Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.
Attention correlated appearance and motion feature followed temporal learning...IJECEIAES
Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.
On the High Dimentional Information Processing in Quaternionic Domain and its...IJAAS Team
There are various high dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. The conventional real-valued neural networks are tried to solve the problem associated with high dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a well generalized learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is presented through a wide spectrum of simulations which demonstrate the significance of the work.
Competent scene classification using feature fusion of pre-trained convolutio...TELKOMNIKA JOURNAL
In view of the fact that the development of convolutional neural networks (CNN) and other deep learning techniques, scientists have become more interested in the scene categorization of remotely acquired images as well as other algorithms and datasets. The spatial geometric detail information may be lost as the convolution layer thickness increases, which would have a significant impact on the classification accuracy. Fusion-based techniques, which are regarded to be a viable way to express scene features, have recently attracted a lot of interest as a solution to this issue. Here, we suggested a convolutional feature fusion network that makes use of canonical correlation, which is the linear correlation between two feature maps. Then, to improve scene classification accuracy, the deep features extracted from various pre-trained convolutional neural networks are efficiently fused. We thoroughly evaluated three different fused CNN designs to achieve the best results. Finally, we used the support vector machine for categorization (SVM). In the analysis, two real-world datasets UC Merced and SIRI-WHU were employed, and the competitiveness of the investigated technique was evaluated. The improved categorization accuracy demonstrates that the fusion technique under consideration has produced affirmative results when compared to individual networks.
Machine learning based augmented reality for improved learning application th...IJECEIAES
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
This document reviews object detection techniques using convolutional neural networks (CNNs). It begins with introducing object detection and CNNs. It then discusses the problem of object detection in computer vision and the need for more precise and accurate detection systems. The majority of the document reviews eight previous works that developed algorithms to improve object detection systems, including R-CNN and approaches using K-SVD, deep equilibrium models, non-local networks, transformers, and selective kernel networks. It evaluates these approaches and their abilities to achieve high detection rates while requiring fewer computations or model parameters. The document provides an overview of recent research aiming to advance CNN-based object detection.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
Attention correlated appearance and motion feature followed temporal learning...IJECEIAES
Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.
On the High Dimentional Information Processing in Quaternionic Domain and its...IJAAS Team
There are various high dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. The conventional real-valued neural networks are tried to solve the problem associated with high dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a well generalized learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is presented through a wide spectrum of simulations which demonstrate the significance of the work.
Competent scene classification using feature fusion of pre-trained convolutio...TELKOMNIKA JOURNAL
In view of the fact that the development of convolutional neural networks (CNN) and other deep learning techniques, scientists have become more interested in the scene categorization of remotely acquired images as well as other algorithms and datasets. The spatial geometric detail information may be lost as the convolution layer thickness increases, which would have a significant impact on the classification accuracy. Fusion-based techniques, which are regarded to be a viable way to express scene features, have recently attracted a lot of interest as a solution to this issue. Here, we suggested a convolutional feature fusion network that makes use of canonical correlation, which is the linear correlation between two feature maps. Then, to improve scene classification accuracy, the deep features extracted from various pre-trained convolutional neural networks are efficiently fused. We thoroughly evaluated three different fused CNN designs to achieve the best results. Finally, we used the support vector machine for categorization (SVM). In the analysis, two real-world datasets UC Merced and SIRI-WHU were employed, and the competitiveness of the investigated technique was evaluated. The improved categorization accuracy demonstrates that the fusion technique under consideration has produced affirmative results when compared to individual networks.
Machine learning based augmented reality for improved learning application th...IJECEIAES
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
This document reviews object detection techniques using convolutional neural networks (CNNs). It begins with introducing object detection and CNNs. It then discusses the problem of object detection in computer vision and the need for more precise and accurate detection systems. The majority of the document reviews eight previous works that developed algorithms to improve object detection systems, including R-CNN and approaches using K-SVD, deep equilibrium models, non-local networks, transformers, and selective kernel networks. It evaluates these approaches and their abilities to achieve high detection rates while requiring fewer computations or model parameters. The document provides an overview of recent research aiming to advance CNN-based object detection.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
This document discusses comparing the performance of different convolutional neural networks (CNNs) when trained on large image datasets using Apache Spark. It summarizes the datasets used - CIFAR-10 and ImageNet - and preprocessing done to standardize image sizes. It then provides an overview of CNN architecture, including convolutional layers, pooling layers, and fully connected layers. Finally, it introduces SparkNet, a framework that allows training deep networks using Spark by wrapping Caffe and providing tools for distributed deep learning on Spark. The goal is to see if SparkNet can provide faster training times compared to a single machine by distributing training across a cluster.
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Easily Trainable Neural Network Using TransferLearningIRJET Journal
This document discusses using transfer learning to train a neural network to detect a specific window structure quickly using a limited number of training samples. It uses the pre-trained Tiny YOLOv3 model and compares training it from scratch, using ImageNet weights, and using COCO weights. The best results were obtained by pre-training the model on a dataset of real-world window images, then using those weights to train on the target window structure. This approach achieved a 98% mean average precision after only 300 iterations, an 83% decrease in time to convergence compared to training from scratch. Transfer learning was able to improve performance even for detecting a relatively featureless object like a window.
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
Accuracy study of image classification for reverse vending machine waste segr...IJECEIAES
This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
Optimizer algorithms and convolutional neural networks for text classificationIAESIJAI
Lately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems.
