This research paper explores the hybrid models for Javanese character recognition using 15600 characters gathered from digital and handwritten sources. The hybrid model combines the merit of deep learning using convolutional neural networks (CNN) to involve feature extraction and a machine learning classifier using support vector machine (SVM). The dropout layer also manages overfitting problems and enhances training accuracy. For evaluation purposes, we also compared CNN models with three different architectures with multilayer perceptron (MLP) models with one and two hidden layer(s). In this research, we evaluated three variants of CNN architectures and the hybrid CNN-SVM models on both the accuracy of classification and training time. The experimental outcomes showed that the classification performances of all CNN models outperform the classification performances of both MLP models. The highest testing accuracy for basic CNN is 94.2% when using model 3 CNN. The increment of hidden layers to the MLP model just slightly enhances the accuracy. Furthermore, the hybrid model gained the highest accuracy result of 98.35% for classifying the testing data when combining model 3 CNN with the SVM classifier. We get that the hybrid CNN-SVM model can enhance the accuracy results in the Javanese characters recognition.
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Tifinagh handwritten character recognition using optimized convolutional neu...IJECEIAES
Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feedforward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.
Design and Development of a 2D-Convolution CNN model for Recognition of Handw...CSCJournals
Owing to the innumerable appearances due to different writers, their writing styles, technical environment differences and noise, the handwritten character recognition has always been one of the most challenging task in pattern recognition. The emergence of deep learning has provided a new direction to break the limits of decades old traditional methods. There exist many scripts in the world which are being used by millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. Feature based approaches derive important properties from the test patterns and employ them in a more sophisticated classification model. Feature extraction using Zernike moment and Polar harmonic transformation techniques was also performed and a moderate classification accuracy was also achieved. The problems faced while using these techniques led us to use CNN based recognition approach which is capable of learning the feature vector from the training character image samples in an unsupervised manner in the sense that no hand-crafting is employed to determine the feature vector. This paper presents a deep learning paradigm using a Convolution Neural Network (CNN) which is implemented for handwritten Gurumukhi and devanagari character recognition (HGDCR). In the present experiment, the training of a 34-layer CNN for a 35 class self-generated handwritten Gurumukhi and 60 class (50 alphabet and 10 digits) handwritten Devanagari character dataset was performed on a GPU (Graphic Processing Unit) machine. The experiment resulted with an average recognition accuracy of more than 92% in case of Handwritten Gurumukhi Character dataset and 97.25% in case of Handwritten Devanagari Character dataset. It was also concluded that the training and classification through our network design performed about 10 times faster than on a moderately fast CPU. The advantage of this framework is proved by the experimental results.
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.
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.
Spoken language identification using i-vectors, x-vectors, PLDA and logistic ...journalBEEI
This document discusses spoken language identification using i-vectors and x-vectors for feature extraction, and PLDA and logistic regression for classification. It examines extracting features from Javanese, Sundanese, and Minangkabau languages, then classifying the languages using various parameters. The study finds that x-vector outperforms i-vector when using PLDA classification, except when using logistic regression, where i-vector performs better. It tunes parameters for i-vector UBM size, i-vector dimension, x-vector max frame size, and num repeats, reporting equal error rates to evaluate performance on test segments of 3, 10 and 30 seconds.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Tifinagh handwritten character recognition using optimized convolutional neu...IJECEIAES
Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feedforward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.
Design and Development of a 2D-Convolution CNN model for Recognition of Handw...CSCJournals
Owing to the innumerable appearances due to different writers, their writing styles, technical environment differences and noise, the handwritten character recognition has always been one of the most challenging task in pattern recognition. The emergence of deep learning has provided a new direction to break the limits of decades old traditional methods. There exist many scripts in the world which are being used by millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. Feature based approaches derive important properties from the test patterns and employ them in a more sophisticated classification model. Feature extraction using Zernike moment and Polar harmonic transformation techniques was also performed and a moderate classification accuracy was also achieved. The problems faced while using these techniques led us to use CNN based recognition approach which is capable of learning the feature vector from the training character image samples in an unsupervised manner in the sense that no hand-crafting is employed to determine the feature vector. This paper presents a deep learning paradigm using a Convolution Neural Network (CNN) which is implemented for handwritten Gurumukhi and devanagari character recognition (HGDCR). In the present experiment, the training of a 34-layer CNN for a 35 class self-generated handwritten Gurumukhi and 60 class (50 alphabet and 10 digits) handwritten Devanagari character dataset was performed on a GPU (Graphic Processing Unit) machine. The experiment resulted with an average recognition accuracy of more than 92% in case of Handwritten Gurumukhi Character dataset and 97.25% in case of Handwritten Devanagari Character dataset. It was also concluded that the training and classification through our network design performed about 10 times faster than on a moderately fast CPU. The advantage of this framework is proved by the experimental results.
