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8th International Conference on e-Society, e-Learning and e-Technologies, ICSLT 2022

作者： Praga, Dhan Joseph P. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Chuenyindee, Thanatorn School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok Thailand

ISBN: (纸本)9781450396660

Due to constant growth of technology it results to increase efficiency to work remotely, as a result, there is an increase in percentage of I.T. professionals working from home. This study used Conjoint Analysis in order to determine the most preferred work from home setup attributes by I.T. professionals. Using the attributes such as having a dedicated workspace, type of internet connection, type of communication tool, audio tool, accessories for their computer or laptop, type of chair, and frequency of team socials. Using conjoint analysis with orthogonal design, this study was able to evaluate the preference among I.T. professionals. The result of the study showed that the type of Internet Connection is the most important (30.975%). Next is the preference on dedicated workspace (22.633%), the type of accessories (13.059%), Communication tool (10.982%), frequency of team socials (9.152%), type of chairs (6.723%), least is the type of audio tools (6.477%). The result of this study could be used by the employers to improve the work from home conditions of their employees. © 2022 Association for Computing Machinery.

关键词： Statistical methods

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： John Raphael G. Cuevas John Kenneth R. Rili Anny Neolicia G. Santillan Vivien A. Agustin Mary Grace N. Gonzales Criselle J. Centeno Richard C. Regala Department of Information Technology College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Associate Dean of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Faculty of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Director of Graduate Program of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Computer Lab Administrator of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design.

关键词： Solid modeling Three-dimensional displays Layout Decision making Clustering algorithms Real-time systems User experience Mobile applications Augmented reality Optimization

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Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

Mendel

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Mendel 2023年第2期29卷 261-272页

作者： Supriyadi, Didi Purwanto, Purwanto Warsito, Budi Doctorate Program of Information Systems School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Information System Faculty of Informatics Institut Teknologi Telkom Purwokerto Banyumas Indonesia

The features present in large datasets significantly affect the performance of machine learning models. Redundant and irrelevant features will be rejected and cause a decrease in machine learning model performance. This paper proposes HyFeS-ROS-ANN: Hybrid Feature Selection and Resampling combination method for binary classification using artificial neural network multilayer perceptron (MLP). The first stage of this approach is to use a combination of two feature selection methods to select essential features that are highly correlated with model performance. The second stage of this approach is to use a combination of resampling methods to handle unbalanced data classes. Both approaches are applied to the academic performance classification model using the MLP neural network. This research dataset is obtained using three-dimensional (3D) frameworks such as the Big Five Personality to determine the Personality that affects academic performance from the student dimension, the Family Influence Scale (FIS), which measures factors that affect academic performance from the family dimension, and Higher Education Institutions Service Quality (HEISQUAL) to measure service quality and its influence on academic performance from the Education institution dimension. Previous research shows that the CoR-ANN algorithm has a model accuracy rate of 94%. The research results based on the dataset show that our proposed method can improve accuracy by selecting more relevant and essential features in improving model performance. The results show that the features are reduced from 135 to 108, while the HyFS-ROS-ANN model for binary classification accuracy increases to 100%. © 2023, Brno University of Technology. All rights reserved.

关键词： Feature Selection

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Determination of Planck's Constant Using the Photoelectric Effect Experiment 11

Determination of Planck's Constant Using the Photoelectric E...

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11th National Physics Seminar, SNF 2022

作者： Fitri, U.R. Ziveria, M. Muhara, I. Department of Physics Education Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Information Systems Study Program Kalbis Institute of Technology and Business Jl. Pulomas Selatan No.22 Jakarta Indonesia Chemical Engineering Study Program Faculty of Industrial Technology Institut Teknologi Bandung West Java Bandung Indonesia

