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Information Technology (ACIT)

作者： Abdallah Moubayed Nouh Alhindawi Jamal Alsakran MohammadNoor Injadat Mohammad Kanan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Department of Software Engineering Jadara University Irbid Jordan Fairleigh Dickinson University Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees.

关键词： software testing Machine learning algorithms software algorithms software quality software systems software reliability Regression tree analysis Optimization Standards software development management

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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Machine Learning for Traffic-Driven Services Provisioning in Optical Networks

Machine Learning for Traffic-Driven Services Provisioning in...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： L. Sanker Magadevan N. Shanmugalakshmi M. Giridhar M. Balaji R. Saravanakumar S. Jeya Lakshmi Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi Tamil Nadu India Department of Artificial Intelligence & Data Science Vel Tech High Tech Dr. Rangarajan and Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The increasing demand for high-capacity and dynamic services in optical networks necessitates intelligent and adaptive provisioning mechanisms. This study investigates the application of machine learning (ML) techniques for traffic-driven service provisioning in optical networks, addressing challenges such as traffic prediction, resource allocation, and fault management. The proposed framework employs supervised and unsupervised learning models, including Long Short-Term Memory (LSTM) networks, Random Forests, and K-Means clustering, to analyze traffic patterns and optimize service deployment. The system leverages real-time and historical traffic data to predict demand, enabling proactive resource allocation and dynamic wavelength assignment. Furthermore, reinforcement learning is explored to automate decision- making for adaptive routing and spectrum management under varying network conditions. Performance evaluation on simulated and real-world datasets demonstrates that ML-based approaches significantly improve network efficiency, reduce latency, and enhance the quality of service (QoS). This research highlights the potential of machine learning in revolutionizing service provisioning in optical networks, offering scalable, data-driven solutions to meet the growing demands of next-generation communication systems.

关键词： Reviews Buildings Machine learning Quality of service Optical fiber networks Elastic optical networks Resource management Monitoring Long short term memory Random forests

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An investigation of ensemble learning techniques for obesity risk prediction using lifestyle data

Decision Analytics Journal

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Decision Analytics Journal 2025年 14卷

作者： Ganie, Shahid Mohammad Reddy, Bobba Bharath K, Hemachandran Rege, Manjeet AI Research Centre Woxsen University Telangana Hyderabad 502345 India School of Technology Woxsen University Telangana Hyderabad 502345 India Department of Software Engineering and Data Science Director of the Center for Applied Artificial Intelligence University of St. Thomas United States

Obesity disease is a significant health issue and has affected millions of people worldwide. Identifying underlying reasons for the onset of obesity risk in its early stage has become challenging for medical practitioners. The growing volume of lifestyle data related to obesity has made it imperative to employ effective machine-learning algorithms that can gather insights from the underlying data trends and identify critical patient conditions. In this study, an ensemble learning approach including boosting, bagging, and voting techniques was used for obesity risk prediction based on lifestyle dataset. Specifically, XGBoost, Gradient Boosting, and CatBoost models are used for boosting, Bagged Decision Tree, Random Forest, and Extra Tree models are used for bagging, and Logistic Regression, Decision Tree, and Support Vector Machine models are used for voting. Different preprocessing steps were employed to improve the quality assessment of the data. Hyperparameter tuning and feature selection and ranking are also used to achieve better prediction results. The considered models are extensively evaluated and compared using various metrics. Among all the models, XGBoost performed better with an accuracy of 98.10%, precision and recall of 97.50%, f1-score of 96.50%, and AUC-ROC of 100%, respectively. Additionally, weight, height, and age features were identified and ranked as the most significant predictors using the recursive feature elimination method for obesity risk prediction. Our proposed model can be used in the healthcare industry to support healthcare providers in better predicting and detecting multiple stages of obesity diseases. © 2024 The Author(s)

关键词： Bagging Boosting Ensemble learning Machine learning Obesity risk prediction Voting

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Hasan M. Mahmud S. Poudel K. Donthula K. Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computer Science Middle Tennessee State University Murfreesboro TN USA Senior Software Engineer Optum Inc Murfreesboro TN USA

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface.

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IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

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Measurement: Sensors 2024年 33卷

作者： Hassan, Abdelwahab Said Thakare, Anuradha Bhende, Manisha Prasad, K.D.V. Singh, Pavitar Parkash Byeon, Haewon Department of Mathematics Faculty of Science Suez Canal University Egypt Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune411044 India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Hyderabad Pune India Department of Management Lovely Professional University India Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine. © 2024 The Authors

关键词： Internet of things

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WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

arXiv

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arXiv 2023年

作者： Huo, Dongjian Zhang, Zehong Su, Hanjing Li, Guanbin Fang, Chaowei Wu, Qingyao School of Software and Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Peng Cheng Laboratory China Tencent Wechat Department School of Computer Science and Engineering Sun Yat-sen University China Xidian University China

Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches——one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks;precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multiscale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach’s remarkable superiority, surpassing existing state-ofthe-art methods by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

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Authentic Learning of Machine Learning to Ransomware Detection and Prevention

Authentic Learning of Machine Learning to Ransomware Detecti...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Md Jobair Hossain Faruk Mohammad Masum Hossain Shahriar Kai Qian Dan Lo Department of Software Engineering and Game Development Kennesaw State University USA School of Data Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department Computer Science Kennesaw State University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.

关键词： Conferences Machine learning software Internet Ransomware Computer security

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File Type Identification Classifier with Classification-by-Retrieval and One-Shot Learning

SSRN

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SSRN 2024年

作者： Lisker, Simona Butman, Ayelet Dubin, Ran Dvir, Amit Hajaj, Chen Department of Computer Science Holon Institute of Technology Holon 52 Golomb Street POB 305 Holon5810201 Israel Department of Computer Science Ariel Cyber Innovation Center Ariel University Kiryat Hamada St. Ariel4070000 Israel Department of Computer and Software Engineering Ariel Cyber Innovation Center Ariel University Kiryat Hamada St. Ariel4070000 Israel Department of Industrial Engineering and Management Data Science and Artificial Intelligence Research Center Ariel University Kiryat Hamada St. Ariel4070000 Israel

In classical malware detection methodologies, digitalforensics and file carving, file type classification holds a pivotalrole. However, it is still a challenge to accurately categorize an ever-growing diversity of file formats and *** approaches to address the issue have been introducedin recent years. Most approches use hand-crafted features, andsome use a compact neural network architectureapproach with explicit feature extraction. Nonetheless, thesemethods encounter some challenges, including training time fornew file types, dealing with an insufficient number of trainingexamples, the complexity of training, and their accuracylevel. Our paper introduces three novel, content-basedretrieval and identification, file type classificationapproaches, proactively addressing these identified *** three approaches significantly enhance accuracy andprioritize an intuitive method for swift integration of new filetypes using pre-trained models, thereby enhancing speed andreliability in file type detection. The first approach is basedon the principles of Natural Language Processing (NLP) andtransformer architecture. The second approach amalgamates anensemble of statistical features, OOXML files identifier and a pre-trained model usingtransfer *** meticulously evaluated the classification task, achieving an accuracy of 72.4% and 69.2%, respectively, for our first two approaches, surpassing the performance of the state-of-the-art convolutional neural network (CNN) based models described in the literature in terms of accuracy. Finally, our third approach of one-shot learning enables the identification of files with minimal examples, facilitating efficient training in a short period, and demonstrating a remarkable 100% accuracy in several scenarios. © 2024, The Authors. All rights reserved.

关键词： Malware

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