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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi department of computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia department of computer science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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computer Modeling in Engineering & sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India department of AI and software Engineering Kangwon National UniversitySamcheok25913Republic of Korea department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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computer Modeling in Engineering & sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia department of Artificial Intelligence&software Kangwon National UniversitySamcheok25913Republic of Korea department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

Review of Techniques for Integrating Security in software Development Lifecycle

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computers, Materials & Continua 2025年第1期82卷 139-172页

作者： Hassan Saeed Imran Shafi Jamil Ahmad Adnan Ahmed Khan Tahir Khurshaid Imran Ashraf College of Electrical and Mechanical Engineering National University of Sciences and Technology(NUST)Rawalpindi46604Pakistan department of computer science Abasyn University Islamabad CampusIslamabad45510Pakistan Military College of Signals National University of Sciences and Technology(NUST)Rawalpindi46600Pakistan department of Electrical Engineering Yeungnam UniversityGyeongsan38541Republic of Korea department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Republic of Korea

software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our *** conduct research in this field,periodic comparative analysis is needed with the new techniques coming up *** purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way *** review follows Kitchenham’s review *** review has been divided into three main stages including planning,execution,and *** the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation *** risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation *** a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat *** review reveals that threat analysis and security testing are needed to develop automated tools for the *** estimation of effort required to prioritize potential security risks is a big challenge in software *** accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep *** is also imperative to validate these effort estimation methods to ensure all potential security threats are *** challenge is selecting the right model for each specific security *** achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.

关键词： software development lifecycle systematic literature review critical software security risks national institute of standards and technology DevSecOps open web application security project McGraw’s touch points

A revisit of three studies related to random testing

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science China(Information sciences) 2015年第5期58卷 49-57页

作者： CHEN Tsong Yueh KUO Fei-Ching TOWEY Dave ZHOU Zhi Quan department of computer science and software Engineering Swinburne University of Technology School of computer science The University of Nottingham Ningbo China School of computer science and software Engineering University of Wollongong

software testing is an approach that ensures the quality of software through execution, with a goal being to reveal failures and other problems as quickly as possible. Test case selection is a fundamental issue in software testing, and has generated a large body of research, especially with regards to the effectiveness of random testing(RT), where test cases are randomly selected from the software’s input domain. In this paper, we revisit three of our previous studies. The first study investigated a sufficient condition for partition testing(PT)to outperform RT, and was motivated by various controversial and conflicting results suggesting that sometimes PT performed better than RT, and sometimes the opposite. The second study aimed at enhancing RT itself, and was motivated by the fact that RT continues to be a fundamental and popular testing technique. This second study enhanced RT fault detection effectiveness by making use of the common observation that failure-causing inputs tend to cluster together, and resulted in a new family of RT techniques: adaptive random testing(ART),which is random testing with an even spread of test cases across the input domain. Following the successful use of failure-causing region contiguity insights to develop ART, we conducted a third study on how to make use of other characteristics of failure-causing inputs to develop more effective test case selection strategies. This third study revealed how best to approach testing strategies when certain characteristics of the failure-causing inputs are known, and produced some interesting and important results. In revisiting these three previous studies, we explore their unexpected commonalities, and identify diversity as a key concept underlying their *** observation further prompted us to examine whether or not such a concept plays a role in other areas of software testing, and our conclusion is that, yes, diversity appears to be one of the most important concepts in the fie

关键词： adaptive random testing diversity metamorphic testing proportional sampling strategy random testing software testing

Personalized federated few-shot node classification

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science China(Information sciences) 2025年第1期 204-218页

作者： Yunfeng ZHAO Xintong HE Guoxian YU Jun WANG Yongqing ZHENG Carlotta DOMENICONI School of software Shandong University Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University department of Mathematics National University of Singapore department of computer science George Mason University