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.
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.
Spoken language identification using i-vectors, x-vectors, PLDA and logistic ...journalBEEI
This document discusses spoken language identification using i-vectors and x-vectors for feature extraction, and PLDA and logistic regression for classification. It examines extracting features from Javanese, Sundanese, and Minangkabau languages, then classifying the languages using various parameters. The study finds that x-vector outperforms i-vector when using PLDA classification, except when using logistic regression, where i-vector performs better. It tunes parameters for i-vector UBM size, i-vector dimension, x-vector max frame size, and num repeats, reporting equal error rates to evaluate performance on test segments of 3, 10 and 30 seconds.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
Writing long sentences is bit boring, but with text prediction in the keyboard technology has made
this simple. Learning technology behind the keyboard is developing fast and has become more accurate.
Learning technologies such as machine learning, deep learning here play an important role in predicting the
text. Current trending techniques in deep learning has opened door for data analysis. Emerging technologies
such has Region CNN, Recurrent CNN have been under consideration for the analysis. Many techniques have
been used for text sequence prediction such as Convolutional Neural Networks (CNN), Recurrent Neural
Networks (RNN), and Recurrent Convolution Neural Networks (RCNN). This paper aims to provide a
comparative study of different techniques used for text prediction.
Efficient feature descriptor selection for improved Arabic handwritten words ...IJECEIAES
This document presents a new approach for recognizing Arabic handwritten words that combines three image descriptors for feature extraction: histogram of oriented gradients (HOG), Gabor filter, and local binary pattern (LBP). The approach applies preprocessing techniques to standardize images from the IFN/ENIT dataset before extracting features using the three descriptors. Three k-nearest neighbor models are trained on the extracted features and their predictions are combined using majority voting. Testing achieves a high recognition rate of 99.88%, outperforming previous methods on this dataset. The results demonstrate the effectiveness of combining multiple descriptors and classifiers for Arabic handwriting recognition.
Automated Bangla sign language translation system for alphabets by means of M...TELKOMNIKA JOURNAL
Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.
A Review on Natural Scene Text Understanding for Computer Vision using Machin...IRJET Journal
This document reviews various machine learning techniques for natural scene text understanding in computer vision. It discusses how deep learning methods like convolutional neural networks (CNNs) are more accurate than traditional image processing for text detection and recognition in complex, natural images. The document also summarizes several papers that propose different models using techniques like recurrent neural networks (RNNs), feature pyramid networks (FPNs), attention mechanisms, and connectionist temporal classification to detect and recognize text in scene images with challenges like variations in font, orientation, lighting and background complexity.
We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
Handwritten digit recognition using quantum convolution neural networkIAESIJAI
The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
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.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
Natural language description of images using hybrid recurrent neural networkIJECEIAES
We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset.
Comparative Analysis of Early Stage Cancer Detection Methods in Machine LearningIRJET Journal
This document compares various machine learning methods for early-stage cancer detection. It discusses convolutional neural networks, random forests, support vector machines, k-nearest neighbors, and other algorithms. Several studies that used these methods for cancer detection and classification are reviewed, such as using CNNs to analyze medical images for signs of cancer and random forests to classify gene expression data. The document aims to evaluate different machine learning techniques for their effectiveness in early cancer detection.
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 is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
A Crop Pests Image Classification Algorithm Based on Deep Convolutional Neura...TELKOMNIKA JOURNAL
Conventional pests image classification methods may not be accurate due to the complex
farmland background, sunlight and pest gestures. To raise the accuracy, the deep convolutional neural
network (DCNN), a concept from Deep Learning, was used in this study to classify crop pests image. On
the ground of our experiments, in which LeNet-5 and AlexNet were used to classify pests image, we have
analyzed the effects of both convolution kernel and the number of layers on the network, and redesigned
the structure of convolutional neural network for crop pests. Further more, 82 common pest types have
been classified, with the accuracy reaching 91%. The comparison to conventional classification methods
proves that our method is not only feasible but preeminent.
Intelligent Handwritten Digit Recognition using Artificial Neural NetworkIJERA Editor
The aim of this paper is to implement a Multilayer Perceptron (MLP) Neural Network to recognize and predict handwritten digits from 0 to 9. A dataset of 5000 samples were obtained from MNIST. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden units and the number of iterations. The performance was thereafter compared to obtain the network with the optimal parameters. The proposed system predicts the handwritten digits with an overall accuracy of 99.32%.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
This document is a project report submitted by Mohammad Saiful Islam for a CMPUT 551 course on December 21st, 2010 regarding Bengali handwritten digit recognition using support vector machines. The report discusses building a dataset of Bengali digits written by the author, preprocessing and feature extraction steps, and using a multiclass support vector machine with different kernels for classification. The author hypothesizes that SVM will perform well, RBF kernels will improve performance over linear and polynomial kernels, and using raw pixel values can achieve good accuracy, though testing on different writers may reduce performance. Experiments are planned to test these hypotheses using the collected dataset.