Convolutional auto-encoded extreme learning machine for incremental learning ...IJECEIAES
In real-world scenarios, a system's continual updating of learning knowledge becomes more critical as the data grows faster, producing vast volumes of data. Moreover, the learning process becomes complex when the data features become varied due to the addition or deletion of classes. In such cases, the generated model should learn effectively. Incremental learning refers to the learning of data which constantly arrives over time. This learning requires continuous model adaptation but with limited memory resources without sacrificing model accuracy. In this paper, we proposed a straightforward knowledge transfer algorithm (convolutional auto-encoded extreme learning machine (CAE-ELM)) implemented through the incremental learning methodology for the task of supervised classification using an extreme learning machine (ELM). Incremental learning is achieved by creating an individual train model for each set of homogeneous data and incorporating the knowledge transfer among the models without sacrificing accuracy with minimal memory resources. In CAE-ELM, convolutional neural network (CNN) extracts the features, stacked autoencoder (SAE) reduces the size, and ELM learns and classifies the images. Our proposed algorithm is implemented and experimented on various standard datasets: MNIST, ORL, JAFFE, FERET and Caltech. The results show a positive sign of the correctness of the proposed algorithm.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
The document describes a study that used a convolutional neural network with a ConvNeXtLarge architecture to classify skin cancer images into benign and malignant classes. The CNN model was trained on a dataset of 3,297 skin cancer images from Kaggle. It achieved an AUC of 0.91 for classifying the images, demonstrating the ConvNeXtLarge architecture is effective for this task. The study aims to help early diagnosis and treatment of skin cancers.
This document provides a critical review of recurrent neural networks for sequence learning. It begins with an abstract summarizing the paper. It then discusses why recurrent neural networks are well-suited for modeling sequential data compared to other models like feedforward neural networks and Markov models. Specifically, it notes that RNNs can capture long-range temporal dependencies, unlike models with a finite context window. It also explains that RNNs can represent a vast number of states using real-valued activations, unlike discrete state Markov models.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different
classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training
faster, we used non-saturating neurons and a very efficient GPU implementation
of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry
Deep Learning Neural Networks in the CloudIJAEMSJORNAL
Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.
Machine learning in Dynamic Adaptive Streaming over HTTP (DASH)Eswar Publications
Recently machine learning has been introduced into the area of adaptive video streaming. This paper explores a novel taxonomy that includes six state of the art techniques of machine learning that have been applied to Dynamic Adaptive Streaming over HTTP (DASH): (1) Q-learning, (2) Reinforcement learning, (3) Regression, (4) Classification, (5) Decision Tree learning, and (6) Neural networks.
Quantum Machine Learning is all you Need – PhD Assistance.pdfPhD Assistance
Quantum computing can be used in Deep learning and machine learning to reduce the time taken train the deep neural network.
For #Enquiry:
Website: https://www.phdassistance.com/blog/quantum-machine-learning-is-all-you-need/
India: +91 91769 66446
Email: info@phdassistance.com
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
More Related Content
Similar to Quantum transfer learning for image classification
This document discusses comparing the performance of different convolutional neural networks (CNNs) when trained on large image datasets using Apache Spark. It summarizes the datasets used - CIFAR-10 and ImageNet - and preprocessing done to standardize image sizes. It then provides an overview of CNN architecture, including convolutional layers, pooling layers, and fully connected layers. Finally, it introduces SparkNet, a framework that allows training deep networks using Spark by wrapping Caffe and providing tools for distributed deep learning on Spark. The goal is to see if SparkNet can provide faster training times compared to a single machine by distributing training across a cluster.
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Easily Trainable Neural Network Using TransferLearningIRJET Journal
This document discusses using transfer learning to train a neural network to detect a specific window structure quickly using a limited number of training samples. It uses the pre-trained Tiny YOLOv3 model and compares training it from scratch, using ImageNet weights, and using COCO weights. The best results were obtained by pre-training the model on a dataset of real-world window images, then using those weights to train on the target window structure. This approach achieved a 98% mean average precision after only 300 iterations, an 83% decrease in time to convergence compared to training from scratch. Transfer learning was able to improve performance even for detecting a relatively featureless object like a window.
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
Accuracy study of image classification for reverse vending machine waste segr...IJECEIAES
This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
Optimizer algorithms and convolutional neural networks for text classificationIAESIJAI
Lately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems.
Convolutional auto-encoded extreme learning machine for incremental learning ...IJECEIAES
In real-world scenarios, a system's continual updating of learning knowledge becomes more critical as the data grows faster, producing vast volumes of data. Moreover, the learning process becomes complex when the data features become varied due to the addition or deletion of classes. In such cases, the generated model should learn effectively. Incremental learning refers to the learning of data which constantly arrives over time. This learning requires continuous model adaptation but with limited memory resources without sacrificing model accuracy. In this paper, we proposed a straightforward knowledge transfer algorithm (convolutional auto-encoded extreme learning machine (CAE-ELM)) implemented through the incremental learning methodology for the task of supervised classification using an extreme learning machine (ELM). Incremental learning is achieved by creating an individual train model for each set of homogeneous data and incorporating the knowledge transfer among the models without sacrificing accuracy with minimal memory resources. In CAE-ELM, convolutional neural network (CNN) extracts the features, stacked autoencoder (SAE) reduces the size, and ELM learns and classifies the images. Our proposed algorithm is implemented and experimented on various standard datasets: MNIST, ORL, JAFFE, FERET and Caltech. The results show a positive sign of the correctness of the proposed algorithm.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
The document describes a study that used a convolutional neural network with a ConvNeXtLarge architecture to classify skin cancer images into benign and malignant classes. The CNN model was trained on a dataset of 3,297 skin cancer images from Kaggle. It achieved an AUC of 0.91 for classifying the images, demonstrating the ConvNeXtLarge architecture is effective for this task. The study aims to help early diagnosis and treatment of skin cancers.
This document provides a critical review of recurrent neural networks for sequence learning. It begins with an abstract summarizing the paper. It then discusses why recurrent neural networks are well-suited for modeling sequential data compared to other models like feedforward neural networks and Markov models. Specifically, it notes that RNNs can capture long-range temporal dependencies, unlike models with a finite context window. It also explains that RNNs can represent a vast number of states using real-valued activations, unlike discrete state Markov models.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different
classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training
faster, we used non-saturating neurons and a very efficient GPU implementation
of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry
Deep Learning Neural Networks in the CloudIJAEMSJORNAL
Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.
Machine learning in Dynamic Adaptive Streaming over HTTP (DASH)Eswar Publications
Recently machine learning has been introduced into the area of adaptive video streaming. This paper explores a novel taxonomy that includes six state of the art techniques of machine learning that have been applied to Dynamic Adaptive Streaming over HTTP (DASH): (1) Q-learning, (2) Reinforcement learning, (3) Regression, (4) Classification, (5) Decision Tree learning, and (6) Neural networks.
