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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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A Game-theoretic Approach for Dynamic Service Scheduling at Charging Facilities

arXiv

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arXiv 2022年

作者： Hajibabai, Leila Mirheli, Amir The Department of Industrial and Systems Engineering North Carolina State University NC27695 United States The Operations Research Program North Carolina State University NC27695 United States

Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies. Copyright © 2022, The Authors. All rights reserved.

关键词： Scheduling

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Wahyu Haris Kusuma Atmaja Yaya Heryadi Ilvico Sonata Lukas Wayan Suparta Computer Science Department Binus Graduate Program - Computer Science Bina Nusantara University West Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy.

关键词： Support vector machines Java Accuracy Web and internet services Artificial neural networks Predictive models Internet Broadband communication Optical fiber subscriber loops Robots

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Vitrification of hazardous wastes from ship recycling: circular economy solutions for asbestos and synthetic vitreous fibers

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Environmental Science and Pollution research 2025年第13期32卷 8183-8200页

作者： Pereira, Newton Narciso de Castro Bertagnolli, Denise da Silva, Ladário Faria, Eliel Eleutério Junior, José Mauro Moraes de Araujo de Monteiro Barros, Munique Eva Paiva Miranda, Tainara Alves de Oliveira Lima, Eduardo da Silva Almeida, Vitor Duarte Department of Production Engineering School of Industrial Engineering and Metallurgy at Volta Redonda - EEIMVR – Federal Fluminense University UFF Av. dos Trabalhadores 420 – Sala C77 Vila Sta. Cecília RJ Volta Redonda27255-125 Brazil Department of Chemistry Exact Sciences Institute Federal Fluminense University UFF Rua Desembargador Ellis Hermydio Figueira 783 RJ AterradoVolta Redonda27213-145 Brazil Department of Physics Exact Sciences Institute Federal Fluminense University UFF Rua Desembargador Ellis Hermydio Figueira 783 RJ AterradoVolta Redonda27213-145 Brazil Department of Physics Universidade Federal de Roraima Av. Cap. Ene Garcez N. 2413 Paricarana Roraima Boa Vista69310-000 Brazil Departament of Metallurgical Engineering Federal Fluminense University UFF Av. dos Trabalhadores 420 Vila Sta. Cecília RJ Volta Redonda27255-125 Brazil Physics Post-Graduate Program Brazilian Center for Physics Research Rua Doutor Xavier Sigaud 150 Urca RJ Rio de Janeiro22290-180 Brazil Physics Post-Graduate Program Physics Institute Federal Fluminense University UFF Av. Gal. Milton Tavares de Souza S/nº Praia Vermelha Campus RJ Niterói24210-346 Brazil

Ship recycling at the end of its useful life presents significant environmental and health challenges due to the presence of hazardous materials, such as asbestos and synthetic vitreous fibers (SVFs). This study aimed to convert SVFs and asbestos into safe, reusable co-products by incorporating them into a glassy matrix. The materials were melted at 1200 °C for 1 h with utilization of niobium pentoxide (Nb2O5) to lower fusion temperatures. Samples collected from an offshore vessel and platform being recycled in Rio de Janeiro, Brazil, were analyzed using energy-dispersive x-ray spectroscopy (EDS) and scanning electron microscopy (SEM), revealing the presence of SVFs but not asbestos. Seven types of glass were produced, with their non-crystalline, glassy nature confirmed by the presence of an amorphous halo in X-ray diffraction (XRD) and a glass transition observed in differential scanning calorimetry (DSC). These findings demonstrate the potential to transform hazardous waste into value-added products, advancing a circular economy in ship recycling operations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Asbestos

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Structural Analysis of Thumb Mechanism Exoskeleton Design for Post-Stroke Rehabilitation

Structural Analysis of Thumb Mechanism Exoskeleton Design fo...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and industrial Automation (ICAMIMIA)

