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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models Software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Biomass Microgrids Integrating Fuel Cell Electric Vehicles

Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Bi...

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International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)

作者： Soheil Mohseni Alan C Brent Sustainable Energy Systems Wellington Faculty of Engineering School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

This paper presents a novel approach for stochastic planning of multi-dimensional microgrids (MGs) integrating solar photovoltaic panels, wind turbines, a micro-hydro power plant, a biomass power plant, battery storage, a super-capacitor bank, hydrogen storage, and fuel cell electric vehicles (FCEVs). More specifically, the paper introduces a stochastic planning framework that incorporates uncertainties in renewable energy generation, energy demand, and wholesale electricity price dynamics. An advanced metaheuristic optimization algorithm is applied to optimize the MG planning, considering the stochastic nature of renewable energy sources and dynamic component interactions. The proposed approach aims to improve the reliability and robustness of MGs under uncertain conditions. The performance of the proposed approach is evaluated through simulation studies and compared with standard deterministic methods to demonstrate its effectiveness in addressing uncertainties whilst optimizing multi-dimensional MGs. Importantly, the results indicate percentage changes of −22%, +4%, and +23% in the whole-life system cost in the best-case, most likely case, and worst-case stochastic scenarios compared to the deterministic methods.

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Development of Granular Fuzzy Relation Equations Based on a Subset of Data

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1416-1427页

作者： Dan Wang Xiubin Zhu Witold Pedycz Zhenhua Yu Zhiwu Li School of Computer Science and Technology Xi’an University of Science and TechnologyXi’an 710054China School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of AlbertaEdmonton T6R 2V4 ABCanada Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Institute of Systems Engineering Macao University of Science and TechnologyMacaoChina

Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy *** each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant *** this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy *** subset of rules is selected by maximizing their performance of the obtained *** originality of this study is conducted in the following *** with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and ***,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.

关键词： A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization(PSO)

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Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling (G-S-M) approach

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computers in Biology and Medicine 2025年 187卷 109813-109813页

作者： Bakir-Gungor, Burcu Temiz, Mustafa Canakcimaksutoglu, Beyza Yousef, Malik Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Electrical and Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Zefat Academic College Israel

Colorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier Ltd

关键词： Colorectal cancer Human gut microbiome Machine learning Taxonomic biomarkers MicroBiomeGSM

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A non-cooperative game-theoretic approach for the flexible operation of multi-microgrids

A non-cooperative game-theoretic approach for the flexible o...

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IEEE General Meeting Power& Energy Society

作者： Soheil Mohseni Alan C Brent Sustainable Energy Systems School of Engineering and Computer Science Wellington Faculty of Engineering Victoria University of Wellington Wellington New Zealand

The literature on incentive-driven, market-oriented demand-side management in microgrids has focused almost entirely on minimizing the operating cost, but failed to characterize the competitive relationships in decentralized energy markets. Accordingly, this has led to producing clear evidence of their underperformance when applied in real-world settings. In response, using ideas from non-cooperative game theory to address behavioral risk factors, this paper introduces an aggregator-mediated, demand response scheduling framework in a two-layer arrangement, and integrates it into an optimal day-ahead energy management model of grid-connected multi-microgrids. The results obtained by applying the proposed model to a conceptual multi-microgrid system, have demonstrated its effectiveness in yielding the best compromise solution between demand response utilization and electricity imports. More specifically, the results indicate that the suggested model can reduce the test-case system’s daily operating cost by up to ~41% by finding a well-balanced solution with respect to the optimal decisions made by competing players in a strategic setting.

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Exploring the Use of Machine Learning and the Internet of Things in Intelligent Agriculture: Short Survey

Exploring the Use of Machine Learning and the Internet of Th...

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International Conference on Optimization and Applications (ICOA)

作者： Karima El Majdouby Mohamed El Mohadab Rachid Ait Daoud Karim Abouelmehdi Computer Science department ELITES laboratory Faculty of Sciences University of Chouaib Doukkali Eljadida Morocco Computer Science department Systems Engineering Laboratory Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Agriculture is evolving towards more sustainable practices thanks to the integration of the machine learning and Internet of Things, which addresses many of the issues related to agricultural production and leads to increased yields with reduced input and labor costs. Smart farming has developed solutions to many conventional agricultural problems to automatically manage and monitor farmland. These technologies reduce the risk of field management in the event of bad weather, and also provide benefits to farmers in the event of labor shortages. The Internet of Things ensures the connection of physical objects that are equipped with sensors to the internet, enabling them to collect and exchange data and communicate with each other and with computer systems to gather information, perform analysis and take actions in real time that require rapid data analysis, and this task can be accomplished through machine learning algorithms with greater efficiency and high-quality decision-making. In addition, we carry out an in-depth state-of-the-art survey to identify very recent work that has implemented the Internet of Things and machine learning in smart agriculture, analyzing the development of practical solutions and discussing recent research trends and future prospects.

关键词： Smart agriculture Surveys Machine learning algorithms Crops Machine learning Transforms Real-time systems Internet of Things Faces Water resources

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids

Reinforcement Learning based MIMO Controller for Virtual Ine...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skiparev, Vjatseslav Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios. © 2022 IEEE.

关键词： Reinforcement learning

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Foreword

Foreword

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作者： Abawajy, Jemal Xu, Zheng Atiquzzaman, Mohammed Zhang, Xiaolu Faculty of Science Engineering and Built Environment Deakin University GeelongVIC Australia School of Computer Engineering and Sciences Shanghai Polytechnic University Shanghai China School of Computer Science University of Oklahoma NormanOK United States Department of Information Systems and Cyber Security The University of Texas at San Antonio San AntonioTX United States

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