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Classification and Comprehension of Software Requirements Using Ensemble Learning

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

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar Imran Talha Imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem Alzahrani Muhammad Shafiq College of Information Science and technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets

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Journal of Data and Information Science 2024年第2期9卷 81-103页

作者： Shuo Xu Yuefu Zhang Xin An Sainan Pi College of Economics and management Beijing University of TechnologyBeijing 100124China School of Economics&management Beijing Forestry UniversityBeijing 100083China

Purpose:Many science,technology and innovation(STI)resources are attached with several different *** assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the ***,several open-source tools implementing these approaches have also been ***,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ***,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world *** limitations:Three real-world datasets differ in the following aspects:statement,data quality,and ***,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification *** the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter *** implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification *** leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among *** ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable ***/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and

关键词： Multi-label classification Real-World datasets Hierarchical structure Classification system Label correlation Machine learning

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Maclaurin Symmetric Mean Aggregation Operators and Their Application to Hesitant Q-Rung Orthopair Fuzzy Multiple Attribute Decision Making

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Journal of Systems Science and Information 2024年第4期12卷 515-542页

作者： Qian YU Xudong LI Jun CAO Fangsu ZHAO Longxiao LI Ling TAN School of Business and Administration Chongqing University of Science & Technology School of Economics and management Chongqing Jiaotong University

The Maclaurin symmetric mean(MSM) operator exhibits a desirable characteristic by effectively capturing the correlations among multiple input parameters, and it serves as an extension of certain existing aggregation operators through adjustments to the parameter k. The hesitant q-rung orthopair set(Hq-ROFSs) can serve as an extension of the existing orthopair fuzzy sets, which provides decision makers more freedom in describing their true opinions. The objective of this paper is to present an MSM operator to aggregate hesitant q-rung orthopair numbers and solve the multiple attribute decision making(MADM) problems in which the attribute values take the form of hesitant q-rung orthopair fuzzy sets(H-qROFSs). Firstly, the definition of H-qROFSs and some operational laws of H-qROFSs are proposed. Then we develop a family of hesitant q-rung orthopair fuzzy maclaurin symmetric mean aggregation operators, such as the hesitant q-rung orthopair fuzzy maclaurin symmetric mean(Hq-ROFMSM) operator, the hesitant q-rung orthopair fuzzy weighted maclaurin symmetric mean(Hq-ROFWMSM) operator, the hesitant q-rung orthopair fuzzy dual maclaurin symmetric mean(Hq-ROFDMSM) operator, the hesitant q-rung orthopair fuzzy weighted dual maclaurin symmetric mean(Hq-ROFWDMSM) operator. And the properties and special cases of these proposed operators are studied. Furthermore, an approach based on the Hq-ROFWMSM operator is proposed for multiple attribute decision making problems under hesitant q-rung orthopair fuzzy environment. Finally,a numerical example and comparative analysis is given to illustrate the application of the proposed approach.

关键词： hesitant q-rung orthopair fuzzy sets multiple attribute decision making Maclaurin symmetric mean operator hesitant q-rung orthopair fuzzy weighted dual MSM (Hq-ROFWDMSM) operator hesitant q-rung orthopair fuzzy weighted MSM (Hq-ROFWMSM) operator

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A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

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工程与科学中的计算机建模(英文) 2024年第1期138卷 459-472页

作者： Huanhua Liu Wei Wang Hanyu Liu Shuheng Yi Yonghao Yu Xunwen Yao School of Information technology and management Hunan University of Finance and EconomicsChangsha410205China

Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image *** practice,images are often affected by various types of degradation which can significantly impact the performance of *** this work,we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model(DTA-ICM)to improve the existing CNNs’classification accuracy on degraded *** proposed DTA-ICM comprises two key components:a Degradation Type Predictor(DTP)and a Degradation Type Specified Image Classifier(DTS-IC)set,which is trained on existing CNNs for specified types of *** DTP predicts the degradation type of a test image,and the corresponding DTS-IC is then selected to classify the *** evaluate the performance of both the proposed DTP and the DTA-ICMon the Caltech 101 *** experimental results demonstrate that the proposed DTP achieves an average accuracy of 99.70%.Moreover,the proposed DTA-ICM,based on AlexNet,VGG19,and ResNet152,exhibits an average accuracy improvement of 20.63%,18.22%,and 12.9%,respectively,compared with the original CNNs in classifying degraded *** suggests that the proposed DTA-ICM can effectively improve the classification performance of existing CNNs on degraded images,which has important practical implications.

