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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Sasikala P. Sakthi K. Agalya U. Vidhya M. Karthik R. Nareshkumar Department of Artificial Intelligence and Data Science CARE College of Engineering Trichy Department of Information Technology SRM Valliammai Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Department Of Information Technology Indra Ganesan College of Engineering Tiruchirappalli Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The aim of this paper, is to gain insights in to dynamics of Kabaddi. This sport has become increasingly popular, by analyzing teams’ performance in national leagues. To do so, the results of each round of matches the participating teams play are used to create a dissimilarity matrix. This matrix is then processed through two algorithms, A visualization of the outcomes of each team was achieved by the utilization of novel dimensionality techniques. This novel approach for dimensionality reduction enables the teams’ performance to be represented in a lower-dimensional manner, facilitating the visualization of complex data connections. The t-SNE algorithm, on the other hand, captures the non-linear relationships in the teams’ performance. This study attempts to discover crucial characteristics that impact team performance and give a greater knowledge of the dynamics of the sport by comparing the findings acquired using a novel dimensionality reduction technique. The data for this study comes from the Kabaddi season that took place during 2017-2018.

关键词： Dimensionality reduction Training Fans Heuristic algorithms data visualization Games Complexity theory

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Deep Convolutional Neural Network Framework for Brain Tumor Classification using MRI Images

Deep Convolutional Neural Network Framework for Brain Tumor ...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： U. Sakthi K. Thangaraj A Tamizhselvi M.K. Kirubakaran Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai TN India Department of Information Technology Sona College of Technology Junction Main Road Salem TN India Department of Information Technology St. Joseph’s College of Engineering Chennai TN India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai TN India

Brain cancer is one of the high-risk diseases and increases the death rate in all countries, affecting both men and women. The early diagnosis and severity of brain cancer leads to better medical treatment and save people’s lives. The machine learning procedure has been applied for early detection and treatment of brain cancer in the biomedical field by classifying them into low-risk and high-risk groups. In cancer research, the predictive and classification model has been developed using Deep Convolutional Neural Network (DCNN) algorithms for accurate decision making. The Magnetic Resonance Image (MRI) classification technique DCNN is advanced to detect and match feature points of training and test images. The DCNN classifier based on the outcome of feature points then classifies images. The key notion of this proposed research effort is to implement and execute the proposed DCNN algorithm on cancer patient datasets for risk level classification. The brain cancer affected patient details are collected from UCI machine learning data repository for experimental analysis. In this research study, the DCNN algorithm is proposed and it gives better accuracy and faster than the KNN, CNN and SVM.

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Optimal Quantum State Tomography via Weak Value

arXiv

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arXiv 2024年

作者： Zhu, Xuanmin Zhang, Dezheng Gao, Runping Wei, Qun Liu, Lixia Luo, Zijiang Center for Quantum Information School of Information Guizhou University of Finance and Economics Guizhou Guiyang550025 China International Joint Research Center for Data Science and High-Performance Computing School of Information Guizhou University of Finance and Economics Guizhou Guiyang550025 China School of Physics and Optoelectronic Engineering Xidian University Xi’an710071 China School of Mathematics and Statistics Xidian University Xi’an710071 China Institute of Intelligent Manufacturing Shunde Polytechnic Guangdong Shunde 528300 China

To improve the efficiency of the state tomography strategy via weak value, we have searched the optimal coupling strength between the system and measuring device. For an arbitrary d-dimensional quantum system, the optimal strengths being used in measuring the real and imaginary parts of the density matrix are obtained. The optimal efficiency of the state tomography has also been studied by using mean square error. The minimal mean square errors in the reconstructed density matrices have been derived. The state tomography strategy studied in this article may be useful in the measurement of the unknown quantum states. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Federated Learning in STAR-RIS Aided SAGINs

IEEE Communications Standards Magazine

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IEEE Communications Standards Magazine 2025年

作者： Wu, Min Guo, Kefeng Dev, Kapal Hamadi, Hussam Al Singh, Keshav Khowaja, Sunder Ali Li, Xingwang Space Engineering University School of Space Information Beijing101407 China Munster Technological University CONNECT Centre Department of Computer Science Ireland College of Engineering and IT University of Dubai United Arab Emirates National Sun Yat-sen University Institute of Communications Engineering Kaohsiung Taiwan Technological University Dublin School of Computing Faculty of Computing Digital and Data Ireland Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China