Customized mask region based convolutional neural networks for un-uniformed ...IJECEIAES
In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015.
Analysis of machine learning algorithms for character recognition: a case stu...nooriasukmaningtyas
This paper covers the work done in handwritten digit recognition and the
various classifiers that have been developed. Methods like MLP, SVM,
Bayesian networks, and Random forests were discussed with their accuracy
and are empirically evaluated. Boosted LetNet 4, an ensemble of various
classifiers, has shown maximum efficiency among these methods.
FACE EXPRESSION RECOGNITION USING CONVOLUTION NEURAL NETWORK (CNN) MODELS ijgca
This paper proposes the design of a Facial Expression Recognition (FER) system based on deep
convolutional neural network by using three model. In this work, a simple solution for facial expression
recognition that uses a combination of algorithms for face detection, feature extraction and classification
is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models
are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended
Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this
study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that
AlexNet model achieved the best accuracy (88.2%) compared to other models.
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.
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Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.
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This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
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The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
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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.
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This is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
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This document is a project report submitted by Mohammad Saiful Islam for a CMPUT 551 course on December 21st, 2010 regarding Bengali handwritten digit recognition using support vector machines. The report discusses building a dataset of Bengali digits written by the author, preprocessing and feature extraction steps, and using a multiclass support vector machine with different kernels for classification. The author hypothesizes that SVM will perform well, RBF kernels will improve performance over linear and polynomial kernels, and using raw pixel values can achieve good accuracy, though testing on different writers may reduce performance. Experiments are planned to test these hypotheses using the collected dataset.
Customized mask region based convolutional neural networks for un-uniformed ...IJECEIAES
In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015.
Analysis of machine learning algorithms for character recognition: a case stu...nooriasukmaningtyas
This paper covers the work done in handwritten digit recognition and the
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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.
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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.
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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.
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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.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
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- Basics of IAM in AWS
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Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
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manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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Mechanical Engineering on AAI Summer Training Report-003.pdf
Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 21, No. 2, April 2023, pp. 346~353
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v21i2.24266 346
Journal homepage: http://telkomnika.uad.ac.id
Hybrid convolutional neural networks-support vector machine
classifier with dropout for Javanese character recognition
Diyah Utami Kusumaning Putri1
, Dinar Nugroho Pratomo2
, Azhari1
1
Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada,
Yogyakarta, Indonesia
2
Department of Electrical Engineering and Informatics, Vocational School, Universitas Gadjah Mada, Yogyakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Sep 24, 2021
Revised Mar 16, 2022
Accepted Jun 02, 2022
This research paper explores the hybrid models for Javanese character
recognition using 15600 characters gathered from digital and handwritten
sources. The hybrid model combines the merit of deep learning using
convolutional neural networks (CNN) to involve feature extraction and
a machine learning classifier using support vector machine (SVM).
The dropout layer also manages overfitting problems and enhances training
accuracy. For evaluation purposes, we also compared CNN models with
three different architectures with multilayer perceptron (MLP) models with
one and two hidden layer(s). In this research, we evaluated three variants of
CNN architectures and the hybrid CNN-SVM models on both the accuracy
of classification and training time. The experimental outcomes showed that
the classification performances of all CNN models outperform the
classification performances of both MLP models. The highest testing
accuracy for basic CNN is 94.2% when using model 3 CNN. The increment
of hidden layers to the MLP model just slightly enhances the accuracy.
Furthermore, the hybrid model gained the highest accuracy result of 98.35%
for classifying the testing data when combining model 3 CNN with the SVM
classifier. We get that the hybrid CNN-SVM model can enhance the
accuracy results in the Javanese characters recognition.
Keywords:
Convolutional neural networks
Dropout
Javanese character recognition
Multilayer perceptron
Support vector machine
This is an open access article under the CC BY-SA license.
Corresponding Author:
Diyah Utami Kusumaning Putri
Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences
Universitas Gadjah Mada, Yogyakarta, Indonesia
Email: diyah.utami.k@ugm.ac.id
1. INTRODUCTION
The Indonesian archipelago has many ethnic groups and a diversity of languages. Javanese tribble is
the largest ethnic group and Javanese is the most widely spoken regional language in Indonesia. The Javanese
language has its own traditional letter called Aksara Jawa or Javanese script [1]. Javanese script is a historic
Javanese character that has been used by the Mataram Kingdom since the 17th century. Nowadays, the use of
Javanese script just can be found in historical relics or wall cravings. Sometimes, it also can be used in place
name signboards, street signboards, or decorations as the transcription for the Roman alphabet [2].