Quantum Machine Learning is all you Need – PhD Assistance.pdfPhD Assistance
Quantum computing can be used in Deep learning and machine learning to reduce the time taken train the deep neural network.
For #Enquiry:
Website: https://www.phdassistance.com/blog/quantum-machine-learning-is-all-you-need/
India: +91 91769 66446
Email: info@phdassistance.com
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
Similar to Quantum transfer learning for image classification (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
Quantum transfer learning for image classification
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 21, No. 1, February 2023, pp. 113~122
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v21i1.24103 113
Journal homepage: http://telkomnika.uad.ac.id
Quantum transfer learning for image classification
Geetha Subbiah1
, Shridevi S. Krishnakumar2
, Nitin Asthana1
, Prasanalakshmi Balaji3
,
Thavavel Vaiyapuri4
1
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
2
Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, India
3
Department of Computer Science, King Khalid University, Abha, Saudi Arabia
4
Department of Computer Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia
Article Info ABSTRACT
Article history:
Received Mar 22, 2021
Revised Nov 04, 2022
Accepted Nov 14, 2022
Quantum machine learning, an important element of quantum computing,
recently has gained research attention around the world. In this paper, we have
proposed a quantum machine learning model to classify images using a
quantum classifier. We exhibit the results of a comprehensive quantum
classifier with transfer learning applied to image datasets in particular.
The work uses hybrid transfer learning technique along with the classical
pre-trained network and variational quantum circuits as their final layers on
a small scale of dataset. The implementation is carried out in a quantum
processor of a chosen set of highly informative functions using PennyLane a
cross-platform software package for using quantum computers to evaluate
the high-resolution image classifier. The performance of the model proved to
be more accurate than its counterpart and outperforms all other existing
classical models in terms of time and competence.
Keywords:
Hybrid neural networks
Quantum computing
Transfer learning
Variational quantum circuits
This is an open access article under the CC BY-SA license.
Corresponding Author:
Geetha Subbiah
School of Computer Science and Engineering, Vellore Institute of Technology
Chennai, India
Email: geetha.s@vit.ac.in
1. INTRODUCTION
Designing learning algorithms has become a focus for machine learning engineers, as they strive to
make machine learn like human. In several automations, such as image and speech recognition, the machine
learning research has seen tremendous improvement in the past few years using the concept of transfer
learning. Transfer learning is a learning model that gains information in a specific context which can be
included in another model to reuse the knowledge for related predictions. Transfer learning has been applied
to all kinds of unsupervised, supervised and reinforced learning tasks. The integration of transfer learning
with quantum computing and machine learning seems to be more promising among all prevailing learning
algorithms. Transfer learning model and its importance is explored in the perspective of quantum machine
learning in this work. Quantum transfer learning proves to be efficient in classifying traditional images
utilizing the quantum state classification. Quantum classification operations could be effectively solved in an
optimal duration using quantum algorithms. Transfer learning is the use of the experience learned by
performing one task to help solve another, but linked, problem. Information is leveraged from a source task
during transfer learning, in order to enhance learning in a new task. It has been a proven scenario to be
successful when the model development starts from a pre-trained deep network rather than training a complete
network from its initial stages, then optimize any or more of the initial layers for a given function and appropriate
datasets. If the transfer process ends up reducing the new task’s output it is considered a negative transfer.
2. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 1, February 2023: 113-122
114
A key problem is to maintain successful transfer between related things while preventing negative transfer
among less related things when implementing transfer learning.
Modern machine learning and deep learning algorithms have traditionally been designed to work in
isolation. These algorithms are qualified for the resolution of complex tasks. If the feature-space distribution
shifts, the models must be rebuilt from scratch. Microsoft proposed a deep residual learning architecture to
address this issue. Many problems can be handled by employing the residual network, including:
− Although residual network (ResNets) are simple to optimise, “plain” networks (those that merely stack
layers) have a larger training error as the depth increases.
− ResNets can readily gain accuracy from more depth, resulting in better results than earlier networks.
It is evident that the ResNet convolutional neural network (CNN) models provide superior object detection
and classification accuracies. Considering the higher accuracy and affordable complexity of this CNN
paradigm, we employed this model’s transfer learning into the quantum machine learning so as to get a
promising performance by the quantum machine learning strategy. Our experimental observations also
substantiate that the chosen transfer learning coupled with quantum machine learning provides good
classification accuracy.
In this work, we implemented transfer learning-based quantum machine learning method to classify
images in order to achieve high performance and the paper focuses on applying the quantum neural network
(QNN) by using transfer learning method in order to classify images as ant/bee and potato leaf image
datasets. QNN is modelled by using computer quantum simulator circuit which is designed by making use of
PennyLane, a cross platform software library, tested in Google Colab. Evaluating the possibility and level of
probability to develop a transfer learning model in the perspective of quantum learning serves as the main
objective of this work.
2. RELATED WORKS
Quantum machine learning is evolving day by day and this work is to mainly analyze the capability
of quantum processors in exploring the transfer learning concept. Both the classical and quantum model has
been hybrid to create a neural network that can perform all type of computations. Three new forms of
learning transition arise naturally: quantum to classical (QC), classical to quantum (CQ), and quantum to
quantum (QQ) [1]. CQ transfer learning is especially appealing nowadays with its noisy intermediate-scale
quantum (NISQ) devices as it enables the chance of using some great deep neural network to pre-process
large input samples of high-resolution in a traditional way and manipulating few but extremely insightful
features successively with a variational quantum circuit. Currently, CNNs are the deep learning models that
are most widely used and are most frequently used for image classification. They make supervised use of
stochastic gradient descent and back-propagation for training. In addition, learning to pass QC and QQ may
be very promising techniques notably once wide quantum computers are accessible. In this case, it might be
possible to pre-train fixed quantum circuits as generic quantity extractors, imitating the well-known classical
models and use them as pre-trained blocks e.g. ResNet, visual geometry group (VGG)Net, inception, and
AlexNet (for image processing), or, transformer, bidirectional encoder representations from transformers
(BERT) [2]. The goal is to establish CQ to a specific dataset with the existing resource, and make it precisely
suitable to the evolving approaches of hybrid neural networks with variational quantum circuits.
