Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
IRJET- Deep Learning Techniques for Object DetectionIRJET Journal
The document discusses deep learning techniques for object detection in images. It provides an overview of convolutional neural networks (CNNs), the most popular deep learning approach for computer vision tasks. The document describes the basic architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It then discusses several state-of-the-art CNN models for object detection, including ResNet, R-CNN, SSD, and YOLO. The document aims to help newcomers understand the key deep learning techniques and models used for object detection in computer vision.
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
IRJET- Recognition of Handwritten Characters based on Deep Learning with Tens...IRJET Journal
This paper proposes a convolutional neural network model to recognize handwritten digits using the MNIST dataset. The model is built using TensorFlow and consists of convolutional, pooling and fully connected layers. The model is trained on 60,000 images and tested on 10,000 images, achieving 98% accuracy on the training set and classifying digits with low error of 0.03% on the test set. Previous methods for handwritten digit recognition are discussed and the CNN approach is shown to provide superior performance with faster training times compared to other models.
This document describes a neural network model for generating image captions to help visually impaired people understand images. A convolutional neural network extracts image features, which are fed into a recurrent neural network or long short-term memory network to generate natural language captions. The model achieves state-of-the-art performance on image captioning tasks and has the potential to greatly improve the lives of visually impaired individuals by allowing them to understand images through automatically generated captions.
This document provides an introduction to speech recognition with deep learning. It discusses how speech recognition works, the development of the field from early methods like HMMs to modern deep learning approaches using neural networks. It defines deep learning and explains why it is called "deep" learning. It also outlines common deep learning architectures for speech recognition, including CNN-RNN models and sequence-to-sequence models. Finally, it describes the layers of a CNN like convolutional, pooling, ReLU and fully-connected layers.
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
IRJET- Deep Learning Techniques for Object DetectionIRJET Journal
The document discusses deep learning techniques for object detection in images. It provides an overview of convolutional neural networks (CNNs), the most popular deep learning approach for computer vision tasks. The document describes the basic architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It then discusses several state-of-the-art CNN models for object detection, including ResNet, R-CNN, SSD, and YOLO. The document aims to help newcomers understand the key deep learning techniques and models used for object detection in computer vision.
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
IRJET- Recognition of Handwritten Characters based on Deep Learning with Tens...IRJET Journal
This paper proposes a convolutional neural network model to recognize handwritten digits using the MNIST dataset. The model is built using TensorFlow and consists of convolutional, pooling and fully connected layers. The model is trained on 60,000 images and tested on 10,000 images, achieving 98% accuracy on the training set and classifying digits with low error of 0.03% on the test set. Previous methods for handwritten digit recognition are discussed and the CNN approach is shown to provide superior performance with faster training times compared to other models.
This document describes a neural network model for generating image captions to help visually impaired people understand images. A convolutional neural network extracts image features, which are fed into a recurrent neural network or long short-term memory network to generate natural language captions. The model achieves state-of-the-art performance on image captioning tasks and has the potential to greatly improve the lives of visually impaired individuals by allowing them to understand images through automatically generated captions.
This document provides an introduction to speech recognition with deep learning. It discusses how speech recognition works, the development of the field from early methods like HMMs to modern deep learning approaches using neural networks. It defines deep learning and explains why it is called "deep" learning. It also outlines common deep learning architectures for speech recognition, including CNN-RNN models and sequence-to-sequence models. Finally, it describes the layers of a CNN like convolutional, pooling, ReLU and fully-connected layers.
This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.
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.
IRJET- Machine Learning based Object Identification System using PythonIRJET Journal
This document presents a machine learning based object identification system using convolutional neural networks (CNNs) in Python. The system is trained on a dataset of cat and dog images and aims to identify objects in input images. The document compares different CNN structures using various activation functions and classifiers. It finds that a model with a ReLU activation function and sigmoid classifier achieved the highest classification accuracy of around 90.5%. The system demonstrates how CNNs can be used for image classification tasks in machine learning.
GUI based handwritten digit recognition using CNNAbhishek Tiwari
This project is to create a model which can recognize the digits as well as also to create GUI which is user friendly i.e. user can draw the digit on it and will get appropriate output.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
CHARACTER RECOGNITION USING NEURAL NETWORK WITHOUT FEATURE EXTRACTION FOR KAN...Editor IJMTER
Handwriting recognition has been one of the active and challenging research areas in the
field of pattern recognition. It has numerous applications which include, reading aid for blind, bank
cheques and conversion of any hand written document into structural text form[1]. As there are no
sufficient number of works on Indian language character recognition especially Kannada script
among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten
Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized
into 60x40 pixel.The resized character is used for training the neural network. Once the training
process is completed the same character is given as input to the neural network with different set of
neurons in hidden layer and their recognition accuracy rate for different kannada characters has been
calculated and compared. The results show that the proposed system yields good recognition
accuracy rates comparable to that of other handwritten character recognition systems.
This document is a resume for Manoj Alwani providing his contact information, education history, professional experience, skills, projects, publications, and courses. It details that he has a M.S. in Computer Science from Stony Brook University and a B.Tech from India. His professional experience includes research roles at Element Inc and Stony Brook University focused on deep learning and computer vision. His skills and projects involve areas such as deep learning, computer vision, parallel computing, robotics, and natural language processing.
