The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse t... more The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108.
Advanced Computing: An International Journal, 2014
Face recognition is A technique that has been widely used in various important field, this proces... more Face recognition is A technique that has been widely used in various important field, this process helps in the identification of an individual by a machine for the purpose of security and ease of work. The normal technique of face recognition usually works better when there are multiple samples for a single person (MSSP) is available. In present applications where this technique is to be used such as in social networks, security systems, identification cards there is only a single sample per person (SSPP) that is readily available. This less availability of the samples causes failure in the working of conventional face recognition techniques which require multiple samples for a particular individual. To overcome this drawback which sets back the system from the accurate functioning of face recognition this paper puts forward a novel technique which makes use of discriminative multi-manifold analysis (DMMA) that extracts distinctive features using image patches. Recognition is done by the process of manifold to manifold matching. Hence there is an increment in the accuracy rate of face recognition.
Received Jul 8, 2021 Revised Oct 19, 2021 Accepted Oct 26, 2021 The Coronavirus disease (COVID-19... more Received Jul 8, 2021 Revised Oct 19, 2021 Accepted Oct 26, 2021 The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using ...
In face recognition Local appearance based methods has achieved greater performance. In this pape... more In face recognition Local appearance based methods has achieved greater performance. In this paper, we have proposed single sample per class using Center Symmetric Local Binary Pattern and Gabor Filter. Gabor Filter extracts the textual feature and generates a binary face templat and the binary face template acts like a mask to extract local texture information using Center Symmetric Local Binary Pattern. Face features which are evaluated from CS-LBP has better performance.
An Efficient Face Recognition Using DCT, Adaptive LBP and Gabor Filter with Single Sample Per Class
Nowadays face recognition plays an important role in today's world. It has achieved greater i... more Nowadays face recognition plays an important role in today's world. It has achieved greater importance in the field of information security, law enforcement and surveillance. Now this face recognition approach is applied to many areas like Airport security, Driver's License, Passport, Customs and Immigration. In face recognition Local appearance based methods had achieved greater performance. In this paper we have proposed single sample per class using Discrete Cosine Transform, Adaptive Local Binary Pattern and Gabor Filter based on local selective feature extraction approach. Discrete Cosine Transform is used to extract the facial features from the face image .It helps to extract the facial features efficiently. Then the Gabor filter extracts the textual feature and generates a binary face template based on that features. And this binary face template act like a mask to extract local texture information using Adaptive Local binary pattern. Adaptive Local binary pattern met...
Map Reduce programming paradigm plays a vital role in the development of cloud computing applicat... more Map Reduce programming paradigm plays a vital role in the development of cloud computing application using the Distributed file system where nodes concurrently provide computing as well as storage functions. Initially a file is partitioned into number of chunks allocated into different nodes so that Map Reduce technique can be performed in the nodes. Since cloud computing is a dynamic environment upgrading, replacing and adding new nodes to the environment is a frequent concern. This confidence is obviously insufficient in a large-scale, failure-prone atmosphere since the central load balancer is put under significant workload that is linearly scaled with the structure of the system range, and may lead to a performance bottleneck the single point of failure. To overcome the failure in this paper, a fully distributed load rebalancing algorithm is presented to handle the load imbalance problem. The proposed algorithm is compared alongside a centralized approach in a production system ...
The technological growth has a serious impact on security which has its own significance. The cor... more The technological growth has a serious impact on security which has its own significance. The core objective of this project is to extract the facial features using the local appearance based method for the accurate face identification with single sample per class .The face biometric based person identification plays a major role in wide range of applications such as Airport security, Driver’s license, Passport, Voting System, Surveillance. This project presents face recognition based on granular computing and robust feature extraction using Scale Invariant Feature Transform (SIFT) approach. The Median filter is used to extract the hybrid features and the pyramids are generated after the face granulation. Then, DoG pyramid will be formed from successive iterations of Gaussian images. By this granulation, facial features are segregated at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. In feature extraction stage, SIFT desc...
Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms
Diabetes mellitus is characterized as a chronic disease may cause many complications. The machine... more Diabetes mellitus is characterized as a chronic disease may cause many complications. The machine learning algorithms are used to diagnosis and predict the diabetes. The learning based algorithms plays a vital role on supporting decision making in disease diagnosis and prediction. In this paper, traditional classification algorithms and neural network based machine learning are investigated for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation on patients suffering from diabetes in future. The results of this work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with lowest MSE of 0.19. The MLP gives the lowest false positive rate and false negative rate with highest area under curve of 86 %.
Thin films of zinc sulfide (ZnS) with different concentrations of zinc acetate have been made by ... more Thin films of zinc sulfide (ZnS) with different concentrations of zinc acetate have been made by chemical bath deposition technique in acidic medium (pH = 5) on glass substrate using zinc acetate and sodium sulfide as sources of Zn+2 ion and S−2 ion, respectively, and ethylenediaminetetraacetate as complexing agents and sulfuric acid to adjust pH value at a constant deposition temperature of 85°C, and the deposition time of 90 minutes was used. The effect of the concentration of metallic precursor on the structural, morphological, and optical properties of chemical bath deposited zinc sulfide thin films was investigated in this study. The XRD result confirmed mixed phases of crystalline and amorphous structure dominating other phases, which is witnessed by larger crystallite size than other phases. It reveals that the thin films had hexagonal structure at the medium concentration with preferred orientation along (111) plane, and at lower and higher concentration, it showed that film...
An Effective Facial Expression Locator Applying WLD and DCT with Single Sample Per Class
Topical appearance-based methods havebeen successfully employed to automatic face recognition and... more Topical appearance-based methods havebeen successfully employed to automatic face recognition and achieved state-of-the-art performance. The aim of this paper is to extract the facial features applying the topical appearance based method for the exact facerecognition with single sample per class. The topical appearance based method finds facial features suchas eyes, nose, mouth and chin and also finds attributes of and relations (e.g. areas, distances, angles) between the characteristics are applied as descriptors for face recognition. In this paper we proposed single sample per class applying Weber's Local Descriptor (WLD) and Discrete Cosine Transform (DCT).Discrete Cosine Transform is used to extract the facial features from the image .It helps to extract the facial features efficiently. The WLD descriptor acts an image as a histogram of differential excitements and clarification changes, formal detection of edges and powerful image representation.This proposed approach reduc...
Introduction: COVID-19 is an acute respiratory illness that directly affects the lungs It is much... more Introduction: COVID-19 is an acute respiratory illness that directly affects the lungs It is much needed to predict the possibility of occurrence of COVID-19 based on their characteristics Objective: This paper studies the different machine learning classification algorithms to predict the COVID-19 recovered and deceased cases Methods: The k-fold cross-validation resampling technique is used to validate the prediction model Aim and The prediction scores of each algorithm are evaluated with performance metrics such as prediction accuracy, precision, recall, mean square error, confusion matrix, and kappa score For the preprocessed dataset, the k-nearest neighbour (KNN) classification algorithm produces 80 4 % of predication accuracy and 1 5 to 3 3 % of improved accuracy over other algorithms Results: The KNN algorithm predicts 92 % (true positive rate) of the deceased cases correctly, with 0 077% of misclassification Further, the KNN algorithm produces the lowest error rate as 0 19 on...
Dominant Local Tetra Patterns: A New Feature Descriptor for Face Recognition
International Journal of Emerging Trends in Engineering Research
Local binary pattern (LBP) and local ternary pattern (LTP) of the gray-level difference of the co... more Local binary pattern (LBP) and local ternary pattern (LTP) of the gray-level difference of the connection between the identified pixel and its nearby neighbours are encoded based on vertical and horizontal directions using the n th order local derivatives. In our proposed approach Dominant local tetra patterns (LTrPs) the n th order LTrP is computed using n th order horizontal and vertical local derivatives. The efficiency and the effectiveness of our proposed algorithm for face recognition is calculated and analysed by combining it with the Difference of Gaussian. In order to find the strength of the descriptor various images under different lighting, facial expression and pose are tested. The experimental results show that the proposed approach provides a good representation and advanced of face recognition rates than LBP and its by-products.
