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1st Engineering and Science International Conference 2024, ESIC 2024

作者： Sadikan, S.F.N. Aziz, M.L.H.A. Umor, M.A.S. Mahzan, S. Marjudi, S. Salamat, M.A. Dunne, R.S. Faculty of Plantation and Agrotechnology Universiti Teknologi MARA Merlimau Melaka77300 Malaysia College of Computing Informatics and Media Universiti Teknologi MARA Merlimau Melaka77300 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Faculty of Communication Visual art and Computing Universiti Selangor Kuala Selangor45600 Malaysia

Rubber plantations are an integral part of economy in Malaysia. However, inadequate soil moisture poses a significant problem for rubber trees as it can lead to water stress, ultimately hindering their growth, leaf development, and latex production. Water stress occurs when the trees are unable to obtain sufficient water from the soil, which resulting in stunted growth, leaf development, leading to fewer and smaller leaves. There is needed an effective water management strategy to ensure optimal growth of rubber trees for latex production. Thus, this project is aim to design and build an active soil monitoring system based on the Internet of Things (IoT). This system is made up of two major components: soil moisture and light intensity. The first component is a soil moisture sensor, which determines the soil's moisture content;light intensity sensor, which detects the intensity of the light and ANSELF Brushless DC Pump connected to an Arduino. This system provides sustainable solutions to increase crop output, reduce waste, and develop a greener plant cultivation. This smart monitoring plant growth system prototype is important in agriculture and plant production. It has potential to promote healthy plant growth and increase agricultural production. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Soil moisture

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FedCSpc: A Cross-Silo Federated Learning System with Error-Bounded Lossy Parameter Compression

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1372-1386页

作者： Zhang, Zhaorui Di, Sheng Zhao, Kai Jin, Sian Tao, Dingwen Ji, Zhuoran Liu, Benben Alharthi, Khalid Ayed Cao, Jiannong Cappello, Franck The Hong Kong Polytechnic University Department of Computing Hong Kong Florida State University Department of Computer Science United States Temple University Department of Computer and Information Sciences United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering United States Shandong University School of Cyber Science and Technology China The University of Hong Kong LSCM R&D Center Hong Kong University of Bisha Department of Computer Science College Of Computing And Information Technology Saudi Arabia Argonne National Laboratory United States

Cross-Silo federated learning is widely used for scaling deep neural network (DNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. To bridge this gap, we propose FedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designed FedCSpc proposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. Results show that FedCSpc can achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4Gb size model, FedCSpc significantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality). © 1990-2012 IEEE.

关键词： Bandwidth compression

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Potential and Challenges of Assurance Cases for Simulation Validation 24

Potential and Challenges of Assurance Cases for Simulation V...

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Proceedings of the Winter Simulation Conference

作者： Pia Wilsdorf Steffen Zschaler Fiete Haack Adelinde M. Uhrmacher Institute for Visual and Analytic Computing University of Rostock Rostock GERMANY Department of Informatics King's College London London UK

ISBN: (纸本)9798331534202

Simulation studies require thorough validation to ensure model accuracy, reliability, and credibility. While validation typically focuses on the simulation model itself, additional artifacts also influence study outcomes. Conceptual models, comprising research questions, requirements, inputs and outputs, model content, assumptions, and simplifications, provide context information for interpreting results and assessing model suitability. Validating other simulation artifacts for their fitness-for-purpose is complex, necessitating structured arguments to increase confidence. This paper explores when and how validation arguments should be constructed throughout the modeling and simulation lifecycle. Drawing on concepts from safety assurance cases, it defines key claims for the various artifacts, discusses validation arguments from different perspectives - including process, product, people, and project - and illustrates them through a computational biology case study. We conclude with a discussion of the suitability of such structured arguments for the comprehensive validation of simulation studies.

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Flexible Target Grasping Strategy of Industrial Robot Based on Eye-in-Hand 3D Machine Vision

Flexible Target Grasping Strategy of Industrial Robot Based ...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International Conference on

作者： Zhang XiaoYang Muhammad Azmi Ayub Fazlina Ahmat Ruslan Shuzlina Abdul-Rahman Sukarnur Che Abdullah College of Engineering UiTM Shah Alam Shah Alam Selangor Malaysia College of Computing Informatics and Media UiTM Shah Alam Shah Alam Selangor Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Object grasping is the most common application direction of robots, and machine vision technology is also the most common application of grasping. With the deepening of the research of machine vision technology, the initial application in static target capture has gradually expanded to some moving target tracking and capture. Estimating and predicting the moving state of a target by detecting its position is a common method for obtaining a stable moving target. Once the target motion changes position due to unpredictable interference, the original predicted position will be invalid. Therefore, it is necessary to constantly locate and track the target and judge the deviation, so as to constantly approach the new target position and finally achieve the capture. In this paper, we utilize the open-source Robot Operating System (ROS) platform to investigate dynamic grasping strategies based on visual feedback. In this paper, we use ROS, based on UR5 robot arm and RealSense D435i camera, based on the eye-in-hand mode, to study the dynamic grasping strategy based on visual feedback. A visual feedback control strategy based on fixation approximation is designed to enable the grasping of moving targets on a conveyor belt. Finally, it is verified that the machine vision-driven feedback capturing algorithm substantially meets the practical application requirements of engineering, demonstrating high feasibility and accuracy.