Hierarchical deep learning architecture for 10 k objects classificationcsandit
Evolution of visual object recognition architectures based on Convolutional Neural Networks &
Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science.
These architectures extract & learn the real world hierarchical visual features utilizing
supervised & unsupervised learning approaches respectively. Both the approaches yet cannot
scale up realistically to provide recognition for a very large number of objects as high as 10K.
We propose a two level hierarchical deep learning architecture inspired by divide & conquer
principle that decomposes the large scale recognition architecture into root & leaf level model
architectures. Each of the root & leaf level models is trained exclusively to provide superior
results than possible by any 1-level deep learning architecture prevalent today. The proposed
architecture classifies objects in two steps. In the first step the root level model classifies the
object in a high level category. In the second step, the leaf level recognition model for the
recognized high level category is selected among all the leaf models. This leaf level model is
presented with the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or unsupervised learning
approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific
leaf level models. Currently the training of leaf level models is in progress; where we have
trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models.
Also we demonstrate that the validation error of the leaf level models saturates towards the
above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5
error rate for the entire two-level architecture needs to be computed in conjunction with the
error rates of root & all the leaf models. The realization of this two level visual recognition
architecture will greatly enhance the accuracy of the large scale object recognition scenarios
demanded by the use cases as diverse as drone vision, augmented reality, retail, image search
& retrieval, robotic navigation, targeted advertisements etc.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
This document presents a case study on using deformable templates for recognizing handwritten digits. Deformable templates match unknown images to known templates by deforming the contours of templates to fit the edge strengths of unknown images. The dissimilarity measure is derived from the deformation needed for the match. This technique achieved recognition rates up to 99.25% on a dataset of 2,000 handwritten digits. Statistical and structural features are extracted to represent characters for classification using deformable templates.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
IRJET - Deep Learning Applications and Frameworks – A ReviewIRJET Journal
This document reviews deep learning applications and frameworks. It begins by defining deep learning and discussing how deep neural networks can be used to automatically identify patterns in large datasets. It then discusses several applications of deep learning, including self-driving cars, news aggregation, natural language processing, virtual assistants, and visual recognition. The document also describes artificial neural networks and deep neural networks. Finally, it reviews several popular deep learning frameworks, including TensorFlow, PyTorch, Keras, Caffe, and Chainer.
This document describes a project using a neural network and MATLAB for handwritten character recognition. The goal is to train a neural network to classify individual handwritten characters. The solution approach involves preprocessing images to extract characters, extracting features from the characters, training the neural network, and creating a graphical user interface application. Image preprocessing includes converting to grayscale, thresholding to binary, connectivity testing, and cropping characters. Feature extraction calculates 17 attributes for each character like position, size, pixel counts and distributions. The neural network is then trained on this dataset to classify characters for the application.
IRJET- Automatic Data Collection from Forms using Optical Character RecognitionIRJET Journal
1) The document presents an automated system for collecting user data from paper forms using optical character recognition (OCR).
2) It involves scanning paper forms, segmenting the user input fields, performing OCR on the input text using a convolutional recurrent neural network model, and updating the data to a database.
3) This system aims to reduce the time and effort required to manually collect and process form data compared to current methods.
This document summarizes a research paper that proposes a new approach to visual cryptography using pextral coding in a cellular automaton framework. It begins by reviewing existing visual cryptography techniques that use pixel expansion to encrypt an image. The proposed method considers a 3x3 cellular automaton matrix and uses two symmetric pextral patterns to form signatures from an ASCII image pixel. These signatures are combined to reconstruct the original image without loss of information. The goal is to improve security over traditional visual cryptography while avoiding issues like noise and loss of quality during encryption and decryption.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The main objective of this work is the uniting and streamlining of an automatic face detection application and recognition system for video indexing applications. Human identification means the classification of gender which can increase the identification accuracy. So, accurate gender classification algorithms may increase the accuracy of the applications and can reduce its complexity. But, in some applications, some challenges are there such as rotation, gray scale variations that may reduce the accuracy of the application. The main goal of building this module is to understand the values in image, pattern, and array processing with OpenCV for effective processing faces for building pipe-lining, SVM models.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
From Pixels to Understanding: Deep Learning's Impact on Image Classification ...IRJET Journal
This document discusses how deep learning has significantly improved image classification and recognition abilities compared to traditional machine learning methods. It provides an overview of different deep learning network structures used for these tasks, including deep belief networks, convolutional neural networks, and recurrent neural networks. Deep learning algorithms are able to extract abstract feature representations from unlabeled image data using multi-layer neural networks, leading to more accurate image categorization than earlier approaches.
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.
IRJET- Face Recognition using Machine LearningIRJET Journal
This document presents a modified CNN architecture for face recognition that adds two batch normalization operations to improve performance. The CNN extracts facial features using convolutional layers and max pooling, and classifies faces using a softmax classifier. The proposed approach was tested on a face database containing images of 4 individuals with varying lighting conditions. Experimental results showed the modified CNN with batch normalization achieved better recognition results than traditional methods.
This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.
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.
IRJET- Machine Learning based Object Identification System using PythonIRJET Journal
This document presents a machine learning based object identification system using convolutional neural networks (CNNs) in Python. The system is trained on a dataset of cat and dog images and aims to identify objects in input images. The document compares different CNN structures using various activation functions and classifiers. It finds that a model with a ReLU activation function and sigmoid classifier achieved the highest classification accuracy of around 90.5%. The system demonstrates how CNNs can be used for image classification tasks in machine learning.
