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Resource Allocation for Fairness Enhancement in Multi-Cell Vehicular VLC system With Optical IRS: A Cooperative Transmission Approach

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IEEE Internet of Things Journal 2025年

作者： An, Nan Yang, Fang Liu, Chang Cheng, Ling Song, Jian Han, Zhu Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Center of National Railway Intelligent Transportation System Engineering and Technology Beijing100081 China China Academy of Railway Sciences Corporation Limited Signal and Communication Research Institute Beijing100081 China University of the Witwatersrand School of Electrical and Information Engineering Johannesburg2050 South Africa Tsinghua University Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In multi-cell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and inter-cell interference, leading to unfairness among vehicles. In this paper, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application. © 2014 IEEE.

关键词： Resource allocation

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Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer

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Medical and Biological Engineering and Computing 2025年 1-14页

作者： Khorshid Sokhangouy, Saeideh Zeinali, Mohsen Fathi, Sina Nazari, Elham Department of Medical Biotechnology School of Medicine Mashhad University of Medical Sciences Mashhad Iran Computer Engineering Department Islamic Azad University Khomeinishahr Branch Isfahan Iran Department of Health Information Technology and Management School of Allied Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Proteomics Research Center System Biology Institute Faculty of Paramedical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the development of more precise and specific methods for diagnosis. In this study, a deep learning model was applied to identify ten key genes from a pool of 68 common differentially expressed genes in the three transcriptomic datasets. The model demonstrated high performance, with the accuracy of 0.969, R2 of 0.88, and PR-AUC of 0.98. Notably, selected genes have been previously reported as functionally important in various cancers. Among them, SCUBE2 stands out as a novel potential diagnostic biomarker in prostate cancer, showing a strong diagnostic performance in the TCGA dataset with AUC = 0.84, sensitivity = 0.76, and specificity = 0.84. SCUBE2 is a secreted glycoprotein known for its ability to suppress tumor growth, cell migration, and epithelial-mesenchymal transition (EMT) in several cancer types, including gliomas, breast, and colorectal cancers, mainly through its regulation of signaling pathways such as Hedgehog (Shh). Although its role in prostate cancer (PCa) has not been previously explored, its consistent downregulation across multiple PCa datasets in this study suggests it may act as a tumor suppressor, warranting further investigation. Another candidate, SLC16A5, showed moderate performance individually (AUC = 0.62, SP = 0.81, SE = 0.42 in GSE88808), but its combination with SCUBE2 significantly enhanced diagnostic accuracy (combined AUC = 0.76, SE = 0.75, SP = 0.71). SLC16A5 is a monocarboxylate transporter involved in metabolic reprogramming, and prior studies have linked its downregulation to immune infiltration and poor prognosis in PCa. Functional enrichment analysis of the ten identified genes revealed strong involvement of these genes in cancer-related processes, including gap junction assembly, tight junction formation, efflux transporter activity, and pathways such as Hedgehog signaling, leukocyte transend

关键词： Deep learning

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A secure and trustworthy blockchain-assisted edge computing architecture for industrial internet of things

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Scientific Reports 2025年第1期15卷 1-17页

作者： Asaithambi, Sasikumar Nallusamy, Senthilkumar Yang, Jing Prajapat, Sunil Kumar, Gyanendra Rathore, Pramod Singh Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science & Technology Tamil Nadu Kattankulathur 603203 India Centre for Applied Nanomaterials Chennai Institute of Technology Tamil Nadu Chennai 600069 India Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics Central University of Himachal Pradesh Dharamshala 176215 India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur 303007 India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur 303007 India

