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HybridHR-Net:Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework

Computers, Materials & Continua 2023年第9期76卷 3275-3295页

作者： Muhammad Naeem Akbar Seemab Khan Muhammad Umar Farooq Majed Alhaisoni Usman Tariq Muhammad usman Akram Department of Computer Engineering National University of Sciences and Technology(NUST)Islamabad46000Pakistan Department of Robotics SMME NUSTIslamabad45600Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The combination of spatiotemporal videos and essential features can improve the performance of human action recognition(HAR);however,the individual type of features usually degrades the performance due to similar actions and complex backgrounds.The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information.This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net.On a few selected datasets,deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model.Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep model.Instead of fully connected layer features,we considered the average pooling layer features and performed two feature selection techniques-an improved artificial bee colony and an entropy-based approach.Using a serial nature technique,the features that were selected are combined into a single vector,and then the results are categorized by machine learning classifiers.Five publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%,98.7%,100%,99.7%,and 96.8%,respectively.Additionally,a comparison of the proposed framework with contemporarymethods is done to demonstrate the increase in accuracy.

关键词： Action recognition entropy deep learning transfer learning artificial bee colony feature fusion

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Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

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Computer Systems Science & Engineering 2023年第8期46卷 2429-2445页

作者： tariq Ahamed Ahanger Fadl Dahan Usman Tariq Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Management Information Systems College of Business Administration-Hawtat Bani TamimPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Al-TurbahTaiz UniversityTaiz9674Yemen

In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.

关键词： Internet of things artificial bee colony bat algorithm elitist strategy web service composition

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Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

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Computers, Materials & Continua 2022年第3期70卷 5039-5058页

作者： Khuram Nawaz Khayam Zahid Mehmood Hassan Nazeer Chaudhry Muhammad usman Ashraf Usman Tariq Mohammed Nawaf Altouri Khalid Alsubhi Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electronics Information and BioengineeringPolitecnico di MilanoMilano20122Italy Department of Computer Science University of Management and TechnologySialkotPakistan College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Computer Science Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah21589Saudi Arabia

Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.

关键词： Face recognition local tetra patterns spatial pyramid matching robust kernel representation max-pooling

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Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

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Computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

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A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

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Computer Systems Science & Engineering 2023年第5期45卷 1001-1016页

作者： Muhammad Junaid Umer Muhammad Sharif Majed Alhaisoni Usman Tariq Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusWah CanttPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.BOX 84428Riyadh11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

关键词： Breast cancer deep learning FS IDC ML SVM

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Denoising Medical Images Using Deep Learning in IoT Environment

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Computers, Materials & Continua 2021年第12期69卷 3127-3143页

作者： Sujeet More Jimmy Singla Oh-Young Song Usman Tariq Sharaf Malebary School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Software Department Sejong UniversitySeoulKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21911Saudi Arabia

Medical Resonance Imaging(MRI)is a noninvasive,nonradioactive,and meticulous diagnostic modality capability in the field of medical imaging.However,the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation.Therefore,to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network(SANR_CNN)for eliminating noise and improving the MR image reconstruction quality.The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality,and SARN algorithm is used for building a dictionary learning technique for denoising large image datasets.The proposed SANR_CNN model also preserves the details and edges in the image during reconstruction.An experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and mean squared error(MSE).The proposed SANR_CNN model achieved higher PSNR,SSIM,and MSE efficiency than the other noise removal techniques.The proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.

关键词： Medical resonance imaging convolutional neural network denoising contrast enhancement internet of things rheumatoid arthritis

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Image Segmentation Based on Block Level and Hybrid Directional Local Extrema

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Computers, Materials & Continua 2022年第2期70卷 3939-3954页

作者： Ghanshyam Raghuwanshi Yogesh Gupta Deepak Sinwar Dilbag Singh Usman Tariq Muhammad Attique Kuntha Pin Yunyoung Nam Manipal University Jaipur Jaipur302026India BMLMunjal University GurugramIndia Bennett University Greater NoidaIndia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of ICT Convergence Soonchunhyang UniversityAsan 31538Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

In the recent decade,the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities.Image segmentation is a key step in digitalization.Segmentation plays a key role in almost all areas of image processing,and various approaches have been proposed for image segmentation.In this paper,a novel approach is proposed for image segmentation using a nonuniform adaptive strategy.Region-based image segmentation along with a directional binary pattern generated a better segmented image.An adaptive mask of 8×8 was circulated over the pixels whose bit value was 1 in the generated directional binary pattern.Segmentation was performed in three phases:first,an image was divided into sub-images or image chunks;next,the image patches were taken as input,and an adaptive threshold was generated;and finally the image chunks were processed separately by convolving the adaptive mask on the image chunks.Gradient and Laplacian of Gaussian algorithms along with directional extrema patterns provided a double check for boundary pixels.The proposed approach was tested on chunks of varying sizes,and after multiple iterations,it was found that a block size of 8×8 performs better than other chunks or block sizes.The accuracy of the segmentation technique was measured in terms of the count of ill regions,which were extracted after the segmentation process.