The main emphasis of the work carried out stays on the hybrid model development. Hence the
paradigm that is conceivable to jointly train quantum variational circuits and classical neural networks to
perform difficult data processing are taken up for study. Inherent parallelism in the execution is the great
benefit of any quantum models. The speed of execution and most importantly, in comparison to typical
classical models, the number of qubits required to encode the information is reduced by an order of
magnitude. For example, just 6 qubits are required to encode a 64-dimensional pattern; hence a 32768×32768
binary image may be encoded with just 30 qubits, which is more than a billion-dimensional planar vector [3], [4].
Using superposition states and quantum entanglement only this reduction could be made possible. When compared
to bits in conventional systems, the superposition property of quantum states entanglement permits the parallel
reconstruction of images from a smaller number of qubits and quantum computation image representation systems
to take advantage of this feature and proves to be highly promising for problems with image classification [5]–[7].
3. RESEARCH METHODOLOGY
This section discusses about the different existing hybrid classical and quantum networks available
for classification. The section covers the conceptual technical details of classical neural networks, variational
quantum networks, and dressed quantum circuits. Also, it discusses the background of transfer learning,
classical to classical transfer learning, and transfer learning from classical to quantum.
3. TELKOMNIKA Telecommun Comput El Control
Quantum transfer learning for image classification (Geetha Subbiah)
115
3.1. Classical neural networks
This is a very common model of comprehensive neural networks for classical machine learning.
The elementary deep neural network block is referred as a layer and it converts interface vectors of 𝑛0 real
objects to output vectors of n1 real objects [5].
𝐸[𝜓] =
⟨𝜓|𝐻|𝜓⟩
⟨𝜓|𝜓⟩
≥ 𝐸0 (1)
Here the notation 𝑛0 = 𝑛1 denotes the number of input and output variables, 𝑊 is a matrix when 𝑛1 = 𝑛0
and 𝑏 is a vector which never changes its state and this contributes the purpose of optimization.
The nonlinear functio 𝜙 is random and the inflammatory tangent or the resolute linear component is specified
as 𝑅𝑒𝐿𝑈(𝑥) = 𝑚𝑎𝑥(0, 𝑥). A standard deep neural network is a multilayer connectivity, taking the output of
(𝑛 − 1) layer contributing as the input to the layer 𝑛 as in (2)
𝐶 = 𝐿𝑛𝑑−1→𝑛𝑑
𝜊 … 𝐿𝑛1→𝑛2
𝜊 … 𝐿𝑛0→𝑛1
(2)
Its depth 𝑑 (number of layers) and the number of instances (number of variables) for each layer, i.e. the range
of integers, are defining hyper-parameters of a deep network is specified for 𝑛0, 𝑛1, 𝑛𝑑 − 1.
3.2. Variational quantum network
The variational quantum circuit is the one with properties of defining tunable parameters that needs
to be iteratively optimized. In artificial neural networks, certain parameters can be used as weights. Due to
the absence of quantum error correction and fault-tolerant quantum computing, the parameters can be
absorbed during the iterative optimization process by the corresponding deviations leading the variational
circuits to become reasonably sensitive to noise [7]. Also, the machine learning algorithms enabled variational
quantum circuits to bypass the complex quantum errors that may occur in variety of devices. A variational
quantum circuit of depth q is a combination of several quantity layers that corresponds to the sum of several
units parameterized by weight variation as (3).
𝒬 = ℒ𝑞° … … . . ℒ2°ℒ1 (3)
A given vector 𝑥 is to be incorporated into a quantum state to inject classical data into a quantum
network. A variational 𝑥-dependent embedding layer can also do this and can be applied to any relevant state [8] as
in (4).
𝜀: 𝑥 → |𝑥⟩ = 𝐸(𝑥)|0⟩ (4)
It would be challenging to simulate the quantum circuits with large number of qubits via classical computers,
which implies that the variational quantum circuits own a better expressive power than the classical function
approximations like a neural network. Although it can entail a quantum computation hidden in the quantum
circuit, 𝑄 is merely a black-box comparable to the classic deep network as viewed from a global point of
view. Nonetheless, when working with actual NISQ systems in particular, there are technological drawbacks
and physical restrictions that should be taken into account. Traditional feed-forward networks [9] may take
any number of characteristics chosen for layers defined in the quantum network as in (4). In general,
embedding layers encode each classic aspect of 𝑥 into some kind of specific framework. This is a constraint
for any embedding deep neural network. A quantum network could overcome this common constraint by:
− Adding and discarding/measuring of ancillary subsystems in the center of the circuit.
− Including intricate technologies of embedding and measuring layers.
− Adding the classical layers pre-processing and post-processing.
3.3. Dressed quantum circuits
An association of traditional neural networks with quantum variational circuits is to be done to
introduce transfer learning to the classical quantum framework. Analyzing the size of the classical and
quantum nodes is usually not feasible. The circuit of variation set out and subsystems dependent on nq could
be tested. To add some simple input and output data especially pre-processing and post-processing,
introducing a classic layer at the middle and end of the quantum network could be termed as a dressed
quantum circuit:
𝑄 = 𝐿𝑛𝑞→𝑛𝑜𝑢𝑡
° 𝑄 ° 𝐿𝑛𝑖𝑛→𝑛𝑞
(5)
4. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 1, February 2023: 113-122
116
Where in (2) 𝑛0 is defined and 𝑄 is the bare quantum circuit connected to it [10]. Here the
computation is not shared as the hybrid complex network with the classical and quantum processors.
It functions while the quantum circuits conduct the main calculation, where the traditional layers are
responsible for supplying the data and fetching data from the model again. Any related hybrid model has
been made one of them is helmholtz quantum computer. A dressed quantum circuit is almost identical to a
naked one from a hardware standpoint [9]. On the other hand, there are two big advantages of dressed
quantum circuit include:
− The two classical layers should be fitted to integrate the input data optimally.
− Amount of variables in input and output is independent of the number of subsystems, making it possible
to link flexibly to other networks that could possibly be classical or quantum networks.
Although implementation of the dressed quantum circuits seems to be a simpler application of transfer
learning systems, it is also in itself a very powerful paradigm of machine learning and is a non-trivial
contribution to this work.
