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Introduction to the Special Issue on Thriving Amidst Disruptive Technologies

Distributed Ledger Technologies: Research and Practice 2024年第3期3卷 1-2页

作者： Jairo Gutierrez Amarolinda Klein Patrick C. K. Hung Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Unisinos - Campus de Porto Alegre Porto Alegre Brazil Faculty of Business and IT Ontario Tech University Oshawa Canada

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Navigating cloud computing security: strategies, risks, and best practices

Navigating cloud computing security: strategies, risks, and ...

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8th IET Smart Cities Symposium (SCS 2024)

作者： Ammar Odeh Qasem S. Abu Al-Haija Tareq Alhajahjeh Raed Mesleh Computer Science Princess Sumaya University For Technology Amman Jordan Department of Cybersecurity Jordan University of Science and Technology Irbid Jordan Tareq Alhajahjeh Faculty of Computing Engineering and Media De Montfort University Leicester UK Electrical Engineering Department German Jordanian University Amman Jordan

ISBN: (数字)9781837243105

This research analyzes cloud computing environments' inherent security and privacy risks. It examines crucial aspects of cloud security, including data encryption, access control mechanisms, identity management, and the implementation of firewalls and intrusion detection systems. The study emphasizes the significance of regular security audits, compliance checks, and the development of robust data backup and disaster recovery plans. Furthermore, it highlights the importance of employee training and awareness in mitigating security threats. The paper delves into the complexities of secure APIs, segmentation, isolation strategies, and the necessity of regular system updates and patching. Additionally, it examines the evolving landscape of cloud security through the lenses of Cloud Security Posture Management (CSPM), the Shared Responsibility Model, and Zero Trust Architecture. This comprehensive approach provides valuable insights into enhancing security measures in cloud computing, thereby safeguarding sensitive data and maintaining user trust in an era where data security is paramount.

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A Proposal for a New E-Waste Image Dataset Based on the UNU-KEYS Classification

A Proposal for a New E-Waste Image Dataset Based on the UNU-...

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International Symposium on Electrical Apparatus and Technologies (SIELA)

作者： Dimitar I. Iliev Marin B. Marinov Frank Ortmeier Faculty of German Engineering and Industrial Management Technical University of Sofia Sofia Bulgaria Department of Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria Faculty of Computer Science Chair of Software Engineering Otto-von-Guericke- University Magdeburg Magdeburg Germany

ISBN: (数字)9798350382365

ISBN: (纸本)9798350382372

This paper introduces the largest and most diverse e-waste dataset to date, consisting of 19,613 pictures, 28,941 annotations, and 77 classes, where each class represents one visually distinctive type of electronic device. Furthermore, our dataset was structured using the United Nations University's UNU-KEYS classification, which ensures the compatibility of our dataset with the different e-waste classifications used worldwide and allowed us to choose more relevant dataset classes. The proposed methodology for image collection, annotation, and labeling is explained. The advantages and disadvantages of this dataset and how it can be improved in the future are presented. This dataset has been made open source with a permissive CC by 4.0 license so that it can be used to stimulate scientific progress in the field of automatic e-waste recognition, with the end goal of solving the global e-waste problem. The dataset is available online at https://***/electronic-waste-detection/e-waste-dataset-r0ojc.

关键词： Annotations Data acquisition Documentation Electronic waste Licenses Planning Proposals

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Fair and Verifiable Identity-Based Broadcast Proxy Re-Encryption With Designated Sender Feasible for Medical Internet of Things

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IEEE Transactions on Information Forensics and Security 2025年 20卷 6033-6045页

作者： Liqing Chen Shiyu Chen Hao Zhang Jian Weng Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an Jiangsu China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou Fujian China College of Information Science and Technology Jinan University Guangzhou Guangdong China

With the development of big data, cloud computing is widely used by individual and enterprise users due to its powerful computing and storage capabilities. How to share the encrypted data stored in cloud servers flexibly to a group of users and provide effective access control to the users is a hot topic in current research. For that, this paper proposes fair and verifiable identity-based broadcast proxy re-encryption with designated sender (FV-IBBPRE-DS) for Medical Internet of Things (MIoT), which gives the system model, formal definition, and security model of FV-IBBPRE-DS, and constructs a concrete FV-IBBPRE-DS scheme. The data owner in the FV-IBBPRE-DS scheme is able to designate a sender, which is given the decryption privilege of the encrypted data to a set of receivers. The scheme ensures that the participating entities perform the process of data sharing fairly and are able to verify the consistency of the ciphertexts. The FV-IBBPRE-DS scheme achieves chosen ciphertext attack security. The performance analysis demonstrates that the scheme in this paper has significant advantages in re-encrypted ciphertext generation and decryption phase, and can be applied to resource-constrained MIoT devices.

