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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 322-332页

作者： Zhipeng Hu Gaoshi Li Xinlong Luo Jiafei Liu Jingli Wu Wei Peng Xiaoshu Zhu Key Lab of Education Blockchain and Intelligent Technology Ministry of Education The Guangxi Key Lab of Multi-source Information Mining & Security The College of Computer Science and Engineering Guangxi Normal University Guilin China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer and Information Security & School of Software Engineering Guilin University of Electronic Science and Technology Guilin China

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a phenomenon among features is found, called weak consensus, then a weak consensus model to fuse these features is proposed in this paper. After analyzing the relationship between a protein and its neighbors in protein-protein interaction networks, a new centrality, namely neighborhood aggregation centrality(NAC) is developed in this paper. Then, a Max-Min strategy is used to integrate NAC with Pearson correlation coefficient and Jaccard similarity coefficient based on gene expression data to obtain local importance score. In addition, orthologous feature score is used to measure proteins conservation. Finally, by using the weak consensus model to fuse orthologous feature score with local importance score, a new method WOL is proposed in this paper. Then experiments are performed on *** data. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate.

关键词： Proteins Computational biology Bioinformatics Feature extraction Biological system modeling Fuses Computational modeling Shape Reliability Minimax techniques

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FunDa: scalable serverless data analytics and in situ query processing

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Journal of Big Data 2025年第1期12卷 1-33页

作者： Lounissi, Elyes Das, Suvam Kumar Peter, Ronnit Zhang, Xiaozheng Ray, Suprio Jia, Lianyin University of Manouba Manouba Tunisia Faculty of Computer Science University of New Brunswick FrederictonNB Canada Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

The pay-what-you-use model of serverless Cloud computing (or serverless, for short) offers significant benefits to the users. This computing paradigm is ideal for short running ephemeral tasks, however, it is not suitable for stateful long running tasks, such as complex data analytics and query processing. We propose FunDa, an on-premises serverless data analytics framework, which extends our previously proposed system for unified data analytics and in situ SQL query processing called DaskDB. Unlike existing serverless solutions, which struggle with stateful and long running data analytics tasks, FunDa overcomes their limitations. Our ongoing research focuses on developing a robust architecture for FunDa, enabling true serverless in on-premises environments, while being able to operate on a public Cloud, such as AWS Cloud. We have evaluated our system on several benchmarks with different scale factors. Our experimental results in both on-premises and AWS Cloud settings demonstrate FunDa’s ability to support automatic scaling, low-latency execution of data analytics workloads, and more flexibility to serverless users. © The Author(s) 2025.

关键词： Query languages

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OpenAI-Powered NLP for SQL Generation: Enhancing Database Access for Non-Technical Users

OpenAI-Powered NLP for SQL Generation: Enhancing Database Ac...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Yuenyong Nilsiam Piyaphiphat Khammaw Siranee Nuchitprasitchai Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Information Technology Faculty of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Database accessibility can be challenging for users without technical expertise, particularly when generating SQL queries to retrieve information. This study addresses this gap by developing an OpenAI-powered system designed to automate SQL command generation, enabling non-expert users to interact with databases using natural language. Implemented as a web application, the system was evaluated by expert users, assessing both performance in query accuracy and user satisfaction. Results indicate high usability and satisfaction scores, with the system effectively enhancing accessibility to database functions, thus providing a user-friendly solution for database management.

关键词： Structured Query Language Accuracy Translation Databases Decision making Data structures User experience Systems support Usability Artificial intelligence

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer Science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Green IoT for energy efficiency: Enabling technologies, challenges, and future research directions

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Thermal Science and engineering Progress 2025年 62卷

作者： Das, Resul Adam, Iliyasu Yahaya Oztop, Hakan F. Firat University Faculty of Technology Department of Software Engineering Elazig23119 Turkey Kano State Polytechnic Institute for Information Technology Department of Computer Science Kano Nigeria Firat University Faculty of Technology Department of Mechanical Engineering Elazig23119 Turkey

This study presents an overview of important aspects of the Green Internet of Things G-IoT with emphasis on fused G-IoT layered architecture, G-IoT enabling technologies, G-IoT opportunities, and applications, renewable energy-assisted Heating, ventilation, and air conditioning HVAC systems, as well as the challenges and research issues in green Internet of Things. The G-IoT is becoming a popular subject due to the ever-growing application of IoT systems. The Prevalence of G-IoT technology across society, industry, scientific institutions, and technology has led to billions of devices that work with the technology. However, this results in increased pollution, including increased carbon dioxide emissions, electronic waste, and excessive energy consumption. The need for eco-friendly and energy-efficient IoT systems necessitates the emergence of a G-IoT. This work discusses the fundamentals of G-IoT, issues, challenges, opportunities, and different applications with wide literature. Also, control of HVAC systems with G-IoT applications is studied. It is observed that there is a lack of sufficient research work on Green IoT at the application layer of IoT Architecture. The special need for incorporating renewable energy sources and local grids into HVAC systems for sustainability and to complement the trade-off between energy efficiency and building occupants’ thermal comfort is being discussed. Finally, a framework for integrating local renewable grids with the main grid has been proposed to offload some HVAC energy consumption load. © 2025 Elsevier Ltd