3.4. Transfer learning
Transfer learning is the idea of transferring an attained knowledge rom one network to the other in
which either of them could be classical or quantum [11]. As recorded in several surveys, transfer learning has
been applied to all kinds of unsupervised, supervised and reinforced learning tasks. Transfer learning has
been implemented for various tasks such as reinforcement learning and classification on restricted Boltzmann
machines. Ortiz et al. [12] applied transfer learning to neural networks and noted it increases both performance
and effectiveness. In specific terms, our method uses unlabeled data to acquire a concise, higher-level function
representation of the higher-level structure specification of the inputs; the implementation simplifies the role of
value classification. Trying to follow the rule that is seen in classical machine learning, all circumstances in
which the dataset 𝐷𝐵6 = 𝐷𝐴 is modified and/or the final task 𝑇𝐵6 = 𝑇𝐴 is modified could be described as
methods of learning transfer.
#𝑖𝑛𝑝𝑢𝑡𝑠 = # 𝑠𝑢𝑏𝑠𝑦𝑠𝑡𝑒𝑚𝑠 = # 𝑜𝑢𝑡𝑝𝑢𝑡𝑠 (6)
The basic concept behind this approach is that it can always serve as a handy function extractor for
another problem, even though 𝐴 has been developed for a particular problem. This workaround is enhanced
by enumerating the final layers of 𝐴 (step 2), in spite of the fact that the network’s final implementations are
typically more suited to the particular situation, intermediate elements are more common and thus more
appropriate for transfer learning. It includes the definitions of a context and a mission. A field 𝐷 is a function
space 𝑋 and a conditional probability distribution 𝑃(𝑋) over the function space, where 𝑋 = 𝑋1 … … … 𝑋𝑛
ubiquitous 𝑋 is located. 𝑋 is the space for all document representations when classifying documents with a
bag-of-word representation, 𝑥𝑖 is the 𝑖𝑡ℎ
term vector referring to some text and 𝑋 is a set of training
documents [13]. A similar idea is used to inspire generative models: it is assumed that the capacity to
generate meaningful images requires an understanding of the underlying image structure, which can be
applied to many other activities in turn, while training generative models. This presumption itself is based on
the idea that all images are built on a low-dimensional multiplicity, i.e. there is a certain basic picture
structure that a model can derive. Recent success with generative adversarial networks in the development of
photorealistic images suggests that such a network may theoretically exist [14].
3.5. Classical to classical transfer learning
Until now, modern machine learning and deep learning algorithms have traditionally been designed to
work in isolation. These algorithms are qualified for the resolution of complex tasks. If the feature-space
distribution shifts, the models must be rebuilt from scratch. Transfer learning is the concept of overcoming the
independent learning model by using learned knowledge for one problem in order to solve similar ones [15].
If there are substantially more data available for 𝑇1 task, it could be utilized for its learning and generalize
this information (features, weights) for 𝑇2 task (which has far less data). It is the first step in the whole
method, and the most critical. The aim is to obtain answers to queries related to which part of the information
can be moved from source to goal in order to enhance the goal task efficiency.
In attempting to address this question, we seek to classify the source-specific portion of information
and what’s common between the source and the target. Given source and target domains 𝐷𝑠 and 𝐷𝑡 where
𝐷 = {𝑋, 𝑃(𝑋)} are source and target tasks 𝑇𝑠 and 𝑇𝑠 where 𝑇 = {Y, P(Y|X)}. Source and target requirements
will differ in four ways – i) the source and target domain featues spaces are distinct from each other; ii) the
relative confidence intervals of the input and the output domain are different; iii) two different scenario
spaces have two different label spaces; and iv) source and target conditional probability distributions are
different; all these aspects could be explained as an example of classification:
5. TELKOMNIKA Telecommun Comput El Control
Quantum transfer learning for image classification (Geetha Subbiah)
117
1) 𝑋𝑠 ≠ 𝑋𝑡 Referred to as, the source and target domain feature spaces are distinct, e.g. there are two
different languages in which the documents are written. This is typically adaptation in the sense of
natural language processing-cross-lingual.
2) 𝑃(𝑋) ≠ 𝑃(𝑋) The relative confidence intervals of the input and the output domain are different, e.g.,
the papers cover various subjects. This circumstance is commonly known as domain adaptation.
3) 𝑌𝑟 ≠ 𝑌𝑟 This situation typically happens for scenarios because it is exceptionally unusual for two different
tasks to have different label spaces, but the same conditional probability distributions. The label spaces
between the two tasks are different, e.g. documents need to be allocated different labels in the target task.
4) 𝑃(𝑌𝑠 |𝑋𝑠) ≠ 𝑃(𝑌𝑡 | 𝑋𝑡) Source and target tasks’ conditional probability distributions are different, e.g.
source and target documents are unbalanced with respect to their classes. In practice and methods such
as over-sampling, under-sampling, this scenario is very prevalent.
In several simulation data, the proposed algorithm is applied to the task of image processing and
also the model is tested with a sample dataset of ants and bees [16]. It can be understood on the image
processing domain, for every sample in each class, the model trains itself adhering to the aforementioned
source-target specifications. For the complete learning of the solution space, the source and the target spaces
are mapped optimally only because of the source-target requirements. The quantum learning algorithm learns
this mapping and attempts to produce the optimal classification results.
3.6. Classical to quantum transfer learning
The transformation from classical to quantum transfer learning in the modern technological era
using the implementation of NISQ devices is perhaps the most promising one [17]. Also, now, the conditions
exist that machines exist with intermediate quantum computers hit the plateau of quantum domination and
simultaneously, classical deep learning approaches could be applied that are very successful and well-tested.
The existing algorithms are commonly accepted as the best performing in machine learning, particularly for
the processing of images and texts using transfer learning. The CQ conversion research comprises of using
such classic pre-trained models specifically as information extractors and then post-processing this
information on a quantum computer. This hybrid method helps in manipulating high-resolution images
because a quantum computer is implemented in this setup only to a comparatively small number of abstract
elements, which is far more practical than embedding millions of individual pixels in a quantum device [18].
The alternate solutions are also to be analyzed to handle large image datasets. And it is also being tested with
the quantum simulator provided by the PennyLane platform as in Figure 1.