Recognizing Javanese script is difficult, this is due to the writing of each character being complex
and some characters are almost similar, so it is more difficult to recognize. Furthermore, if the manuscript
that will be recognized is hand-written because it was written by many different writers who have different
writing styles [3]. Some Javanese are not able to write and read Javanese scripts, particularly adolescents.
It is going to erase the presence of Javanese characters and have an effect on Javanese culture in general.
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Therefore, to contribute to the preservation of Javanese script, we need a tool that has the ability to
automatically recognize Javanese characters [4].
Machine learning has been used before as a solution to the recognition of handwritten characters.
Popli et al. [5] recognized handwritten alphabets samples and classified them into one of the alphabet classes
using machine learning models, i.e. ensemble learning, ensemble bagged trees, k-nearest neighbor (KNN),
support vector machine (SVM) and Naïve Bayes. This research proposed a simplified methodology based on
engineered features that are verified using the MatLab tool, then achieved the highest accuracy of 89.3%
using ensemble subspace model 1.
There have been several kinds of research for recognizing Javanese characters using artificial neural
networks (ANN) and deep learning techniques in order to get better accuracy. Dewa et al. [6] developed
software that used the digital convolutional neural networks (CNN) method for classifying the segmented
image of offline handwritten Javanese characters into 20 classes. In this research, CNN is compared to the
multilayer perceptron (MLP). The results of experiments show that the CNN model outperforms MLP which
achieves the highest accuracy of 89%. Fauziah et al. [7] also used the CNN method to classify 48 classes
including Javanese script types, namely basic letters (carakan) and voice-modifying scripts (sandhangan).
The CNN architecture consists of three convolution layers with max-pooling operations. This research used
hyperparameters including the number of filters for each convolution layer 32, 64, or 128 filters, with a
learning rate of 0.0001, a dropout value is 0.5, and the number of neurons in the fully-connected layer is
1,024 neurons. The average accuracy performance value was 87.65%, the average precision value was
88.01%, and the average recall value was 87.70%. Rismiyati et al. [8] performed CNN and deep neural
network (DNN) for classifying 20 handwritten Javanese characters. The experiment used 2.470 images with
an input image size is 32×32 pixels. The accuracy result with k-fold cross-validation obtained is 70.22% for
CNN and 64.65% for DNN. Wibowo et al. [9] used the CNN method with two different numbers of layers
and the dataset contains 11500 characters. The experimental results obtained that CNN has ensured to
recognition of simple Javanese characters with a 94.57% accuracy score. Currently, the CNN model is a deep
learning technique that is very powerful in solving classification problems with the input image. CNN model
takes pixel neighbour information using extraction of feature task with convolution and pooling operation
between a combination of many layers. Then, the features obtained are used to determine its class using the
softmax activation.
Several other studies have been conducted to improve the performance of CNN, such as developing a
hybrid model that integrates a CNN and support SVM [10]–[15]. In the hybrid model, CNN takes as a feature
extractor and SVM performs as a classifier. This hybrid approach automatically extracts features from the input
raw images using CNN and yields the predictions using SVM. Niu and Suen [10] experimented with the
Modified National Institute of Standards and Technology (MNIST) digit database and compared the hybrid
model with different studies on the equal database. The results imply that this hybrid model has accomplished
better results. Ahlawat and Choudhary [11] additionally confirmed the effectiveness of hybrid CNN-SVM by
producing an accuracy score of 99.28% on the recognition task using the MNIST handwritten digits dataset.
Elleuch et al. [12] explored a new hybrid model CNN-SVM and applied the dropout technique for offline
Arabic handwriting recognition. Simulation results proved that the novel CNN-SVM model with dropout shows
extensively and efficiently better than the CNN-SVM architecture without dropout and the fundamental CNN
classifier.
In this study, an architecture model using hybrid CNN-SVM with dropout for Javanese characters
recognition has been offered to improve the accuracy score of character recognition. The focus of this
research is to learn and extract the features from the raw images of Javanese characters using CNN. Then,
these learned patterns are continued to the SVM classifier for executing the Javanese characters recognition.
Dropout training is one of the effective approaches to manage overfitting issues via randomly ignoring subsets of
features at each iteration of a training stage. The dropout layer will make the convergence speed different, weaken
the effect of the initial parameters on the model, and enhance the training accuracy [16], [17]. For evaluation
purposes, we additionally evaluated CNN models with three different architectures with MLP on the
performance of accuracy and training time.
The rest of this paper is organized as: the basic concepts of CNN, SVM and the hybrid CNN-SVM
model designed for Javanese characters recognition explained in section 2. Furthermore, section 3 presents
our experimental method. Then, the experimental results are given and analyzed in section 4. Lastly, section
4 concludes some remarks and explains the future scope.
2. RESEARCH METHODS
The research methods employed in this work are outlined in this section. We provide an overview of
the CNN and SVM models. Then, we explain our proposed model, the hybrid CNN-SVM.
