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Preliminary Plan for Data Collection on Stakeholder information Interdependence in Area-Business Continuity management: A Case Study of Flood management in Thai Industrial Zones

Preliminary Plan for Data Collection on Stakeholder Informat...

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International Symposium on Communications and information Technologies (ISCIT)

作者： Penpitcha Arayachookiat Kumpol Saengtabtim Akira Kodaka Natt Leelawat Jing Tang Kenji Watanabe Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Graduate School of System Design and Management Keio University Kanagawa Japan Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Faculty of Engineering International School of Engineering Chulalongkorn University Bangkok Thailand Graduate School of Social Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

The 2011 flood in Thailand exposed significant vulnerabilities in industrial areas, highlighting the necessity for enhanced disaster risk management through Area-Business Continuity management (Area-BCM). This preliminary study focuses on identifying how stakeholders rely on information from each other to improve disaster preparedness and response. The research involves systematically identifying Area-BCM stakeholders and designing interview questions, which are evaluated by experts using the Index of Item-Objective Congruence (IOC) to ensure relevance and clarity. All interview questions surpassed the IOC threshold of 0.5, confirming their effectiveness in capturing information interdependencies. Expert feedback led to refinements in the questions, underscoring the importance of tailored data collection. This initial plan provides a critical foundation for understanding information interdependence and highlights the importance of well-designed data collection methods. The findings have significant implications for developing more resilient disaster management strategies in industrial areas, emphasizing the need for precise and relevant stakeholder communication to enhance Area-BCM effectiveness.

关键词： Disasters Disaster management Data collection information and communication technology Stakeholders Floods Risk management Iterative methods Interviews Resilience

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An approach to parameter optimizer for music mood classification in machine learning

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AIP Conference Proceedings 2025年第1期3223卷

作者： Nunik Destria Arianti T. Thoyyibah Achmad Udin Zailani Neny Rosmawarni Rinna Rachmatika Achmad Lutfi Fuadi Information System University of Nusaputra Sukabumi Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten Indonesia Faculty of Information Technology Veterans National Development University Indonesia

Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an object that can be studied both through lyrics, audio, biographies and others. This research focuses on the mood of Indonesian music in the 70s and 80s. The data set used consists of text data and audio data. The mood tested consisted of sad, happy and neutral. This area of interest was investigated using the Crisp-DM method derived from business understanding, data understanding, data preparation, modelling, evaluation, deployment. In building the Confusion Matrix, this study uses several parameters, namely epoch, dimensions, set size, learning rate and others. The value generated by the confusion matrix contains a good 85 % value.

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Classification machine learning with chromagram and word embedding feature

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AIP Conference Proceedings 2025年第1期3223卷

作者： Thoyyibah Ines Heidiani Ikasari Perani Rosyani Wasis Haryono Sofa Sofiana Neny Rosmawarni Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten South Tangerang Indonesia Faculty of Information Technology Veterans National Development University Jakarta Jakarta Indonesia

Indonesia is a country that is rich in artistic diversity. This art can be a song. In addition to traditional songs, Indonesia is rich in songs from various genres. Some of the genres used by the author are pop, ballad, dangdut. Song or music has become an interesting area of research in computer science. Music recognition is a computational problem in the field of computer vision. Music introduction aims to classify music into several categories such as genre, mood, or music generation. Although in the last ten years a number of studies have been published, the problem of music recognition is still a difficult problem to solve by computers. In addition, it is necessary to explore several features that represent music, including audio spectrograms, chromagrams, and song lyrics. The author processes the lyrics and audio features in the form of a chromagram using many songs. Music can be a collection of data that can be processed by machine learning. The data used for classification is song data in the form of datasets. The author uses his own dataset or manual methods in collecting and processing datasets. The song is downloaded from YouTube after that it is converted into mp3 and cut. The author processed 1181 songs from the 70’s and 80’s era which only took the chorus part. Lyrics in chorus form are saved in excel. The audio chorus is saved in .wav. The results of the Mood classification used by the author in music consist of 3, namely happy, sad and neutral which are processed using machine learning. Using 2 experiments on Chromagram and word embedding feature has a higher value of 81%.