关键词： Image recognition image degradation machine learning deep convolutional neural network

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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of management Science and Engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China Department of Industrial Engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing *** DFJSP research only considers machine constraints and ignores worker *** one critical factor of production,effective utilization of worker resources can increase ***,energy consumption is a growing concern due to the increasingly serious environmental ***,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this *** solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling *** further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are *** strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto *** effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 *** results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning

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Combined Application of Biostimulants and EDTA Improved Wheat Productivity under Cadmium Stress

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Phyton-International Journal of Experimental Botany 2024年第7期93卷 1647-1665页

作者： Abida Aziz Shafiqa Bano Mubshar Hussain Muhammad Farooq Azhar Ghulam Yasin Naila Hadayat Iqra Arooj Abeer Hashem Ajay Kumar Elsayed Fathi Abd_Allah Qamar uz Zaman Department of Botany The Women UniversityMultan54500Pakistan Institute of Agronomy Bahauddin Zakariya UniversityMultan60800Pakistan Department of Forestry and Range management Bahauddin Zakariya UniversityMultan60800Pakistan Department of Botany Division of Science&TechnologyUniversity of EducationLahore54770Pakistan Department of Microbiology&Molecular Genetics The Women UniversityMultan54500Pakistan Botany and Microbiology Department College of ScienceKing Saud UniversityRiyadh201313Saudi Arabia Amity Institute of Biotechnology Amity UniversityNoida201313India Plant Production Department College of Food and Agricultural SciencesKing Saud UniversityRiyadh11451Saudi Arabia School of Agriculture Yunnan UniversityKunmingYunan650504China Department of Environmental Sciences The University of LahoreLahore54590Pakistan

Wheat(Triticum aestivum L.)exhibits a greater capacity for cadmium(Cd)absorption compared to other cereal crops,leading to elevated daily Cd intake,and posing a signiﬁcant threat to public *** the mitigation of Cd stress in sustainable and environmentally friendly way,a pot study was designed by using exogenous application of various biostimulants,i.e.,Nigella sativa and Ocimum sanctum extracts:0%,10%,and 20%in combination with the chelating agent ethylenediaminetetraacetic acid(EDTA)using 0 and 5 mg kg^(-1) under various levels of Cd stress(i.e.,0,5,10,and 15 mg kg^(-1) soil).Results revealed that Cd stress signiﬁcantly reduced the seed emergence,growth,root,and allometric characters and yield-related parameters of wheat *** most observable reduc-tion was documented in wheat plants exposed to a higher Cd concentration(15 mg kg^(-1)),followed by the lower Cd level(control).The combined application of bio-stimulants and EDTA minimized the negative impacts of Cd *** highest increase in seedling emergence(5.44%),leaf area(50.60%),number of tillers(31.02%),grain yield per plant(24.28%),biological yield(13.97%),and decrease in Cd levels in grains(40%)was noticed where 20%foliar application of *** and 10%of *** biostimulants were done using 5 mg kg^(-1) of soil-applied *** intervention demonstrated a notable reduction in Cd-induced negative effects,highlighting the potential of these substances in promoting sustainable wheat cultivation in contaminated ***,it is an eco-friendly and approachable method at theﬁeld level able to ensure food safety.

关键词： Abiotic stress allometric characters antioxidant activity crop yield seed emergence grain yield

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Recent advances and perspectives in MXene-based cathodes for aqueous zinc-ion batteries

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International Journal of Minerals,Metallurgy and Materials 2024年第7期31卷 1752-1765页

作者： Aiduo Wu Tianhao Wang Long Zhang Chen Chen Qiaomin Li Xuanhui Qu Yongchang Liu Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyState Key Laboratory for Advanced Metals and MaterialsUniversity of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Economics and management University of Science and Technology BeijingBeijing 100083China

Aqueous zinc-ion batteries(AZIBs)show great potential for applications in grid-scale energy storage,given their intrinsic safety,cost effectiveness,environmental friendliness,and impressive electrochemical ***,strong electrostatic interactions exist between zinc ions and host materials,and they hinder the development of advanced cathode materials for efficient,rapid,and stable Zn-ion *** and their derivatives possess a large interlayer spacing,excellent hydrophilicity,outstanding electronic conductivity,and high redox *** materials are considered“rising star”cathode candidates for *** comprehensive review discusses recent advances in MXenes as AZIB cathodes from the perspectives of crystal structure,Zn-storage mechanism,surface modification,interlayer engineering,and conductive network design to elucidate the correlations among their composition,structure,and electrochemical *** work also outlines the remaining challenges faced by MXenes for aqueous Zn-ion storage,such as the urgent need for improved toxic preparation methods,exploration of potential novel MXene cathodes,and suppression of layered MXene restacking upon cycling,and introduces the prospects of MXene-based cathode materials for high-performance AZIBs.

关键词： aqueous zinc-ion batteries MXenes terminal groups interlayer engineering conductive network design

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