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International Conference on Electrical Engineering and Informatics (ICELTICs)

作者： Murhaban Syahrial Teuku Yuliar Arif Fitri Arnia Departement Information Technology Engineering Doctoral Program School of Engineering Universitas Syiah Kuala Universitas Teuku Umar Banda Aceh Aceh Barat Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350366822

ISBN: (纸本)9798350366839

This study develops an adaptive handover strategy in 5G networks to tackle high mobility challenges and improve service continuity and quality. The research method uses a 5G network emulator with a topology of four base stations (gNBs) to test user equipment (UE) speeds of 20 km/h and 130 km/h. The adaptive algorithm developed utilizes data from the Received Signal Strength Indicator (RSSI) as well as the speed and direction of user mobility to make optimal handover decisions. The results show that at a speed of 130 km/h, the average handover latency reaches 12 ms, compared to 8 ms at 80 km/h. Additionally, the signal quality (RSSI) is lower at higher speeds, negatively affecting service quality. This adaptive algorithm successfully reduces handover latency and improves the stability and efficiency of the overall network. The testing also indicates that this adaptive algorithm can predict handover targets by considering UE speed and direction and uses hysteresis mechanisms to prevent overly frequent handovers. By employing technologies such as Multiple Input Multiple Output (MIMO) and beamforming, this strategy is expected to maintain high service quality even under high mobility conditions.

关键词： Adaptive systems 5G mobile communication Network topology Adaptive algorithms Handover Topology Received signal strength indicator Informatics Optimization Testing

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Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning

arXiv

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arXiv 2024年

作者： Masuya, Nozomu Sato, Hiroshi Yamane, Koki Kusume, Takuya Sakaino, Sho Tsuji, Toshiaki Master's Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Doctoral Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Department of Intelligent Interaction Technologies Institute of Systems and Information Engineering University of Tsukuba Japan Graduate School of Science and Engineering Saitama University Saitama Japan

Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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Dynamic Model for Determining Disaster Evacuation Locations with Game Theory 1

Dynamic Model for Determining Disaster Evacuation Locations ...

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1st International Conference on Advanced information Scientific Development, ICAISD 2020

作者： Safii, M. Efendi, Syahril Zarlis, Muhammad Mawengkang, Herman Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

Disaster management can be grouped into three stages with several activities that can be carried out starting from pre-disaster, during emergency response and post-disaster. In the pre-disasterstage one of which is mitigation, which is a series of efforts to reduce disaster risk, both through physical development and awareness raising and capacity to face the threat of disaster. In mitigation one important factor is about evacuation. Evacuation locations in disaster transportation management are urgently needed for an effective evacuation process. Evacuation location determination is a benchmark in handling evacuation that can minimize the impact of losses from the disaster. To determine the evacuation location dynamically using game theory can solve the problem by providing several alternative locations by showing the dominant factor in the variables in the selection of the evacuation site. Game theory will use pure and mixed strategies to provide the most optimum choice of criteria. With this method will produce a decision support system to determine the optimal evacuation location or safe area in disaster mitigation © 2020 Published under licence by IOP Publishing Ltd.

关键词： Site selection

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Multi-tenant Cloud SaaS Application for a meeting to task transition via deep learning models

Multi-tenant Cloud SaaS Application for a meeting to task tr...

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Viktor Walter-Tscharf Department of Information Engineering and Computer Science Doctoral Program Industrial Innovation Fondazione Bruno Kessler University of Trento European Institute of Innovation and Technology

ISBN: (纸本)9798350309850

Thought digitalisation and the post pandemic period online meetings increase by 12.9% and number of attendees by 13.5%. According to Zoom, Google Meets, and MS Teams they collectively increased there amount of active users from March 2020 until june 2021 by 253%. Additionally virtual meetings are becoming inefficient, unproductive and unstructured, where results are not documented correctly. The intention is to shift those statistics by revolutionising the way online meeting are held. The paper outlines the technical requirements for a user-friendly web application that allows to reproduce a virtual meeting including agreements, its participants interactions and highlighting relevant sequences, is outlined. The developed AI meeting wizard autonomously documents the discussed content in the form of notes, to-dos tasks and integrates them directly into task management, mail or calendar tools. Reinforcing the focus on the discussion by increased interaction and productivity instead of task management and note taking.

关键词： Technical requirements Productivity Pandemics Software as a service Speech recognition Internet of Things Task analysis

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A Systematic Integrated Literature Review of Clinical Data Management Platforms of HL7 FHIR

A Systematic Integrated Literature Review of Clinical Data M...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Franz Frederik Walter Viktor Walter-Tscharf Department of Information Engineering and Computer Science Fondazione Bruno Kessler University of Trento European Institute of Innovation and Technology Doctoral Program Industrial Innovation

This study is dedicated to a critical investigation of the existing approaches toward a data visualization management platform based on Fast Health Interoperability Resources (FHIR). Despite the growing popularity of FHIR, the available evidence provides a compelling reason to believe that its use is still accompanied by a significant number of technical challenges. Therefore, the development of tools to ensure its convenient visualization remains a pressing need for a variety of stakeholders operating in the healthcare industry. Results of the systematic literature review that included 26 studies provide valuable information about the most popular instruments in this area, such as the OpenEHR CKM approach. The review offers a critical discussion of the most important advantages and disadvantages of different approaches. It provides valuable recommendations for further research as well as practical recommendations concerning the best ways to utilize data visualization platforms based on FHIR. It defines the tradeoff of functionality versus the ease of dissemination as one of the most important problems facing most platforms and suggests avenues for developing additional tooling support for visualizing FHIR. The results of the research could be useful both for scientists and practitioners.

关键词： Industries Systematics Bibliographies Instruments Data visualization Pressing Medical services

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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A Survey on the Impact of Data Analytics and Machine Learning Techniques in E-commerce 5

A Survey on the Impact of Data Analytics and Machine Learnin...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Dheva Rajan, S. Vavilapalli, Sivajee Hasan, Shahriar Kumar, Ritesh Rafa, Nafisa Muda, Iskandar University of Technology and Applied Sciences Almusannah Information Technology Oman Duvvada Department of Management Studies Visakhapatnam India University of Dhaka Department of Finance Bangladesh Maharishi School of Engineering and Technology Uttar Pradesh Lucknow India BBA in Marketing Institute of Business Administration University of Dhaka Dhaka Bangladesh Universitas Sumatera Utara Faculty Economic and Business Department of Doctoral Program Medan20222 Indonesia

ISBN: (纸本)9798350398267

Current article has been developed for understanding the impact of data analytics and machine learning technique in e-commerce. If the observation has been developed for the current market of this technology it can be detected that real-life industry increases their investment which showed the interest in getting advantages. Identification of the problem of costing and different algorithm suitability objectives has been set for understanding the real impact of e-commerce. Secondary data collection has been used for understanding the different usability of machine learning and data analytics for e-commerce. It has been detected that using machine learning brings the benefits of automation and improves continuous growth. Data analytics provide the scope of accuracy build-up. Apart from this different area of application and the different techniques has been analyzed. Understanding become easier and the scope of future benefits can be identified. © 2022 IEEE.

关键词： Data Analytics

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route

Optimation Cavacity Vehicle Routing Problem with K-Nearest N...

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International Conference of Computer Science and information technology

作者： Muhammad Syahputra Novelan Syahril Efendi Poltak Sihombing Herman Mawengkang Doctoral Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of ‘nodes’ on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods.

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