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2.1. Convolutional neural networks
A convolutional neural network model proposed by LeCun et al. [18] can be considered as an
elaboration for conventional ANN models, such as MLP. A CNN model is arranged of certain layers which are
called convolution and pooling layers to learn and extract features from raw image input and a fully-connected
neural network (FCN) which is actually an MLP model to predict the output class. The features obtained from
those special layers are called feature maps and become inputs for a fully-connected layer (FCL) [6].
Figure 1 represents an example of CNN architecture that consists of a set of many layers. To begin,
the input is convoluted with a set of filters (with C hidden layers) in the convolution layer to get the feature
maps value. Next, the dimensionality (with S hidden layers) of the spatial resolution of the feature maps is
reduced, every convolution layer is continued to a subsampling (or pooling) layer. Convolutional layers
alternate subsampling layers denote as the feature extractor to retrieve discriminating features from the input
images. In the end, a flattened function is implemented to transform each feature map into a one-dimensional
matrix. Then, these matrices will be further passed into the output layer which is the FCL or MLP with a
softmax activation function that generates possibilities of each class of the input image [6], [10], [12].
The various combinations of the numbers of hidden layers, epochs and architectures of CNN may produce
different performances [19]–[21].
Figure 1. An example of CNN model
architecture [10]
Figure 2. An example of hybrid CNN-SVM model
architecture [10]
2.2. Support vector machine
Support vector machine, which has been proposed by Vapnik [22], Cortes and Vapnik [23] is a robust
discriminative classifier. SVM is assumed to be a sophisticated tool to accomplish linear and also non-linear
classification issues with flexibility, stinginess, prediction ability and the global optimum solution. Difference
from ANN that minimizes the empirical risk, the foundation of SVM formulation is the minimization of
structural risk [22].
Kernel functions in the SVM model can convert a nonlinear into a linear problem by transforming data
into high-dimensional feature spaces and finding the best hyperplane to separate the features. The modification
was conducted using various kernel functions such as sigmoid, linear, polynomial, and radial basis function
(RBF) kernels. The best hyperplane is reached by solving a quadratic programming problem that depends on
parameters of regularization [22], [23].
2.3. Hybrid CNN-SVM
The hybrid CNN–SVM architecture combines the CNN model and SVM classifier. A CNN has a
supervised learning mechanism that includes convolution, subsampling (pooling), and fully connected layers.
CNN can learn invariant local features conveniently and extract the most discriminating features from pixel
image patterns. Furthermore, the SVM classifier can turn down the generalization error on invisible data.
SVM intends to represent the dataset features into multi-dimensional feature spaces where an optimal
hyperplane splits the features of image data belonging to variant classes. This model works by replacing the
latest output layer with the SVM classifier. In this model, CNN becomes a feature extractor and SVM as a
classifier and substitutes the softmax layer of CNN. Thus, the output of the hidden layer result can be
assumed as the input features for the SVM classifier [12].
Figure 2 shows an example of the hybrid CNN–SVM model. First, the raw images are continued to
the input layer and are pursued by the CNN model is trained until the training process converges with several
iterations or epochs. Next, the SVM model with kernel function substitutes the output layer of the CNN
model. The SVM uses the outputs from the last hidden layer of CNN as a new feature vector for the input
training process. After that, the SVM classifier has been trained well, it performs the recognition task and
produces new determinations to predict the classes on testing image datasets [10].
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3. EXPERIMENTS
The objective of this study is to accomplish the task of Javanese character recognition and improve its
performance. In order to achieve the objective, we investigated three CNN architectural variations and a hybrid
CNN-SVM model with dropout. The focus of this research is to extract the patterns from the raw images of
handwritten Javanese characters using CNN. Then, these learned features proceeded to the SVM classifier to
perform the Javanese characters recognition. Dropout is also used to control overfitting. We also compared CNN
models with three different architectures with MLP models with one and two hidden layer(s) for evaluation
purposes. We evaluated all models on both the accuracy of classification and training time.
The details of experiments conducted in this paper are described in this section. The experiments
section includes data acquisition for digital fonts and handwritten Javanese characters, the architecture of CNN
models and compared MLP models, the SVM hyperparameters, and also experiment scenarios for Javanese
characters recognition. Each part of the experiment will be presented in the subsection parts.
3.1. Data acquisition
The data is Javanese characters acquired from digital fonts and handwritten texts which are scanned
into documents. The Javanese digital fonts were gathered from 10 different Javanese fonts with normal, bold,
and italic text converted into a document file. Furthermore, the collection of handwriting Javanese script data
was carried out using different pen thicknesses written by Javanese people, then we scanned the handwritten
texts and converted them into a document file. After that, all Javanese documents are segmented into
characters. A total of 100 sets of Javanese handwriting scripts had been acquired yielding 12000 characters
(120 characters per set), besides, we also collected 30 sets of digital Javanese text from digital Javanese fonts,
resulting in 3600 characters.
