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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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Design of a Multifactor Unidentified Remote End User Authentication Mechanism for IoT Network

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第10期49卷 191-204页

作者： Sharma, Neha Dhiman, Pankaj Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat H. P. Solan India

The rapid proliferation of Internet of Things (IoT) devices, coupled with the rollout of advanced 5G networks, has generated significant concerns regarding security breaches. These concerns stem from the expanded attack surfaces that come with improved connectivity, making IoT systems more vulnerable to malicious threats. A crucial strategy to counter these security challenges is the implementation of robust user authentication methods. Despite numerous proposals for multi-factor authentication mechanisms, many of these systems exhibit weaknesses, particularly in their susceptibility to user impersonation attacks and the risks posed by stolen mobile devices. Furthermore, several schemes fail to incorporate essential features such as session key agreements or backup solutions for instances of lost or stolen devices and compromised private keys. To address urgent security challenges in IoT environments, we developed a three-factor user authentication system tailored for low-cost IoT devices. This system tackles critical vulnerabilities while maintaining low computing and communication costs, enhancing security without compromising usability and efficiency. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 5G mobile communication systems

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Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography

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International Journal of Advanced computer science and Applications 2025年第4期16卷 542-551页

作者： Maria, Mahfuza Akter Akhand, M.A.H. Kamal, Abdus Samad Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Graduate School of Science and Technology Gunma University Kiryu376-8515 Japan

In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity’s patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency. © (2025), (science and Information Organization). All rights reserved.

关键词： Emotion Recognition

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A Comprehensive Survey on Clinical Models for AI-Powered Medical Applications 4

A Comprehensive Survey on Clinical Models for AI-Powered Med...

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

作者： Javagal, Bhavya N Sharma, Sonal Department of Computer Science & Engineering Karnataka Bengaluru562112 India

ISBN: (纸本)9798331523923

Automatic Clinical Text Identification and Classification is a Natural Language Processing (NLP) technique for decoding clinical texts to uncover hidden information. Clinical text analysis has a wide range of applications, including document classification based on clinical significance, disease diagnosis using Electronic Health Records (EHRs), medical chatbots, and more. The widespread application of clinical text analysis has pushed researchers to automate medical text analysis. NLP can capture, process, and organize information from unstructured text data with minimal manual effort, allowing employees to focus on more productive tasks. While NLP is effective for handling unstructured text, the vast amount of medical data requires more advanced methods. To process such large volumes of data, a Large Language Model (LLM), a type of NLP, achieves more effective results. This review aims to provide comprehensive guidance on clinical text classification, starting with unstructured text collection and progressing to the development and evaluation of LLMs. Over 40 research articles are reviewed. Although several LLM models are available, the review focuses on Clinical BERT, Clinical BigBird, and Clinical Longformer. These three models are evaluated based on various characteristics, including model size, architecture, performance, error-handling capability and more. The review helps in determining the most appropriate LLM based on clinical requirements. This review article will be a valuable resource for researchers working on clinical text classification. © 2025 IEEE.

关键词： Electronic health record

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Comprehensive insights into healthcare IoT: the role of machine learning and deep learning approaches

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Multimedia Tools and Applications 2025年 1-42页

作者： Kumar, Atul Dewang, Rupesh Kumar Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Conventional healthcare systems have long struggled to address the varied needs of large patient populations, often leading to inefficiencies and less-than-optimal outcomes. Yet, the advent of machine learning (ML) and deep learning (DL) models has heralded a transformative shift toward value-based care, enabling healthcare providers to offer personalized and remarkably effective treatments. Contemporary medical equipment and devices are equipped with internal applications that gather and store extensive patient data, forming a valuable resource for ML-powered predictive *** have conducted a systematic literature review (SLR) which mainly focuses on Theoretical background, challenges, Application framework, Organization of IoT in healthcare and HIoT *** the SLR process some efficient methodologies has been used. In the SLR process we have conducted Quality metadata analysis (QMA) which provides the complete statistical analysis by considering different *** learning algorithms enable the analysis of the extensive data produced by IoT devices, uncovering patterns and irregularities that may escape human detection. Deep learning, leveraging sophisticated neural network models, enhances the accuracy of medical image interpretation, disease prognosis, and forecasting patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diagnosis

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Modeling a novel modified DeepLabV3Plus network for localization of tampered region in an image and video

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Multimedia Tools and Applications 2025年 1-28页

作者： Anbu, T. Joe, M. Milton Murugeswari, G. Department of Computer Science and Engineering Manonmaniam Sundaranar University Tamil Nadu Tirunelveli India Tamil Nadu Tirunelveli India

