Article
Computer Science, Artificial Intelligence
Syed Rashid Aziz, Tamim Ahmed Khan, Aamer Nadeem
Summary: The study aims to validate the helpfulness of inheritance metrics in classifying unlabeled datasets and propose a new mechanism to label clusters as faulty or fault-free. Results showed a significant impact of inheritance metrics in SFP, specifically in classifying unlabeled datasets and correctly labeling instances.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Syed Rashid Aziz, Tamim Ahmed Khan, Aamer Nadeem
Summary: This study evaluated the exclusive use and viability of inheritance metrics in software fault prediction through experiments with about 40 datasets containing inheritance metrics, finding that ic, noc, and dit metrics are helpful in reducing error entropy rate.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Sushant Kumar Pandey, Anil Kumar Tripathi
Summary: The quality of defect datasets is crucial in software defect prediction, and the Class Imbalance (CI) problem is a significant challenge. Training defect prediction models using noisy and imbalanced data can lead to inconsistent and unsatisfactory results. This study investigates the impact of noise and CI on five baseline SDP models and suggests a model with the highest noise tolerance.
Article
Computer Science, Hardware & Architecture
Rakesh Kumar, Amrita Chaturvedi, Lakshmanan Kailasam
Summary: This study proposes an automated fault prediction approach that does not require labeled datasets and can identify faulty and nonfaulty software artifacts in unlabeled datasets. By using logarithmic transformation to obtain metric thresholds and utilizing the random forest algorithm for fault prediction, this approach demonstrates superior performance in empirical evaluations.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Multidisciplinary Sciences
Vinod Kumar Kulamala, Lov Kumar, Durga Prasad Mohapatra
Summary: Software fault prediction is a process that helps to identify fault-prone modules in early stages of software development, with the goal of improving software quality with optimized effort and cost. This study explores Least Square Support Vector Machines (LSSVM) and compares software fault prediction models using different kernels. Experimental results show that LSSVM with polynomial kernel performs better than LSSVM with linear kernel and is similar to RBF kernel.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Jonathan Bryan, Pablo Moriano
Summary: The complexity of software today necessitates collaboration among thousands of developers, which increases the likelihood of introducing defect-prone changes. However, determining when these changes occur has proven difficult using traditional machine learning methods. This study uses contribution graphs of developers and source files to capture the complexity of software changes and shows the potential of using graph-based machine learning for improved just-in-time defect prediction. Testing on 14 open-source projects, the results demonstrate significantly better performance compared to the state-of-the-art, with an F1 score as high as 77.55% and a Matthews correlation coefficient (MCC) as high as 53.16%.
Article
Computer Science, Artificial Intelligence
Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
Summary: This paper introduces a new approach for defect prediction, utilizing a large feature set and incorporating deep temporal convolutional networks with hierarchical attention layers. Evaluation on a dataset from six Java open-source systems shows the effectiveness of the proposed approach in timely predicting defect proneness of code components.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Rizwan Muhammad, Aamer Nadeem, Muddassar Azam Sindhu
Summary: This study evaluates the effectiveness of two coupling metrics, Vovel-in and Vovel-out, in determining fault-prone entities in software systems. The results show that these metrics significantly improve the prediction of fault-prone classes and cover a significant amount of unique information.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Khoa Phung, Emmanuel Ogunshile, Mehmet Aydin
Summary: Identifying software faults is crucial in software development as they reduce software quality and increase development costs. Previous studies have provided insufficient information for fault prediction, making the task difficult. This paper proposes a new set of software metrics called Error-type software metrics, which incorporate information about different types of Java runtime errors. The authors also propose a methodology using Stream X-Machine and machine learning techniques to model, extract, and evaluate error patterns. Experimental results demonstrate that the proposed metrics significantly improve fault-proneness prediction performance.
Article
Computer Science, Artificial Intelligence
Merve Odabasi, Ensar Gul
Summary: In this study, the impact of environmental metrics on software fault prediction was explored, in addition to software metrics. It was found that when combined with machine learning algorithms, environmental metrics can significantly improve the success rate of software fault prediction.
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Francesco Lomio, Sergio Moreschini, Valentina Lenarduzzi
Summary: This study empirically investigates the impact of code quality on fault proneness and predicts fault-inducing commits using different variables and techniques. The results identify a set of features that can accurately predict faults, with deep learning models outperforming machine learning models in terms of accuracy.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Sofian Kassaymeh, Salwani Abdullah, Mohammed Azmi Al-Betar, Mohammed Alweshah
Summary: This paper proposes a combination of the salp swarm algorithm and backpropagation neural network to solve the software fault prediction problem. The proposed method, SSA-BPNN, outperforms the conventional BPNN and state-of-the-art methods in terms of prediction accuracy on various datasets.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Somya Goyal
Summary: Software fault prediction is crucial for ensuring high quality in software development, and machine learning classifiers are widely used in this field. However, the curse of high dimensionality affects the accuracy of these classifiers, and feature preprocessing is proposed as a solution. This study introduces a novel feature selection method using mathematical diversification and conducts experiments on five datasets. The results show that the proposed method outperforms other models in terms of performance.
Article
Multidisciplinary Sciences
Xiaowei Wang, Yanqiao Chen, Jiashan Jin, Baohua Zhang
Summary: This paper proposes a prediction model based on a hybrid fuzzy c-means clustering algorithm and fuzzy network method, which combines expert knowledge and numerical data to achieve excellent interpretability, transparency, and accuracy.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Software Engineering
Iqra Batool, Tamim Ahmed Khan
Summary: Software fault prediction (SFP) techniques aim to identify faults in the early stages of the software development life cycle (SDLC). Machine learning techniques are commonly used for SFP and can provide more accurate results compared to deep learning methods. In this study, we use three deep learning methods, namely long short-term memory (LSTM), bidirectional LSTM (BILSTM), and radial basis function network (RBFN), to predict software faults and compare our results with existing models. We conclude that LSTM and BILSTM perform better, while RBFN is faster in producing the required results. Our proposed models provide software developers with a more accurate and efficient SFP mechanism.
SOFTWARE QUALITY JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Cong Jin, Shu-Wei Jin
APPLIED SOFT COMPUTING
(2016)
Article
Computer Science, Information Systems
Cong Jin, Shu-Wei Jin
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2016)
Article
Computer Science, Information Systems
Cong Jin, Shu-Wei Jin
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2018)
Article
Automation & Control Systems
Cheng Jin, Xianguang Kong, Jiantao Chang, Han Cheng, Xiaojia Liu
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2020)
Article
Education & Educational Research
Cong Jin
Summary: This paper proposes a method to predict students' dropout status using their learning behavior data. By designing a feature extraction method and an intelligently optimized SVR model, this method outperforms other benchmark models in predictive performance.
INTERACTIVE LEARNING ENVIRONMENTS
(2023)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
