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Review and Empirical Analysis of machine learning-Based Software Effort Estimation

IEEE ACCESS 2024年 12卷 85661-85680页

作者： Rahman, Mizanur Sarwar, Hasan Kader, MD. Abdul Goncalves, Teresa Tin, Ting Tin Western Illinois Univ Sch Comp Sci Macomb IL 61455 USA United Int Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh Univ Malaysia Pahang Al Sultan Abdullah Fac Comp Pekan 26600 Malaysia Univ Evora Dept Informat P-7004516 Evora Portugal INTI Int Univ Fac Data Sci & Informat Technol Nilai 71800 Malaysia

The average software company spends a huge amount of its revenue on Research and Development (R&D) for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation can pose significant challenges, highlighting the need for continuous improvement in estimation techniques. This study reviews recent machine learning approaches employed to enhance the accuracy of software effort estimation (SEE), focusing on research published between 2020 and 2023. The literature review employed a systematic approach to identify relevant research on machine learning techniques for SEE. Additionally, comparative experiments were conducted using five commonly employed machine learning (ML) methods: K-Nearest Neighbor, Support Vector machine, Random Forest, Logistic Regression, and LASSO Regression. The performance of these techniques was evaluated using five widely adopted accuracy metrics: Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation was carried out on seven benchmark datasets: Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, and COCOMO, which are publicly available and extensively used in SEE research. By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE);secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation;and

关键词： Estimation machine learning algorithms Software reliability Software algorithms Research and development Software development management Linear regression Support vector machines Random forests Software effort estimation software development efforts estimation linear regression support vector machine random forest LASSO KNN R&D investment

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Efficient Incremental Variable-Fidelity machine-learning-Assisted Hybrid Optimization and Its Application to Multiobjective Antenna Design

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第12期72卷 9347-9354页

作者： Chen, Weiqi Wu, Qi Han, Biying Yu, Chen Wang, Haiming Hong, Wei Southeast Univ Sch Informat Sci & Engn Nanjing 211189 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 211189 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

Online-model-based machine-learning-assisted optimization (MLAO) methods are widely used to reduce the computational burden of complex electromagnetic (EM) optimization problems. Multidesign parameter and multiobjective EM problems are common in engineering practice. As the problem design dimensionality increases, the training time of the surrogate model in the optimization process becomes nonnegligible. The performance of optimization algorithms degrades for high design dimensions and multiple objectives, and many full-wave simulation calculations are required before convergence. In this work, an incremental variable-fidelity machine-learning-assisted hybrid optimization (IVF-MLAHO) algorithm is proposed to solve a multiobjective EM problem with medium-scale (i.e., 20-50) design variables. First, reliable variable-fidelity models are used for initial sampling to reduce the computational cost of sampling. Then, in the training process, incremental learning or retraining is adaptively selected to update the surrogate models, which reduces the training burden. Furthermore, a hybrid global multiobjective and local single-objective optimization algorithm is adopted to markedly improve the convergence performance. Finally, the superiority of the IVF-MLAHO algorithm is verified on a substrate-integrated waveguide (SIW) broadband millimeter-wave slot antenna array, in which the training time is greatly reduced.

关键词： Computational modeling Optimization Training Predictive models Data models Antennas and propagation Adaptation models machine learning algorithms Antenna arrays Millimeter wave technology Antenna design incremental learning machine learning multiobjective optimization variable-fidelity optimization

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Entropy generation and heatline visualization of magnetized thermal convection in an irregular enclosure filled with nanofluid and subject to non-uniform heating: FEM-machine learning hybrid approach

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CASE STUDIES IN THERMAL ENGINEERING 2024年 60卷

作者： Shahzad, Hasan Li, Zhiyong Tang, Tingting Xie, Yongyu Lin, Zhuobin Dongguan Univ Technol Fac Energy & Power Engn Sch Chem Engn & Energy Technol Dongguan Peoples R China Univ Sci & Technol Dept Chem Engn & Energy Technol Hefei Peoples R China