Digital Image and Digital Video forgery describes the process of manipulating or modifying digital photos or videos in order to trick viewers or misrepresent reality. Due to the widespread availability of multimedia editing tools, it becomes simple to manipulate digital images and videos which cause many problems in real life. Using the manipulated images or videos people might spread wrong information or fake news and also can cause trouble in courts, hurt people’s reputation or lead to financial scams. The challenge of detecting and localizing forged regions in images and videos has become increasingly significant. Existing forgery detection techniques often rely on format-dependent forgery localization approach and also inefficient in feature extraction. In this research, we propose a novel methodology for localizing forged regions in an image and video through semantic segmentation using Modified DeepLabV3Plus Network. The DeepLabV3Plus Network is modified using MobileNetV2 network and Attention layer. This work improves feature extraction by empowering the network to actually focus on appropriate spatial regions and catch both local as well global features effectively and also it works on different format images. The result demonstrates that the tampered regions are localized accurately in tampered image and video. The experimental evaluations on diverse dataset demonstrates the proposed model’s superior performance in accurately localizing forged regions with an global accuracy of 98.56% and 98.07% in an image and video, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Text Summarization for Light Weight Devices

Text Summarization for Light Weight Devices

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Akshatha, K. Praveen, M. Taleki, Shravan Rakshith Yadav, K V Nataraj Revathi, T. Alliance University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

In this paper, we propose a text summarization method for low-compute devices with limited hardware capabilities like smartphones, tablets and IOT devices. Traditional methods for text summarization face significant challenges in the settings where these devices are used, given their limited computational power and memory. We present optimization algorithms that target reducing computation and memory effort whilst retaining quality in the summarization. Our solution focuses on lightweight, efficient methods to runtime-compress the summaries generated by a powerful but heavy model -in order to allow them to be computed with high mobility and little energy consumption by small devices- for example through techniques of model compression, quantization or similarity detection in datasets- thus achieving tractable abstraction of useful information from digital content [2], enhancing their accessibility experience while taking advantage of relevant mobile behaviors. Our approach enables efficient summarization with high performance and minimal resource costs, as shown by the corresponding experimental results. © 2025 IEEE.

关键词： Edge Computing Lightweight Devices Model Compression Natural Language Processing Quantization Text Summarization

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DualGuard: Obfuscated Federated Learning with Two-Party Secure Robust Aggregation

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IEEE Internet of Things Journal 2025年第11期12卷 16595-16609页

作者： Pan, Hao Bao, Haiyong Guan, Menghong Li, Zhiqiang Huang, Cheng Dai, Hong-Ning East China Normal University Software Engineering Institute Shanghai200062 China Fudan University School of Computer Science Shanghai200438 China Hong Kong Baptist University Department of Computer Science Hong Kong

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, data privacy leakage and Byzantine clients are common challenges in the FL aggregation phase. While extensive research has been conducted to explore defenses for these risks independently, there is a notable lack of scholarly work on integrated defense strategies to address both challenges simultaneously. To bridge this gap, we propose a novel Two-Party Secure Robust Aggregation (TPSRA) framework. The critical insight of TPSRA is to couple client-side gradient obfuscation with server-side secure two-party computation to achieve robust and private FL aggregation. Specifically, clients obfuscate and split local gradients using matrix theory, while servers utilize a novel secure multi-party computation protocol based on mutually orthogonal matrices to preserve the privacy of local gradients. Additionally, TPSRA designs and integrates state-of-the-art robust aggregation algorithms into compatible sub-protocols, enabling efficient parallel computation. This establishes a highly efficient and versatile secure robust aggregation framework for FL. Experiments demonstrate that our TPSRA framework not only effectively resists gradient leakage attacks and detects malicious gradients, but also exhibits superior computational and communication efficiency. We also prove theoretically that TPSRA is secure under the semi-honest adversary model. © 2014 IEEE.

关键词： Federated learning

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Using the Digital Twin Collage Model in CollectiveEyes 26th

Using the Digital Twin Collage Model in CollectiveEyes

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26th International Conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Kimura, Risa Nakajima, Tatsuo Department of Computer Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9783031780929

This paper introduces an application of the digital twin-collage model in CollectiveEyes, a digital platform designed to facilitate the collective sharing of diverse human perspectives and the development of innovative services. The digital twin-collage model represents the real world from the perspective of human vision. By conceptualizing the world as a collection of nudges and leveraging information technology to make these nudges explicit, the platform can create services that influence people’s decision-making and thought processes. The paper also showcases two case studies in which the digital twin-collage model was applied to examine CollectiveEyes, highlighting the potential for developing innovative services based on collective human perspectives. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Choice Architecture Digital Twin Nudges Sharing Economy World Model

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TippyMirror: Recollecting People’s Positive Moments Based on Mirror Metaphor 26th

TippyMirror: Recollecting People’s Positive Moments Based o...

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26th International Conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Kato, Yukina Kimura, Risa Nakajima, Tatsuo Department of Computer Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9783031780929

Reflecting on positive moments in daily life can amplify emotions and improve well-being. In this study, we develop a life-logging service called TippyMirror, focusing on a display method using the mirror metaphor. TippyMirror helps people collect and reflect on their daily positive moments. This study compares TippyMirror with TippyVR-Plus, which uses Virtual Reality (VR) technology, from psychological and motivational perspectives, where previous studies have shown that VR technology has strong effects on emotions. The experiment showed that TippyMirror has similar effects to TippyVR-Plus in reducing negative emotions. While TippyVR-Plus was better at amplifying positive emotions, the introduction of a function focusing on individual positive moments indicates that TippyMirror can provide effects similar to TippyVR-Plus. For that, we found that a contact-less interaction method such as gaze-based interaction should be added to TippyMirror. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Virtual environments

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