The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse t... more The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108.
Advanced Computing: An International Journal, 2014
Face recognition is A technique that has been widely used in various important field, this proces... more Face recognition is A technique that has been widely used in various important field, this process helps in the identification of an individual by a machine for the purpose of security and ease of work. The normal technique of face recognition usually works better when there are multiple samples for a single person (MSSP) is available. In present applications where this technique is to be used such as in social networks, security systems, identification cards there is only a single sample per person (SSPP) that is readily available. This less availability of the samples causes failure in the working of conventional face recognition techniques which require multiple samples for a particular individual. To overcome this drawback which sets back the system from the accurate functioning of face recognition this paper puts forward a novel technique which makes use of discriminative multi-manifold analysis (DMMA) that extracts distinctive features using image patches. Recognition is done by the process of manifold to manifold matching. Hence there is an increment in the accuracy rate of face recognition.
Received Jul 8, 2021 Revised Oct 19, 2021 Accepted Oct 26, 2021 The Coronavirus disease (COVID-19... more Received Jul 8, 2021 Revised Oct 19, 2021 Accepted Oct 26, 2021 The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using ...
In face recognition Local appearance based methods has achieved greater performance. In this pape... more In face recognition Local appearance based methods has achieved greater performance. In this paper, we have proposed single sample per class using Center Symmetric Local Binary Pattern and Gabor Filter. Gabor Filter extracts the textual feature and generates a binary face templat and the binary face template acts like a mask to extract local texture information using Center Symmetric Local Binary Pattern. Face features which are evaluated from CS-LBP has better performance.
An Efficient Face Recognition Using DCT, Adaptive LBP and Gabor Filter with Single Sample Per Class
Nowadays face recognition plays an important role in today's world. It has achieved greater i... more Nowadays face recognition plays an important role in today's world. It has achieved greater importance in the field of information security, law enforcement and surveillance. Now this face recognition approach is applied to many areas like Airport security, Driver's License, Passport, Customs and Immigration. In face recognition Local appearance based methods had achieved greater performance. In this paper we have proposed single sample per class using Discrete Cosine Transform, Adaptive Local Binary Pattern and Gabor Filter based on local selective feature extraction approach. Discrete Cosine Transform is used to extract the facial features from the face image .It helps to extract the facial features efficiently. Then the Gabor filter extracts the textual feature and generates a binary face template based on that features. And this binary face template act like a mask to extract local texture information using Adaptive Local binary pattern. Adaptive Local binary pattern met...
Map Reduce programming paradigm plays a vital role in the development of cloud computing applicat... more Map Reduce programming paradigm plays a vital role in the development of cloud computing application using the Distributed file system where nodes concurrently provide computing as well as storage functions. Initially a file is partitioned into number of chunks allocated into different nodes so that Map Reduce technique can be performed in the nodes. Since cloud computing is a dynamic environment upgrading, replacing and adding new nodes to the environment is a frequent concern. This confidence is obviously insufficient in a large-scale, failure-prone atmosphere since the central load balancer is put under significant workload that is linearly scaled with the structure of the system range, and may lead to a performance bottleneck the single point of failure. To overcome the failure in this paper, a fully distributed load rebalancing algorithm is presented to handle the load imbalance problem. The proposed algorithm is compared alongside a centralized approach in a production system ...
The technological growth has a serious impact on security which has its own significance. The cor... more The technological growth has a serious impact on security which has its own significance. The core objective of this project is to extract the facial features using the local appearance based method for the accurate face identification with single sample per class .The face biometric based person identification plays a major role in wide range of applications such as Airport security, Driver’s license, Passport, Voting System, Surveillance. This project presents face recognition based on granular computing and robust feature extraction using Scale Invariant Feature Transform (SIFT) approach. The Median filter is used to extract the hybrid features and the pyramids are generated after the face granulation. Then, DoG pyramid will be formed from successive iterations of Gaussian images. By this granulation, facial features are segregated at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. In feature extraction stage, SIFT desc...
Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms
Diabetes mellitus is characterized as a chronic disease may cause many complications. The machine... more Diabetes mellitus is characterized as a chronic disease may cause many complications. The machine learning algorithms are used to diagnosis and predict the diabetes. The learning based algorithms plays a vital role on supporting decision making in disease diagnosis and prediction. In this paper, traditional classification algorithms and neural network based machine learning are investigated for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation on patients suffering from diabetes in future. The results of this work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with lowest MSE of 0.19. The MLP gives the lowest false positive rate and false negative rate with highest area under curve of 86 %.
Thin films of zinc sulfide (ZnS) with different concentrations of zinc acetate have been made by ... more Thin films of zinc sulfide (ZnS) with different concentrations of zinc acetate have been made by chemical bath deposition technique in acidic medium (pH = 5) on glass substrate using zinc acetate and sodium sulfide as sources of Zn+2 ion and S−2 ion, respectively, and ethylenediaminetetraacetate as complexing agents and sulfuric acid to adjust pH value at a constant deposition temperature of 85°C, and the deposition time of 90 minutes was used. The effect of the concentration of metallic precursor on the structural, morphological, and optical properties of chemical bath deposited zinc sulfide thin films was investigated in this study. The XRD result confirmed mixed phases of crystalline and amorphous structure dominating other phases, which is witnessed by larger crystallite size than other phases. It reveals that the thin films had hexagonal structure at the medium concentration with preferred orientation along (111) plane, and at lower and higher concentration, it showed that film...
An Effective Facial Expression Locator Applying WLD and DCT with Single Sample Per Class
Topical appearance-based methods havebeen successfully employed to automatic face recognition and... more Topical appearance-based methods havebeen successfully employed to automatic face recognition and achieved state-of-the-art performance. The aim of this paper is to extract the facial features applying the topical appearance based method for the exact facerecognition with single sample per class. The topical appearance based method finds facial features suchas eyes, nose, mouth and chin and also finds attributes of and relations (e.g. areas, distances, angles) between the characteristics are applied as descriptors for face recognition. In this paper we proposed single sample per class applying Weber's Local Descriptor (WLD) and Discrete Cosine Transform (DCT).Discrete Cosine Transform is used to extract the facial features from the image .It helps to extract the facial features efficiently. The WLD descriptor acts an image as a histogram of differential excitements and clarification changes, formal detection of edges and powerful image representation.This proposed approach reduc...
Introduction: COVID-19 is an acute respiratory illness that directly affects the lungs It is much... more Introduction: COVID-19 is an acute respiratory illness that directly affects the lungs It is much needed to predict the possibility of occurrence of COVID-19 based on their characteristics Objective: This paper studies the different machine learning classification algorithms to predict the COVID-19 recovered and deceased cases Methods: The k-fold cross-validation resampling technique is used to validate the prediction model Aim and The prediction scores of each algorithm are evaluated with performance metrics such as prediction accuracy, precision, recall, mean square error, confusion matrix, and kappa score For the preprocessed dataset, the k-nearest neighbour (KNN) classification algorithm produces 80 4 % of predication accuracy and 1 5 to 3 3 % of improved accuracy over other algorithms Results: The KNN algorithm predicts 92 % (true positive rate) of the deceased cases correctly, with 0 077% of misclassification Further, the KNN algorithm produces the lowest error rate as 0 19 on...
Dominant Local Tetra Patterns: A New Feature Descriptor for Face Recognition
International Journal of Emerging Trends in Engineering Research
Local binary pattern (LBP) and local ternary pattern (LTP) of the gray-level difference of the co... more Local binary pattern (LBP) and local ternary pattern (LTP) of the gray-level difference of the connection between the identified pixel and its nearby neighbours are encoded based on vertical and horizontal directions using the n th order local derivatives. In our proposed approach Dominant local tetra patterns (LTrPs) the n th order LTrP is computed using n th order horizontal and vertical local derivatives. The efficiency and the effectiveness of our proposed algorithm for face recognition is calculated and analysed by combining it with the Difference of Gaussian. In order to find the strength of the descriptor various images under different lighting, facial expression and pose are tested. The experimental results show that the proposed approach provides a good representation and advanced of face recognition rates than LBP and its by-products.
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