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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kumar, Hardik Bhaidasna, Hetal Patel, Kajol Parul Institute of Engineering and Technology Computer Engineering Vadodara India Parul Institute of Engineering and Technology-Diploma Computer Engineering Vadodara India

ISBN: (纸本)9798331523923

The rise of Bluetooth tech & wearable gadgets has greatly changed our personal lives. These devices offer amazing convenience & help us connect in many areas like healthcare, fitness, and daily activities. But, with this fast growth, we need to think about important & privacy issues too. Bluetooth, which many people use to communicate wirelessly, faces various security risks. There are threats like eavesdropping, man-in-the-middle attacks, and device spoofing. All these vulnerabilities could lead to unauthorized access, data leaks, or even messing up how devices work. Wearable devices are especially enticing for cyber attackers since they collect & share sensitive personal information, including health metrics and location info. Protecting these gadgets is important because if something goes wrong with the data, it doesn't just affect personal privacy;it can also impact safety & well-being. This review paper takes a close look at the security challenges that come with Bluetooth and wearable gadgets. It dives into main security threats and vulnerabilities, discussing how attackers might exploit these weaknesses to gain access or disrupt device operations. Plus, it highlights the consequences of security breaches in wearables. This shows why we need strong security measures to keep our data safe and private. Understanding these security challenges is super important for finding ways to reduce risks. The goal of this paper is to emphasize how vital it is to set up strong security frameworks & good practices. Doing this can help keep our private information safe & make sure users feel secure in a world that's increasingly connected. © 2025 IEEE.

关键词： Differential privacy

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Implementation of Multi Attribute Utility Theory Method for Government Assistance Recipient Recommendation System in Dadapayu Village, Semanu District, Gunung Kidul Regency 15

Implementation of Multi Attribute Utility Theory Method for ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Yumarlin, M.Z. Rahayu, Sri Bororing, Jemmy Edwin Kurniawan, Safar Dwi Fitriastuti, Fatsyahrina Rozak, Muhamad Department of Informatics Faculty of Engineering Universitas Janabadra Indonesia Diploma Program in Computer Engineering Politeknik Harapan Bersama Indonesia

ISBN: (纸本)9798331300579

The Indonesian government is furnishing help in the form of" Bantuan Langsung Tunai(BLT)" or the Direct Cash backing program in order to make up for the recent increase in the price of energy oil painting(BBM), which has easily had an impact on the lives of the destitute and the general public. The BLT program is enforced throughcross-sectoral collaboration, where institutions unite grounded on their separate functions and duties. Aid distribution opinions are grounded on multiple applicable rudiments and aren't primarily determined by one criterion when the MAUT fashion is applied. A system's recommended feasibility score of 78 is attained from the evaluation findings of system testing, which were carried out using a questionnaire exercising the System Usability Scale(SUS) approach. With a score in the "Acceptable" range, the system is supposed good since it's adequately satisfactory.. © Grenze Scientific Society, 2024.

关键词： Electric grounding

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Security in Bluetooth and Wearable Devices: A Survey

Security in Bluetooth and Wearable Devices: A Survey

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Hardik Kumar Hetal Bhaidasna Kajol Patel Computer Engineering Parul Institute of Engineering And Technology Vadodara India Computer Engineering Parul Institute of Engineering And Technology-Diploma Vadodara India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The rise of Bluetooth tech & wearable gadgets has greatly changed our personal lives. These devices offer amazing convenience & help us connect in many areas like healthcare, fitness, and daily activities. But, with this fast growth, we need to think about important & privacy issues too. Bluetooth, which many people use to communicate wirelessly, faces various security risks. There are threats like eavesdropping, man-in-the-middle attacks, and device spoofing. All these vulnerabilities could lead to unauthorized access, data leaks, or even messing up how devices work. Wearable devices are especially enticing for cyber attackers since they collect & share sensitive personal information, including health metrics and location info. Protecting these gadgets is important because if something goes wrong with the data, it doesn't just affect personal privacy; it can also impact safety & well-being. This review paper takes a close look at the security challenges that come with Bluetooth and wearable gadgets. It dives into main security threats and vulnerabilities, discussing how attackers might exploit these weaknesses to gain access or disrupt device operations. Plus, it highlights the consequences of security breaches in wearables. This shows why we need strong security measures to keep our data safe and private. Understanding these security challenges is super important for finding ways to reduce risks. The goal of this paper is to emphasize how vital it is to set up strong security frameworks & good practices. Doing this can help keep our private information safe & make sure users feel secure in a world that's increasingly connected.

关键词： Surveys Data privacy Sentiment analysis Bluetooth Reviews Current measurement Vectors Security Wearable devices Standards

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Smart Home Security System Using Face Recognition Based on IoT- CNN 2

Smart Home Security System Using Face Recognition Based on I...

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2nd International Conference on Information Technology Research and Innovation, ICITRI 2023

作者： Febriantono, M. Aldiki Zuhair, Alvin Khaeruddin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Islam Blitar Electrical Engineering Department Blitar Indonesia University of Muhammadiyah Malang Diploma III Electronic Technology Vocational Study Program Malang Indonesia

ISBN: (纸本)9798350324945

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners. © 2023 IEEE.

关键词： Face recognition

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Twenty years of e-learning in health science: A bibliometric 7

Twenty years of e-learning in health science: A bibliometric

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Raharja, Dwi Risza Budi Khaeruddin Herasmara, Ridho Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index. © 2022 Elsevier B.V.. All rights reserved.