作者： Rika Dwi Hidayatul Qoryah Harus Laksana Guntur Hendro Nurhadi Handri Prastyo Franciscus Xaverius Kristianta Shendy Chandika Graduate Program of Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Center of Excellence for Mechatronics and Industrial Automation Research Center (PUI-PT MIA-RC) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Undergraduate Program Mechanical Engineering Department University of Jember Jember Indonesia Mechanical Engineering Department University of Jember Jember Indonesia

Stroke is the second cause of death and the third cause of disability in the world. Stroke is a disease caused by a clot in the artery that supplies blood to the brain (ischemia). Rehabilitation is a solution to restore the function of parts of the body that have been disturbed by a stroke. Therefore, exoskeleton robots for stroke therapy are one solution to provide good rehabilitation. The exoskeleton robot supports healthy human hand movements and considers the safety and comfort factors during rehabilitation. The exoskeleton in this research uses a cable and sheath transmission mechanism with a locally situated motor as a design mechanism that guarantees safety and comfort. The design strength can be determined using ANSYS static structural assistance software so that the maximum stress in links 1, 2, and 3 is known within safe limits with anthropometric loading. Apart from that, the static structural simulation results show that link 1 can withstand a maximum load of 20 N or more in the X-axis loading, a load of 15 N in the Y-axis loading, and a load of 15 N in the Y-axis loading, and a load of 10 N in the Z-axis loading. Link 2 can withstand a maximum load of 10 N in the X-axis load and 20 N or more in the Y-axis load. Meanwhile, link 3 can withstand a maximum load of 20 N in the X-axis load and below 10 N in the Y-axis load.

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Online Allocation and Learning in the Presence of Strategic Agents

arXiv

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arXiv 2022年

作者： Yin, Steven Agrawal, Shipra Zeevi, Assaf Department of Industrial Engineering and Operations Research Columbia University New YorkNY10027 United States Graduate School of Business Columbia University New YorkNY10027 United States

We study the problem of allocating T sequentially arriving items among n homogeneous agents under the constraint that each agent must receive a pre-specified fraction of all items, with the objective of maximizing the agents’ total valuation of items allocated to them. The agents’ valuations for the item in each round are assumed to be i.i.d. but their distribution is a priori unknown to the central planner. Therefore, the central planner needs to implicitly learn these distributions from the observed values in order to pick a good allocation policy. However, an added challenge here is that the agents are strategic with incentives to misreport their valuations in order to receive better allocations. This sets our work apart both from the online auction design settings which typically assume known valuation distributions and/or involve payments, and from the online learning settings that do not consider strategic agents. To that end, our main contribution is an online learning based allocation mechanism that is approximately Bayesian incentive compatible, and when all agents are truthful, guarantees a sublinear regret for individual agents’ utility compared to that under the optimal offline allocation policy. © 2022, CC BY.

关键词： E-learning

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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Computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： Computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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Computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： Computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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A blockchain-based dynamic energy pricing model for supply chain resiliency using machine learning

Supply Chain Analytics

引用

Supply Chain Analytics 2024年 6卷

作者： Abadi, Moein Qaisari Hasan Sadeghi, Russell Hajian, Ava Shahvari, Omid Ghasemi, Amirehsan Department of Operations Management & Operations Research ESSEC Business School Cergy 95000 France Department of Information Technology and Decision Sciences G. Brint Ryan College of Business University of North Texas Denton TX 76203 United States School of Industrial Engineering College of Engineering University of Tehran Tehran 14399 Iran Department of Information Science College of Information University of North Texas Denton TX 76203 United States Department of Industrial and Systems Engineering College of Engineering University of Missouri Columbia 65211 MO United States The Bredesen Center for Interdisciplinary Research and Graduate Education University of Tennessee Knoxville TN 37996 United States

The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the autoregressive integrated moving average (ARIMA) algorithm has the highest accuracy compared to other algorithms. © 2024 The Authors

关键词： Blockchain Energy consumption management Machine learning Power outage Supply chain resiliency

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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