Satellite-aerial-ground integrated network (SAGIN) is considered to be an advanced architecture that offers seamless coverage, flexible access, and efficient connectivity. However, several challenges emerge during the construction of SAGINs, which include complex propagation environments and limited spectrum resources. The aforementioned challenges can be addressed in a cost effective manner by integrating simultaneous transmission and reflecting reconfigurable intelligent surfaces (STAR-RISs). The STAR-RISs are capable of reflecting and transmitting the incident signals passively in order to serve users on both sides of the panel. Given the self-organizing nature of STAR-RIS-aided SAGINs, meticulous resource allocation and transmission strategy adaptation are imperative to accommodate changes in multiple communication node environments. Recently, federated learning has been envisioned as a pioneering distributed machine learning (ML) approach for managing the multi-objective optimization problem in STAR-RIS-aided SAGINs. This approach strives to balance the trade-offs between wireless communication performance and model efficiency. In this work, we present the advantages and potential directions for employing federated learning in STAR-RIS-aided SAGINs through multi-objectives optimization. Furthermore, to illustrate the practical application of federated learning in STAR-RIS-aided SAGINs, we present two case studies utilizing federated deep reinforcement learning, based on the non-orthogonal multiple access (NOMA) scheme. © 2017 IEEE.

关键词： Deep reinforcement learning

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems information management Monitoring Read only memory

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Joint Vehicle Connection and Beamforming Optimization in Digital Twin Assisted Integrated Sensing and Communication Vehicular Networks

arXiv

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arXiv 2024年

作者： Ding, Weihang Yang, Zhaohui Chen, Mingzhe Liu, Yuchen Shikh-Bahaei, Mohammad Department of Engineering King’s College London London United Kingdom The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Hangzhou310007 China The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

This paper introduces an approach to harness digital twin (DT) technology in the realm of integrated sensing and communications (ISAC) in the sixth-generation (6G) Internet-of-everything (IoE) applications. We consider moving targets in a vehicular network and use DT to track and predict the motion of the vehicles. After predicting the location of the vehicle at the next time slot, the DT designs the assignment and beamforming for each vehicle. The real time sensing information is then utilized to update and refine the DT, enabling further processing and decision-making. In the DT, an extended Kalman filter (EKF) is used for precise motion prediction. This model incorporates a dynamic Kalman gain, which is updated at each time slot based on the received echo signals. The state representation encompasses both vehicle motion information and the error matrix, with the posterior Cramér-Rao bound (PCRB) employed to assess sensing accuracy. We consider a network with two roadside units (RSUs), and the vehicles need to be allocated to one of them. To optimize the overall transmission rate while maintaining an acceptable sensing accuracy, an optimization problem is formulated. Since it is generally hard to solve the original problem, Lagrange multipliers and fractional programming are employed to simplify this optimization problem. To solve the simplified problem, this paper introduces both greedy and heuristic algorithms through optimizing both vehicle assignments and predictive beamforming. The optimized results are then transferred back to the real space for ISAC applications. Recognizing the computational complexity of the greedy and heuristic algorithms, a bidirectional long short-term memory (LSTM)-based recurrent neural network (RNN) is proposed for efficient beamforming design within the DT. Simulation results demonstrate the effectiveness of the DT-based ISAC network. Notably, the LSTM-based RNN method achieves similar transmission rates as the heuristic algorithm

关键词： Cramer-Rao bounds

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A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms

arXiv

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arXiv 2023年

作者： Jiang, Meiyu Shen, Jun Jiang, Xuetao Luo, Lihui Zhou, Rui Zhou, Qingguo School of Information Science and Engineering Lanzhou University Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Gansu Provincial Industry Technology Center of Intelligent Equipment & Big Data for Disaster Prevention Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Gansu Lanzhou730000 China