关键词： Image segmentation HDEP block-level processing adaptive threshold

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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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Human Gait Recognition Based on Sequential Deep Learning and Best Features Selection

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Computers, Materials & Continua 2023年第6期75卷 5123-5140页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Usman Tariq Ye Jin Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxilaPakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Gait recognition is an active research area that uses a walking theme to identify the subject correctly.Human Gait Recognition(HGR)is performed without any cooperation from the individual.However,in practice,it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a coat.Researchers,over the years,have worked on successfully identifying subjects using different techniques,but there is still room for improvement in accuracy due to these covariant factors.This paper proposes an automated model-free framework for human gait recognition in this article.There are a few critical steps in the proposed method.Firstly,optical flow-based motion region esti-mation and dynamic coordinates-based cropping are performed.The second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames;the training has been conducted using static hyperparameters.The third step proposed a fusion technique known as normal distribution serially fusion.In the fourth step,a better optimization algorithm is applied to select the best features,which are then classified using a Bi-Layered neural network.Three publicly available datasets,CASIA A,CASIA B,and CASIA C,were used in the experimental process and obtained average accuracies of 99.6%,91.6%,and 95.02%,respectively.The proposed framework has achieved improved accuracy compared to the other methods.

关键词： Human gait recognition optical flow deep learning features fusion feature selection

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平扫单源双能CT成像对鉴别肾上腺高密度腺瘤与嗜铬细胞瘤的鉴别价值

平扫单源双能CT成像对鉴别肾上腺高密度腺瘤与嗜铬细胞瘤的鉴别价...

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作者： tariq usman 大连医科大学

学位级别：硕士

目的探讨平扫单源双能CT成像对肾上腺高密度腺瘤（hyperdense adrenal adenomas,HAA）与嗜铬细胞瘤（Pheochromocytomas）的鉴别价值。方法回顾性分析大连医科大学附属第一医院2011年至2016年接受单源双能CT能谱成像扫描且经组织病理证... 详细信息

目的探讨平扫单源双能CT成像对肾上腺高密度腺瘤（hyperdense adrenal adenomas,HAA）与嗜铬细胞瘤（Pheochromocytomas）的鉴别价值。方法回顾性分析大连医科大学附属第一医院2011年至2016年接受单源双能CT能谱成像扫描且经组织病理证实的肾上腺实性肿块患者。入组病灶直径≥2cm,CT值≥20HU,最终入组病灶36个,其中HAA病灶17个,嗜铬细胞瘤病灶19个。机器型号和扫描参数。由两名观察者进行测量,将70ke V单能量图像导入工作站中,选择层厚及层间距为2.5mm,避开囊变、出血及钙化区测量GSI病灶最大层面,获得40-140ke V单能量CT值、脂（水）值、血（水）值、有效原子序数（z）,并计算能谱曲线斜率（k）。两观察者数据一致性检验使用Spearman相关检验;两组病例患者年龄、病灶直径、40-140ke V单能量CT值、能谱曲线斜率（k）、脂（水）值、血（水）值及有效原子序数（z）采用MannWhitney U检验;对两组病例能谱曲线类型进行卡方检验:上升型（k＞1）、水平型（-1≤k≤1）、下降型（k＜-1）。选择有差异指标绘制受试者工作特征曲线（ROC曲线）,得出曲线下面积（AUC）,同时,通过ROC曲线分析可以得出敏感度和特异度两者兼顾的阈值。结果两组患者年龄、性别无统计学差异（P＞0.05）;嗜铬细胞瘤组肿瘤中位直径大于HAA组（4.16,2.56cm,P＜0.05）。两观察者数据一致性皆良好（r＞0.9,P＜0.05）。40-60ke V单能量下中位CT值及有效原子序数值HAA皆小于嗜铬细胞瘤,分别为（40.66,58.61 HU,40 ke V）,（30.95,48.67 HU,50ke V）,（28.05,43.25HU,60 ke V）和（7.70,7.82）,差异有统计学意义（P＜0.05）。而70-140ke V单能量CT值差异皆无统计学意义（P＞0.05）。HAA的中位脂（水）值高于嗜铬细胞瘤（-170.55,-284.74 mg/cm3）,而血（水）值HAA低于嗜铬细胞瘤（719.11,1198.57 mg/cm3）,两者差异有统计学意义（P＜0.05）。HAA的中位能谱曲线斜率k大于嗜铬细胞瘤（-0.172,-0.290）,差异有统计学意义（P＜0.05）;HAA能谱曲线类型分别为:上升型5.9%（1/17）,水平型35.3%（6/17）,下降型58.8%（10/17）,而嗜铬细胞瘤能谱曲线类型皆为下降型100.0%（19/19）,两者差异有统计学意义。当平扫40ke V单能量下CT值诊断阈值为43.65HU时,鉴别HAA与嗜铬细胞瘤的敏感度为84.2%,特异度为70.6%,AUC为0.805;当有效原子序数诊断阈值为7.76时,鉴别HAA与嗜铬细胞瘤的敏感度为68.4%,特异度为82.4%,AUC为0.802;当脂（水）值及血（水）值的诊断阈值分别为-198.06mg/cm3及835.65mg/cm3时,敏感度分别为64.7%、94.7%,特异度分别为94.7%、64.7%,AUC皆为0.833。当能谱曲线斜率诊断阈值为-0.202时,鉴别HAA与嗜铬细胞瘤的敏感度为58.8%,特异度为94.7%,AUC为0.786。结论常规（70ke V）CT值不能鉴别两组病例。利用单源双能CT能谱成像平扫时单能量（40-60ke V）CT值、有效原子序数、能谱曲线斜率、脂（水）值及血（水）值可以较好的鉴别HAA及嗜铬细胞瘤。40ke V单能量下CT值有较好的诊断敏感度及特异度,能谱曲线斜率及脂（水）值有较高的诊断特异度,而血（水）值有较高的诊断敏感度。

关键词：单源双能CT 高密度肾上腺腺瘤嗜铬细胞瘤

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