Personalized federated learning(PFL) aims to train customized models for individual clients in a decentralized setting, with the account of non-independent and identically distributed data across clients. However, most PFL methods adopt uniform classification layers for diverse clients and give rise to error-prone predictions, due to the task heterogeneity notably prominent in decentralized graph data scenarios. Although some PFL solutions setup client-specific classification layers for each client and optimize them only locally, they are corrupted with limited local training data. We propose an innovative solution called federated parameter decoupling and node augmentation(Fed PANo) to address these problems and to achieve personalized federated few-shot node classification, which is a prevalent and challenging but unexplored topic. Specifically, Fed PANo first separates the local model into the GNN and classifier to handle unique client-specific task variations. The GNN is trained through federated learning to capture shared knowledge of graph nodes across clients, while the classifier is custom-designed and trained individually for each client. Additionally, a generic classifier shared among clients is adopted to encourage the GNN's grasp of shared information. Then Fed PANo further proposes the node generator along with its local and collaborative training strategies to deal with the node scarcity of clients. Extensive experimental results on benchmark datasets confirm that Fed PANo outperforms eight competitive baselines across different settings.

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A Review on the Architecture of software Defined Network

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Chinese Journal of Electronics 2018年第6期27卷 1111-1117页

作者： LIN Chuang HU Jie LI Guoliang CUI Laizhong College of computer science and software Engineering Shenzhen University department of computer science and Technology Tsinghua University

software defined network(SDN) is a promising network architecture which can simplify network management, ease deployment of network applications, and enable independent evolution of control plane and data plane. However, the separation of control plane and data plane intensifies performance concerns. In this paper, we review the performance of SDN from the perspective of I Ching which has a history of more than two and half millennia of commentary and interpretation, and provides inspiration to the worlds of business, military and religion. We utilize I Ching to model and abstract the architecture of SDN, and discuss the architecture and performance of SDN based on the statements of I Ching. Then we study current works and researches on the performance and architecture of SDN, and discuss the promising directions to the development and evolvement of SDN. Finally,we study common control functions in SDN and the deployment of SDN in different networking settings.

关键词： software defined networking(SDN) Network architecture I Ching

Spatial geometric constraints histogram descriptors based on curvature mesh graph for 3D pollen particles recognition

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Chinese Physics B 2014年第6期23卷 123-130页

作者：谢永华徐赵飞 Hans Burkhardt School of computer and software Nanjing University of Information Science and Technology department of computer science Freiburg University

This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.

关键词： pollen recognition curvature mesh graph spatial geometric constraints bidirectional histogram

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Improving the Ambient Intelligence Living Using Deep Learning Classifier

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computers, Materials & Continua 2022年第10期73卷 1037-1053页

作者： Yazeed Yasin Ghadi Mouazma Batool Munkhjargal Gochoo Suliman AAlsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park department of computer science and software Engineering Al Ain UniversityAl Ain15551UAE department of computer science Air UniversityIslamabad44000Pakistan department of computer science and software engieering United Arab Emirates UniversityAl Ain15551UAE department of computer science College of ComputerQassim UniversityBuraydah51452Saudi Arabia department of Humanities and Social science Al Ain UniversityAl Ain15551UAE department of computer Engineering Tech University of KoreaGyeonggi-do15073Korea

Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live *** a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s *** can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for *** this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)*** first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode *** convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal *** assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,*** reveal that hybrid method gives more accuracy than single classifier in AAL *** suggested system can assists persons with impairments,assisting carers and medical personnel.

关键词： Ambient assisted living convolutional neural network dimensionality reduction frequency-time features wearable technology

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2327-2339页

作者： Min Yang Guanjun Liu Ziyuan Zhou Jiacun Wang the department of computer science Tongji University IEEE the computer science and software Engineering department Monmouth UniversityWest Long Branch

Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model ***, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

关键词： Deep reinforcement learning(DRL) performance improvement framework probabilistic automata real-time monitoring the key probabilistic decision-making units(PDMU)-action pair