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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： R. Dhanalakshmi A.V. Kalpana J. Umamageswaran B. Praveen Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Information Technology R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering K L University Andhra Pradesh India

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. Data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery.

关键词： Histograms Neural networks Redundancy Approximation algorithms Data models Classification algorithms Data mining

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Diabetes Classification Using Support Vector Machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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Updating Top-k Dominate Individuals with Incomplete Data Addition

Updating Top-k Dominate Individuals with Incomplete Data Add...

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IEEE International Symposium on Information Theory

作者： Jimmy Ming-Tai Wu Ke Wang Huizhen Yan Chao-Chun Chen Pei-Wei Chen Jerry Chun-Wei Lin College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Manufacturing Information and Systems National Cheng Kung University Tainan Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Top-k dominance (TKD) query is an extended query method of skyline query and top-k query, which reveals the top-k dominant individuals in an incomplete dataset by analyzing the dominance relationships between individuals and is a common decision tool in intelligent recommendation applications. This research proposes two parallel query algorithms based on Spark computing engine to address the shortcomings of the parallel top-k-dominated query algorithms for dynamic incomplete datasets. The designed model achieves good performance in terms of runtime performance compared to previous studies.

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An Artificial Neural Network for Image Classification Inspired by Aversive Olfactory Learning Circuits in Caenorhabditis Elegans

arXiv

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arXiv 2024年

作者： Wang, Xuebin Liu, Chunxiuzi Zhao, Meng Zhang, Ke Di, Zengru Liu, He Department of Systems Science Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China International Academic Center of Complex Systems Beijing Normal University Zhuhai519087 China School of Systems Science Beijing Normal University Beijing100875 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning circuits of the nematode Caenorhabditis elegans (C. elegans). Despite the remarkable performance of ANNs in a variety of tasks, they face challenges such as excessive parameterization, high training costs and limited generalization capabilities. C. elegans, with its simple nervous system comprising only 302 neurons, serves as a paradigm in neurobiological research and is capable of complex behaviors including learning. This research identifies key neural circuits associated with aversive olfactory learning in C. elegans through behavioral experiments and high-throughput gene sequencing, translating them into an image classification ANN architecture. Additionally, two other image classification ANNs with distinct architectures were constructed for comparative performance analysis to highlight the advantages of bio-inspired design. The results indicate that the ANN inspired by the aversive olfactory learning circuits of C. elegans achieves higher accuracy, better consistency and faster convergence rates in image classification task, especially when tackling more complex classification challenges. This study not only showcases the potential of bio-inspired design in enhancing ANN capabilities but also provides a novel perspective and methodology for future ANN design. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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On the Performance of RIS-enabled NOMA for Aerial Networks

On the Performance of RIS-enabled NOMA for Aerial Networks

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IEEE Conference on Wireless Communications and Networking

作者： Lina Bariah Fouzi Boukhalfa Wael Jaafar Sami Muhaidat Halim Yanikomeroglu Technology Innovation Institute Masdar City Abu Dhabi UAE Department of Software and Information Technology Engineering École de Technologie Supérieure (ÉTS) Montreal QC Canada Department of Electrical Engineering and Computer Science Center for Cyber-Physical Systems Khalifa University Abu Dhabi UAE Department of Systems and Computer Engineering Carleton University ON Canada

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted aerial communications, where a ground base station (GBS) communicates with distant terrestrial and/or aerial users through the assistance of a RIS-equipped unmanned aerial vehicle (RIS-UAV). The GBS uses the non-orthogonal multiple access (NOMA) scheme to transmit its signal, which is directed to the users via the RIS-UAV. First, the end-to-end channel is characterized, by considering the shadowed Rician fading, then the outage probability performance metric is derived for the underlying system model. Through numerical results, we demonstrate the impact of several system parameters on the performance of NOMA users. Specifically, we found that RIS elements need to be carefully allocated among different NOMA users, according to their channel conditions, in order to achieve the needed quality of service.

关键词： Measurement NOMA Base stations Rician channels Quality of service Probability Power system reliability

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Autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems

Autoencoder and Teaching-learning-based Optimizer for Mobile...

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IEEE International Conference on systems, Man and Cybernetics

作者： Dian Xu Mengchu Zhou Haitao Yuan Institute of Systems Engineering Macau University of Science and Technology Macao China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA School of Automation Science and Electrical Engineering Beihang University Beijing China

By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problems through several widely used function problems. However, the following two crucial issues have not been resolved, 1) ATLBO should be verified by solving real-life optimization problems; and 2) how autoencoder parameters and structures impact AEO's performance. In this work, ATLBO is verified by an energy consumption minimization problem (ECM) in mobile edge computing systems. To design an effective autoencoder for ATLBO, this work proposes a parameter tuning optimization strategy for autoencoders. By using the proposed Autoencoder Parameter Tuning (APT) strategy, ATLBO can enjoy higher robustness than those without it. The experimental results show that it is three to six times better than state-of-the-art methods in solving ECM. We consider the strategy-induced overhead and take the execution time as the primary criterion to evaluate them. In addition, the experimental results show that, against the conventional wisdom that higher-accuracy auto encoders bring higher system performance, lower-accuracy ones can actually assist ATLBO in locating the best solutions. This work promotes a novel application of autoencoders in optimization theory and practice.

