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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models computer science Climate change Environmental monitoring Economics

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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2025 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Filianin, Ivan Borisov, Nikita Bogatyrev, Vladimir Itmo University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia Itmo University Faculty of Infocommunication Technologies St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department of Information Systems Security St. Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it. © 2025 IEEE.

关键词： Edge computing

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Optimizing Secure Multi-User ISAC systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Security Aspects of Container Orchestration in Kubernetes Environments 24

Security Aspects of Container Orchestration in Kubernetes En...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanišić, Sava Vesković, Milan Ristić, Olga Dordević, Borislav Faculty of Technical Sciences Department of Computer and Software Engineering Čačak Serbia Mihajlo Pupin Institute Imp Computer Systems Belgrade Serbia

ISBN: (纸本)9798331515799

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described. © 2025 IEEE.

关键词： Docker Kubernetes security

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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Towards Drought Modeling in South Asia: Machine Learning Approaches, Challenges and Opportunities

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IEEE Access 2025年

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, M.A. Rajab, Khairan Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering Istanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state- of-the-art in ML and DL (Complex Machine learning and deep learning models) for drought modeling, highlighting current challenges such as incomplete and inconsistent datasets, Lack of explainability and interpretability in complex ML/DL models, unavailability of benchmarks. Based on these challenges, this review suggests the following techniques to address these challenges: Data integration (Data fusion), distributed machine learning (Federated Learning) and explainable AI (XAI, SHAP, LIME, etc.). © 2013 IEEE.

关键词： Federated learning

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