关键词： Cloud computing Energy efficiency Green IoT Heating, ventilation, and air conditioning HVAC Renewable energy Smart city

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Improved smart city security using a deep maxout network-based intrusion detection system with walrus optimization

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PeerJ computer Science 2025年 11卷 e2743-e2743页

作者： Rajeh, Wahid Aborokbah, Majed Manimurugan, S. Albalawi, Umar Aljuhani, Ahamed Younes, Osama Shibl Abdalghany Periyasami, Karthikeyan Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia School of Computer Science and Engineering RV University Karnataka Bengaluru India

Background: Smart cities, enabled by the Internet of Things (IoT), leverage technology to optimize urban living and enhance infrastructure. As urban environments become interconnected hubs of digital innovation, securing critical components like public transportation infrastructure becomes increasingly important. Methods: This research addresses the need for robust intrusion detection systems (IDS) tailored to the unique challenges of securing public transportation within smart cities. Focused on the Tabuk region in Saudi Arabia, the study introduces an IDS model integrating the deep maxout network with walrus optimization (DMN-WO). The DMN is configured with an architecture that includes multiple layers with maxout activation functions. These layers are capable of capturing complex patterns in the data, making the DMN particularly effective for identifying anomalies in IoT network traffic. The DMN-WO model is ensured to be resource-efficient and suitable for real-time deployment on constrained devices like Raspberry Pi, typical in IoT systems. Results: Training and validation are conducted using the CIC-IDS-2018 dataset, CIC-IDS -2029 dataset and real-time data from Raspberry Pi devices deployed in the smart city’s public transportation network. Real-time data application maintains robust performance, with 98.06% accuracy, 98.50% detection rate, 98.81% precision, 98.24% specificity, and a 98.57% F1-score. Conclusions: This research advances cybersecurity measures in smart city applications by providing a resilient solution for detecting and mitigating security threats in public transportation infrastructure. It lays the groundwork for further refinements and real-world deployments in the dynamic landscape of smart cities. Copyright 2025 Rajeh et al.

关键词： Cybersecurity

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MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network

arXiv

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arXiv 2025年

作者： Ahire, Vrushank Shah, Kunal Khan, Mudasir Nazir Pakhale, Nikhil Sookha, Lownish Rai Ganaie, M.A. Dhall, Abhinav Department of Computer Science and Engineering Indian Institute of Technology Punjab Ropar India Faculty of Information Technology Monash University Melbourne Australia

—This paper introduces MAVEN (Multi-modal Attention for Valence-Arousal Emotion Network), a novel architecture for dynamic emotion recognition through dimensional modeling of affect. The model uniquely integrates visual, audio, and textual modalities via a bi-directional cross-modal attention mechanism with six distinct attention pathways, enabling comprehensive interactions between all modality pairs. Our proposed approach employs modality-specific encoders to extract rich feature representations from synchronized video frames, audio segments, and transcripts. The architecture’s novelty lies in its cross-modal enhancement strategy, where each modality representation is refined through weighted attention from other modalities, followed by self-attention refinement through modality-specific encoders. Rather than directly predicting valence-arousal values, MAVEN predicts emotions in a polar coordinate form, aligning with psychological models of the emotion circumplex. Experimental evaluation on the Aff-Wild2 dataset demonstrates the effectiveness of our approach, with performance measured using Concordance Correlation Coefficient (CCC). The multistage architecture demonstrates superior ability to capture the complex, nuanced nature of emotional expressions in conversational videos, advancing the state-of-the-art (SOTA) in continuous emotion recognition in-the-wild. Code can be found at: https://***/Vrushank-Ahire/MAVEN_8th_ABAW. © 2025, CC BY.

关键词： Emotion Recognition

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Selfish-Free Influential Nodes Detection in Mobile Social Networks

Selfish-Free Influential Nodes Detection in Mobile Social Ne...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Doaa AbdelMohsen Tamer Abdelkader Mohamed Hamdy Khaled ElBahnasy Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering Galala Unversity Suez Egypt Faculty of Computing and Information Technology King Abdul Aziz University Jeddah International College KSA

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Mobile networks where users group in communities based on their interests, and disseminate data mainly to their community using their mobile devices, are called Mobile Social Networks (MSN). However, the efficiency of data dissemination becomes a challenging problem in these networks. Using influential users can improve the efficiency of data dissemination. An influential user should have an effective activity on the network and influence other users. The participation behavior of nodes in the network dramatically affects the detection process of influential nodes. Self-sufficient nodes who selectively participate in the dissemination of data in the network may reduce the influence of the other nodes. This paper proposes detecting influential nodes that consider the negative effects of selfish behavior. The performance analysis of the proposed selfish-free influential node detection protocol is presented by comparing it to another one that does not consider the selfishness problem. This comparison shows that the proposed protocol achieves efficiency up to 30% in a environment of selfish behavior.

关键词： Performance evaluation Technological innovation Protocols Social networking (online) Smart cities Data dissemination Mobile handsets Performance analysis Mobile ad hoc networks

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clip Art Generation Dataset Collection Generative Adversarial Network Pix2Pix

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