GUI based handwritten digit recognition using CNNAbhishek Tiwari
This project is to create a model which can recognize the digits as well as also to create GUI which is user friendly i.e. user can draw the digit on it and will get appropriate output.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
CHARACTER RECOGNITION USING NEURAL NETWORK WITHOUT FEATURE EXTRACTION FOR KAN...Editor IJMTER
Handwriting recognition has been one of the active and challenging research areas in the
field of pattern recognition. It has numerous applications which include, reading aid for blind, bank
cheques and conversion of any hand written document into structural text form[1]. As there are no
sufficient number of works on Indian language character recognition especially Kannada script
among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten
Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized
into 60x40 pixel.The resized character is used for training the neural network. Once the training
process is completed the same character is given as input to the neural network with different set of
neurons in hidden layer and their recognition accuracy rate for different kannada characters has been
calculated and compared. The results show that the proposed system yields good recognition
accuracy rates comparable to that of other handwritten character recognition systems.
This document is a resume for Manoj Alwani providing his contact information, education history, professional experience, skills, projects, publications, and courses. It details that he has a M.S. in Computer Science from Stony Brook University and a B.Tech from India. His professional experience includes research roles at Element Inc and Stony Brook University focused on deep learning and computer vision. His skills and projects involve areas such as deep learning, computer vision, parallel computing, robotics, and natural language processing.
Hierarchical deep learning architecture for 10 k objects classificationcsandit
Evolution of visual object recognition architectures based on Convolutional Neural Networks &
Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science.
These architectures extract & learn the real world hierarchical visual features utilizing
supervised & unsupervised learning approaches respectively. Both the approaches yet cannot
scale up realistically to provide recognition for a very large number of objects as high as 10K.
We propose a two level hierarchical deep learning architecture inspired by divide & conquer
principle that decomposes the large scale recognition architecture into root & leaf level model
architectures. Each of the root & leaf level models is trained exclusively to provide superior
results than possible by any 1-level deep learning architecture prevalent today. The proposed
architecture classifies objects in two steps. In the first step the root level model classifies the
object in a high level category. In the second step, the leaf level recognition model for the
recognized high level category is selected among all the leaf models. This leaf level model is
presented with the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or unsupervised learning
approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific
leaf level models. Currently the training of leaf level models is in progress; where we have
trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models.
Also we demonstrate that the validation error of the leaf level models saturates towards the
above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5
error rate for the entire two-level architecture needs to be computed in conjunction with the
error rates of root & all the leaf models. The realization of this two level visual recognition
architecture will greatly enhance the accuracy of the large scale object recognition scenarios
demanded by the use cases as diverse as drone vision, augmented reality, retail, image search
& retrieval, robotic navigation, targeted advertisements etc.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
This document presents a case study on using deformable templates for recognizing handwritten digits. Deformable templates match unknown images to known templates by deforming the contours of templates to fit the edge strengths of unknown images. The dissimilarity measure is derived from the deformation needed for the match. This technique achieved recognition rates up to 99.25% on a dataset of 2,000 handwritten digits. Statistical and structural features are extracted to represent characters for classification using deformable templates.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
IRJET - Deep Learning Applications and Frameworks – A ReviewIRJET Journal
This document reviews deep learning applications and frameworks. It begins by defining deep learning and discussing how deep neural networks can be used to automatically identify patterns in large datasets. It then discusses several applications of deep learning, including self-driving cars, news aggregation, natural language processing, virtual assistants, and visual recognition. The document also describes artificial neural networks and deep neural networks. Finally, it reviews several popular deep learning frameworks, including TensorFlow, PyTorch, Keras, Caffe, and Chainer.
This document describes a project using a neural network and MATLAB for handwritten character recognition. The goal is to train a neural network to classify individual handwritten characters. The solution approach involves preprocessing images to extract characters, extracting features from the characters, training the neural network, and creating a graphical user interface application. Image preprocessing includes converting to grayscale, thresholding to binary, connectivity testing, and cropping characters. Feature extraction calculates 17 attributes for each character like position, size, pixel counts and distributions. The neural network is then trained on this dataset to classify characters for the application.
IRJET- Automatic Data Collection from Forms using Optical Character RecognitionIRJET Journal
1) The document presents an automated system for collecting user data from paper forms using optical character recognition (OCR).
2) It involves scanning paper forms, segmenting the user input fields, performing OCR on the input text using a convolutional recurrent neural network model, and updating the data to a database.
3) This system aims to reduce the time and effort required to manually collect and process form data compared to current methods.
This document summarizes a research paper that proposes a new approach to visual cryptography using pextral coding in a cellular automaton framework. It begins by reviewing existing visual cryptography techniques that use pixel expansion to encrypt an image. The proposed method considers a 3x3 cellular automaton matrix and uses two symmetric pextral patterns to form signatures from an ASCII image pixel. These signatures are combined to reconstruct the original image without loss of information. The goal is to improve security over traditional visual cryptography while avoiding issues like noise and loss of quality during encryption and decryption.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The main objective of this work is the uniting and streamlining of an automatic face detection application and recognition system for video indexing applications. Human identification means the classification of gender which can increase the identification accuracy. So, accurate gender classification algorithms may increase the accuracy of the applications and can reduce its complexity. But, in some applications, some challenges are there such as rotation, gray scale variations that may reduce the accuracy of the application. The main goal of building this module is to understand the values in image, pattern, and array processing with OpenCV for effective processing faces for building pipe-lining, SVM models.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
From Pixels to Understanding: Deep Learning's Impact on Image Classification ...IRJET Journal
This document discusses how deep learning has significantly improved image classification and recognition abilities compared to traditional machine learning methods. It provides an overview of different deep learning network structures used for these tasks, including deep belief networks, convolutional neural networks, and recurrent neural networks. Deep learning algorithms are able to extract abstract feature representations from unlabeled image data using multi-layer neural networks, leading to more accurate image categorization than earlier approaches.
