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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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A Novel Neural Network Approach to Proactive 3-D Beamforming

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1576-1596页

作者： Mallioras, Ioannis Yioultsis, Traianos V. Kantartzis, Nikolaos V. Lazaridis, Pavlos I. Zaharis, Zaharias D. Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki54124 Greece Santarcangelo di Romagna R and D Department Maggioli SpA Rimini47822 Italy University of Huddersfield School of Computing and Engineering HuddersfieldHD1 3DH United Kingdom

This study explores three-dimensional proactive beamforming at millimeter wave frequencies using transformer neural networks (TNNs), long short-term memory networks (LSTMs) and gated-recurrent units (GRUs). The proposed scheme aims to reduce beamforming latency by predicting future directions of arrival (DoAs) based on past observations, allowing the system to prepare beamforming weights proactively. We simulate an urban environment using OpenStreetMap data to generate realistic movement paths, creating a comprehensive dataset for training and evaluation. Our focus is on the predictive capacity of TNNs, LSTMs and GRUs to anticipate future DoAs, even in non-line-of-sight scenarios influenced by urban infrastructure. We detail the environment simulation setup, the ray-tracing mechanism as well as the movement generation process for pedestrians and vehicles. A statistical analysis on the prediction accuracy and response time is presented to assess the most accurate model and discuss the trade-offs between the architectures. In addition, an end-to-end AI-based proactive beamforming scenario is examined where zero-forcing is applied on moving users. This is to further demonstrate and evaluate the capabilities and the performance of each model. Our findings suggest that proactive beamforming can significantly enhance performance in dynamically changing urban landscapes, offering a promising avenue for future research and development in adaptive communication systems. © 2024 IEEE.

关键词： Direction of arrival

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Dates Fruit Recognition: From Classical Fusion to Deep Learning

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computer Systems Science & engineering 2022年第1期40卷 151-166页

作者： Khaled Marji Alresheedi Suliman Aladhadh Rehan Ullah Khan Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

There are over 200 different varieties of dates fruit in the ***,every single type has some very specific features that differ from the *** recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research *** this regard,this paper focuses on the detection and recognition of dates using computer vision and machine *** experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates *** machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning *** feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented *** fusion of various features is also extensively explored in this *** MLP achieves the highest detection performance with an F-measure of ***,deep learning shows better accuracy than the classical machine learning *** fact,deep learning got 2%more accurate results as compared to the MLP and the Random *** also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

关键词： features Deep separating

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Nuclei Pathology Classification using YoloV9

Nuclei Pathology Classification using YoloV9

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Singh, Deependra K. Anitha, G. Pattanaik, Balachandra Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Chennai601103 India School of Electrical and Computer Engineering Wollaga University P.O.BOX:395 Nekemte Ethiopia

ISBN: (纸本)9798350379716

Nuclei pathology is important for diagnosing various diseases and cancer. It is responsible for examining cellular structures found in tissue samples. Laboratories have the challenge of accurate nuclei classification in order to identify illnesses and support treatment. Here, we introduce a new strategy to identify four types of nuclei (,Other', 'Inflammatory', 'Epithelial' and 'Spindle-Shaped'), using YOLOv9 deep learning. The present approach provides an overall accuracy of 98.9% in classifying nuclei, with other metrics also ranked strongly. A high rate of consistency is guaranteed by the use of this model, meaning that the associated expertise is capable of increasing the diagnosis aid in pathology. Brought by the increase in precision, diagnosis in all other fields is also able to be increased more quickly. The utilization of YOLOv9 opens up a better approach to faster pipelines of better quality for the diagnosis of patients, where reliability and speed are the key to help guide proper funding to lived improvement outcomes. © 2024 IEEE.

关键词： Diseases

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Enhancing Access Control, Authorization, and Accountability in Cyber-Physical Systems Using Machine Learning

Enhancing Access Control, Authorization, and Accountability ...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Adom, Isaac Awais, Muhammad Raza, Mohsin Khan, Umar Chughtai, Omer School of Computing and Digital Technologies Staffordshire University Stoke-On-Trent United Kingdom School of Computing Sciences University of East Anglia Norwich United Kingdom Department of Electrical and Electronic Engineering Edge Hill University Ormskirk United Kingdom Department of Electrical and Computer Engineering Comsats University Wah Cantt Islamabad Pakistan

ISBN: (纸本)9798331510503

Cyber-Physical Systems (CPS) integrate networking, computing, and physical processes, forming the backbone of critical industries such as healthcare, energy, and transportation. The increasing complexity and interconnection of CPS have led to significant compliance and security challenges. This research introduces a novel framework that leverages Machine Learning (ML) techniques to enhance access control, authorization, and accountability within CPS environments. By combining these techniques with traditional access control methods, the framework addresses the unique demands of CPS, including scalability and adaptability to dynamic conditions. A key innovation lies in applying ensemble methods like Random Forest, AdaBoost, and Gradient Boosting, which outperform individual models by mitigating overfitting and improving generalizability. The framework also incorporates sophisticated feature engineering and regularization strategies tailored to CPS, ensuring robust and efficient security solutions. Through rigorous data preprocessing, relationship analysis, and model validation, this study demonstrates how machine learning can significantly advance the security posture of CPS, offering a scalable and effective approach tailored to the specific needs of these critical systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning 59

Joint Optimization of Model Splitting and Device Task Assign...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Wei, Dongyu Zhang, Qianqian Liu, Yuchen Chen, Mingzhe Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Nc State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331513269

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm. © 2025 IEEE.

关键词： Deep reinforcement learning

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The Current State of Research on Malware Written in the Rust Programming Language 11

The Current State of Research on Malware Written in the Rust...

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11th International Conference on Information Technology, ICIT 2023

作者： Praveen, Meghna Almobaideen, Wesam Rochester Institute of Technology Department of Electrical Engineering and Computing Dubai United Arab Emirates The University of Jordan Department of Computer Science Amman Jordan

ISBN: (纸本)9798350320060

Malware is conventionally written in the C/C++ programming languages. However, a recent trend has been observed where other languages are being used to write malware. One such language is the Rust programming language. In this paper, we conduct a systematic literature review to identify the current state of research on malware written in the Rust programming language. We outline the research gaps and challenges faced by researchers when studying malware written in the Rust programming language. The study found that while the industry is doing well in terms of researching and developing tools and techniques, much of it is not openly available. Moreover, there is a lack of academic research in this field, highlighting the need for academia to conduct more research to keep up with the evolving threat of malware. The study concludes that open research initiatives should be encouraged to ensure that the latest developments in malware research are accessible to all. The material in this paper is not endorsed, reviewed or approved by the Rust Foundation. © 2023 IEEE.

关键词： C++ (programming language)

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Advancements and applications of position-sensitive detector(PSD): a review

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optoelectronics Letters 2024年第6期20卷 330-338页

作者： Shaher Dwik G.Sasikala S.Natarajan Department of Electronics&Communication Engineering School of Electrical&CommunicationVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidyapeethamChennai 601103India1

This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.

关键词： Advancements and applications of position-sensitive detector a review PSD

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