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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Bi Ziqi Wang Haitao Yuan Junfei Qiao Jia Zhang MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Evolutionary algorithms are commonly used to solve many complex optimization problems in such fields as robotics, industrial automation, and complex system design. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-learning-based Optimizer is designed to dynamically adjust parameters for balancing exploration and exploitation during its solution process. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate it by comparing it with several state-of-the-art algorithms through six benchmark functions. We further test it by applying it to solve a real-world computational offloading problem.

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Detection of a Novel Object-Detection-Based Cheat Tool for First-Person Shooter Games Using Machine Learning

Detection of a Novel Object-Detection-Based Cheat Tool for F...

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International Conference on Software engineering Research, Management and Applications (SERA)

作者： Zhang Xiao Takaaki Goto Partha Ghosh Tadaaki Kirishima Kensei Tsuchida Graduate School of Information Sciences and Arts Toyo University Saitama Japan Faculty of Information Sciences and Arts Toyo University Saitama Japan Department of Computer Science and Engineering Academy of Technology West Bengal India

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection.

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Typhoon Track Image Prediction Using a Comprehensive LSTM Attention Model in Deep Learning

Typhoon Track Image Prediction Using a Comprehensive LSTM At...

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Neural Networks, information and Communication engineering (NNICE), International Conference on

作者： Yiping Jiang Yi Yu Yuelong Ma Jiahao Zhuang Xianze Ye Yifeng Yuan Guanpeng Su School of Computer Science Guangdong University of Science and Technology Dongguan China Faculty of Science and Technology FST University of Macau Macau SAR China School of Information and Optoelectronic Science and Engineering South China Normal University Guangzhou China Faculty of Applied Sciences Macao Polytechnic University Macau SAR China School of Computer Zhuhai College of Science and Technology Zhuhai China

Typhoons are exceptionally destructive tropical cyclones that possess the capacity to inflict significant harm upon society. For institutions engaged in risk assessment and disaster mitigation, accurately forecasting typhoon paths and trajectories is of paramount importance. In this study, we propose a Typhoon Track Image Prediction model based on a Comprehensive Long Short-Term Memory (LSTM) Attention Model in the field of Deep Learning. This model significantly improves the accuracy of typhoon track prediction. To assess its performance, we utilize typhoon data from the China Typhoon Network spanning from 1949 to 2022. The Comprehensive LSTM Attention Model is compared against other popular models such as Temporal Convolutional Networks (TCN), traditional LSTM, and Seq2Seq models for forecasting the typhoon track over a 12-hour period. The results demonstrate the superiority of our proposed model, showcasing its effectiveness in terms of superior accuracy in typhoon track prediction.

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Empirical Based Irrigation Model Using Predicted Soil Moisture for Durian Plantation 3rd

Empirical Based Irrigation Model Using Predicted Soil Moistu...

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3rd International Conference on Control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Ramli, Muhammad Shahrul Azwan Abidin, Mohamad Shukri Zainal Hean, Pui Boon Rahman, Mohd Amiruddin Abd Perumal, Thinagaran Reba, Mohd Nadzri Md Division of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai Malaysia IoT Department MIE Industrial Sdn. Bhd Selangor Puchong Malaysia Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Skudai Malaysia

ISBN: (纸本)9789811939228

It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil moisture sensors in each tree is complicated or excessively expensive. Forecasting the value using climate data is a viable solution in this scenario. Climate data are used to forecast soil moisture and then utilized in this irrigation model. This study uses an Artificial Neural Network (ANN) to forecast soil moisture values. The statistical method is used to determine the predicted values’ correctness. After the process, the irrigation volume and schedule are calculated based on the most accurate prediction findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soil moisture

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Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Priya K. Priyadharshini R. Santhana Krishnan J. Relin Francis Raj Imran Javeed Settu A. Srinivasan Department of Computer Science and Engineering Ramco Institute of Technology India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

关键词： Adaptive systems Smart cities Roads Traffic control Predictive models Air quality Real-time systems Fuels Time factors Traffic congestion

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Multi-swarm Genetic Gray Wolf Optimizer with Embedded Autoencoders for High-dimensional Expensive Problems

Multi-swarm Genetic Gray Wolf Optimizer with Embedded Autoen...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Bi Jiahui Zhai Haitao Yuan Ziqi Wang Junfei Qiao Jia Zhang MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.

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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

Group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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Blockchain-based e-Government: Exploring Stakeholders Perspectives and Expectations

Blockchain-based e-Government: Exploring Stakeholders Perspe...

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International Conference on Electrical, Telecommunication and computer engineering (ELTICOM)

作者： F. Rizal Batubara Syahril Efendi Mahyuddin K. M. Nasution Fahmi Poltak Sihombing Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Electrical Engineering Department Faculty of Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9798350320725

Blockchain, as a rapidly expanding area of practice and study, has drawn players from a wide range of backgrounds. The goal of this study is to demonstrate how various stakeholders and their various perspectives on blockchain-based e-Government systems may be studied in a given situation. Using Indonesia as an example of a prospective blockchain-based e-Government system user, stakeholder analysis is used to determine and classify stakeholder categories based on their relative power and interest. A literature analysis identifies four distinct viewpoints on blockchain-based e-Government systems: bureaucratic, political, technological, and economic. This demonstrates the dominance of bureaucratic and political perspectives over the other two, as well as the presence of a politico-economic viewpoint not discovered in the previous existing literature. Overall, this study suggests that stakeholder analysis and perspective analysis are feasible approaches for Blockchain-based e-Government development that add value by revealing crucial actors’ names, positions of authority, motivations, and worldviews. They provide the foundational knowledge needed for both academics and practitioners to understand the many interpretations of Blockchain-based e-Government in each case.

关键词： Economics Communications technology Cognition Blockchains Stakeholders Electronic government

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MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler

MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Thanh Trung Nguyen Minh Hai Vu Thi Ha Ly Dinh Phi Le Nguyen Kien Nguyen Faculty of Computer Science Phenikaa University Hanoi Vietnam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Vietnam Institute for Advanced Academic Research Chiba University Japan Graduate School of Engineering Chiba University Japan

Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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