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.
IRJET- Face Recognition using Machine LearningIRJET Journal
This document presents a modified CNN architecture for face recognition that adds two batch normalization operations to improve performance. The CNN extracts facial features using convolutional layers and max pooling, and classifies faces using a softmax classifier. The proposed approach was tested on a face database containing images of 4 individuals with varying lighting conditions. Experimental results showed the modified CNN with batch normalization achieved better recognition results than traditional methods.
TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITIONijaia
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.
TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITION gerogepatton
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.
Transfer Learning with Convolutional Neural Networks for IRIS Recognitiongerogepatton
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize
persons with a high degree of assurance. Extracting effective features is the most important stage in the
iris recognition system. Different features have been used to perform iris recognition system. A lot of
them are based on hand-crafted features designed by biometrics experts. According to the achievement of
deep learning in object recognition problems, the features learned by the Convolutional Neural Network
(CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed
an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The
proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for
features extracting and classification. The performance of the iris recognition system is tested on four
public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The
results show that the proposed system is achieved a very high accuracy rate.
This document presents a study on object detection using SSD-MobileNet. The researchers developed a lightweight object detection model using SSD-MobileNet that can perform real-time object detection on embedded systems with limited processing resources. They tested the model on images and video captured using webcams. The model was able to detect objects like people, cars, and animals with good accuracy. The SSD-MobileNet framework provides fast and efficient object detection for applications like autonomous driving assistance systems that require real-time performance on low-power devices.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
A Survey on Image Processing using CNN in Deep LearningIRJET Journal
This document discusses the use of convolutional neural networks (CNNs) for image processing tasks. It provides an overview of CNNs and their application in image classification. The document then reviews several papers that have applied CNNs to tasks like image classification, object detection, and image segmentation. Some key advantages of CNNs discussed are their ability to directly take images as input without needing separate preprocessing steps. However, challenges include overfitting when training data is limited and complex images can confuse networks. The document concludes that CNN performance improves with more network layers and training data. CNNs are widely used for computer vision tasks due to their strong image feature extraction capabilities.
Image Classification and Annotation Using Deep LearningIRJET Journal
This document presents a new deep learning model for jointly performing image classification and annotation. The model uses a convolutional neural network (CNN) to extract features from images and classify semantic objects. It then annotates the images based on the identified objects. The model is evaluated on standard datasets like CIFAR-10, CIFAR-100 as well as a new dataset collected by the authors. Results show the model achieves comparable or better performance than baseline methods, while also enabling fast image annotation. A novel scalable implementation allows annotating large datasets within seconds.
IRJET- Comparative Study of Different Techniques for Text as Well as Object D...IRJET Journal
This document discusses and compares different techniques for object and text detection from real-time images, including OCR, RCNN, Mask RCNN, Fast RCNN, and Faster RCNN algorithms. It finds that Mask RCNN, an extension of Faster RCNN, is generally the best algorithm for object detection in real-time images, as it outperforms other models in accuracy for tasks like object detection, segmentation, and captioning challenges. The document provides background on machine learning and neural networks approaches to image recognition and object detection.
This document discusses the layers of convolutional neural networks (CNNs). It provides an overview of common CNN layers including convolutional layers, max pooling layers, padding, rectified linear unit (ReLU) nonlinearity, and fully connected layers. Convolutional layers extract features from input images using small filter matrices in a sliding window approach. Max pooling layers reduce the dimensionality of feature maps. Padding handles edge effects when filters are smaller than inputs. ReLU introduces nonlinearity. Fully connected layers flatten feature maps into vectors for classification. The document reviews the functions of these key CNN layers.
This document provides a summary of recent developments in artificial intelligence for object detection. It discusses different architectures based on convolutional neural networks for tasks like face detection, object detection, and video object co-segmentation. Region-based models like R-CNN, Fast R-CNN, and Faster R-CNN are described along with single-shot detectors like YOLO. The document also discusses using fuzzy logic for 3D modeling and the author's own work on face and motion detection in Python. In conclusion, real-time object detection has advanced through these techniques but there remains room for improvement, especially addressing issues in models like YOLO.
IRJET- Object Detection in an Image using Deep LearningIRJET Journal
The document summarizes object detection in images using deep learning. It introduces common object detection methods like convolutional neural networks (CNNs) and regional-based CNNs. CNNs are effective for object detection as they can automatically learn distinguishing features without needing manually defined features. The document then describes the methodology which uses a CNN with layers like convolution, ReLU, pooling and fully connected layers to perform feature extraction and classification. It concludes that CNNs provide an efficient method for real-time object detection and segmentation in images through deep learning.