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Model of Sustainable Development Based on FinTech in Financial and Banking Industry: A Mixed-Method Research 3

Model of Sustainable Development Based on FinTech in Financi...

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3rd International Conference on Computer and Informatics Engineering, IC2IE 2020

作者： Legowo, Mercurius Broto Subanidja, Steph Sorongan, Fanky Antoneus Information Technology Faculty Institute of Perbanas Information System Study Program Jakarta Indonesia Post Graduate School Institute of Perbanas Master of Management Program Jakarta Indonesia Economic and Business Faculty Institute of Perbanas Accounting Study Program Jakarta Indonesia

ISBN: (纸本)9781728182476

In the past decade, it stated that the discourse on Sustainable Development and the Financial technology(FinTech) role in the financial and banking industries, has attention on researchers, practitioners, regulators, and policymakers. The real problem arises from the discourse on whether FinTech has an effect on sustainable development in the financial and banking industry in Indonesia. This discourse needed to be understood better and more deeply. For credible research results, it is necessary to use a mixedmethod research approach. This research aims to develop a model that will help uncover the phenomenon of FinTech to sustainable development in the Financial and Banking Industry in Indonesia, using mixed methods research. In this study, Exploratory Sequential Mixed Method used as a type of mixedmethod approach. The main results of this study reveal that FinTech, which is influenced by business drivers, has a significant role in sustainable development in the Financial and Banking Industry in Indonesia. The contribution of this research can provide insight and understanding for researchers and practitioners in the financial and banking industry who want to implement a sustainable development model based on FinTech in more depth. © 2020 IEEE.

关键词： Sustainable development

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Unbiasing on the Fly: Explanation-Guided Human Oversight of Machine Learning System Decisions

arXiv

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arXiv 2024年

作者： Mamman, Hussaini Basri, Shuib Balogun, Abdullateef O. Imam, Abdullahi Abubakar Kumar, Ganesh Capretz, Luiz Fernando Department of Computer and Information Sciences Universiti Teknologi PETRONAS Bandar Seri Iskandar Perak32610 Malaysia Department of Management and Information Technology Abubakar Tafawa Balewa University Bauchi740272 Nigeria School of Digital Sciences Universiti Brunei Darussalam BE1410 Brunei Dept. of Electrical & Computer Eng. Western University London Canada MCS Program Yale-NUS College 138527 Singapore

The widespread adoption of ML systems across critical domains like hiring, finance, and healthcare raises growing concerns about their potential for discriminatory decision-making based on protected attributes. While efforts to ensure fairness during development are crucial, they leave deployed ML systems vulnerable to potentially exhibiting discrimination during their operations. To address this gap, we propose a novel framework for on-the-fly tracking and correction of discrimination in deployed ML systems. Leveraging counterfactual explanations, the framework continuously monitors the predictions made by an ML system and flags discriminatory outcomes. When flagged, post-hoc explanations related to the original prediction and the counterfactual alternatives are presented to a human reviewer for real-time intervention. This human-in-the-loop approach empowers reviewers to accept or override the ML system decision, enabling fair and responsible ML operation under dynamic settings. While further work is needed for validation and refinement, this framework offers a promising avenue for mitigating discrimination and building trust in ML systems deployed in a wide range of domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Integrating educational robot and low-cost self-made toys to enhance STEM learning performance for primary school students

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Behaviour and information technology 2024年第8期43卷 1614-1635页

作者： Hu, Chih-Chien Yang, Yu-Fen Cheng, Ya-Wen Chen, Nian-Shing Department of Information Management Tatung University Taipei Taiwan Department of Applied Foreign Languages National Yunlin University of Science and Technology Douliou Taiwan Department of Early Childhood Education Asia University Taichung Taiwan Institute for Research Excellence in Learning Sciences and Program of Learning Sciences National Taiwan Normal University Taipei Taiwan