Figure 1. Hybrid quantum transfer learning model
3.7. Proposed algorithm
The steps and procedures discussed above are summarised as a single algorithm. The logical aspets
for each step summarized, has been explained in the previous sections. The algorithm outline for the
implemented hybrid transfer learning model is given [19], [20]:
1) A pre-trained CNN model is loaded with a large dataset.
2) The weights of hyper parameters in model’s lower convolutional layers are frozen.
3) Replace the upper layers of the network with a custom quantum classifier and the number of outputs
must be set equal to the number of classes.
4) After the custom quantum classifier with required layers of trainable parameters are added to the model,
the layers to freeze are adjusted depending on similarity of new task to original dataset.
6. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 1, February 2023: 113-122
118
5) The classifier layers on training data available for task is trained.
6) Train only the custom quantum classifier layers for the task thereby optimizing the model for the given
dataset.
7) Hyper parameters are fine-tuned to give optimal accuracy and more layers are unfrozen if required.
We concentrate on the classical quantum transfer learning algorithm and provide a specific example
to illustrate the training process.
− We employ ResNet18, a deep residual neural network that has been pre-trained on the ImageNet
dataset, as our pre-trained network named ‘A’.
− We acquire a pre-processing block, named ‘A’, after eliminating its final layer, which translates any
input high-resolution image into 512 abstract features.
− A 4-qubit “dressed quantum circuit” –‘B’, which is a variational quantum circuit sandwiched between
two classical layers, is used to classify such features.
− The hybrid model is trained on a subclass of ImageNet comprising images of ants and bees, while
maintaining A’ constant’
The model is experimented to get trained for classification of ants and bees as in Figure 2 and also
to classify potato leaf diseases as in Figure 3. We have about 800 training images and 400 testing images for
ants and bees. The potato leaf disease dataset consists of 1500 image files of 3 different classes, namely early
blight, late blight, and healthy. Usually, this is a very small dataset to generalize upon, if trained from scratch.
Since we are using transfer learning, we should be able to generalize reasonably well. The dataset used is
same for both the classical and classical to quantum transfer learning.
Figure 2. Ants and bee’s dataset Figure 3. Potato leaf dataset
In classical transfer learning the image classification in done by using RestNet 50 pre-trained model.
First CNN in applied for bees and ant’s classification using Keras API. Then the data is trained form the
retained model, now transfer learning is applied, CNN is trained with ImageDataGenerator. Same process is
applied for leaf diseases dataset. On imagenet image recognition tasks such as VGG, genesis, and ResNet,
Keras provides easy access to many high performing templates [21]. The aim is to find maximal values for
each of these filter matrices, so that when the image is propagated through the network, output crypto keys
can be used correctly. The method used to identify these values. Classical to quantum transfer learning is
implemented with the same Dataset consists of ant and bees. It is first trained numerically and the model is
tested on the open source platform of quantum computing PennyLane with the help of PennyLane libraries [22].
PennyLane provides two quantum simulators for running quantum codes. So, this example has also been run
into PennyLane default simulator strawberryfields.fock.
− dev = qml.device (‘strawberryfields.fock’, wires = 1, cutoff_dim = 10)
− This model is divided into different phases; it has a public dataset with 1000 images which is provided
by image net, a pre-trained residual neural network RestNet 18 which is created by microsoft in early
2016. RestNet 18 removing the fully connected final layer, receiving a 512-function pre-trained
extractor. And images of two different classes: ant and bees separated into two different sets one for
training that is of 800 images and second set is of 400 images for testing.
− 𝐵 = 𝑄 = 𝐿4−2
o
𝑄 o
𝐿512−4: i.e., a 4-qubit dressed quantum circuit (9) with 512 input features and 2 real
outputs.
− 𝑇𝐵 = classification (2 labels).
The Figure 4 gives the details of the full data processing pipeline of classical to quantum transfer learning.
7. TELKOMNIKA Telecommun Comput El Control
Quantum transfer learning for image classification (Geetha Subbiah)
119
Figure 4. Full data processing pipeline of quantum transfer learning
4. Results and accuracy
The proposed method is evaluated using the following performance metrics and the results are
compated with the existing deep learning models. The result states that the dressed quantum circuit is very
useful in quantum machine learning, this will help to classify highly non-linear dataset. The variational
parameters which are used in this quantum program is 4 qubits with learning rate of 0.0004, the batch size for
testing is 4. Number of training epochs is 50 and the main quantum depth is 6. After each epoch the quantum
model will be validated by the test dataset. The hybrid model’s performance is measured with metrics such as
accuracy, precision, recall, F1-score and specificity. True positive (TP) gives the number of correctly
estimated positive cases and is provided in Table 1, false positive (FP) gives the number of falsely predicted
cases, true negative (TN) gives the number of negative samples correctly estimated and false negative (FN) is
the number of samples that are falsely predicted. The accuracy rate indicates the details of correctly classified
test data. Recall and precision are two important metrics where the percentage of correctly classified is
defined as the recall and there is a trade-off between them. F1 score seek a balance between recall and
precision. Specificity discuss about the correctly predicted. The performance of the proposed system is
comapared against the other deep learning systems and is provided in Table 2.
Cross entropy is used as a loss function and reduced using the Adam optimizer. The classical
entities were conventional recombination and mutation operators are also used. In the case of numerical
variables, there are several variance operators present in the literature [22], [23]. The statistical evidence that
a quantum network truncation does not necessarily decrease the efficiency of the calculated attributes are
expressed by close inspection that is beneficial to increase the classical depth but even after two layers it
saturates the precision. In the other side, it is evident that the quantum depth has an entropy value of
approximately 𝑞 = 15, although the accuracy is goes down for larger values.
Figure 5 gives the number of training iterations happened with respect to the evolution of the loss
function. This is a representation of total 6 quantum depths trained from the scratch. According to the
quantum existence, the qubit state generated by the truncated variational circuit will be interconnected and/or
not associated with the principle of estimation. But may in reality be a handy transfer learning technique, is a
noteworthy finding. For the classical part, validation is done by the help of validation generator and the
losses are binary cross entropy. Figure 6 and Figure 7 depicts the details of training and validation accuracy
and loss of the developed hybrid model for the given datasets.