PADDY CROP DISEASE DETECTION USING SVM AND CNN ALGORITHMIRJET Journal
- The document discusses a study on detecting diseases in paddy/rice crops using deep learning algorithms like convolutional neural networks (CNN) and support vector machines (SVM).
- A dataset of rice leaf images was created and a CNN model using transfer learning with MobileNet was developed and trained on the dataset to classify rice diseases.
- The proposed method aims to automatically classify rice disease images to help farmers more accurately identify diseases, as manual identification can be difficult and inaccurate. This could help improve treatment and support farmers.
Scene recognition using Convolutional Neural NetworkDhirajGidde
The document discusses scene recognition using convolutional neural networks. It begins with an abstract stating that scene recognition allows context for object recognition. While object recognition has improved due to large datasets and CNNs, scene recognition performance has not reached the same level of success. The document then discusses using a new scene-centric database called Places with over 7 million images to train CNNs for scene recognition. It establishes new state-of-the-art results on several scene datasets and allows visualization of network responses to show differences between object-centric and scene-centric representations.
Satellite Image Classification with Deep Learning Surveyijtsrd
Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. Roshni Rajendran | Liji Samuel ""Satellite Image Classification with Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30031.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30031/satellite-image-classification-with-deep-learning-survey/roshni-rajendran
Convolutional Neural Network Based Real Time Object Detection Using YOLO V4IRJET Journal
This document describes a project to develop a real-time object detection system using convolutional neural networks (CNNs) implemented in MATLAB. The system will leverage deep learning techniques like CNNs to accurately detect and localize objects in real-time video streams. The key objectives are to achieve state-of-the-art accuracy and reliability in object detection with minimal false positives/negatives, while also enabling real-time performance by processing video frames swiftly. The document provides background on CNNs and object detection, and outlines the proposed system architecture which includes collecting an annotated dataset, preprocessing the data, training a CNN model, and evaluating model performance on a test set.
Deep Learning Applications and Image Processingijtsrd
With the rapid development of digital technologies, the analysis and processing of data has become an important problem. In particular, classification, clustering and processing of complex and multi structured data required the development of new algorithms. In this process, Deep Learning solutions for solving Big Data problems are emerging. Deep Learning can be described as an advanced variant of artificial neural networks. Deep Learning algorithms are commonly used in healthcare, facial and voice recognition, defense, security and autonomous vehicles. Image processing is one of the most common applications of Deep Learning. Deep Learning software is commonly used to capture and process images by removing the errors. Image processing methods are used in many fields such as medicine, radiology, military industry, face recognition, security systems, transportation, astronomy and photography. In this study, current Deep Learning algorithms are investigated and their relationship with commonly used software in the field of image processing is determined. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "Deep Learning Applications and Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49142.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49142/deep-learning-applications-and-image-processing/ahmet-özcan
Similar to Unsupervised learning models of invariant features in images: Recent developments in multistage architecture approach for object detection (20)
Design and Implementation of Smart Cooking Based on Amazon EchoIJSCAI Journal
Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking
food. People may speak to Amazon Echo during the cooking in order to get the information and situation of
the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services,
and speaks to people the results that cloud services make by querying the embedded cooking knowledge and
achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile
application are well-designed and implemented to monitor the temperature of cooking food
Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysi...IJSCAI Journal
The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual
effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the
World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic
oriented economic policy, an objective recommendation for creating an economic policy that improves
people’s everyday lives might be derived by the analysis results. It was found that these more advanced
algorithms achieve a considerably stronger correlation between both indices than pure statistical means
yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven
economic policy.
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Nov 2018 Table of contents; current issue -International Journal on Soft Comp...IJSCAI Journal
The document discusses using machine learning algorithms to analyze the relationship between economic freedom and quality of life. It examines the Economic Freedom of the World index and the Human Development Index with modern machine learning methods. The analysis finds that these advanced algorithms achieve a stronger correlation between the indices than statistical means alone. However, there is still some room for non-liberal interpretations of how to design economic policies to improve people's lives. The goal is to objectively evaluate the impact of economic freedom through a data-driven approach.
6th international conference on artificial intelligence and applications (aia...IJSCAI Journal
6th International Conference on Artificial Intelligence and Applications (AIAP-2019) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Generating images from a text description is as challenging as it is interesting. The Adversarial network
performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of
Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With
generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS
step-by-step in this paper that could be easily understood. This paper presents visual comparative study of
other models attempting to generate image conditioned on the text description. One sentence can be related
to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also
performed. The performance of Stack-GAN is better in generating images from captions due to its unique
architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the
defects yielding a high-resolution image.
Temporally Extended Actions For Reinforcement Learning Based Schedulers IJSCAI Journal
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Knowledgebase Systems in Neuro Science - A StudyIJSCAI Journal
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
An Iranian Cash Recognition Assistance System For Visually Impaireds IJSCAI Journal
In economical societies of today, using cash is an inseparable aspect of human’s life. People use cash for
marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and
the necessity of knowing the monetary value of it caused one of the most challenging problems for visually
impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a
picture taken from a banknote using some image processing and machine vision techniques. While the
developed approach is very fast, it can recognize the value of the banknote by an average accuracy rate of
about 97% and can overcome different challenges like rotation, scaling, collision, illumination changes,
perspective, and some others.
