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An asymmetrical multilevel inverter with minimum voltage stress and fewer components for photovoltaic renewable-energy system

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Clean Energy 2024年第1期8卷 1-22页

作者： Rabail Memon Mukhtiar Ahmed Mahar Abdul Sattar Larik syed asif Ali shah Department of Electrical Engineering Mehran University of Engineering and TechnologyJamshoroPakistan

The enhanced power quality provided by multilevel inverters(MLIs)has made them more appropriate for medium-and high-power applications,including photovoltaic ***,a prevalent limitation involves the necessity for numerous switches and increased voltage stress across these switches,consequently increasing the overall system *** address these challenges,a new 17-level asymmetrical MLI with fewer components and low voltage stress is proposed for the photovoltaic *** innovative MLI configuration has four direct current(DC)sources and 10 *** on the trinary sequence,the proposed topology uses photovoltaics with boost converters and fuzzy logic controllers as its DC *** equations are used to calculate cru-cial parameters for this proposed design,including total standing voltage per unit(TSVPU),cost function per level(CF/L),component count per level(CC/L)and voltage stress across the *** comparison is conducted by considering switches,DC sources,TSVPU,CF/L,gate driver circuits and CC/L with other existing MLI *** analysis is carried out under various conditions,encompassing different levels of irradiance,variable loads and modulation *** reduce the total harmonic distortion of the suggested topology,the phase opposition disposition approach has been *** suggested framework is simulated in MATLAB®/Simulink®.The results indicate that the proposed topology achieves a well-distributed stress profile across the switches and has CC/L of 1.23,TSVPU of 5 and CF/L of 4.58 and 5.76 with weight coefficients of 0.5 and 1.5,*** values are not-ably superior to those of existing MLI *** results demonstrate that the proposed topology maintains a consistent output at varying irradiance levels with FLCs and exhibits robust performance under variable loads and diverse modulation ***,the total harmonic distortion achieved with phase opposition disposi

关键词： asymmetrical multilevel inverter photovoltaic system renewable energy component count per level cost function per level total standing voltage per unit phase opposition disposition technique

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

Deep Learning Based Efficient Crowd Counting System

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Computers, Materials & Continua 2024年第6期79卷 4001-4020页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Muhammad Hamza Faheem syed shah Amanullah Quadri Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Centre of Excellence in Cybercrime and Digital Forensics Naif Arab University for Security SciencesRiyadh14812Saudi Arabia Center of Excellence for Information Assurance(COEIA) King Saud UniversityRiyadh12372Saudi Arabia

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different *** is challenging to determine the approximate crowd size from an image of the crowd’s *** in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the *** employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s *** analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd *** results show the superiority of the proposed system.

关键词： Crowd counting EfficientNet multi-head attention convolutional neural network transfer learning

Classification and Comprehension of Software Requirements Using Ensemble Learning

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Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad syed Muqsit shaheed Rubia Fatima Sajid shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

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Computers, Materials & Continua 2024年第5期79卷 2565-2583页

作者： syed Ayaz Ali shah Aamir shahzad Musaed Alhussein Chuan Meng Goh Khursheed Aurangzeb Tong Boon Tang Muhammad Awais Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusAbbottabad22060Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Information and Communication Technology University Tunku Abdul RehmanKampar CampusKampar31900Malaysia Centre for Intelligent Signal and Imaging Research University Teknologi PETRONASBandar Seri IskandarPerak32610Malaysia School of Computing Sciences University of East AngliaNorwichNR47TJUK

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood *** task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different ***,the speed of the retinal vessel segmentation system is of utmost *** the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the *** address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical *** algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)*** experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE ***,our algorithmexhibits competitive performance with state-of-the-art ***,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE *** is worth noting that these results were achieved using Matlab scripts containing multiple *** suggests that the processing time can be further reduced by replacing loops with *** the proposed algorithm can be deployed in real time *** summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

关键词： Line detector vessel detection localization mathematical morphology image processing

A Framework to Assess Possible Sources of Stakeholder Exploitation:A Developing Country Case

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International Journal of Disaster Risk Science 2024年第1期15卷 32-44页

作者： Kamran Shafique syed Shams Tapan Sarker School of Business University of Southern QueenslandSpringfeldQLD 4300Australia Business School University of QueenslandSt LuciaQLD 4072Australia

Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stakeholder ***,small,more organized groups,such as government parastatals,private developers,and contractors often exploit large,unorganized groups,such as affected communities,leaving them more vulnerable to future *** on data collected from a case study in Pakistan,this study proposed a framework to assess,anticipate,and mitigate the exploitation of vulnerable stakeholders in post-disaster reconstruction *** framework draws on influential management theories and utilizes reciprocal relationships between stakeholder attributes(power,legitimacy,and urgency),participation,and *** study also argued for non-binary treatment of stakeholder *** framework will allow practitioners to address issues around the exploitation of stakeholder interests in future post-disaster reconstruction projects.

关键词： Collective action Pakistan Post-disaster reconstruction-Stakeholder participation Stakeholder exploitation

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah shahid shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi syed Aziz shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud Al Naimi syed Alwee Aljunid syed Junid Muhammad lmran Ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

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Computers, Materials & Continua 2024年第7期80卷 1381-1405页

作者： Khursheed Aurangzeb syed Irtaza Haider Musaed Alhussein Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of *** this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus *** DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding *** reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network *** modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained *** evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and *** results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD *** used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,*** OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,***,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

关键词： Refined Anam-Net parameter tuning deep learning optic cup optic disc cup-to-disc ratio glaucoma diagnosis