The network built from scratch produces the same or better performance in favour of the network
with a set initial layer over a reasonably long training time. Comparing quantum accuracy to classical
accuracy there is a phenomenal difference. It can be expressed that the classical to quantum learning model
for this example which is being trained from the scratch is a more powerful construction to the classical
model because it has extra more variational quantum depth parameters. However, the resources are limited
for this scenario though they work properly. In this kind of practical situation quantum models could be used
as a convenient transfer learning strategy [24], [25].
Table 1. Result Matrix Formulae
Method Formula
Sensitivity 𝑇𝑃𝑅 = 𝑇𝑃/(𝑇𝑃 + 𝐹𝑁)
Specificity 𝑇𝑁𝑅 = 𝑇𝑁/(𝐹𝑃 + 𝑇𝑁)
Precision 𝑃𝑃𝑉 = 𝑇𝑃/(𝑇𝑃 + 𝐹𝑃)
Negative predictive value 𝑁𝑃 = 𝑇𝑁/(𝑇𝑁 + 𝐹𝑁)
False positive rate 𝐹𝑃𝑅 = 𝐹𝑃/(𝐹𝑃 + 𝑇𝑁)
False discovery rate 𝐹𝐷𝑅 = 𝐹𝑃/(𝐹𝑃 + 𝑇𝑃)
False negative rate 𝐹𝑁𝑅 = 𝐹𝑁/(𝐹𝑁 + 𝑇𝑃)
Accuracy 𝐴𝐶𝐶 = (𝑇𝑃 + 𝑇𝑁)/(𝑃 + 𝑁)
F1 score 𝐹1 = 2𝑇𝑃/(2𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁)
*TP–true positive; TN–true negative; FP–false positive; FN–false negative.
8. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 1, February 2023: 113-122
120
Table 2. Confusion matrix details of hybrid model for both datasets
Method Performance metrics Ants/bee’s dataset Potato leaf dataset
ResNet18 Sensitivity 0.94 0.92
Specificity 0.92 0.92
Precision 0.92 0.93
Negative predictive value 0.95 0.92
False positive rate 0.07 0.07
False discovery rate 0.08 0.07
False negative rate 0.05 0.07
Accuracy 0.935 .925
F1 score 0.93 0.925
AlexNet Sensitivity 0.90 0.92
Specificity 0.90 0.92
Precision 0.9 0.92
Negative predictive value 0.91 0.93
False positive rate 0.099 0.079
False discovery rate 0.090 0.070
False negative rate 0.1 0.08
Accuracy 0.905 0.925
F1 score 0.90 0.92
VGG16 Sensitivity 0.89 0.90
Specificity 0.88 0.89
Precision 0.88 0.89
Negative predictive value 0.9 0.91
False positive rate 0.78 0.10
False discovery rate 0.11 0.091
False negative rate 0.10 0.11
Accuracy 0.89 0.90
F1 score 0.88 0.89
Quantum deep learning Sensitivity 0.94 0.95
Specificity 0.94 0.96
Precision 0.94 0.97
Negative predictive value 0.95 0.95
False positive rate 0.059 0.03
False discovery rate 0.050 0.049
False negative rate 0.06 0.03
Accuracy 0.945 0.96
F1 score 0.944 0.96
Figure 5. Training with respect to loss function evolution
Figure 6. Traning and validation accuracy Figure 7. Traning and validation loss
9. TELKOMNIKA Telecommun Comput El Control
Quantum transfer learning for image classification (Geetha Subbiah)
121
5. CONCLUSION
Researchers started to pay attention to the convergence and implementation of those two disciplines
with the accelerated growth of machine learning and quantum computing. Image classification is the key in
the pattern recognition. The study of the quantum critical exponents, which describe the action of the order
parameters close to the phase transitions, is a natural extension of this work. The theoretical implications
could be explored further and algebraic construction of quantum neural networks. The key attribute of
quantum models is their exponential acceleration over their classical equivalents. The work is proved by
giving two examples of different size of dataset. Considering the accuracy for classical and quantum models
it could be confined that the quantum hybrid models are more successful in comparison of classical model.
From the experimental and theoretical study, it is inferred that transfer learning is a successful technique
which, in the sense of near-term quantum devices, can be a very promising model for classification.
REFERENCES
[1] A. Gupta, Y. -S. Ong, and L. Feng, “Insights on transfer optimization: because experience is the best teacher,” IEEE Transactions
on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 51–64, 2018, doi: 10.1109/TETCI.2017.2769104.
[2] C. -H. Huang and H. -S. Goan, “Robust quantum gates for stochastic time-varying noise,” Physical Review A, vol. 95, no. 6, 2017,
doi: 10.1103/PhysRevA.95.062325.
[3] T. Iwata and Z. Ghahramani, “Improving output uncertainty estimation and generalization in deep learning via neural network
gaussian processes,” arXiv – Statistics-Machine Learning, pp. 1–9, 2017, doi: 10.48550/arXiv.1707.05922.
[4] N. Killoran, J. Izaac, N. Quesada, V. Bergholm, M. Amy, and C. Weedbrook, “Strawberry fields: a software platform for photonic
quantum computing,” Quantum, vol. 3, p. 129, 2019, doi: 10.22331/q-2019-03-11-129.
[5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015, doi: 10.1038/nature14539.
[6] M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemporary Physics, vol. 56, no. 2,
pp. 172–185, 2015, doi: 10.1080/00107514.2014.964942.
[7] N. Killoran, T. R. Bromley, J. M. Arrazola, M. Schuld, N. Quesada, and S. Lloyd, “Continuous-variable quantum neural
networks,” Physical Review Research, vol. 1, no. 3, 2019, doi: 10.1103/PhysRevResearch.1.033063.
[8] D. Li, S. Wang, S. Yao, Y. -H. Liu, Y. Cheng, and X. -H. Sun, “Efficient design space exploration by knowledge transfer,” in
Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis,
2016, no. 12, pp. 1–10, doi: 10.1145/2968456.2968457.