Figure 3. Sample of digital (from 1 until 30) and handwritten (from 31 until 130) “HA” Javanese character
after preprocessing methods applied
A total of 15600 Javanese characters obtained from digital (30 sets × 120 characters) and handwritten
(100 sets × 120 characters) resources. The characters obtained from the hand-writing process can be noisy, not
aligned, slightly blurry (because of the pen inks), and so on must be enhanced. Hence, some image enhancement
techniques have been adjusted to all the original Javanese characters images in the pre-processing step. Thus,
the handwritten dataset gathered is clean and can be used robustly. Every data in the dataset is converted and
normalized into an 8-bit grayscale image which has a fixed image of the size of 28×28 pixels and is positioned
at the center. Figure 3 displays samples of digital (from 1 until 30) and handwritten (from 31 until 130) “HA”
Javanese characters after preprocessing methods were applied. This dataset will be split into training, validation
and testing datasets.
3.2. The architecture of CNN models
The CNN model consists of several layers with convolution and subsampling layers and the FCN
layer as an output layer with a softmax function. Xavier weight initialization, also known as Glorot uniform
initializer was used to initialize weight neurons in the CNN model. Adam optimizer was used in this work to
obtain optimized performance. We adjusted dropout regularization and also the rectified linear unit (ReLU)
activation function to entire layers in the CNN model. The implementation of the CNN model using a deep
learning library in python language, tensorflow keras library [24], [25]. For evaluation objectives, we also
compared CNN models with three different architectures with MLP models with one and two hidden layer(s).
The details of different CNN architectures utilized in this research are presented in Table 1.
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Table 1. The details of different CNN model architectures
Model architecture Layer Size Output shape
Architecture of model 1 CNN Input (1, 28, 28) -
Conv + ReLU 32 (3×3) filters (28, 28, 32)
MaxPooling + Dropout (0.2) (2×2) (14, 14, 32)
Conv + ReLU 64 (2×2) filters (14, 14, 64)
MaxPooling + Dropout (0.2) (2×2) (7, 7, 64)
Conv + ReLU 128 (3 × 3) filters (7, 7, 128)
MaxPooling + Dropout (0.2) (2×2) (4, 4, 128)
FullyConnected + ReLu + Dropout (0.2) 1000 neurons 1000
FullyConnected 120 neurons 120
Softmax 120 way 120
Architecture of model 2 CNN Input (1, 28, 28) -
Conv + ReLU 32 (5×5) filters (28, 28, 32)
MaxPooling + Dropout (0.2) (2×2) (14, 14, 32)
Conv + ReLU 64 (5×5) filters (14, 14, 64)
MaxPooling + Dropout (0.2) (2×2) (7, 7, 64)
Conv + ReLU 128 (5×5) filters (7, 7, 128)
MaxPooling + Dropout (0.2) (2×2) (4, 4, 128)
Conv + ReLU 64 (5×5) filters (4, 4, 64)
MaxPooling + Dropout (0.2) (2×2) (2, 2, 64)
Conv + ReLU 32 (5×5) filters (2, 2, 32)
MaxPooling + Dropout (0.2) (2×2) (1, 1, 32)
FullyConnected + ReLu + Dropout (0.2) 1024 neurons 1024
FullyConnected 120 neurons 120
Softmax 120 way 120
Architecture of model 3 CNN Input (1, 28, 28) -
Conv + ReLU 64 (3×3) filters (28, 28, 64)
Conv + ReLU 64 (3×3) filters (28, 28, 64)
MaxPooling + Dropout (0.2) (2×2) (14, 14, 64)
Conv + ReLU 128 (3×3) filters (14, 14, 128)
Conv + ReLU 128 (3×3) filters (14, 14, 128)
MaxPooling + Dropout (0.2) (2×2) (7, 7, 128)
Conv + ReLU 256 (3×3) filters (7, 7, 256)
Conv + ReLU 256 (3×3) filters (7, 7, 256)
Conv + ReLU 256 (3×3) filters (7, 7, 256)
MaxPooling + Dropout (0.2) (2×2) (4, 4, 256)
Conv + ReLU 512 (3×3) filters (4, 4, 512)
Conv + ReLU 512 (3×3) filters (4, 4, 512)
Conv + ReLU 512 (3×3) filters (4, 4, 512)
MaxPooling + Dropout (0.2) (2×2) (2, 2, 512)
Conv + ReLU 512 (3×3) filters (2, 2, 512)
Conv + ReLU 512 (3×3) filters (2, 2, 512)
Conv + ReLU 512 (3×3) filters (2, 2, 512)
MaxPooling + Dropout (0.2) (2×2) (1, 1, 512)
FullyConnected + ReLu + Dropout (0.2) 4096 neurons 4096
FullyConnected + ReLu + Dropout (0.2) 4096 neurons 4096
FullyConnected 120 neurons 120
Softmax 120 way 120
In this experiment, we also trained MLP models with single and two hidden layer(s) with the same
Javanese characters dataset. The detailed architecture of MLP models is given in Table 2. In this research,
we utilized pixel values of the Javanese character images as inputs for MLP without the feature extraction
approach previously.
