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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 IEEE.

关键词： Energy management systems

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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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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

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

ISBN: (纸本)9798350357769

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.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pandey, Ravindra Rongali, Aruna Sri Gopinath, S. Anute, Nilesh Nama, Forda Pund, Sachin Hult International Business School United States University of the Cumberlands Doctoral Information Technology Information Technology Emphasis Information Technology United States Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India ASM's Institute of Business Management and Research Pune India University of Lampung Doctoral Program of Environtmental Science Department of Informatics Bandar Lampung Indonesia Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets. © 2023 IEEE.

关键词： approximately gaining knowledge mastering performance regression

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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Advanced Data Instrumentation for Stunting Analysis in Physical-Cyber Architecture 17

Advanced Data Instrumentation for Stunting Analysis in Physi...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Nugraha, Daniel Nurtam, M. Riza Mistialustina, Hartuti Saepudin, Didin Surjono, Welly Prihatmanto, Ary Setijadi Institut Teknologi Bandung Doctoral Program of School of Electrical Engineering and Infomatics Bandung Indonesia Universitas Widya Dharma Pontianak Kalimantan Barat Indonesia Universitas Sangga Buana Faculty of Engineering Electrical Engineering Department Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Institut Teknologi Bandung School of Electical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350309164

In Indonesia, stunting is a serious problem that has drawn a lot of attention from the government. This study offers a novel strategy for preventing stunting by combining the 5-Desk Model used in integrated health posts (Posyandu) with the Cyber-Physical System (CPS) architecture. Chronic malnutrition and childhood diseases can cause stunting, which can result in long-Term health issues and cognitive deficits. Despite advancements, it still poses a serious threat to almost every Indonesian province. For the purpose of gathering data in Posyandu, such as registration, growth measurement, recording, counseling, and health services, the 5-Desk Model provides an organized framework. This promotes the well being of the community and gives healthcare professionals the ability to thoroughly monitor important health indicators. This strategy is improved with CPS integration, which results in a four-layered, comprehensive system that includes gamification, business intelligence, information systems, and instrumentation. To maximize efforts to avoid stunting, this architecture facilitates accurate monitoring, real-Time data collecting, data analysis, and stakeholder communication. This study imagines a data-driven future for Indonesian stunting prevention, where cooperation, technology, and data are combined to address this pressing problem and safeguard the future prospects and wellbeing of Indonesia's youth. Implementing this goal and expanding stunting prevention initiatives will be the main goals of future research. © 2023 IEEE.

关键词： Cyber Physical System

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Using Entropy for Modeling Difficulty in the Asteroid Escape Sliding Puzzle

Using Entropy for Modeling Difficulty in the Asteroid Escape...

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CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Jose A. del Solar-Zavala Hans Schaa Nicolas A. Barriga Doctoral Program in Engineering Systems Universidad de Talca Curicó Chile Faculty of Engineering Universidad de Talca Talca Chile

Developing a standarized metric for difficulty that can be applied to a large number of puzzles is not trivial. This does not come as a surprise because of the subjective factors involved in defining difficulty. However, studies that address the design and application of metrics that can be applied to multiple games are still important, because they can still be used to increase the quality of content generated by automated agents. We take a look at applying information entropy concepts in the game Asteroid Escape and compare the results with other metrics to assess difficulty used in games of similar characteristics. This approach is largely based on future work proposal from a previous study done on The Witness puzzles that yielded positive results on using an automatic method to assess the difficulty of user rated puzzles. This study also includes a modification of BFS and A* capable of finding every shortest solution. From the implementation, it is possible to look at contradicting results with the literature, where basic metrics had a better result correlating with an official definition of difficulty from the game designers. The impact expected from this study is to propose an update to the the corpus of puzzle games where the uncertainty metric for difficulty has been applied. As having an accurate assessment of difficulty can open the doors to other studies, such as improving the quality of the output from procedural content generation methods in games or propose an automated method of difficulty adjustment based on this metric.

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Evaluating Standard Search Enhancements Performance in Zen Puzzle Garden

Evaluating Standard Search Enhancements Performance in Zen P...

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CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Hans Schaa Jose A. del Solar-Zavala Nicolas A. Barriga Doctoral Program in Engineering Systems Universidad de Talca Curicò Chile Faculty of Engineering Universidad de Talca Talca Chile

Sokoban-type puzzles and their variants involve complex planning and decision-making challenges in wide and deep search spaces. Such scenarios are interesting for testing path-planning algorithms. This document uses A * and IDA * (Iterative Deepening A *) algorithms to solve Zen Puzzle Garden game boards; a Sokoban-type puzzle where you need rake the sandy area completely. The algorithms are evaluated in terms of speed and node expansion in moderately complex boards. Additionally, we implement three improvements: transposition tables, move ordering, and deadlock tables. The results show that IDA * with the three proposed improvements is on average faster than A *. Although its strength is its low memory usage, it can be seen that it is very competitive versus A * in terms of time usage. Transposition tables and deadlocks tables reduce by 500 times the number of nodes expanded by the IDA * without improvements. On the other hand, the ordering of nodes is not very effective for some boards, which causes a worse performance of the IDA * algorithm, giving rise to greater exploration in boards that do not lead to the solution. Studying these algorithms can be beneficial in the field of path-finding for Sokoban-type game domains or solver-based procedural content generators.

