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Multi-Layered Deep Learning features fusion for Human Action Recognition

Computers, Materials & Continua 2021年第12期69卷 4061-4075页

作者： Sadia Kiran Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Yunyoung Nam Robertas Damaševicius Muhammad Sharif Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Mathematics Silesian University of TechnologyGliwicePoland Department of Computer Science COMSATS University IslamabadWah CampusPakistan

Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of computing.In this article,we proposed a new method for the use of deep learning for HAR.In the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.The Resnet-50 Pre-Trained Model is used as a deep learning model in this work.features are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold function.The selected features are finally passed to multiple classifiers for final classification.Experiments are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and KTH.The accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,respectively.Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR.Also,the proposed method is computationally fast based on the time of execution.

关键词： Action recognition transfer learning features fusion features selection classification

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Human Gait Recognition:A Deep Learning and Best Feature Selection Framework

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Computers, Materials & Continua 2022年第1期70卷 343-360页

作者： Asif Mehmood Muhammad Attique Khan Usman Tariq Chang-Won Jeong Yunyoung Nam Reham R.Mostafa Amira ElZeiny Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of Information Systems Faculty of Computers and InformationDamietta UniversityDamiettaEgypt

Background—Human Gait Recognition(HGR)is an approach based on biometric and is being widely used for surveillance.HGR is adopted by researchers for the past several decades.Several factors are there that affect the system performance such as the walking variation due to clothes,a person carrying some luggage,variations in the view angle.Proposed—In this work,a new method is introduced to overcome different problems of HGR.A hybrid method is proposed or efficient HGR using deep learning and selection of best features.Four major steps are involved in this work-preprocessing of the video frames,manipulation of the pre-trained CNN model VGG-16 for the computation of the features,removing redundant features extracted from the CNN model,and classification.In the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named PSbK.After that,the features of PSbK are fused in one materix.Finally,this fused vector is fed to the One against All Multi Support Vector Machine(OAMSVM)classifier for the final results.Results—The system is evaluated by utilizing the CASIA B database and six angles 00◦,18◦,36◦,54◦,72◦,and 90◦are used and attained the accuracy of 95.80%,96.0%,95.90%,96.20%,95.60%,and 95.50%,respectively.Conclusion—The comparison with recent methods show the proposed method work better.

关键词： Human gait recognition deep features extraction features fusion features selection

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Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

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Computers, Materials & Continua 2022年第5期71卷 3667-3684页

作者： Usra Yasmeen Muhammad Attique Khan Usman Tariq Junaid Ali Khan Muhammad Asfand EYar ChAvais Hanif Senghour Mey Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of EE HITEC University TaxilaTaxilaPakistan Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in farming.In Pakistan,several kinds of fruits are produced and exported the other countries.Citrus is an important fruit,and its production in Pakistan is higher than the other fruits.However,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this Fruit.The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure.In the agriculture sector,deep learning showing significant success in the last five years.This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification.In the proposed framework,the augmentation technique is applied initially by creating more training data from existing samples.They were then modifying the two pre-trained models named Resnet18 and Inception V3.The modified models are trained using an augmented dataset through transfer learning.features are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few more.The experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus Fruits.On these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,respectively.The proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

关键词： Citrus diseases data augmentation deep learning features selection features fusion

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GaitDONet: Gait Recognition Using Deep features Optimization and Neural Network

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Computers, Materials & Continua 2023年第6期75卷 5087-5103页

作者： Muhammad Attique Khan Awais Khan Majed Alhaisoni Abdullah Alqahtani Ammar Armghan Sara A.Althubiti Fayadh Alenezi Senghour Mey Yunyoung Nam Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityKorea

Human gait recognition(HGR)is the process of identifying a sub-ject(human)based on their walking pattern.Each subject is a unique walking pattern and cannot be simulated by other subjects.But,gait recognition is not easy and makes the system difficult if any object is carried by a subject,such as a bag or coat.This article proposes an automated architecture based on deep features optimization for HGR.To our knowledge,it is the first architecture in which features are fused using multiset canonical correlation analysis(MCCA).In the proposed method,original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and Efficientnet.Deep transfer learning was used to train both fine-tuned models on selected angles,yielding two new targeted models that were later used for feature engineering.features are extracted from the deep layer of both fine-tuned models and fused into one vector using MCCA.An improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network classifier.The experimental process was conducted on all 11 angles of the large multi-view gait dataset(CASIA B)dataset and obtained improved accuracy than the state-of-the-art techniques.Moreover,a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.

关键词： Human gait recognition biometric deep learning features fusion optimization neural network

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Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

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Computers, Materials & Continua 2022年第5期71卷 2477-2495页

作者： Amina Bibi Muhamamd Attique Khan Muhammad Younus Javed Usman Tariq Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa Rasha H.Sakr Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Computer Science Department Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt

Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques.

关键词： Skin cancer lesion segmentation deep learning features fusion classification

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A Hybrid Duo-Deep Learning and Best features Based Framework for Action Recognition

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Computers, Materials & Continua 2022年第11期73卷 2555-2576页

作者： Muhammad Naeem Akbar Farhan Riaz Ahmed Bilal Awan Muhammad Attique Khan Usman Tariq Saad Rehman National University of Sciences and Technology(NUST) Islamabad46000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj 11942Saudi Arabia

Human Action Recognition(HAR)is a current research topic in the field of computer vision that is based on an important application known as video surveillance.Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning,but they still face many challenges such as similarity in various actions and redundant features.We proposed a framework for accurate human action recognition(HAR)based on deep learning and an improved features optimization algorithm in this paper.From deep learning feature extraction to feature classification,the proposed framework includes several critical steps.Before training fine-tuned deep learning models–MobileNet-V2 and Darknet53–the original video frames are normalized.For feature extraction,pre-trained deep models are used,which are fused using the canonical correlation approach.Following that,an improved particle swarm optimization(IPSO)-based algorithm is used to select the best features.Following that,the selected features were used to classify actions using various classifiers.The experimental process was performed on six publicly available datasets such as KTH,UT-Interaction,UCF Sports,Hollywood,IXMAS,and UCF YouTube,which attained an accuracy of 98.3%,98.9%,99.8%,99.6%,98.6%,and 100%,respectively.In comparison with existing techniques,it is observed that the proposed framework achieved improved accuracy.

关键词： Action recognition deep learning features fusion features selection recognition

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A Cascaded Design of Best features Selection for Fruit Diseases Recognition

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Computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert.Hence,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images.According to the literature,many automated methods have been developed for the recognition of fruit diseases at the early stage.However,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant features.Image processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these challenges.Therefore,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based selection.The proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final recognition.The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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HRNetO:Human Action Recognition Using Unified Deep features Optimization Framework

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计算机、材料和连续体（英文） 2023年第4期75卷 1089-1105页

作者： Tehseen Ahsan Sohail Khalid Shaheryar Najam Muhammad Attique Khan Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Riphah International UniversityPeshawar Rdnear Hajj ComplexI-14IslamabadIslamabad Capital Territory46000Pakistan Departmnt of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart cities.Many attempts have been made in theliterature to develop an effective and robust framework for HAR.Still,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant features.In this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate HAR.The proposed design consists of a fewsignificantsteps.In the first step,frame preprocessing is performed.In the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video frames.In the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value approach.The fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning classifiers.Theexperiment was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.

关键词： Action recognition features fusion deep learning features selection

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A Framework of Deep Optimal features Selection for Apple Leaf Diseases Recognition

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Computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the quantity.As a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer learning.Then, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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An Integrated Deep Learning Framework for Fruits Diseases Classification

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Computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

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