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All Journal Socio-Economic and Humanistic Aspects for Township and Industry English and Tourism Studies
Amitesh Kumar Singh
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Religion Arts Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Security and Privacy in Healthcare Applications Using Federated Learning - Review Amitesh Kumar Singh; Aishwarya M; Shishir Shastry BH; Usharani MP
Socio-Economic and Humanistic Aspects for Township and Industry Vol. 3 No. 1 (2025): Socio-Economic and Humanistic Aspects for Township and Industry
Publisher : Tinta Emas Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/sehati.v3i1.371

Abstract

This review explored data security and privacy in Internet of Health Things (IoHT) networks, focusing on local training for AI models and Federated Learning (FL) to ensure privacy and integrity. This highlights the importance of leveraging technological advancements for disease prediction and data exchange. Functional programming can be used in various industries and applications under real-world scenarios. In the medical industry, safeguarding the confidentiality of patient records and medical status is essential. This is where collaborative or federated learning becomes relevant. On the other hand, creating an intelligent system that helps medical personnel without exposing data can result in a Federated Learning concept. An example is an AI-based intelligent system for diagnosing brain tumors, which can effectively operate within a teamwork setting. Advancements in smart devices and applications designed for the Internet of Healthcare Things have created an ideal setting for implementing Machine Learning techniques. Nevertheless, conventional ML solutions work by collecting and processing data in a centralized manner. Federated Learning (FL) offers a potential solution for training machine-learning models on numerous separate devices without the need to share private data. As a result, FL provides a secure framework for managing extremely sensitive data in the context of the IoHT. This survey provides a detailed overview of new data security and privacy tools for Federated Learning in Internet of Health Things networks. Initially, we introduced the fundamental concepts of Federated Learning (FL) as it is applied to the IoHT.
Determinant Factors Affecting the Number of Tourist Visits to Gili Trawangan, North Lombok Iwan Harsono; Ahmad Jouhari; Neelu Seetaram; Amitesh Kumar Singh
English and Tourism Studies Vol. 2 No. 2 (2024): December
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/ets.v2i2.376

Abstract

This research aims to determine the effect of tourist income, travel time, travel costs on the number of tourist visits to Gili Trawangan. Data collection was carried out using primary data (questionnaires) which were filled out by visitors to tourist attractions on Gili Trawangan. Data processing uses classical assumption tests (normality test, reliability test, heteroscedasticity test, multicollinearity test), multiple linear regression and hypothesis testing. The research results show that the coefficient of determination (R2) is 0.72. The research results simultaneously show that tourist income, travel time, travel costs and attractiveness have a significant effect on the number of tourist visits. The research results show that partially income, travel time, travel costs and attractiveness have a significant effect on the number of tourist visits. It is hoped that tourist attraction managers will continue to innovate so that they always have attractions that can make tourists come back to tourist attractions on Gili Trawangan.
Co-Authors Ahmad Jouhari Aishwarya M Iwan Harsono Neelu Seetaram Shishir Shastry BH Usharani MP