An Experimental Study of Feature Extraction Techniques in Opinion MiningIJSCAI Journal
This document summarizes an experimental study on different feature extraction techniques for opinion mining and sentiment analysis. The study used a freely available dataset containing 200 reviews from TripAdvisor. Various feature extraction methods were applied, including part-of-speech tags, entities, phrases, document-level features, and term weighting. The results found that term frequency-inverse document frequency (tf-idf) weighting at the single word and multi-word levels achieved the highest accuracy of 98.36% and 98.26% respectively. Overall accuracy across the different feature sets ranged from 95.91% to 98.36%. The study aims to evaluate different feature extraction methods for sentiment classification tasks in opinion mining.
Monte-Carlo Tree Search For The "Mr Jack" Board Game IJSCAI Journal
Recently the use of the Monte-Carlo Tree Search algorithm, and in particular its most famous
implementation, the Upper Confidence Tree can be seen has a key moment for artificial intelligence in
games. This family of algorithms provides huge improvements in numerous games, such as Go, Havannah,
Hex or Amazon. In this paper we study the use of this algorithm on the game of Mr Jack and in particular
how to deal with a specific decision-making process.Mr Jack is a 2-player game, from the family of board
games. We will present the difficulties of designing an artificial intelligence for this kind of games, and we
show that Monte-Carlo Tree Search is robust enough to be competitive in this game with a smart approach.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
A Study on Graph Storage Database of NOSQLIJSCAI Journal
This document summarizes a research paper on graph storage databases in NoSQL. It discusses big data and the need for alternative databases to handle large, diverse datasets. It defines the key aspects of big data including volume, velocity, variety and complexity. It also describes different types of NoSQL databases, focusing on the basic structure of graph databases. Graph databases use nodes and relationships to model connected data. The document compares several graph database systems and discusses advantages like performance and flexibility as well as disadvantages like complexity. It outlines several applications of graph databases in areas like social networks and logistics.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Estimation Of The Parameters Of Solar Cells From Current-Voltage Characterist...IJSCAI Journal
This paper presents a method for calculating the light generated current, the series resistance, shun
resistance and the two components of the reverse saturation current usually encountered in the double
diode representation of
the solar cell from the experimental values of the current
-
voltage characteristics
of the cell using genetic algorithm. The theory is able to regenerate the above mentioned parameters to
very good accuracy when applied to cell data that was generated from
pre
-
defined parameters. The
method is applied to various types of space quality solar cells and sub cells. All parameters except the
light generated current are seen to be nearly the same in the case of a cell whose characteristics under
illumination and i
n dark were analyzed. The light generated current is nearly equal to the short
-
circuit
current in all cases. The parameters obtained by this method and another method are nearly equal
wherever applicable. The parameters are also shown to represent the cur
rent
-
voltage characteristics
well
Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval ...IJSCAI Journal
In the magnitude of internet one need to devote extra time to investigate an
ticipated resource, especially
when one need to search information from documents. For the higher range internet there is serious need
to demand the essentiality to discover the reserved resources. One of the solutions for information retrieval
from docume
nt repository is to attach tags to documents. Numerous online social bookmarking services
permit users to attach tags with resources which are eventually meta
-
data, frequently stated as folksonomy.
In current paper, authors implemented this model for infor
mation retrieval by utilizing these tags, after
retrieving by using delicious API and synthesize tag cloud in an Indian University to search and retrieve
information from document repository
Study of Distance Measurement Techniques in Context to Prediction Model of We...IJSCAI Journal
Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce
related applications. Sometimes people of organization feel delay while accessing internet in
spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this
delay problem. Prediction model analysing history of internet user from server raw log files and determine
future sequence of web objects and placed all web objects to nearer to the user so access latency could be
reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects,
it is necessary to determine proximity of one web object with other by identifying proper distance metric
technique related to web caching and prefetching. This paper studies different distance metric techniques
and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching
and Web Prefetching
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATAIJSCAI Journal
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
Design of Dual Axis Solar Tracker System Based on Fuzzy Inference SystemsIJSCAI Journal
Electric power is a basic need in today’s life. Due to the extensive usage of power, there is a need to look
for an alternate clean energy source. Recently many researchers have focused on the solar energy as a
reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into
electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as
they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased
with an ability to change the panels angel according to the sun position. The main goal of such systems is
to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times.
This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The
system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling.
Models are compared in terms of the correlation between the actual testing data output and their
corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used
to measure the models error size. In order to measure the effectiveness of the proposed models, we
compare the output power produced by a fixed photovoltaic panels with the output which would be
produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce
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Unsupervised learning models of invariant features in images: Recent developments in multistage architecture approach for object detection
1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
DOI :10.5121/ijscai.2016.5107 63
UNSUPERVISED LEARNING MODELS OF
INVARIANT FEATURES IN IMAGES: RECENT
DEVELOPMENTS IN MULTISTAGE
ARCHITECTURE APPROACH FOR OBJECT
DETECTION
Sonia Mittal
CSE Dept. Institute of Technology Nirma University, Ahmedabad
ABSTRACT
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
KEYWORDS
Unsupervised feature learning, CNNs, Tiled CNNs, Deep learning
1. INTRODUCTION
A lot of research is being done in the area of object recognition and detection during last two
decades. It involves multi-disciplinary field knowledge like image processing, machine learning,
statistics /probability, optimization etc. In object detection and recognition the learning invariant
representations of features is an important problem and it is the sub domain of computer vision.