This investigation's primary objective is to analyze the fluid dynamics and heat transfer enhancement in an irregular enclosure. Several physical phenomena such as Alumina-water nanofluids, magnetic field, non-uniform heating and amplitude of non-uniform heating are taken into account. A thorough investigation using machine learning algorithms (MLAs) and computational fluid dynamics (CFD) is carried out. Initially, for CFD phase the coupled non-linear dimensionless governing equations of the considered problem are solved numerically by adapting a Finite Element method (FEM) using appropriate boundary conditions. The analysis considers a range of physical parameters, such as the Hartmann number (0 - 20), nanoparticle volume friction (0 - 0.04), Ryleigh number (10 3-10 5 ), offset temperature (0.1 - 1), frequency (1-10) and nonuniform heating amplitude (0.1 - 1). The numerical results are explicated in the form of streamlines, isotherms, heatlines structures. The impact of these factors on entropy generation is also explored. In addition, three popular MLAs, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector machine (SVM) were used to save the computational costs. The finding explore Hartmann number and nanoparticle volume friction has decreasing trend on total entropy production and heat transfer rate. The entropy production decreases up to 42.02 % for Hartman number and for nanoparticles it decreases 12.61 %. However, the non-uniform heating amplitude has opposite effect on irreversibility factor. Also, compared to other MLAs, the ANN model demonstrates more accurate predictions. The objective is to propose novel concept for enhancing the performance of cooling systems and optimizing energy utilization across diverse technical domain.

关键词： machine learning algorithms Nanofluids Irregular cavity Offset temperature Half-sinusoidal heating magnetohydrodynamic (MHD) Finite element method (FEM)

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Using Methods From Dimensionality Reduction for Active learning With Low Query Budget

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 4317-4330页

作者： Tharwat, Alaa Schenck, Wolfram Hsch Bielefeld Univ Appl Sci & Arts Ctr Appl Data Sci CfADS Bielefeld Germany

Recently, it has been challenging to generate enough labeled data for supervised learning models from a large amount of free unlabeled data due to the high cost of the labeling process. Here, the active learning technique provides a solution by annotating a small but highly informative set of unlabeled data. This ensures high generalizability in space and improves classification performance with test data. The task is more challenging when the query budget is small, the data is imbalanced, multiple classes are present, and no predefined knowledge is available. To address these challenges, we present a novel active learner geometrically based on principal component analysis (PCA) and linear discriminant analysis (LDA). The proposed active learner consists of two phases: The PCA-inspired exploration phase, in which regions with high variances are explored, and the LDA-inspired exploitation phase, in which boundary points between classes are selected. The proposed geometric strategy improves the search capabilities of the active learner, allowing it to explore the space of minority classes even with multiple minority classes and a small query budget. Experiments on synthetic and real binary and multi-class imbalanced data show that the proposed algorithm has significant advantages over multiple known active learners.

关键词： Data models Training data Space exploration Principal component analysis Labeling Adaptation models machine learning algorithms Active learning dimensionality reduction imbalanced data LDA PCA

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Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

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ENVIRONMENTAL TOXICOLOGY 2024年第5期39卷 2908-2926页

作者： Xu, Qi-Tong Qiang, Jian-Kun Huang, Zhi-Ye Jiang, Wan-Ju Cui, Xi-Mao Hu, Ren-Hao Wang, Tao Yi, Xiang-Lan Li, Jia-Yuan Yu, Zuoren Zhang, Shun Du, Tao Liu, Jinhui Jiang, Xiao-Hua Tongji Univ Shanghai East Hosp Sch Med Dept Gastrointestinal Surg Shanghai Peoples R China Tongji Univ Sch Med Key Lab Arrhythmias Minist Educ China Shanghai Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Gynecol Nanjing Peoples R China Tongji Univ Shanghai East Hosp Sch Med Dept Gastrointestinal Surg Shanghai 200092 Peoples R China

BackgroundColorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis *** retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic *** integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC *** current study highlights the potential of integrating machine learning models to enhance the pr

关键词： colorectal cancer (CRC) experimental validation machine learning algorithms prognostic signature programmed cell death (PCD)

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Resilient Event Detection Algorithm for Non-Intrusive Load Monitoring Under Non-Ideal Conditions Using Reinforcement learning