The Industrial Internet of Things (IIoT) seeks to improve smart factory productivity by leveraging automation and scalability. For automation in industry, optimization, collaboration and protection, and scalability, the IoTs paradigm, technology for communication and information, and intelligent systems are integrated as a single organism. This article presents a blockchain-assisted safe data-sharing mechanism that provides security requirements in the industry using IoT. End-to-end authentication is developed based on the blockchain’s reputation, and the smart contract is used to validate nodes’ security measures. Through integrity verification and categorization in node terminals and industry, the blockchain paradigm manages data collection and dissemination. An efficient proof of authentication (PoAh) consensus mechanism is created using the blockchain network to build a collaborative network to preserve logs and verification data in the industrial IoT. It accomplishes trustworthy authentication and endpoint activity tracing, and edge computing is used in blockchain nodes to offer device authentication processes that utilize smart contracts and PoAh. According to an experimental study, the proposed architecture lowers the authentication time and obtains a high response rate. The proposed system’s service time shows the efficiency of PoAh-based blockchain architecture for industrial IoT compared with existing works. Finally, we evaluated various block sizes to ensure an efficient transaction rate. The outcomes demonstrate the practicality of the suggested and put-into-practice architecture, distinguished by enhanced data audibility, device data ownership, security, and privacy while utilizing decentralized storage. © The Author(s) 2025.

关键词： Authentication Blockchain Edge computing Industrial internet of things PoAh

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Sustainable Secure Blockchain Assisted AIoT and Green Multi-Constraints Supply Chain system

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IEEE Internet of Things Journal 2025年

作者： Lakhan, Abdullah Alyasseri, Zaid Abdi Alkareem Mohammed, Mazin Abed Al-Attar, Bourair Nedoma, Jan Alubady, Raaid Memon, Sajida Martinek, Radek Dawood University of Engineering and Technology Department of Computer Science Sindh Karachi74800 Pakistan VSB-Technical University of Ostrava Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic VSB-Technical University of Ostrava Department of Telecommunications Ostrava70800 Czech Republic Najaf54001 Iraq University of Warith Al-Anbiyaa College of Engineering Karbala Iraq University of Al-Ameed College of Medicine Karbala1238 Iraq Al-Ayen University Engineering Technical College Iraq College of Information Technology University of Babylon Thi-Qar Babel Iraq Dawood University of Engineering and Technology Department of Computer System Engineering Pakistan

In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multi-constraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This paper presents sustainable, secure blockchain-assisted AIoT and green multi-constraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multi-constraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multi-constraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes. © 2014 IEEE.

关键词： AIoT Blockchain CO2 Deep Convolutional Neural Network Genetic Algorithm Green Scheduling Sustainability

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification

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IEEE Access 2025年 13卷 83580-83590页

作者： Muhammad Shadab Alam Hashmi Azam Mehmood Qadri Ali Raza Saleem Ullah Aseel Smerat Changgyun Kim Muhammad Syafrudin Norma Latif Fitriyani Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Software Engineering The University of Lahore Lahore Pakistan Department of Precision Medicine School of Medicine Sungkyunkwan University Suwon Republic of Korea Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact and Outcome Chitkara Institute of Engineering and Technology Chitkara University Rajpura Punjab India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Department of Electronic and AI System Engineering Kangwon National University Samcheok Republic of Korea Department of Artificial Intelligence and Data Science Sejong University Seoul Republic of Korea

Although coffee is a crucial commodity for developing nations’ economies, processing defects can significantly impact the safety, commercial value, and quality of coffee beans. Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. This study proposes a novel transfer learning-based deep neural network, VGGBM-Net, for classifying coffee bean diseases. It focuses on the timely and precise identification of diseases to improve export quality without compromising safety during production. In this research, the proposed approach is trained using a dataset USK-Coffee of coffee beans to classify healthy and unhealthy beans. The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models, this feature extraction enhances classification accuracy and robustness. This transformation improves feature extraction by capturing more discriminative patterns, leading to superior performance compared to conventional methods. Experimental results highlight the superior performance of the LGBM classifier, achieving an impressive 99% accuracy, recall, f1, and precision score of 98% with a computational runtime of just 0.084 seconds. K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. This research establishes a highly effective framework for automat

