TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vac...TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.展开更多
In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test ...In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%.展开更多
This paper aims to establish a relative study between a relational Microsoft SQL Server database and a non-relational MongoDB database within the unstructured representation of data in JSON format. There is a great am...This paper aims to establish a relative study between a relational Microsoft SQL Server database and a non-relational MongoDB database within the unstructured representation of data in JSON format. There is a great amount of work done regarding comparison of multiple database management applications on the basis of their performances, security etc., but we have limited information available where these databases are assessed on the basis of provided data. This study will mainly focus on looking at all the possibilities that both these database types offer us when handling data in JSON. We will accomplish this by implementing a series of experiments while taking into consideration that the subjected data does not require to be normalized;and therefore, evaluate the outcome to conclude the result.展开更多
In this paper,we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm.The method works on the principle of confusion and diffusion.The proposed scheme containing both conf...In this paper,we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm.The method works on the principle of confusion and diffusion.The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes.Initially,an image(red,green,and blue components)is partitioned into blocks with an equal number of pixels.Each block is then processed with Tinkerbell Chaotic Map(TBCM)to get shuffled pixels and shuffled blocks.Composite Fractal Function(CFF)change the value of pixels of each color component(layer)to obtain a random sequence.Through the obtained random sequence,three layers of plain image are encrypted.Finally,with each encrypted layer,Brownian Particles(BP)are XORed that added an extra layer of security.The experimental tests including a number of statistical tests validated the security of the presented scheme.The results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature.展开更多
Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbase...Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM.展开更多
In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and producti...In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.展开更多
基金This paper is funded by the Deanship of Scientific Research at ImamMohammad Ibn Saud Islamic University Research Group No.RG-21-07-05.
文摘TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.
文摘In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%.
文摘This paper aims to establish a relative study between a relational Microsoft SQL Server database and a non-relational MongoDB database within the unstructured representation of data in JSON format. There is a great amount of work done regarding comparison of multiple database management applications on the basis of their performances, security etc., but we have limited information available where these databases are assessed on the basis of provided data. This study will mainly focus on looking at all the possibilities that both these database types offer us when handling data in JSON. We will accomplish this by implementing a series of experiments while taking into consideration that the subjected data does not require to be normalized;and therefore, evaluate the outcome to conclude the result.
文摘In this paper,we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm.The method works on the principle of confusion and diffusion.The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes.Initially,an image(red,green,and blue components)is partitioned into blocks with an equal number of pixels.Each block is then processed with Tinkerbell Chaotic Map(TBCM)to get shuffled pixels and shuffled blocks.Composite Fractal Function(CFF)change the value of pixels of each color component(layer)to obtain a random sequence.Through the obtained random sequence,three layers of plain image are encrypted.Finally,with each encrypted layer,Brownian Particles(BP)are XORed that added an extra layer of security.The experimental tests including a number of statistical tests validated the security of the presented scheme.The results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature.
文摘Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM.
文摘In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.