关键词： Visualization Target tracking Three-dimensional displays Service robots Machine vision Operating systems Robot vision systems Grasping Object detection Industrial robots

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AI-Driven Innovation Using Multimodal and Personalized Adaptive Education for Students With Special Needs

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IEEE Access 2025年 13卷 69790-69820页

作者： Farhah, Nesren Adnan, Muhammad Abdullah Alqarni, Ahmed Irfan Uddin, M. Aldhyani, Theyazn H. H. King Salman Center for Disability Research Riyadh11614 Saudi Arabia Saudi Electronic University College of Health Science Department of Health Informatics Riyadh11673 Saudi Arabia Institute of Computing Kohat University of Science and Technology Kohat26000 Pakistan Al-Baha University Department of Computer Sciences and Information Technology Al-Aqiq 65779 Saudi Arabia Al Hofuf31982 Saudi Arabia

This study provides an in-depth exploration of the use of multimodal techniques in developing adaptive learning systems designed for students with special needs using various neural network models: a Baseline Neural Network, Convolutional Neural Network, Attention Model, LSTM, GRU, and Transformer models. Adaptive learning systems play a major role in the customization of the educational process in such a way as to ensure the specific cognitive, behavioural and emotional needs of students with special educational needs are catered for. The models were trained using a sizeable amount of multimodal data that recorded factors such as academic achievement, behavioral interactions, and environmental factors. From our observations, it can be noted that transformer models and attention mechanisms allowed for higher performance in predicting and fulfilling the needs of students, and were more effective than simpler neural architectures. These findings suggest that more advanced and intelligent neural networks can improve how students with different learning abilities consume content and, consequently, provide better educational answers and solutions to the market. To identify the important features affecting student performance, various Explainable AI (XAI) techniques were utilized, including SHAP (Shapley Additive Explanations) values, logistic regression-based feature importance analysis, LIME (Local Interpretable Model-Agnostic Explanations), partial dependency plots, and whose global surrogate model was a Decision Tree Regressor. These XAI instruments allowed the model’s decision to be explained and visualized so that teachers could understand the main beneficiaries of the improved educational results. Such XAI methods enable transparency in how models make decisions, thus allowing educators to understand the reasons for certain recommended learning paths. © 2013 IEEE.

关键词： Convolutional neural networks

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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A Semi-decentralized Congestion-Free Multi-path Energy Routing for P2P Energy Trading Systems 8th

A Semi-decentralized Congestion-Free Multi-path Energy Routi...

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8th International Symposium on Modelling and Implementation of Complex Systems, MISC 2024

作者： Hebal, Sara Harous, Saad Mechta, Djamila Louail, Lemia LRSD Laboratory Department of Computer Science Sétif 1 University Ferhat Abbas Sétif19000 Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Université de Lorraine CNRS CRAN Nancy54000 France

ISBN: (纸本)9783031821110

The increased emphasis on clean energy has accelerated the integration of renewable energy sources into electrical grids, resulting in the rise of peer-to-peer energy trading systems. For optimal power dispatch and management the Energy Internet concept advocates using energy routers. Efficient energy routing algorithms are crucial for successful energy transfer between trading pairs in peer-to-peer energy trading across the complex Energy Internet. This research treats the energy routing problem as a non-convex, non-linear optimization problem using a combination technique of graph theory and non-convex nonlinear programming. It efficiently generates a multi-path solution for a trading pair, minimizing energy transmission loss while considering power flow direction constraints and accurately computing power losses during transmission which has not been considered in existing energy routing algorithms. A semi-decentralized approach with a priority concept is proposed for managing simultaneous power transmissions from multiple energy trading pairs, to avoid congestion and shift computational workloads to individual routers and the network system operator. The approach’s performance is assessed through numerical simulations and comparisons with existing energy routing algorithms to showcase its novelty in power loss reduction, congestion management, and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph theory

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ju-Chan Kim Pham Van Nguyen Duc-Tai Le Hyunseung Choo College of Computing and Informatics Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This paper presents NDAS (Noise-Decomposed Ab-normal Segmentation), an innovative framework for robust med-ical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS's superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability.

关键词： Image segmentation Pathology Accuracy Noise Image retrieval Information management Reliability Medical diagnostic imaging

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Application of Generative Artificial Intelligence for Epidemic Modeling 24

Application of Generative Artificial Intelligence for Epidem...

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Proceedings of the Winter Simulation Conference

作者： Hannah Danielle Ladera Villaplana Jaeyoung Kwak Michael H. Lees Hongying Li Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam The Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (纸本)9798331534202

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents' decision-making using random forest model.

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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

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computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon South Korea Technology Management Stony Brook University New York USA Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

关键词： Power management (telecommunication)

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