The ability to learn robust invariant representations from a limited amount of labelled data is a
critical step for the solution of the object recognition problem. In computer vision and pattern
recognition domain various unsupervised methods are used to build feature extractor since long
e.g. for dimensionality reduction or clustering, such as Principal Component Analysis and K-
Means, have been used routinely in numerous vision applications [3].
The general framework for unsupervised feature learning (multistage)[4] is as below: It is
divided broadly into two stages: Feature map learning and classification. First stage is the
extraction of random patches from unlabelled training images. And a pre-processing would be
done to the patches to normalize the data. After that learning a feature – mapping using an
unsupervised learning. In the second stage consists of feature extraction, Pooling and
classification. Feature would be extracted from equally spaced –sub patches covering the input
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
64
images, then pooling of features together over regions of the input images to reduce the number
of feature values. And finally train a classifier to predict the labels given the feature vectors.
Primarily Multistage network for object detection/classification (high level computer vision
tasks) are the Artificial Neural network, because of the shallow architecture (one input, hidden
and out-put layer) [4, 5]. ANNs had not gain much of the success as it could be. Much of the
momentum is not gain by this architecture due to the complexity and computation time required
to execute back propagation methods for learning on relatively slow processor as compared to
current and past years progress in speed of processor. But as the advent of the fast computational
resources these types of architecture again become useful and with new variant i.e. Deep
Learning Network came into existence. Deep learning Network were found very successful in
computer vision problems.
Machine learning is the area where the method are used to provide the ability to machine which
functions like humans Deep learning is one of the area of Machine learning methods i.e. it is
based on learning representation of data. An image, video or audio can be represented as a vector
of intensity value per pixel, or as a more abstract way as a set of edges, region of particular shape
etc.as in Fig. 1. So by raw input, machine cannot understand about the nature of the data or
characteristics of the data. If input data can be represented in some higher level like edges or
shapes then machine can able to predict by combing these abstraction together [1,4,5]. Deep
learning provides the way to represent input data into some higher level of abstraction as shown
in Fig. 1. In deep learning methods main contribution was in 2006 by a few research groups,
starting with Geoff Hinton’s group, who initially focussed on starting unsupervised representation
learning algorithms to obtain deeper representation [3, 5, 6, 11, 12, and 14].
Primarily our focus is on Convolutional Neural Networks (CNNs) and Deep Belief Networks
(DBNs) because they are well established in the deep learning field and show great success. In
literature many more variants of the convolutional networks are found, which are vary in how
learning is done: supervised manner or unsupervised manner. Also [10] efforts was to develop
invariant feature learning while extracting feature in same stage to reduce computation time. We
will be discussing in detailed Tiled CNNs variant of CNNs in following section.
3
rd
layer “Objects”
2
nd
Layer Object parts
1
st
layer “Edges”
Pixels
Fig.: 1 Feature Representation[22]
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
65
2. VARIOUS FRAMEWORKS
2.1 Deep 2learning: Deep belief networks (DBN)
Deep Belief network belongs to the class of generative model in machine learning. It can also be
viewed as an alternating type of deep neural network, which is composed of multiple layer of
latent/hidden variables [4]. Internally hidden layers are connected in a hierarchical manner to each
other. But the units which are lying on the same layer are not connected to each other as shown in
Fig.2. A DBN can learn statics of input data by providing the set of examples in an unsupervised
manner. In this manner this kind of network can learn to probabilistically reconstruct its inputs.
The hidden layers then act as feature detectors on inputs. After this learning step, a DBN can be
further trained in a supervised way to perform classification.
In this type of networks depth of architecture indicates the number of levels of composition of
non-linear operations in the function learned. As the researchers have gain the knowledge and
insight into how our neuroscience functions [4], they are able to get the intuition about how the
information is processed and communication is done in our brain and visual cortex, and are
inspired by this model.
In [15] paper they have discussed about the stackable unsupervised learning system termed as
encoder-decoder paradigm. Encoder will take raw data as an input and transform into
representation known as code or feature vector (low dimensional). And after that a decoder will
reconstructs the input from the representation as shown in Fig.4. Principle component analysis
(PCA) Restricted Boltzmann Machine (RBM), Auto – encoder neural nets, Sparse energy based
are the example of unsupervised learning methods which can be used to pre trained the Deep
network. In [16] paper convolutional Auto-Encoders are discussed with the advantage that they
can scale well to high dimensional inputs over auto-encoders which are used in DBN.
Fig : 2 Schematic overview of a deep belief net. Arrows represent directed connections [23].
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
66
Fig : 3 Auto- encoder [24]
While fully connected Deep architecture do not scale well to realistic-sized high dimensional
images in context to the performance and computational time. In Convolutional neural network
discussed in next section their number of parameters which are used to describe their shared
weights does not depends on the input dimensionality [19, 20, 21].
In paper [11] Hinton et. al have introduced a fast unsupervised learning algorithm for deep belief
network based on greedy layer wise approach. It is a generative model as describe earlier with
many hidden variables in hidden units. In DBN’s RBM (Restricted Boltzmann machine) are
generally used as basic building blocks, which is based on energy models. RBMs are usually
trained using the contrastive divergence learning procedure (Hinton, 2002).
2.2 Convolutional Network
The fundamental concept behind the Convolution Network is biologically inspired trainable
architecture. It is also the type of deep network having the ability to learn invariant features.