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Development of a Hybrid Actuation Hand Exoskeleton with Redundant Compliant Mechanisms 16

Development of a Hybrid Actuation Hand Exoskeleton with Redu...

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Shu, Bairui Sun, Zhenyu Zhang, Xinyu Yokoi, Hiroshi Jing, Xiaobei Li, Guanglin Yong, Xu The Siat Branch Shenzhen518055 China The University of Electro-Communications Department of Mechanical Engineering and Intelligent Systems Tokyo182-8585 Japan The University of Electro-Communications Joint Doctoral Program for Sustainability Research Tokyo182-8585 Japan

ISBN: (纸本)9798350330755

Exoskeleton robots are currently widely used in the field of rehabilitation medicine to effectively assist patients with hand function loss in their rehabilitation training. To accommodate the physiological characteristics of the hand, particularly the deviation of the joint rotation axis, this paper proposes a hybrid-driven hand exoskeleton system with redundant compliant mechanisms and conducts motion and force analysis on it. The exoskeleton system can independently drive three fingers and drive their joints separately. To verify whether the exoskeleton system meets the rehabilitation requirements, angle verification experiments and object grasping experiments were conducted. The results showed that the maximum joint range of motion of the exoskeleton system reached 66 °, with an average joint range of motion of 55 ° , fully meeting the rehabilitation needs. © 2023 IEEE.

关键词： Patient rehabilitation

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Hydrogel derived from water hyacinth and pectin from banana peel as a membrane layer

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Materials Today: Proceedings 2023年 87卷 13-17页

作者： Elma M. Kadek Devi Ananda Saraswati N. Fransiska Afrida Simatupang P. Febriyanti R. Rahma A. Mustalifah F.R. Chemical Engineering Department Engineering Faculty Lambung Mangkurat University Jl A. Yani Km 36 Banajrbaru 70714 South Kalimantan Indonesia Magister Program of Chemical Engineering Faculty of Engineering Lambung Mangkurat University Jl. A. Yani KM 36 Banjarbaru 70714 Indonesia Doctoral Program of Environemntal Science Postgraduate Program Lambung Mangkurat University Jl Brigjen H. Hasan Basri Kayutangi Banjarmasin 70123 South Kalimantan Indonesia ReG) Lambung Mangkurat University Jl. A. Yani KM 36 Banjarbaru 70714 Indonesia

Hydrogels is a unique material that affordable to apply as a membranes layer. In tropical country, such as Indonesia there is easily found water hyacinth (a weed plant) which is potentially used for hydrogels materials fabrication due to high cellulose content. Carboxymethyl cellulose (CMC) from water hyacinth cellulose and banana peels pectin are used to produce hydrogel for ultrafiltration membrane. The aims of this study are to characterize the functionalization of CMC-pectin membrane with different ratio using Fourier transform infrared spectroscopy (FTIR) and to determines the morphology of its membranes structure. The CMC was synthesized from water hyacinth stems through carboxymethylation. The functionalization group of multiple CMC-pectin membrane ratio is domination with carboxyl group (COO-), carbonyl stretching and carbon group. Meanwhile, CMC-pectin 70:30 wt% ratio displays not smooth structure. There is a water hyacinth stems extracted cellulose fibre on the surface of hydrogel CMC:pectin membrane. In other hand, the addition of pectin in all ratio variation exhibit increasing contact angle number from 63.98° to 68.16° (0-30 wt% pectin). The existence of pectin from banana peels on hydrogel membranes contributes to increase hydrophobicity of hydrogel-membrane, in spite of the contact angle value is below than 90°. 2023 The Authors. All rights reserved.

关键词： Carboxymethylcellulose Hydrogel Membrane Peat water Pectin Water hyacinth

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Path Planning for an Autonomous Vehicle based on the Ant Colony Algorithm: A Review

Path Planning for an Autonomous Vehicle based on the Ant Col...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Desi Windi Sari Suci Dwijayanti Bhakti Yudho Suprapto Department of Electrical Engineering Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Doctoral Program in Engineering Science Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

Efficient path planning technology for autonomous vehicles not only yields significant time savings but also contributes to reducing fuel usage. Various methodologies have been introduced and documented in the literature concerning the path planning of autonomous vehicles. While these methodologies may not ensure an optimal solution, they have demonstrated successful applications in their respective contexts, especially the ant colony algorithm. Nevertheless, not many papers discuss the literature review from the viewpoint of ant colony-based path planning. This paper aims to provide a comprehensive review encompassing the modeling, optimization criteria, and solution algorithms related to the ant colony for path planning. In this study, the review of the ant colony algorithm is divided into global and local path planning. Based on the review results, the ant colony algorithm appears suitable for global path planning. To achieve better performance, it can be combined with other algorithms such as artificial potential field (APF), genetic algorithm, and particle swarm algorithm. These studies are conducted solely in simulations. Therefore, further research should be carried out to ensure the performance of combining the ant colony and APF for autonomous vehicles for both global and local path planning.

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