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Comparative analysis of text mining and clustering techniques for assessing functional dependency between manual test cases

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software Quality Journal 2025年第2期33卷 1-36页

作者： Tahvili, Sahar Hatvani, Leo Felderer, Michael de Oliveira Neto, Francisco Gomes Afzal, Wasif Feldt, Robert Compute Platforms Engineering Unit Ericsson AB Stockholm Sweden Department of Industrial AI Systems Mälardalen University Västerås Sweden Institute of Software Technology German Aerospace Center Cologne Germany Department of Mathematics and Computer Science University of Cologne Cologne Germany Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Engineering Blekinge Institute of Technology Karlskrona Sweden Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden

Text mining techniques, particularly those leveraging machine learning for natural language processing, have gained significant attention for qualitative data analysis in software testing. However, their complexity and lack of transparency can pose challenges, especially in safety-critical domains where simpler, interpretable solutions are often preferred unless accuracy is heavily compromised. This study investigates the trade-offs between complexity, effort, accuracy, and utility in text mining and clustering techniques, focusing on their application for detecting functional dependencies among manual integration test cases in safety-critical systems. Using empirical data from an industrial testing project at ALSTOM Sweden, we evaluate various string distance methods, NCD compressors, and machine learning approaches. The results highlight the impact of preprocessing techniques, such as tokenization, and intrinsic factors, such as text length, on algorithm performance. Findings demonstrate how text mining and clustering can be optimized for safety-critical contexts, offering actionable insights for researchers and practitioners aiming to balance simplicity and effectiveness in their testing workflows. © The Author(s) 2025.

关键词： Artificial intelligence Clustering Natural language processing software testing Text mining

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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Leveraging Predictive Maintenance for Photovoltaic systems 9th

Leveraging Predictive Maintenance for Photovoltaic Systems

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9th International Conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston K7K 7B4 ON Canada

ISBN: (纸本)9783031886522

This paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. Through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. The seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Edge device Energy management system Energy monitoring Predictive maintenance Renewable energy Solar energy

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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm with Niching Technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Computer School Beijing Information Science and Technology University Beijing China School of Mathematics and Computer Science Tongling University Tongling China National Engineering Laboratory for Internet Medical Systems and Applications the First Affiliated Hospital of Zhengzhou University Zhengzhou China

In this paper, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multi-modality, we designed a hybrid metaheuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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SSVEPPoolformer: An Improved Poolformer Model With the Adaptive Denoising Algorithm for SSVEP-EEG Signal Classification

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Chunquan Liao, Zhiyuan Cheng, Yuxin Wang, Zitao Wu, Junyun Liu, Ruijun Liu, Peter X. Nanchang University School of Information Engineering Nanchang330031 China Nanchang University School of Mathematics and Computer Science Nanchang330031 China Beihang University School of Software Beijing100191 China Carleton University School of Department of Systems and Computer Engineering OttawaK1S 5B6 Canada

Most EEG classification algorithms based on steady-state visual evoked potentials (SSVEP-EEG) require filtering for denoising. However, manually set thresholds may inadvertently remove useful information, leading to a loss of significant signal features. Additionally, most deep learning-based SSVEP-EEG classification models have limited global feature extraction capabilities, and the self-attention mechanism in Transformers increases computational costs. To address these challenges, this paper proposes a novel SSVEP-EEG classification algorithm, SSVEPPoolformer. SSVEPPoolformer integrates an adaptive denoising algorithm with an improved Poolformer algorithm, enhancing both denoising performance and classification accuracy. The adaptive denoising algorithm dynamically adjusts the threshold using a compensation and adaptive adjustment mechanism, effectively filtering noise while retaining critical signal features. The improved Poolformer algorithm replaces the self-attention mechanism with an average pooling operation, reducing computational costs while maintaining performance. It also uses adaptive average pooling to integrate cross-channel feature information and extract global fine-grained features, improving global feature extraction. The SSVEPPoolformer model's efficacy was validated on two public datasets. Experimental results demonstrate that compared with other state-of-the-art methods, SSVEPPoolformer has higher classification accuracy and Information Transfer Rate (ITR) in both intra-class and inter-class recognition scenarios, and has lower computational cost. © 1975-2011 IEEE.

关键词： Electroencephalography

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston K7K 7B4 ON Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R² score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R² score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review

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PeerJ computer science 2025年 11卷 e2400-e2400页

作者： Gadekallu, Thippa Reddy Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Fang, Kai Chen, Junxin Wang, Wei Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Computer Science Engineering and Information Systems Vellore Institute of Technology University Tamil Nadu India Zhongda Group Jiaxing China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Mathematics and Computer Science Zhejiang Agricultural and Forestry University Hangzhou China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. This review offers a comprehensive analysis of the GPT and its significance for enhancing inclusivity in the field of higher education. In this research, we also examined the possible challenges and constraints associated with the integration of GPT into inclusive higher education, along with potential solutions. Overall, this review is intended for educators, students with and without learning disabilities, policymakers, higher education institutes, researchers, and educational technology developers. This review aims to provide a comprehensive understanding of GPT in promoting inclusive higher education for people with various learning disabilities, its impacts on inclusive higher education, emerging challenges, and potential solutions. Copyright 2025 Gadekallu et al.

关键词： Contrastive Learning

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Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural software Architecture Inference

arXiv

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arXiv 2025年

作者： Herbold, Steffen Knieke, Christoph Rausch, Andreas Schindler, Christian Faculty of Computer Science and Mathematics Universität Passau Passau Germany Institute for Software and Systems Engineering Technische Universität Clausthal Clausthal-Zellerfeld Germany

Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoning over these architectures, enabling neurosymbolic architectural reasoning. We discuss how this approach works in general and outline a research agenda based on six general research question that need to be addressed, to achieve this vision. © 2025, CC BY.

关键词： software design

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Predicting Cardiac Flow Patterns Using a Novel Hemodynamic Neural Network for LVAD Therapy Planning and Evaluation

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The Journal of Heart and Lung Transplantation 2025年第4期44卷 S133-S134页

作者： M. Mattei A. Issitt M.A. Khan B. Diaz R. White V. Chivukula Biomedical Engineering Florida Institute of Technology Melbourne FL Computer Engineering Florida Institute of Technology Melbourne FL Computer Science and Software Engineering Florida Institute of Technology Melbourne FL Mathematics and Systems Engineering Florida Institute of Technology Melbourne FL

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