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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Jing-Yuan Wei, Feng-Feng Chen, Tai-You Hu, Xiao-Min Jeon, Sang-Woon Wang, Yang Chen, Wei-Neng School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of School of Management Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665410205

Traffic signal control (TSC) problems have received increasing attention with the development of the smart city. Reinforcement learning (RL) models TSC as a Markov decision process and learns the timing relationship of traffic scheduling from massive historical data. Due to the uncertainty and mutability of TSC problems, existing RL methods face bottlenecks in diversity and are easy to be trapped into local optima. To alleviate this predicament, this paper combines evolutionary optimization and RL to propose an evolutionary algorithm-assisted reinforcement learning (EARL-Light) method for TSC problems. EARL-Light is a population-based algorithm, in which one individual represents a policy and a population of individuals are evolved to search for near-optimal policies. The diversified search ability of evolutionary optimization can help the algorithm get rid of local optima for global optimization and the rapid learning based on the gradient of RL can achieve fast convergence. Extensive experiments on seven real-world traffic datasets demonstrates that EARL-Light achieves shorter travel time with fast convergence. © 2024 IEEE.

关键词： Travel time

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Leveraging IoT and AI in Precision Agriculture for Efficient Water Management

Leveraging IoT and AI in Precision Agriculture for Efficient...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Mittal, Mudit Parida, Prasanta Kumar, Yogendra Singla, Atul Thandra, Nagarjuna LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India School of Rural Management KIIT University Odisha Bhubaneswar India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

Where water is limited the challenge of getting the best management of the resource become a major issue in farming. In this study, therefore, we focus on the following question: What role may AI and the IoT play in precision agriculture towards enhancing sustainability through the conservation of water resources? IoT devices that can provide information on the environmental conditions include;sensors for moisture level in the soil, and weather systems. To such data artificial intelligence algorithms are applied to automate the process of irrigation, determine possible water consumption and avoid excess. Real-time and historical data can also be evaluated using machine learning approaches of the system. From this it is then able to predict when crops will need water and then align their irrigation times from this information. Moreover, the analytic model using AI may find potential issues with irrigation and present mitigating steps. Juxtaposing IoT and AI, it is revealed in this research how the two technologies can improve water management decisionmaking with the aim of minimizing water depletion as well as over-irrigation. Integrated soil nutrients management and conservation agriculture are recommended and the overall atmospheric water scarce zone sustainable agricultural productivity is enhanced by the proposed framework while increasing water productivity. The study reveals that it is possible to steer the future of water management through the interconnection of IoT and AI in precision agriculture. It may therefore result in higher production, efficiency in resource utilization and ultimate sustainability. © 2025 IEEE.

关键词： Irrigation

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A Novel Approach to Detect Indian High Security Registration Plates 13

A Novel Approach to Detect Indian High Security Registration...

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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Bhardwaj, Arnav Gupta, Sumita Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9781713857938

In recent times, with the growing number of vehicles, road safety has become a paramount issue. To ensure safe and easy passage of vehicles on the road, it is important that we take help of certain techniques that ensure the road safety regulations are properly followed. The Government of India has mandated the usage of High Security Registration Plate (HSRP) to ensure safety of vehicles, since these plates are tamper proof and cannot be counterfeited easily. In this paper, we have proposed a novel approach to detect the HSRP on a car. The most prominent feature detectable through images of an HSRP is the "IND" text inscribed on it. We have used three License Plate (LP) detection models: Warped Planar Object Detection Network (WPOD-Net), You Only Look Once (YOLO) version 3 (YOLOv3) and version 5 (YOLOv5) to detect the license plate on a car. Once a plate is detected, we apply EasyOCR to determine whether the plate has "IND" inscribed on it or not. A comparative analysis is done on these three models and our experimental results show that WPOD-Net with EasyOCR gives and accuracy of 93.01%, making the proposed approach viable for HSRP detection. © Grenze Scientific Society, 2022.

关键词： Object detection

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

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computers, Materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-Madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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Prediction of diabetic patients in Iraq using binary dragonfly algorithm with long-short term memory neural network

AIMS Electronics and Electrical Engineering

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AIMS Electronics and Electrical engineering 2023年第3期7卷 217-230页

作者： Alhakeem, Zaineb M. Hakim, Heba Hasan, Ola A. Laghari, Asif Ali Jumani, Awais Khan Jasm, Mohammed Nabil Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq Real Estate Bank of Iraq Basrah Iraq Software college Shenyang Normal University Shenyang China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Department of Computer Science ILMA University Karachi Sindh Pakistan Basra Oil Company Basrah Iraq Computer Technology Engineering Department Iraq University College Basrah Iraq

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction,features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check *** work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic. © 2023 the Author(s)

关键词： Long short-term memory

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Bio-Inspired PSO for Improving Neural Based Diabetes Prediction System

Journal of ICT Standardization

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Journal of ICT Standardization 2022年第2期10卷 179-200页

作者： Khan, Mohammad Zubair Mangayarkarasi, R. Vanmathi, C. Angulakshmi, M. Department of Computer Science and Information Taibah University Medina Saudi Arabia School of Information Technology and Engineering VIT University Vellore Campus India