[9] C. Lindner, D. Waring, B. Thiruvenkatachari, K. O’Brien, and T. F. Cootes, “Adaptable landmark localisation: applying model
transfer learning to a shape model matching system,” in International Conference on Medical Image Computing and Computer-
Assisted Intervention, 2017, vol. 10433, pp. 144–151, doi: 10.1007/978-3-319-66182-7_17.
[10] J. R. McClean, J. Romero, R. Babbush, and A. A. -Guzik, “The theory of variational hybrid quantum-classical algorithms,” New
Journal of Physics, vol. 18, no. 2, 2016, doi: 10.1088/1367-2630/18/2/023023.
[11] P. Palittapongarnpim, P. Wittek, E. Zahedinejad, S. Vedaie, and B. C. Sanders, “Learning in quantum control: High-dimensional global
optimization for noisy quantum dynamics,” Neurocomputing, vol. 268, pp. 116–126, 2017, doi: 10.1016/j.neucom.2016.12.087.
[12] A. P. -Ortiz, M. Benedetti, J. R. -Gómez, and R. Biswas, “Opportunities and challenges for quantum-assisted machine learning in
near-term quantum computers,” Quantum Science and Technology, vol. 3, 2018, doi: 10.1088/2058-9565/aab859.
[13] J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, 2018, doi: 10.22331/q-2018-08-06-79.
[14] M. Schuld and N. Killoran, “Quantum machine learning in feature hilbert spaces,” Physical Review Letters, vol. 122, no. 4, 2019,
doi: 10.1103/PhysRevLett.122.040504.
[15] R. Zen et al., “Transfer learning for scalability of neural-network quantum states,” Physical Review E, vol. 101, no. 5, 2020,
doi: 10.1103/PhysRevE.101.053301.
[16] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Conference ICLR
2015, 2015, pp. 1–14, doi: 10.48550/arXiv.1409.1556.
[17] S. Piat, N. Usher, S. Severini, M. Herbster, T. Mansi, and P. Mountney, “Image classification with quantum pre-training and auto-
encoders,” International Journal of Quantum Information, vol. 16, no. 08, 2018, doi: 10.1142/S0219749918400099.
[18] N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum algorithms for nearest-neighbor methods for supervised and unsupervised
learning,” Quantum Information and Computation, vol. 15, no. 3&4, pp. 316–356, 2015, doi: 10.26421/QIC15.3-4-7.
[19] C. Wu, B. Qi, C. Chen, and D. Dong, “Robust learning control design for quantum unitary transformations,” IEEE Transactions
on Cybernetics, vol. 47, no. 12, pp. 4405–4417, 2017, doi: 10.1109/TCYB.2016.2610979.
[20] S. Zhou, Q. Chen, and X. Wang, “Deep quantum networks for classification,” in 2010 20th International Conference on Pattern
Recognition, 2010, pp. 2885–2888, doi: 10.1109/ICPR.2010.707.
[21] S. Adhikary, S. Dangwal, and D. Bhowmik, “Supervised learning with a quantum classifier using multi-level systems,” Quantum
Information Processing, vol. 19, 2020, doi: 10.1007/s11128-020-2587-9.
[22] V. Bergholm et al., “PennyLane: Automatic differentiation of hybrid quantum classical computations,” in arXiv-Quantum
Physics, pp. 1–15, 2018, doi: 10.48550/arXiv.1811.04968.
[23] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549,
pp. 195–202, 2017, doi: 10.1038/nature23474.
[24] V. Dunjko, J. M. Taylor, and H. J. Briegel, “Quantum-enhanced machine learning,” Physical Review Letters, vol. 117, no. 13,
2016, doi: 10.1103/PhysRevLett.117.130501.
[25] T. Fösel, P. Tighineanu, T. Weiss, and F. Marquardt, “Reinforcement learning with neural networks for quantum feedback,”
Physical Review X, vol. 8, no. 3, 2018, doi: 10.1103/PhysRevX.8.031084.
10. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 1, February 2023: 113-122
122
BIOGRAPHIES OF AUTHORS
Geetha Subbiah is a Professor and Associate Dean in School of Computer
Science and Engineering, VIT University, Chennai Campus, India. She has 20 years of rich
teaching and research experience. She has published more than 100 papers in reputed
International Conferences and refereed Journals. Her h-Index is 13 and i-10 index is 20. Her
research interests include image processing, deep learning, steganography, steganalysis,
multimedia security, intrusion detection systems, machine learning paradigms and information
forensics. She is a recipient of University Rank and Academic Topper Award in B.E. and M.E.
She is a IEEE Senior Member, ACM Member, Life Member in HKCBEES, ISCA, IACSIT,
and IAENG. She has received grants from DST, MeITy, AICTE and VIT Seed Fund for
carrying out research projects. She can be contacted at email: geetha.s@vit.ac.in.
Shridevi S. Krishnakumar is currently working as Associate Professor in the
Centre for Advanced Data Science at Vellore Institute of Technology, Chennai. She completed
her Doctorate from MS University, Tirunelveli. Her research area of interest includes Semantic
technology, Web mining, Machine Learning, and Deep Learning. She has published and
presented many research papers in reputed International Journals and Conferences. She can be
contacted at email: shridevi.s@vit.ac.in.
Nitin Asthana completed his Masters in Computer Applications in Vellore
Institute of Technology. His area of interest is Quantum computing and deep learning. He has
developed several projects in Deep learning and Quantum computing. He can be contacted at
email: nitin.asthana2019@vitalum.ac.in.
Prasanalakshmi Balaji received her Ph.D. degree in Computer Science from
Bharathiar University, India. She is a full-time professor in the department of Computer
science and a research fellow at Center for Artificial Intelligence, King Khalid University,
Saudi Arabia. Her research Interests include Artificial Intelligence, Computer Vision and Data
Analysis. She has published and presented many research papers in reputed International
Journals and Conferences. She can be contacted at email: prengaraj@kku.edu.sa.
Thavavel Vaiyapuri is a Fellow of HEA (UK). Currently, she is an Assistant
Professor in College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz
Univeristy, Saudi Arabia. Her research field of interest includes data science, security,
computer vision and high-performance computing. With nearly 20 years of research and
teaching experience, she has published more than 50 research publications in impacted
journals. She can be contacted at email: t.thangam@psau.edu.sa.