The MLP architecture with a single hidden layer has 784 neurons in the input layer as Table 2. Each
neuron will accept a single vector as the result of extracted features from a character image that has a size of
28×28 pixels. The MLP architecture with a single hidden layer consists of one hidden layer with 1,000
neurons. Then, the MLP model will generate 120 probability values to which the input image class may
belong. Furthermore, the two hidden layers MLP model utilized 1,000 neurons and 2,000 neurons. MLP
models used the rectified linear unit (ReLU) in hidden layers. Moreover, the softmax activation functions are
also applied in the output layer, respectively.
3.3. The SVM hyperparameters
The generated features from the CNN model are continued to the SVM classifier for training,
validating and then testing the Javanese images. One hundred and twenty values from the last layer of the
trained CNN model were used as a new feature vector to denote the input matrix and were passed to the SVM
for learning, validation, and testing. The parameters of SVM like kernel function, C parameter (regularization
parameter) and gamma parameter are tuned accurately because they are the affecting parameters during SVM
classification.
The kernel functions are observed in SVM using linear, sigmoid, polynomial, and RBF kernels.
The values of the gamma parameter explored are 0.01, 0.001, 0.0001, 0.00001. Furthermore, the values of the
C parameter observed are 1, 10, 100, 1000. The parameters used to apply the SVM method are optimized by
a 5-fold cross-validated grid-search over a parameter grid.
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3.4. Experiment scenario
A CNN model uses a supervised learning scenario to update internal weight value matrices during
the training process, the model uses a lost function that calculates the difference between the predicted class
and the actual class. CNN models utilize the lost (or cost) function as the cross-entropy error formula.
The scenario of our experiment was started with the dataset which was separated into 80% for training and
the rest for testing to perform the model development and evaluation. Then, the training dataset was split into
80% for training and the rest for the validation dataset. In our experiment, we use 128 as the batch size,
the maximum number of epochs 1000 and a learning rate of 0.0001. We used 4 × NVIDIA Tesla V100
DGXS GPU for training the model.
Table 2. The details of different multilayer perceptron model architectures
Model architecture Layer Size Output shape
The MLP model architecture with single hidden layer Input 784 neurons -
FullyConnected + ReLu 1000 neurons 1000
FullyConnected 120 neurons 120
Softmax 120 way 120
The MLP model architecture with two hidden layers Input 784 neurons -
FullyConnected + ReLu 1000 neurons 1000
FullyConnected + ReLu 2000 neurons 2000
FullyConnected 120 neurons 120
Softmax 120 way 120
4. RESULTS AND DISCUSSION
Three different CNN architectures are compared with the MLP model with single and two hidden
layer(s). We used three different variables to evaluate the performances: validation accuracy, testing accuracy
and training time results. Table 3 presents the experiment results of those five different models.
Table 3 shows that MLP with a single hidden layer takes minimal training time among others.
Moreover, model 3 CNN needs more training time than other CNN models. It is caused by the complex
architectures of convolution and subsampling layers, thus increasing the computation and training time.
Overall, the classification accuracies of all CNN models exceed the classification accuracies of both MLP
models for validation and testing datasets. Using of convolutional and pooling layers in the CNN model can
effectively learn the features of the Javanese characters dataset. The highest validation and testing accuracies
from this experiment were gathered when we used model 3 CNN, which are 97.14% and 98.06%,
respectively. We can also learn that the increment of hidden layers to the MLP model slightly enhances its
performance.
In the next experiment, we built and trained a hybrid CNN-SVM model. An SVM classifier changed
the last fully connected layer of CNN to produce classes of the characters. We used different kernel functions
and determined the optimal value of gamma and C parameters to build the SVM in the hybrid model by
applying the 5-fold cross-validation scenario on the training dataset using grid-search. Table 4 provides the
results of the hybrid CNN-SVM model using three different CNN architectures.