The application of STEM (Science, technology, Engineering, and Math) education in solving real-world problems is challenging. To tackle this challenge, a project-based learning approach that integrates robots with cost-effective self-made toys to problem-solving was adopted to assist rural primary school students to apply STEM skills. The purpose of this study was to evaluate the effects of the project-based learning approach which includes lecturing and hands-on activities on primary school students’ STEM learning outcomes and attitudes. An experiment was conducted with 25 primary school students who voluntarily participated in a STEM summer-camp programme. Students were asked to assemble a paper house equipped with an IoT control module and LED light switches, and write and edit robot scripts to produce a robot-based storytelling narrative using the paper house they made as a context. The results show that the project-based learning approach was an effective approach for cultivating primary school students’ STEM knowledge and skills as evidenced from the post-written test. The relationships between the students’ STEM learning outcomes and attitudes were also confirmed by a clustering analysis. Students who had higher learning attitudes also achieved higher STEM learning outcomes;the findings are also supported by the feedback from the open-ended questionnaire items. This study suggests that integrating low-cost self-made toys and robots in project-based learning activities is an effective and practical approach to enhance primary school students’ learning outcomes and learning attitudes in STEM education. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Toys

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IoT Attack Detection and Prevention Through Machine Learning System

IoT Attack Detection and Prevention Through Machine Learning...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Kanishk Agrawal Pavan Kumar Reddy Poli Yash Gupta Garv Juneja Adhip Kishen Bopaiah Dinesh Ashok Arani Department of IET CSE Manglayatan University Aligarh Uttar Pradesh India Managing Information Technology Implementations Program management for fortune 6 Healthcare Company USA Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Delhi India ICT Trainer Trinity College Bangalore India Manglayatan University Aligarh Uttar Pradesh India

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.

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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Improving Team Winning Rate through Experiential and Observational Learning in E-Sports Games: A Multilevel Analysis 42

Improving Team Winning Rate through Experiential and Observa...

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42nd International Conference on information Systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Zhu, Jiantao Xiong, Bingqing Cai, Zhao Lim, Eric School of Management University of Science and Technology of China Jinzhai Road 96 Anhui Hefei China University of Science and Technology of China-City University of Hong Kong Joint Program Jinzhai Road 96 Anhui Hefei China Nottingham University Business School China University of Nottingham Ningbo China 199 Taikang East Road Ningbo China School of Information Systems and Technology Management UNSW Business School SydneyNSW2052 Australia

ISBN: (纸本)9781713893608

Despite participating in a growing number of tournaments, but can e-sports players continuously learn and improve? This question is deeply connected with experiential learning theory since players could acquire experience through repetitive participation but with increasingly smaller marginal returns. Observational learning is touted as a complementary learning mechanism by drawing on external sources of knowledge (or others) to accelerate learning progress. This study hence advances a dynamic and integrated research model that scrutinizes the moderating influence of peer- and rival-based observational learning on the relationship between experiential learning and team performance. Analyzing data gathered on 8,410 teams and 29,078 players in e-Sports games, we reveal that peer-based observational learning attenuates the negative effect of experiential learning on players’ incremental absorptive capacity, whereas rival-based observational learning reinforces the negative relationship between experiential learning and players’ incremental absorptive capacity. Players’ incremental absorptive capacity, in turn, enhances team performance. © 2021 42nd International Conference on information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Sports

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Analysis of Perceptron Quantum Artificial Neural Networks to Classify the Feasibility of Prospective Debtors 1

Analysis of Perceptron Quantum Artificial Neural Networks to...

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

作者： Pujiastuti, Lise Wahyudi, Mochamad Solikhun STMIK Antar Bangsa Information System Study Program Banten Indonesia Universitas Bina Sarana Informatika Faculty of Technical and Informatics Information Technology Study Program DKI Jakarta Indonesia AMIK Tunas Bangsa Pematangsiantar Informatics Management Study Program North Sumatra Indonesia

Bank is a business entity whose activities are collecting funds from the public in the form ofdeposits and channeling them to the public in the form of loans or other forms to improve the livesof many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers usingthe perceptron algorithm with quantum computing. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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