关键词： Accuracy Feature extraction Diseases Deep learning Convolutional neural networks Transfer learning Production Nearest neighbor methods Training Classification algorithms

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Digital nudging model for learning management systems: Saudi Arabia perspective

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Heliyon 2025年第6期11卷

作者： Alotaibi, Salihah Issa, Tomayess Abu-Salih, Bilal Aljaafari, Mohammed Almiani, Muder Information Systems Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Faculty of Business and Law Curtin University Hayman Road Bentley 6102 WA Australia King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan King Faisal University Al-Ahsa 31982 Saudi Arabia Department of Management Information System (MIS) Gulf University for Science and Technology Kuwait City Kuwait

Decisions related to education are complex and have long-term consequences. For higher education students, most of these decisions are made when students are young and with little academic experience;these decisions can potentially influence students to postpone the completion of academic tasks or make unwise decisions. In this research, an e-nudging model is proposed for higher education in Saudi Arabia (SA), offering early and low-cost support for universities to encourage students to make better. A mixed-methods approach (an explanatory sequential design) was adopted, and two online surveys were designed to target academics and IT staff. The survey of academics and IT staff yielded two new factors: training and support for both instructors and students and awareness of e-nudges. The students' survey showed that 45.30% of university students are struggling to maintain attention and 29.66% have issues with memory. The interview phase was conducted with twenty-five expert academics who were invited to give their perceptions about the e-nudging model, and two new factors were generated, namely “define students' barriers: behavior, cognitive and environmental” and “ethics of e-nudge”. The position of the evaluation stage in the e-nudging model was changed to make the purpose and functionality of this stage clearer to the readers. In the future, this research can be re-conducted in other developing countries, especially the Gulf Cooperation Council (GCC) countries which share many common characteristics such as culture, religion, social mores, languages, and economic and financial systems. © 2025 The Author(s)

关键词： E-nudging model Higher education Learning environment Saudi Arabia

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High Accuracy Source Localization Based on Parallel Factor Analysis of TDOA in the Cross Correlation Domain

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IEEE Transactions on Communications 2025年

作者： Li, Jianfeng Jiang, Hang Li, Yingying Zhou, Fuhui Wu, Qihui Quek, Tony Q. S. Al-Dhahir, Naofal Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing China Singapore University of Technology and Design Singapore487372 Singapore Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

It is challenging to ensure both high accuracy and low complexity when localizing radiation sources. To address this challenge, we propose two novel methods leveraging time difference of arrival (TDOA) measurements. Specifically, we introduce a TDOA estimation method and a direct position determination (DPD) method based on parallel factor (PARAFAC) analysis in the cross-correlation domain. Initially, multiple sensors synchronously capture the source signal, and the cross-correlation function between signals received from a reference sensor and other sensors is calculated. Then, the primary cross-spectrum data undergoes an expansion and integration process to establish the PARAFAC model. Through cross-spectrum expansion, virtual nodes are formed, which further improves the estimation performance. The TDOA estimates for each sensor are obtained by normalizing and extracting the phase from this matrix. Additionally, we introduce a novel DPD method tailored for multipath propagation scenarios. Simulations and real-world measurements demonstrate the superiority and effectiveness of our proposed methods compared with cutting-edge methods. © 1972-2012 IEEE.

关键词： Time difference of arrival

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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

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Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chen, Zhuolong Yin, Xiaoqing Yang, Fan Li, Xiaofan Zhao, Zixuan Li, Xueying Liu, Jianghu Zhao, Yubin Xu, Cheng-Zhong Zheng, Fangfang Lin, Yong Sun Yat-Sen University School of Microelectronics Science and Technology 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Department of Psychiatry 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Pediatrics Department 0000 China Jinan University School of Intelligent System Science and Engineering 0000 China University of Macau State Key Laboratory of IoTSC Department of Computer and Information Science Macau0000 China

Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical. © 2013 IEEE.

关键词： Contrastive Learning

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