A high level of glucose in the blood over a long period creates diabetes disease. Undiagnosed diabetes may trigger other complications such as cardiovascular disease, nerve damage, renal failure, and so on. There are many factors age, blood pressure, food habits, lifestyle changes are some of the reasons for diabetes. With increasing cases of diabetes in the smart Internet world, there is a need for an automated prediction system to facilitate the patients, to get know, whether they are affected by the disease or not. There are many diabetes prediction software that is already in use, still, the accurateness of a diabetes prediction is not complete. This paper presents a robust framework (PSO-NNDP), employs a novel hybrid feature selector to improvise the neural-based diabetes prediction system. The novel hybrid feature selector presented in this paper comprises the merits of the correlation coefficient, F-score, and particle swarm optimization methods to influence the feature selection process. The reliability of the proposed framework has been experimented on the benchmarking dataset. By establishing the clear steps, for the replacement of missing values, removal of outliers, the proposed framework obtains 99.5% accuracy. Moreover, the experimented machine learning models also show a great improvement upon the usage of the proposed feature selector. © 2022 River Publishers

关键词： Forecasting

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Online Shipping Container Pricing Strategy Achieving Vanishing Regret with Limited Inventory 39

Online Shipping Container Pricing Strategy Achieving Vanishi...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Gao, Yucen Wei, Xikai Jing, Xi Shi, Yangguang Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350322279

With the growing demand for global trade transportation, the shipping container market has gained an increasingly important position. As a key issue of the market, container pricing is regarded as an important indicator to adjust the market supply and demand as well as the revenue of liner enterprises. Although various methods aimed at increasing enterprise revenue, such as expert pricing and dynamic pricing, have been proposed by industry and academia in recent years, these approaches rarely yield worst-case performance guarantee for the double-sided online scenarios of commodities and *** cater to the double-sided online scenario and provide theoretical performance guarantee, we propose an online learning-based pricing framework named Balancing Inventory and Revenue with -chasing Decider (BIRD). BIRD determines container price by combining advantages of given multiple online pricing strategies. We utilize a strategy selector A to select a proper target strategy and use an ϵ-chasing decider DChasing to determine the price. BIRD is proven to combine the advantages of multiple online pricing strategies to achieve the performance close to the posterior optimal strategy for any sequence of online buyers on realistic sales platforms with inventory limitation. BIRD is proved to yield a vanishing regret for the online posted pricing problem with the features of limited inventory and multi-unit demand. Based on the historical data provided by COSCO, one of the largest liner enterprises in the world, we experimentally demonstrate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Costs

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State-of-the-art: Data Dissemination Techniques in Vehicular Ad-hoc Networks 9

State-of-the-art: Data Dissemination Techniques in Vehicular...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharan, Bhagwati Chhabra, Megha Sagar, Anil Kumar Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544441

Vehicular Ad-hoc Networks (VANETs) is a very fast emerging research area these days due to their contribution in designing Intelligent transportation systems (ITS). ITS is a well-organized group of wireless networks. It is a derived class of Mobile Ad-hoc Networks (MANETs). VANET is an instant-formed ad-hoc network, due to the mobility of vehicles on the road. The goal of using ITS is to enhance road safety, driving comfort, and traffic effectiveness by alerting the drivers at right time about upcoming dangerous situations, traffic jams, road diverted, weather conditions, real-time news, and entertainment. We can consider Vehicular communication as an enabler for future driverless cars. For these all above applications, it is necessary to make a threat-free environment to establish secure, fast, and efficient communication in VANETs. In this paper, we had discussed the overviews, characteristics, securities, applications, and various data dissemination techniques in VANET. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Vehicular ad hoc networks

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Miniaturized Novel UWB Band-Notch Textile Antenna for Body Area Networks

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computer Systems science & engineering 2022年第3期40卷 1183-1198页

作者： Mohammad Monirujjaman Khan Arifa Sultana Mehedi Masud Gurjot Singh Gaba Hesham A.Alhumyani Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics&Electrical Engineering Lovely Professional UniversityPunjabIndia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

This paper presents the design and analysis of a miniaturized and novel wearable ultra-wideband(UWB)band-notch textile antenna for Body Area Networks(BANs).The major goal of building the antenna for wearable applications with band notch in X-band is to reject the downlink band(7.25 to 7.75 GHz)of satellite communication in the UWB frequency ranges of 3.1–10.6 GHz to keep away from *** Simulation technology(CST)TM Microwave Studio,which is user-friendly and reliable,was used to model and simulate the *** radiating element of the antenna is designed on Jeans’textile substrate,which has a relative permittivity of *** thickness of the jeans’fabric substrate has been considered to be 1 *** loss,gain,bandwidth,impedance,radiation,and total efficiency,and radiation patterns are presented and *** antenna is simulated placed on the three layers of the human body model,and the on-body results are summarized in comparison with free *** and analysis indicate that this antenna has good band-notch characteristics in the frequency range of 7.25 GHz to 7.75 *** parametric study varying the relative permittivity of Jeans’fabric substrate of this antenna is also *** addition,effects on the antenna parameters of variation of ground plane size have been *** antenna is 25 mm×16 mm×1.07 mm in total *** reveal that this antenna achieves the design goal and performs well both in free space and on the body.

关键词： UWB band notch textile antenna wearable antenna on-body BANs jeans X-band CST

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