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Blockchain-Based Model and Architecture Public Health System

SSRN

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SSRN 2024年

作者： Fernando, Erick Winanti, Winanti Prabowo, Yulius Denny Johan, Monika Evelin Tjahjana, David Department of Information Systems Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information Systems Universitas Insan Pembangunan Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia STMIK Insan Pembangunan Indonesia

Blockchain has been identified as one of the technologies that can be used in the healthcare system, especially in the issues of data security, privacy, and interoperability. This article explores the application of blockchain in various medical processes ranging from patient registration, visit registration, initial examination, patient examination, diagnostic input, and drug preparation. This study was conducted at a healthcare facility that utilizes blockchain as a technology to manage patient data, medical records, and drug prescriptions. Through a mixed-method approach, this study evaluates the impact of blockchain on operational efficiency, data security, and system reliability. The results show that blockchain not only improves the security and privacy of patient data but also accelerates operational processes in the healthcare chain. An in-depth discussion of the benefits, challenges, and future potential of blockchain in healthcare is outlined to provide insights for blockchain technology developers and healthcare practitioners. Model validation was conducted through Focus Group Discussion (FGD) with domain experts in the health sector. From the experiments conducted, this study concludes that blockchain can be a transformational solution in addressing major problems in the healthcare sector, although cross-sector collaboration is needed to ensure successful implementation. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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

作者： Rohit Sharma Shubham Dahatonde Srimanta Kumar Acharjya Sourabh Singh Verma Vijaypal Singh Dhaka Ramesh Singh Rawat School Of Computing & Intelligent Systems Manipal University Jaipur Jaipur India Department of Computer Science and engineering C. V Raman Global University Bhubaneswar India Dept. of computer Science and Engg Graphic Era deemed to be University Deharadun Uttrakhand India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency.

关键词： Training Image segmentation Accuracy Land surface Feature extraction Data models Robustness Satellite images Marine vehicles Artificial intelligence

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Designing Consensus-Based Distributed Filtering over Directed Graphs

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Lv, Yuezu Duan, Zhisheng Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Systems Science School of Mathematics Southeast University Nanjing211189 China MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper proposes a novel consensus-on-only-measurement distributed filter over directed graphs under the collectively observability condition. First, the distributed filter structure is designed with an augmented leader-following measurement fusion strategy. Subsequently, two parameter design methods are presented, and the consensus gain parameter is devised utilizing local information exclusively rather than global information. Additionally, the lower bound of the fusion step is derived to guarantee a uniformly upper bound of the estimation error covariance. Moreover, the lower bounds of the convergence rates of the steady-state performance gap between the proposed algorithm and the centralized filter are provided with the fusion step approaching infinity. The analysis demonstrates that the convergence rate is, at a minimum, as rapid as exponential convergence under the spectral norm condition of the communication graph. The transient performance is also analyzed with the fusion step tending to infinity. The inherent trade-off between the communication cost and the filtering performance is revealed from the analysis of the steady-state performance and the transient performance. Finally, the theoretical results are substantiated through the validation of two simulation examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality

arXiv

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arXiv 2024年

作者： Fan, Chao Hou, Saihui Liang, Junhao Shen, Chuanfu Ma, Jingzhe Jin, Dongyang Huang, Yongzhen Yu, Shiqi The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Hong Kong The School of Artificial Intelligence Beijing Normal University China Watrix Technology Limited Co. Ltd Beijing China

Gait recognition, a rapidly advancing vision technology for person identification from a distance, has made significant strides in indoor settings. However, evidence suggests that existing methods often yield unsatisfactory results when applied to newly released real-world gait datasets. Furthermore, conclusions drawn from indoor gait datasets may not easily generalize to outdoor ones. Therefore, the primary goal of this work is to present a comprehensive benchmark study aimed at improving practicality rather than solely focusing on enhancing performance. To this end, we first develop OpenGait, a flexible and efficient gait recognition platform. Using OpenGait as a foundation, we conduct in-depth ablation experiments to revisit recent developments in gait recognition. Surprisingly, we detect some imperfect parts of certain prior methods thereby resulting in several critical yet undiscovered insights. Inspired by these findings, we develop three structurally simple yet empirically powerful and practically robust baseline models, i.e., DeepGaitV2, SkeletonGait, and SkeletonGait++, respectively representing the appearance-based, model-based, and multi-modal methodology for gait pattern description. Beyond achieving SoTA performances, more importantly, our careful exploration sheds new light on the modeling experience of deep gait models, the representational capacity of typical gait modalities, and so on. We hope this work can inspire further research and application of gait recognition towards better practicality. The code is available at https://***/ShiqiYu/OpenGait. © 2024, CC BY-NC-ND.

关键词： Benchmarking

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