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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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computer Modeling in Engineering & sciences 2024年第8期140卷 1339-1370页

作者： ali S.alghamdi Mohamed A.Zohdy Saad aldoihi department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and computer Engineering department Oakland UniversityRochesterMI48309USA Institute of Earth and Space science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia computer science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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computer Modeling in Engineering & sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai alsubai Safeeullah Soomro department of computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy department of Information Systems University of Management and TechnologyLahore54770Pakistan department of computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey department of computer science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland department of computer science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second department of computer science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek department of Information Technology Lebanese French UniversityErbilIraq department of computer science University of ZakhoZakhoKurdistan RegionIraq department of computer science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic department of computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of computer sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq department of Applied Data science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed ali Khan Saddaf Rubab Muhammad Shahid Anwar department of computer science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan department of computer science National University of PakistanRawalpindiPunjab46000Pakistan department of computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan department of computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia department of computer science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK department of computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

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computers, Materials & Continua 2024年第9期80卷 3785-3802页

作者： Ahmad alzu’bi Amjad albashayreh Abdelrahman Abuarqoub Mai A.M.alfawair department of computer science Jordan University of Science and TechnologyIrbid22110Jordan department of computer science The University of JordanAmman11942Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversitySalt19117Jordan

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse *** the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain *** paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT *** effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time *** leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world ***,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack *** experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

关键词： DDoS attack classification deep learning explainable AI cybersecurity

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif al Mudawi Mohammed alonazi Samia allaoua Chelloug Jeongmin Park department of Creative Technologies Air UniversityIslamabad46000Pakistan department of computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Cross-comparative review of Machine learning for plant disease detection:apple,cassava,cotton and potato plants

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Artificial Intelligence in Agriculture 2024年第2期12卷 127-151页

作者： James Daniel Omaye Emeka Ogbuju Grace Ataguba Oluwayemisi Jaiyeoba Joseph Aneke Francisca Oladipo department of computer science Federal UniversityLokojaNigeria department of computer science Dalhousie UniversityHalifaxCanada department of computer science Hampton UniversityVirginiaUSA

Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food *** these diseases early can help mitigate their impact and ensure healthy crop *** learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected *** algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant *** early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many *** the years,research has focused on using machine-learning algorithms for plant disease ***,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease *** view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and *** conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the *** filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered *** these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected *** from our re-view show that deep learning and other algorithms performed significantly well in detecting plant *** addition,we fou

关键词： Machine learning Plant diseases Agriculture Plant disease management Convolutional neural networks

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Eco-toxicity and health risk assessment of polycyclic aromatic hydrocarbons in surface sediments of Burullus Lake in Egypt

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China Geology 2024年第3期7卷 460-468页

作者： Hanan E Osman Mohamed H E El-Morsy Hazem T Abd El-Hamid department of Biology Faculty of Applied ScienceUmm Al-Qura UniversityMecca 24382Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah 24372Saudi Arabia National Institute of Oceanography and Fisheries(NIOF) Cairo 11865Egypt Botany and Microbiology department Faculty of ScienceAl-Azhar UniversityCairo 11651Egypt

Polycyclic aromatic hydrocarbons(PAHs)are ubiquitous environmental contaminants of growing concern due to their potential ecological and human health *** study presents a comprehensive assessment of PAHs contamination in the surface sediments of Burullus Lake,a vital and second largest delta lake in *** aim was to evaluate the eco-toxicity and potential health risks associated with the presence of these *** seven sediment samples were collected from various drains in the southern part of Burullus *** extraction method was employed to extract PAHs(16PAHs)from the sediment ***,target compounds were located using *** results showed that samples contained PAHs levels ranging from 0.038×10^(-6)to 0.459×10^(-6),which is considered heavily polluted by the European standard for PAHs ***,there was no apparent source of PAHs in the ElKhashah drain or the Brinbal Canal,as HPLC found none of the *** most prevalent compound in sediment samples along the study area was *** diagnostic indices in the present study indicated that the hydrocarbons in the region originated from pyrolytic and man-made sources along the drains of Burullus *** principal component analysis(PCA)and diagnostic ratios revealed that coal combustion and pyrolytic sources were responsible for the PAHs contamination in the surface *** non-carcinogenic risk(HI),which is the product of the HQs for the adult and child populations,respectively,was *** values under 1,therefore,demonstrated that they had no carcinogenic effects on human *** and MEQs in the sediments of Burullus Lake do not have a cancer-causing impact on *** the safety of nearby wildlife,aquatic life,and people,all activities that raise petroleum hydrocarbon levels in Burullus Lake must be adequately regulated and *** to the ecological risk assessment,there is little chance that PAHs will be found

关键词： Polycyclic aromatic hydrocarbons Diagnostic indices Toxicity Delta lake sediment Burullus Lake Human health risk Aquatic ecosystem HPLC Coal combustion

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Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq Khan Muhammad Ramzan Mudassar Raza Muhammad Attique Khan Khalid Iqbal Taerang Kim Jae-Hyuk Cha department of computer science COMSATS University IslamabadWah CampusWah47040Pakistan department of computer science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon department of computer science HITEC UniversityTaxila47080Pakistan department of computer science COMSATS University IslamabadAttock CampusAttock43600Pakistan department of computer science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS university Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

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Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 34-40页

作者： Joice G.Philip Jaesung Jung Ahmet Onen the department of Energy Systems Research Ajou UniversitySuwonSouth Korea the department of Electrical and Electronic Engineering Abdullah Gul UniversityKayseri38080Turkey the department of Electrical and computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscatOman

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)*** phasors from PMUs are preprocessed to check for the presence of *** the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida *** superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest *** show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

关键词： Empirical wavelet transform(EWT) sub-synchronous oscillation Prony-based method Yoshida algorithm variational mode decomposition phasor measurement unit(PMU)

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