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第2期60卷 2085-2094页

作者： Etezadifar, Mozaffar Karimi, Houshang Aghdam, Amir G. Mahseredjian, Jean Polytech Montral Dept Elect Engn Montreal PQ H3T 1J4 Canada Concordia Univ Elect & Comp Engn Montreal PQ H3G 1M8 Canada

Event detection is critical in a non-intrusive load monitoring (NILM) solution. NILM is essential for the implementation of some demand-side management (DSM) techniques. This article proposes an event detection algorithm based on reinforcement learning for NILM purposes (RLNILM). The proposed method employs a number of simpler traditional event detection algorithms, e.g., LLR voting, or SWDC, to train the RLNILM agent through a feedback system that separates the RLNILM agent from directly accessing consumers' data. The performance of the proposed RLNILM method is validated using the real-world data from the iAWE dataset under ideal and non-ideal conditions. Four test scenario groups include cases where the frequency, input electric signals, or access to crucial grid information varies significantly. In all scenarios, the RLNILM agent outperforms traditional event detection algorithms used in the feedback system. The results show not only the proposed architecture increases the cyber security of the customers connected to NILM services, but also it improves the performance of real-time event detection algorithms in non-ideal grid conditions.

关键词： Event detection Power grids machine learning algorithms Heuristic algorithms Load monitoring Training Real-time systems Non-intrusive load monitoring reinforcement learning smart grid event detection federated learning machine learning NILM

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Analyzing machine learning Algorithm Performance in Predicting Student Academic Performance in Data Structures and algorithms Based on Lifestyles

Analyzing Machine Learning Algorithm Performance in Predicti...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Elisa V. Malasaga Angelo C. Arguson John Kenneth F. Andales Shaneth C. Ambat Rhonnel S. Paculanan May Florence San Pablo Computer Science FEU Institute of Technology Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm (DSA) course which is based on student lifestyle to analyze the factors that affect the high or low performance result. A total number of 251 Bachelor of Science in Computer Science (BSCS) students participated in the study where 207 or 82% were male and 44 or 18% were female. A oneshot case study was conducted that led to data collection through the administration of an online survey on former enrollees of the said course. The dataset was extracted with 43 features and was analyzed using Python on Jupyter Notebook. Randomly selected 70% of these, 176 observations, are used to train the classifier models. The remaining 30%, 75 observations, were used as the test data. In order to classify academic performance students, eight machine learning algorithms were applied based on random forest (RF), decision tree (DT), support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), Gaussian Naive Bayes (GNB), stochastic gradient descent (SGD), and perceptron. Although SGD and Perceptron classifier models show comparably low classification performances, both random forest and decision tree classifiers provided the highest metric performance. The study indicated that the lifestyles of students contributed to whether the student performance became high or low in their grade performance.

关键词： Support vector machines Surveys Logistic regression machine learning algorithms Gaussian processes Prediction algorithms Feature extraction

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Optimal Resource Allocation for AGIN 6G: A learning-Based Three-Sided Matching Approach

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1553-1565页

作者： Qin, Peng Wang, Miao Cai, Ziyuan Ding, Rui Zhao, Xiongwen Fu, Yang Wu, Xue North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China China Acad Elect & Informat Technol Beijing 100041 Peoples R China China Satellite Network Grp Co Ltd Beijing 100029 Peoples R China

As ubiquitous interconnection becomes a reality for human beings, addressing the challenge of seamless coverage in the near future 6G network, particularly for remote area connections, has become increasingly urgent. HAPs and UAVs can efficiently extend the network coverage which is unfeasible by ground base stations. However, due to the high dynamic nature of air ground communications, both the available network resource and channel state information (CSI) are time varying which is typically called the dilemma of information uncertainty, often making the resource allocation intractable. To overcome the challenges, first we propose an air ground integrated 6G heterogeneous network (AGIN 6G) leveraging the wide coverage of HAPs and the hot spot communication enhancement of UAVs. Second we formulate the challenge as a three-sided matching problem with the size and cyclic preference (TMSC) among HAPs, UAVs and users. To find a feasible solution, we transform the problem into a R-TMSC issue by applying some reasonable constraints, especially when the CSI is assumed known, we can develop the HOR$<^>{2}$ A-CSI algorithm. Third, to cope with the information uncertainty, we propose the CVA-UCB matching solution augmented with machine learning. Finally, we conduct extensive experiments comparing with benchmark algorithms. Our works can verify that the proposed algorithm is superior to others in terms of the data transmission rate, system revenue and throughput.