关键词： Publishing

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Twenty years of e-learning in health science: A bibliometric

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Procedia computer Science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

关键词： e-learning health science bibliometric vosviewer education

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Sasirangan Cloth Recognition and Shopping Experience Simulation Based on Virtual Reality Game with Non-Player Character Integration 8

Sasirangan Cloth Recognition and Shopping Experience Simulat...

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8th International HCI and UX Conference in Indonesia, CHIuXiD 2022

作者： Subandi, Subandi Syahidi, Aulia Akhrian Zakiah, Siti Kiyokawa, Kiyoshi Riyadi, Ahmad Noor, M. Helmy Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin Indonesia Universitas Brawijaya Informatics Engineering Undergraduate Program Faculty of Computer Science Malang Indonesia Nara Institute of Science and Technology Cybernetics and Reality Engineering Laboratory Ikoma Japan Politeknik Negeri Banjarmasin Informatics Engineering Diploma Program Banjarmasin Indonesia

ISBN: (纸本)9781665476645

Sasirangan cloth is one of the traditional cloths owned by Indonesia and is a typical cloth originating from the province of South Kalimantan. This Sasirangan cloth has many motifs and is unique. Sasirangan cloth is also one of the potentials that can attract local and foreign tourists to visit South Kalimantan. However, in recent years, tourist visits to South Kalimantan have decreased due to the impact of the COVID-19 pandemic. Then it also has an impact on the delay in the implementation of the Banjarmasin Sasirangan Festival which is usually held annually. The next impact is a decrease in turnover and interest in shopping for Sasirangan cloth, so that employees who work for Sasirangan cloth craftsmen are dismissed and reduce people's income. So the purpose of this study is to report the results of the development of a Virtual Reality Game application called VR-SasiranganKu by integrating the interaction of Non-Player Character (NPC) behavior with the type of narrative in the simulation genre game. VR-SasiranganKu is recommended to introduce and at the same time promote Sasirangan cloth to users, then justify the reaction to knowledge and shopping experience, and evaluate user experience using the UX Honeycomb method. Respondents involved as many as 100 people who are local and national tourists. The results show that the interaction of NPC behavior can provide good direction to users regarding the introduction of Sasirangan cloth by presenting an appropriate interaction design based on the narrative and the theme of the NPC that has been adopted, the results of justification using VR-SasiranganKu can provide knowledge reaction and very good shopping experience, and based on user experience evaluation that VR-SasiranganKu has adopted a very good UX with an average score of 4.91. © 2022 IEEE.

关键词： Virtual reality

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Optimization of Naïve Bayes uses the genetic algorithm for classification data 7

Optimization of Naïve Bayes uses the genetic algorithm for ...

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7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020

作者： Salim, A. Alfian, M.R. Andriani, H. Afifah, N. Department of Plant Production Polytechnic of Jember State Mastrip Street 164 Jember Indonesia Diploma in Computer Engineering University of Mataram Technology Mataram City Indonesia Diploma in Medical Record and Health Information Management Polytechnic of Medica Farma Husada Mataram Mataram City Indonesia Diploma in Information Technology Polytechnic of Mitra Global Jember Jember City Indonesia

Classification is one of the statistical methods to classify data systematically. However, if there is a large amount of data and various features, it often results in low accuracy. For this reason, methods are needed that can handle the data with various types. One method that can handle this problem is Naïve Bayes. Naïve Bayes is one of the methods used for classification data. This method requires a stage of selection of independent variables in increasing the accuracy of the model from Naïve Bayes. So we need an excellent method to fix these deficiencies uses a Genetic Algorithm (GA). Genetic algorithm is one of the metaheuristic methods used in optimization techniques. The data used are septic tank data in East Surabaya with eleven independent for classification data. The result of classification accuracy using Naïve Bayes is 72.7%. When Naive Bayes was used with a genetic algorithm, the classification accuracy was increased is 90.9% © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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IoT-Enabled Traffic Management Systems using CNN-TransLSTM for Next-Generation Smart Cities 3

IoT-Enabled Traffic Management Systems using CNN-TransLSTM f...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Manswini Padhy, Kumari Chattopadhyay, Shouvik Malik, Neeru Patra, Jyoti Prasad Perada, Anvesh Raghapriya, N.R. Centurion University of Technology and Management Department of Cse R.Sitapur India Institute of Engineering and Management University of Engineering and Management Department of Mba Kolkata India School of Engineering and Technology Pimpri Chinchwad University Maharashtra India Nigam Institute of Engineering and Technology Niet UG/PG Diploma Engineering Department of Electrical Cuttack India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA United States Erode Sengunthar Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331508456

The rising quantity of vehicles has intensified traffic congestion, pollution, and road accidents. This paper introduces an IoT-enabled traffic management system utilizing CNN-TransLSTM, a hybrid model that combines convolutional neural networks, LSTM, and transformers for effective traffic prediction. Min-max normalization eliminates outliers and maintains data integrity, whereas feature extraction identifies essential variables such as weather, traffic density, and direction. The model attained a prediction accuracy of 91.25%, exceeding that of individual CNN, LSTM, and Transformer models. This method emphasizes the capacity of smart cities to utilize deep learning and IoT for superior traffic control, resulting in increased efficiency and sustainability. © 2025 IEEE.

关键词： Traffic congestion

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