Wind energy is a widely distributed, renewable, and environmentally friendly energy source that plays a crucial role in mitigating global warming and addressing energy shortages. Nevertheless, wind power generation is characterized by volatility, intermittence, and randomness, which hinder its ability to serve as a reliable power source for the grid. Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources. However, traditional wind power forecasting systems primarily focus on ultra-short-term or short-term forecasts, limiting their ability to address the diverse adjustment requirements of the power system simultaneously. To overcome these challenges, We propose an automatic framework capable of forecasting wind power across multi-time scale. The framework based on the tree-structured Parzen estimator (TPE) and temporal fusion transformer (TFT) that can provide ultra-short-term, short-term and medium-term wind power forecasting power. Our approach employs the TFT for wind power forecasting and categorizes features based on their properties. Additionally, we introduce a generic algorithm to simultaneously fine-tune the hyperparameters of the decomposition method and model. We evaluate the performance of our framework by conducting ablation experiments using three commonly used decomposition algorithms and six state-of-the-art models for forecasting multi-time scale. The experimental results demonstrate that our proposed method considerably improves prediction accuracy on the public dataset Engie https://***. Compared to the second-best state-of-the-art model, our approach exhibits a reduction of 31.75% and 28.74% in normalized mean absolute error (nMAE) for 24-hour forecasting, and 20.79% and 16.93% in nMAE for 48-hour forecasting, respectively. © 2023, CC BY.

关键词： Wind power

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Significance-aware Medication Recommendation with Medication Representation Learning

Significance-aware Medication Recommendation with Medication...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yishuo Li Zhufeng Shao Weimin Chen Shoujin Wang Yuehan Du Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Mental Health Center Jinan China Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The goal of medication recommendation system is to recommend appropriate pharmaceutical interventions based on a patient’s diagnosis. Most of existing approaches often formulate these recommendations use data on diagnoses, procedures, and prescriptions accumulated in the electronic health records (EHR), and despite the great successes, they seem to have limitations on modelling the significance of medication to a patient’s current visit and mining fine-grained medication representation information. To address these issues, we propose a novel Significanceaware Medication Recommendation (SMRec) framework built on significance of medication to patients and fine-grained medication representation learning. Specifically, we first design a encoding mechanism to compute significance information of medications for each patient’s visit. Then, we utilize the set-level medication co-occurrence graph based on patients’ medical history which integrates temporal dependency to learn fine-grained medication representations. Experimental results on the publicly available MIMIC-III dataset demonstrate the superior effectiveness of our model compared to other approaches 1 .

关键词： Representation learning Drugs Federated learning Computational modeling MIMICs Encoding History

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Addressing the traveling salesperson problem with frequency fitness assignment and hybrid algorithms

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Soft computing 2024年第17-18期28卷 9495-9508页

作者： Liang, Tianyu Wu, Zhize Lässig, Jörg Berg, Daan van den Thomson, Sarah L. Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China Fakultät Elektrotechnik und Informatik Hochschule Zittau/Görlitz Brückenstraße 1 Saxony Görlitz02826 Germany Abteilung Kognitive Energiesysteme Fraunhofer IOSB-AST Wilhelmsplatz 11 Saxony Görlitz02826 Germany Department of Computer Science Vrije Universiteit Amsterdam De Boelelaan 1111 Amsterdam1081 HV Netherlands School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Jiulong Road 111 Anhui Hefei230601 China

The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to optimization has recently been proposed: instead of directing the search towards better solutions, the optimization process prefers those with rarely encountered objective values. FFA can be plugged into existing algorithms and can improve their performance on some problems that are hard for them. However, problems like the TSP, where the number of possible objective values (here: tour lengths) is high, cause the performance of FFA-based algorithms to deteriorate. We choose 56 symmetric instances from TSPLib as a testbed for our experiments. We plug FFA both into the (1+1) EA and simulated annealing (SA) and obtain the FEA and FSA, respectively. We find that the resulting FEA substantially outperforms the (1+1) EA and that FSA can reach the global optima more often than SA within our computational budget. We propose two ways to hybridize FFA and traditional optimization in a round-robin scheme where objective function evaluations are alternatingly assigned to both components. The FFA-based parts of the algorithms sometimes send a solution over to the traditional-optimization-based ones. This can lead to further performance improvements. In particular, the hybrids combining simulated annealing with the FEA solve most problems and find close-to-optimal solutions on other instances. Even for several instances with many possible different objective values, which are therefore not suited to FFA, hybrids combining FFA and traditional objective-guided optimization algorithms can offer substantial performance improvements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

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