关键词： 6G mobile communication Resource management Uncertainty Autonomous aerial vehicles machine learning algorithms Heuristic algorithms Throughput Air ground integrated 6G heterogeneous networks (AGIN 6G) matching learning resource allocation three-sided cyclic matching

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Dimension Reduction Techniques for machine learning-Based AC Microgrid Fault Diagnosis: A Systematic Review

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IEEE ACCESS 2024年 12卷 160586-160612页

作者： Zaben, Muiz M. Abido, Mohammad A. Worku, Muhammed Y. Hassan, Mohamed A. King Fahd Univ Petr & Minerals KFUPM Elect Engn Dept Dhahran 31261 Saudi Arabia KFUPM Res Inst Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran 31261 Saudi Arabia Mansoura Univ Elect Engn Dept Mansoura 35516 Egypt

The integration of inverter-interfaced sources into microgrids has led to new protection challenges due to fluctuating fault currents, bidirectional power flow, and low inertia. Traditional protection methods are inadequate for these evolving systems, necessitating the development of novel, fast-acting, and cost-effective strategies that are independent of fault current levels. machine learning-based protection schemes offer potential for improved performance over traditional methods, but their success is highly dependent on the quality and nature of the input data. Raw fault signal data are often noisy and high-dimensional. Processing it without feature extraction or selection can result in poor performance, longer processing times, higher storage needs, overfitting, and reduced generalizability. The dimensionality reduction step, which encompasses feature extraction and/or feature selection algorithms, is crucial for machine learning-based protection schemes. Many such methods have been proposed in the last decade. This research aims to investigate available techniques for reducing the dimensionality of data used for fault diagnosis in AC microgrids. The research will review and compare different dimensionality reduction methods, highlighting their strengths and weaknesses. It will also explore the most used features and their effectiveness in different scenarios. This article highlights the key gaps in AC microgrid fault diagnosis, which include scalability, real-time performance, and generalization, among many others. It emphasizes that future research should focus on unexplored dimension reduction techniques, hybrid approaches, and improving deep learning interpretability for robust and accurate solutions.

关键词： Feature extraction Dimensionality reduction Microgrids Protection machine learning algorithms Fault diagnosis Transforms Reviews machine learning Discrete wavelet transforms fault detection fault location feature extraction machine learning microgrid reviews wavelet transform

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Cluster Workload Allocation: A Predictive Approach Leveraging machine learning Efficiency

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IEEE ACCESS 2024年 12卷 194091-194107页

作者： Sliwko, Leszek Univ Westminster Sch Comp Sci & Engn London W1B 2HW England

This research investigates how machine learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited number of nodes. Using real-world Google Cluster Data (GCD) workload traces and the AGOCS framework, the study extracts node attributes and task constraints, then analyses them to identify suitable node-task pairings. It focuses on tasks that can be executed on either a single node or fewer than a thousand out of 12.5k nodes in the analysed GCD cluster. Task constraint operators are compacted, pre-processed with one-hot encoding, and used as features in a training dataset. Various ML classifiers, including Artificial Neural Networks, K-Nearest Neighbours, Decision Trees, Naive Bayes, Ridge Regression, Adaptive Boosting, and Bagging, are fine-tuned and assessed for accuracy and F1-scores. The final ensemble voting classifier model achieved 98% accuracy and a 1.5-1.8% misclassification rate for tasks with a single suitable node.

关键词： Load management Cloud computing Load modeling Processor scheduling Internet Resource management Optimal scheduling Classification algorithms Prediction algorithms machine learning algorithms machine learning classification algorithms load balancing and task assignment Google cluster data

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