Internal representations are hierarchical. In vision, pixels are assembled into edges, edges into
motifs, motifs into parts, parts into objects, and objects into scenes as shown in Fig. 1.
This gives the motivation that recognition architectures for vision should have multiple trainable
stages stacked on top of each other, one for each level in the feature hierarchy. Each stage in a
ConvNets is consists of a filter bank, some non-linearities, and feature pooling layers. With
multiple stages, a ConvNet can learn multi-level hierarchies of features as shown in Fig. 4.
While ConvNets have been successfully deployed in many commercial applications from OCR to
video surveillance. Initially the convolution network was trained by Gradient-based supervised
learning. The problem with Convolutional network is that it requires a large number of examples
5. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
67
per class for training. If lower layers would be trained in an unsupervised manner then number of
training samples would be reduced considerably. In last few years several research works have
shown the advantages (in terms of speed and accuracy) of pre-training each layer of a deep
network in unsupervised mode, before tuning the whole system with a gradient-based algorithm
[3,9].
In many of the methods they incorporates invariance at its core, e.g. local contrast normalization
(LCN) method, which introduces invariance with respect to illumination changes. That means
method for unsupervised training of invariant feature hierarchies are designed. Once high-level
invariant features have been trained with unlabeled data, a classifier can use these features to
classify images through supervised training on a small number of samples.
Fig. 4 : A typical Convolution architecture with two stages[25]
In Convolutional network or Convolutional neural network (CNNs) translated versions of the
same basis function is used, and translation invariant features are built by pooling them. In CNNs
the same basis function is shared across different image locations i.e. termed as weight tying
CNNs. As a result of this technique have significantly fewer learnable parameters which makes it
possible to train them with fewer examples than if entirely different basis functions were learned
at different locations (untied weights).
Furthermore, Translation invariance is hard coded in CNNs, it is both advantageous and non-
advantageous. Drawback of this hard-coding approach is that the pooling architecture captures
only translational invariance; the network does not, for example, pool across units that are
rotations of each other or capture more complex invariances, such as out-of-plane rotations. So it
is better to let the network learn its own invariances from unlabelled data in place of hard-code
translational invariance in the network. Tiled Convolutional network is the architecture which
employs this scheme. Tiled convolutional neural network is an extension of Convolutional neural
network that support both unsupervised pertaining and weight tiling.
2.3 Tiled Convolutional Network
The tiled convolutional networks (Tiled CNNs) [7], which uses a tiling scheme in which weight
would be tied across the k steps away from each other. By using tied weight mechanism it has the
6. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
68
benefit of significantly reducing the number of learnable parameters while giving the algorithm
flexibility to learn other invariances. This method is based on only constraining weights/basis
functions k steps away from each other as shown in fig : 5 CNN can be considered as a special
case of Tiled CNN, with K=1 then it corresponds to Convolution network.
Fig. 5: Left: CNN Right: Partially untied local receptive field.
Figure 5 [26] can be understand as: left figure is showing the connectivity in the CNN; where local
receptive fields and weights are tied. Right figure shows that in Tiled CNNs units same colour belong to
same map; within each map, units with the same fill texture have tied together. ( Right fig: The circles
which are on front level of same color (blue), the middle ones are of same color(red) and last level are of
same color(green)) .
Tiled CNNs [7] are based on two main concepts: first one is local receptive fields (RF), and
another is weight-tying. The receptive field concepts gives the locality awareness, hence each unit
in the network only ―looks‖ at a small, localized region of the input image. Now second concepts
of weight-tying additionally enforces that each first-layer (simple) unit shares the same weights
(see Figure 4-Left). This will provide the reduction in the number of learnable parameters, and by
pooling over neighbouring units further hard-codes translational invariance into the model.
By Tiling connection of receptive fields instead of full receptive filed as in CNN, computational
complexity would be reduced reasonably and allows the network to grow with more input or
classes.
Weight-tying mechanism is allowing hard coded translational invariance and hence more
invariant features cannot be possible because pooling units is capturing only simple invariance
(illumination), in place of complex invariance such as scale and rotation invariance.
In Tiled CNNs [7], rather than tying all of the weights in the network together, instead a method
is used that leaves nearby bases untied, but far-apart bases tied. This lets second-layer units pool
over simple units that have different basis functions, and hence learn a more complex range of
invariances. The Tiled Convolutional network achieves competitive results on the NORB and
CIFAR-10 object recognition datasets.
7. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
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3. CONCLUSIONS
The convolutional neural networks inherently will not provide unsupervised pre training, whereas
Deep Belief networks, Tiled CNN provide unsupervised pre training. The variant of deep belief
network: Staked Auto encoders are helpful in pertaining deep network. The CNNs are
discriminative methods whereas Deep belief Networks is generative method. Stacked Auto
encoders are discriminative models but denoising encoders maps to generative models.
Depending upon the requirements of the application the choice of the framework can be selected.
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AUTHOR
Sonia Mittal is working as a Faculty with CSE Department for last 16 years. She has
pursued her MCA from MBM Engineering College, Jodhpur. Her area of interest are
Parallel Computing and Multimedia Processing. She has published 2 Research papers in
International Journal. She has also involved in guiding major projects at post graduate
level. Currently she is working as an Assistant Professor in Computer Science and
Engineering department.