Table 3. The results of CNN and MLP models
Model Validation accuracy Testing accuracy Training time
Model 1 CNN 89.9% 90.1% 98.979
Model 2 CNN 94.75% 95.45% 251.456
Model 3 CNN 97.14% 98.06% 379.195
MLP with one hidden layer 80.3% 81.2% 24.987
MLP with two hidden layers 81.7% 82.3% 32.017
Table 4. The results of the hybrid CNN-SVM models
Model Validation accuracy Testing accuracy Training time
Model 1 CNN + SVM 90.99% 91.86% 445.263
Model 2 CNN + SVM 95.22% 95.57% 600.764
Model 3 CNN + SVM 97.38% 98.35% 827.896
After examining the results presented in Table 3 and Table 4, the accuracy of the hybrid CNN-SVM
model for recognition of Javanese characters is outperformed by the accuracy of the basic CNN model. The
highest testing accuracy is 98.35% when combining model 3 CNN with SVM classifier. The training time of the
hybrid CNN-SVM increases because searching the best parameters of SVM using grid search requires more time.
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5. CONCLUSION
This study aims to accomplish the task of recognising Javanese characters and improving their
performance. A total of 15600 Javanese characters were gathered from digital and handwritten sources. In order
to achieve the objective, we investigated three variants of CNN architectures and a model of hybrid CNN-SVM
with dropout. The focus of this research is to involve feature extraction using CNN and then predict the output
using an SVM classifier. The model combines the advantage of deep learning CNN and a machine learning
classifier using SVM in recognizing Javanese characters. Dropout training with the dropout layer is one of the
powerful ways to manage overfitting problems and enhance training accuracy. We also compare CNN models
with three different architectures with multilayer perceptron MLP models with one and two hidden layer(s). In this
research, we assessed all models on both classification performance and training time.
The experimental outcomes showed that the classification performances of all CNN models
outperform the classification performances of both MLP models for validation and testing datasets. The highest
testing accuracy using basic CNN is 98.06% which used model 3 CNN. The increment of hidden layers to the
MLP model slightly enhances the accuracy. Furthermore, the proposed model achieved the highest accuracy
of 98.35% for testing data when combining model 3 CNN with SVM classifier. The CNN-based-SVM model
is a promising classification method in character recognition research. For the training time, MLP with a single
hidden layer needs minimal training time among other models. Moreover, CNN models require more
significant time for training compared to MLP. The training time of hybrid CNN-SVM also increases
because searching the best parameters of SVM using grid search requires more time.
The character recognition research using a hybrid CNN-SVM model can improve further. In future
research, our proposed model can be enhanced to recognise digital and handwritten characters in different
languages such as Japanese, Korean, Bengali, Hindi, and so on. The other optimizing techniques can also be
explored to elevate the overall performance of classification. Different architectures of hybrid CNN such as
CNN-RNN, CNN-HMM can be explored. Evolutionary algorithms also can be investigated for enhancing CNN
learning parameters, i.e. the number of layers and/or neurons, learning rate, kernel size of convolution filters.
ACKNOWLEDGEMENTS
This work was supported in part by a grant from Direktorat Penelitian Universitas Gadjah Mada
[grants number: 2403/UN1.P.III/DIT-LIT/PT/2020].
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BIOGRAPHIES OF AUTHORS
Diyah Utami Kusumaning Putri received B.Sc. and M.Cs. degress in Computer
Science from Universitas Gadjah Mada, Yogyakarta, Indonesia in 2016 and 2019, respectively,
and received the M.Sc. degree in Computer Science according to the dual degree program between
Universitas Gadjah Mada, Yogyakarta, Indonesian and National Central University, Taiwan, in
2019. She became lecturer in Department of Computer Science and Electronics, Universitas
Gadjah Mada in 2020 and joined Intelligent System Laboratory. Her research interests are deep
learning, computer vision, data science and natural language processing. email:
diyah.utami.k@ugm.ac.id.
Dinar Nugroho Pratomo received B.Sc. degree in Informatics Engineering from
Universitas Jenderal Soedirman, Purwokerto, Indonesia in 2016, the M.Sc. degree in Computer
Science from Universitas Gadjah Mada, Yogyakarta, Indonesia in 2019, and received the M.IM.
degree in Information Management according to the dual degree program between Universitas
Gadjah Mada, Yogyakarta, Indonesian and National Taiwan University of Science and
Technology, Taiwan, in 2019. He became a lecturer in Department of Electrical Engineering and
Informatics, Vocational School, Universitas Gadjah Mada in 2020. His research interests include
machine learning, software engineering, and data science.
email: dinar.nugroho.p@ugm.ac.id.
Azhari received B.Sc. degree in Statistics from Universitas Gadjah Mada,
Yogyakarta, Indonesia, the M.T. degree in Software Engineering from Institut Teknologi Bandung,
Bandung, Indonesia, and the Ph.D. degree in Computer Science from Universitas Gadjah Mada,
Yogyakarta, Indonesia. He is currently Associate Profressor and Head of Computer Science
Bachelor Program in Department of Computer Science and Electronics, Universitas Gadjah Mada.
His research interests are software engineering, intelligent agent, and information technology
project management. He can be contacted at email: arisn@ugm.ac.id.