关键词： Receivers Encryption Hospitals Cryptography Cloud computing Genetics Servers Access control Sun software engineering

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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization

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PeerJ computer science 2025年 11卷 1-35页

作者： Mehta, Sumet Sohail, Muhammad Twala, Bhekisipho Ullah, Asad Ullah, Fasee Khan, Arfat Ahmad Ling, Qinghua School of Computer Science & Communication Engineering Jiangsu University Jiangsu Zhenjiang China Department of Computer Software Engineering Military College of Signals NUST Islamabad Islamabad Pakistan Faculty of Information and Communication Technology Tshwane University of Technology Pretoria West Pretoria South Africa The Department of Computing Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar Malaysia Department of Computer Science College of Computing Khon Kaen University Khon Kaen Khon Kaen Thailand School of Computer Science and Engineering Jiangsu University of Science & Technology Jiangsu Zhenjiang China

The analysis of high-dimensional microarray gene expression data presents critical challenges, including excessive dimensionality, increased computational burden, and sensitivity to random initialization. Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. To address these limitations, this study proposes an adaptive neighborhood-preserving multi-objective particle swarm optimization (ANPMOPSO) framework for gene selection. ANPMOPSO introduces four key innovations: (1) a weighted neighborhood-preserving ensemble embedding (WNPEE) technique for dimensionality reduction that retains local structure;(2) Sobol sequence (SS) initialization to enhance population diversity and convergence stability;(3) a differential evolution (DE)-based adaptive velocity update to dynamically balance exploration and exploitation;and (4) a novel ranking strategy that combines Pareto dominance with neighborhood preservation quality to prioritize biologically meaningful gene subsets. Experimental evaluations on six benchmark microarray datasets and eleven multi-modal test functions (MMFs) demonstrate that ANPMOPSO consistently outperforms state-of-the-art methods. For example, it achieves 100% classification accuracy on Leukemia and Small-Round-Blue-Cell Tumor (SRBCT) using only 3–5 genes, improving accuracy by 5–15% over competitors while reducing gene subsets by 40–60%. Additionally, on MMFs, ANPMOPSO attains superior hypervolume values (e.g., 1.0617 ± 0.2225 on MMF1, approximately 10–20% higher than competitors), confirming its robustness in balancing convergence and diversity. Although the method incurs higher training time due to its structural and adaptive components, it achieves a strong trade-off between computational cost and biological relevance, making it a promising tool for high-dimensional gene selection in bioinformatics. © 2025

关键词： Particle swarm optimization (PSO)

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

Transformer-based Text Classification on Unified Bangla Mult...

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Information Technology (ACIT)

作者： Md Sakib Ullah Sourav Mohammad Sultan Mahmud Hua Zheng Mohammad Aljaidi Md. Simul Hasan Talukder Rejwan Bin Sulaiman Abdullah Hafez Nur Ahmad Al-Qerem School of Management Science and Engineering Shandong University of Finance and Economics Jinan China College of Computer Science and Software Engineering Shenzhen University Senzhen China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Dhaka School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom Northumbria UK Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world’s most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.

关键词： Measurement Human computer interaction Sentiment analysis Analytical models Codes Accuracy Web 2.0 Text categorization Transformers Information technology

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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection

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IEEE Access 2025年 13卷 101711-101720页

作者： Zrar Kh Abdul Abdulbasit K. Al-Talabani Wisam Hazim Gwad Entisar Alkayal Halgurd S. Maghdid Safar Maghdid Asaad Computer Department College of Science Charmo University Sulaymaniyah Iraq Department of Software Engineering Faculty of Engineering Koya University Koya Iraq Department of Artificial Intelligence Engineering College of Engineering Alnoor University Ninavah Iraq Information Technology Department Faculty of Computing and Information Technology-Rabigh King Abdulaziz University Jeddah Saudi Arabia Department of Engineering Research Center Deanship of Research and Development Centers Koya University Erbil Governorate Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq

Cepstral features, such as Gammatone Cepstral Coefficients (GTCC), have recently been applied in fault detection and diagnosis. However, GTCC was originally designed for speech feature extraction rather than fault detection, which limits its ability to capture relevant features for fault identification effectively. In this research, three key parameters of GTCC, namely, the number of coefficients, maximum frequency, and minimum frequency, are optimized using two metaheuristic algorithms: Fitness Dependent Optimizer (FDO) and Grey Wolf Optimization (GWO). These parameters are vital for enhancing the effectiveness of GTCC in extracting relevant features from the vibration signal for gear defect identification. Specifically, the maximum and minimum frequency values are critical for capturing the Gear Mesh Frequency (GMF), a common indicator of gear faults. Furthermore, optimizing the number of GTCC coefficients helps reduce model complexity. Experimental results demonstrate that optimizing GTCC parameters with GWO improves fault detection performance using an SVM classifier, achieving over 1% and 3% accuracy improvements on the PHM09 and DDS datasets, respectively.

关键词： Feature extraction Fault detection Optimization Gears Metaheuristics Mel frequency cepstral coefficient Mathematical models Filter banks Support vector machines Cepstrum

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Robust Length of Stay Prediction Model for Indoor Patients

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computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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A Novel Trust Assessment System for Online Social Networking Environment Using Learning Assisted Classification Model

A Novel Trust Assessment System for Online Social Networking...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nithya, S. Deepa, D. Babu, Vunnava Dinesh Pallavi, G. Fawareh, Hamed Kayalvizhy, R.D. Saveetha Engineering College Department of Computer Science Engineering Chennai India St. Joseph's College of Engineering Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Laxminarayan Innovation Technological University Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798331543617

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based Learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks. © 2024 IEEE.

关键词： Adversarial machine learning

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