Planck's constant experiment has been carried out to determine the value of Planck's constant using the regression analysis method. The steps are carried out using the scientific process outlined in the practical work steps. The specification of the experimental equipment uses the Planck constant experimental set with 12v/35w tungsten halogen light, 15v output, +-0.2% accuracy, 220V power requirement, 0.5A fuse rating and red, yellow 1, yellow 2, and red color filters, green and blue. Experiments using the concept of the photoelectric effect phenomenon began by irradiating metallic materials with light using a filter. The wavelength (635, 570, 540, 500, 460) nm is the light filter variation. The wavelength that hits the metal material is selected by placing a light filter on the light propagation path toward the metal material. So the light that passes through the filter is only light with a certain wavelength. After the metal material is irradiated by light with a certain wavelength, then the stopping potential voltage measurement is carried out on the metal material irradiated by light. From the variation of the light filter used, it is possible to calculate Planck's constant using the regression method with the value obtained 6.26x1034. This value is very close to the actual Planck's constant value which is 6.62x1034 so an error value is obtained 4.23%. With 95.77% accuracy, Planck's constant value obtained from the calculation using the linear regression method can be said to be very close to the actual Planck's constant value. © Published under licence by IOP Publishing Ltd.

关键词： Photoelectricity

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Localization of a Crack in Moving Cylindrical Ferromagnetic Rods by Measuring the Fourier Coefficients of the Leakage Magnetic Flux

Localization of a Crack in Moving Cylindrical Ferromagnetic ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Kanta Shiku Masafumi Kuromizu Yuji Gotoh Takaaki Nara Graduate School of Information Science and Technology The University of Tokyo bunkyo-ku Japan Division of Materials Science and Production Engineering Graduate School of Engineering Oita University Oita Japan Intelligent Mechanical Systems Program Faculty of Science and Technology Oita University Oita Japan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Electromagnetic nondestructive inspection for high-speed moving ferromagnetic materials is necessary for manufacturing efficiency. In this paper, we present a novel leakage magnetic flux (LMF) method to estimate the crack position in a moving cylindrical ferromagnetic rod. The method directly measures the first-order Fourier coefficients of the LMF moving with the crack by using only two coils with DC excitation. The location of a crack is estimated by the proposed method, as demonstrated for an S45C cylindrical rod.

关键词： Location awareness Coils Magnetic flux Inspection Ferromagnetic materials Manufacturing Electromagnetics

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Preference Location of Assignment engineering Professionals in the Middle East: A Conjoint Analysis Approach 22

Preference Location of Assignment Engineering Professionals ...

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4th International Conference on Management Science and Industrial engineering, MSIE 2022

作者： Pineda, Jan Russell P. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Chuenyindee, Thanatorn Ayuwati, Irene Dyah School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines Entrepreneurship Department Binus Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok10220 Thailand Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Information Technology Institut Teknologi Telkom Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

This study examines the preference of location of assignment of engineering professionals working in the Middle East. A survey method that allows for the use of structured questionnaire was adopted. The study was conducted to engineering professionals with a total of 55 participants. Data gathered was coded and analyzed using Conjoint Analysis. Results revealed that location of assignment in design offices or cities is still preferred over remote area assignments with attractive salary and other additional compensation packages. The research results recommend employers to also consider non-monetary aspects in order to retain valued employees in their companies. © 2022 ACM.

关键词： Location

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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PBF-AMP-Onto: an ontology for powder bed fusion additive manufacturing processes 1

PBF-AMP-Onto: an ontology for powder bed fusion additive man...

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1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024

作者： Pour, Mina Abd Nikooie Tarafder, Prithwish Wiberg, Anton Li, Huanyu Moverare, Johan Lambrix, Patrick Department of Computer and Information Science Linköping University Linköping Sweden Swedish e-Science Research Centre Linköping University Linköping Sweden Department of Management and Engineering Linköping University Linköping Sweden Wallenberg Initiative Materials Science for Sustainability Linköping University Linköping Sweden Wallenberg AI Autonomous Systems and Software Program Linköping University Linköping Sweden

Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings) or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research and industry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semanticsaware data management, and enhance the understanding and optimization of additive manufacturing processes. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph. © 2024 CEUR-WS. All rights reserved.

关键词： Ontology

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction 11

Using LSTM and Regression Analysis for Railway Passenger Tra...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Tey, Fu Jie Tuan, Chih-Chi Cai, Cheng-En Wu, Ming-Xuan Wu, Yun-Sheng Wu, Tin-Yu National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan

ISBN: (纸本)9781665492324

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data. © 2022 IEEE.

关键词： Long short-term memory

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