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Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

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 *** 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)*** challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS *** 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 *** 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 *** 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 *** datasets are created from real-world datasets and artificially to form different scale sizes of WSC *** results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to *** 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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HybridHR-Net:Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework

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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 *** deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial *** article proposes a new framework called for video surveillance human action recognition dubbed *** a few selected datasets,deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning *** optimization is employed for the tuning of hyperparameters of the fine-tuned deep *** 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 *** a serial nature technique,the features that were selected are combined into a single vector,and then the results are categorized by machine learning *** publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%,98.7%,100%,99.7%,and 96.8%,***,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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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),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** 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 *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** 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 Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

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Computers, Materials & Continua 2023年第11期77卷 1599-1615页

作者： Nadeem Malik Saud Altaf Muhammad usman tariq Ashir Ahmed Muhammad Babar University Institute of Information Technology PMAS Arid Agriculture UniversityRawalpindi46000Pakistan Department of Marketing Operationsand Information SystemAbu Dhabi UniversityAbu Dhabi6844United Arab Emirates College of Computer and Information Sciences Prince Sultan UniversityRiyadh11442Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11442Saudi Arabia

The severity of traffic accidents is a serious global concern,particularly in developing *** the main causes and contributing circumstances may reduce the severity of traffic *** exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and *** such approaches are popular,there exists an issue of data management and low prediction *** article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social ***,a Tweet dataset has been formed by using an exhaustive keyword-based searching *** the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule *** output of this phase has been forwarded to a three-layer deep learning model for final *** experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM *** has been shown that the proposed model achieved 94.2%prediction accuracy.

关键词： Accident XLNet Bi-LSTM association rule mining Twitter

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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 ***,the efficiency of MR image reconstruction is affected by its bulky image sets and slow process ***,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 *** 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 *** proposed SANR_CNN model also preserves the details and edges in the image during *** 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 *** 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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Cache in fog computing design,concepts,contributions,and security issues in machine learning prospective

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Digital Communications and Networks 2023年第5期9卷 1033-1052页

作者： Muhammad Ali Naeem Yousaf Bin Zikria Rashid Ali usman tariq Yahui Meng Ali Kashif Bashir School of Science Guangdong University of Petrochemical TechnologyMaoming525000China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Department of Information and Communication Technologies Universitat Pompeu FabraBarcelonaSpain College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUnited Kingdom Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The massive growth of diversified smart devices and continuous data generation poses a challenge to communication *** deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication *** brings on-demand services proximate to the end devices and delivers the requested data in a short *** computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end *** this end,fog caching plays an imperative role in addressing data dissemination *** study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog ***,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog ***,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog *** also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.

关键词： Internet of things Cloud computing Fog computing Caching Latency

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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 *** a single solution to solve all these problems at a time is a challenging *** 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 *** technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching ***,the input image is recognized using a robust kernel representation method using extracted *** qualitative and quantitative analysis of the proposed method is carried on benchmark image *** 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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Handling Class Imbalance in Online Transaction Fraud Detection

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Computers, Materials & Continua 2022年第2期70卷 2861-2877页

作者： Kanika Jimmy Singla Ali Kashif Bashir Yunyoung Nam Najam UI Hasan usman tariq School of Computer Science and Engineering Lovely Professional UniversityPunjabIndia Department of Computing and Mathematics ManchesterMetropolitan UniversityUK&School of Electrical Engineering and Computer ScienceNational University of Science and TechnologyIslamabadPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Electrical and Computer Engineering Dhofar UniversitySalalahOman College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every ***,the fraud cases have also increased causing the loss of money to the ***,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in ***,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance *** this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional ***,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.

关键词： Class imbalance deep learning fraud detection loss function thresholding

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Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

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Computers, Materials & Continua 2021年第9期68卷 3825-3840页

作者： Ayesha Sarwar Kashif Javed Muhammad Jawad Khan Saddaf Rubab Oh-Young Song usman tariq National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Software Sejong UniversitySeoulGwangjin-guKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware *** recorded brain data is converted directly to the machine that can be used to control external *** are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and *** traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery *** major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy *** this study,motor imagery(MI)signals have been classified using different deep learning *** have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset *** outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning *** the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.

关键词： Brain-computer interface motor imagery artificial neural network long-short term memory classification

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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

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Computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song usman tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of GI *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data *** can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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