Faculty Publications
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Publications by NITK Faculty
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Item An efficient framework and access control scheme for cloud health care(Institute of Electrical and Electronics Engineers Inc., 2016) Saravana, N.; Rajya Lakshmi, G.V.; Annappa, B.Cloud computing is being a potential role in providing services for utilizing a huge data in various application, as it is ubiquitous. In emerging growth of Cloud services been focused on security issues and optimal data storage used by consumers. Eventually, the Cloud storage is the best way to keep essential business data secure and accessible. Along with that, there are few important feature been considered. i.e( file versioning, automatic sync,collaboration tools, security File Encryption). In our research article, the framework is designed for real-time Healthcare business application to be achieved all the essential features with Inter-Cloud data storage.To do additionally, has been implemented and tested by an efficient CP-ABE (Cipher Policy-Attribute Based Encryption) algorithm for secure data transmission. Outcomes were powerful in a such way that can be promised in a designed framework developed in Python 3 in Charm-Cryptography. © 2015 IEEE.Item Context Aware Trust Management Scheme for Pervasive Healthcare(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Karthik, N.; Ananthanarayana, V.S.Medical sensor nodes are used in pervasive healthcare applications like remote patient monitoring, elderly care to collect patients vital signs for identifying medical emergency. These resource restricted sensor nodes are prone to various malicious attacks, data faults and data losses. Presence of faulty data, data loss in collected patient data may lead to incorrect analysis of patient condition, which decreases the reliability of pervasive healthcare system. The aim of this work is to alert the caregiver and raise the alarm only when the patient enters into medical emergency situation. The proposed scheme also reduces the false alarms and alerts caused by data fault and misbehaving sensor nodes. To achieve this, we introduce a context aware trust management scheme for data fault detection, data reconstruction and event detection in pervasive healthcare systems. It employs heuristic functions, data correlation and contextual information based algorithms to identify the data faults and events. It also reconstructs the data faults and data loss for identifying patient condition. Performance of this approach is evaluated with the help of real data samples collected by medical sensor network prototype of remote patient monitoring application. The experimental results show that the proposed trust scheme outperforms state-of-the-art techniques and achieves good detection accuracy in data fault detection and event detection. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item A novel chaotic modulation approach of packaged antenna for secured wireless medical sensor network in E-healthcare applications(John Wiley and Sons Inc. P.O.Box 18667 Newark NJ 07191-8667, 2020) Jayawickrama, C.; Kumar, S.; Chakrabartty, S.; Song, H.This article first time reports the chaotic modulation approach toward RF signal processing for secured wireless medical sensor network (WMSN) in E-healthcare applications. A Lorenz based chaotic modulation approach is implemented which provides lowest bit error rate (BER). The definite analytical expressions for BER in a differential chaos-shift keying (DCSK) modulation scheme is derived and it predicted good correlation between simulated and theoretical. It is observed that proposed Lorenz chaos-based DCSK modulation scheme is a potential candidate to provide high security in the patient data for WMSN. An off-body UWB slotted antenna is designed which could avoid limitation of short-range distance like implanted ones. The entire work includes numerical, simulated and experimental data in three phases. In first phase, Lorenz chaotic oscillator with electronics compatibility is executed which acts as data acquisition unit and demonstrates two-dimensional and three-dimensional chaos attractors. While in the second phase, analysis of BER achieves value of less than 10?4 by providing pseudorandom bit sequence at 5 Gb/s. A chaos modulated envelope using Lorenz based DCSK modulation is obtained by delay element ?. Finally, the third phase is designed on-wafer off-body antenna and demonstrates 3.1 to 10.6 GHz UWB toward RF signal processing in E-healthcare applications. © 2019 Wiley Periodicals, Inc.Item Challenges in Government Inter-Organizational Information Integration in the Context of Measles Rubella Vaccination in India(IGI Global, 2022) Jayan, V.; Alathur, S.Most of the countries are heading to Government 3.0 with the advent of information communication technology (ICT). Information integration has to be done with the support of different stakeholders for an effective e-governance ecosystem. The use of artificial intelligence (AI) and high-end processors solved the issues to some extent. But the socio-political intervention is making the government interorganizational information integration (GIII) difficult when information turns into misinformation. Misinformation in social network sites (SNS) is increasing alarmingly and is also affecting the healthcare sector. The study is focused on the trends in decreasing vaccination rates in India during the vaccination drive. Twitter data, news reports, and social media posts during the MR vaccination program in India are taken into consideration for the analysis. The vaccine hesitancy is also associated with political, religious, psychological, and economic factors. Government 3.0 has got its power to overcome the misinformation in the healthcare programs. © © 2022, IGI Global.Item Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data(Elsevier Ltd, 2023) Reshma, R.; Kamath S․, S.K.; V.s, A.The phenomenal popularity of social media platforms over the past decade has accelerated the development of intelligent applications that leverage social media data for informed decision-making in diverse domains like finance, education, public policy and healthcare management practices. While understanding the colloquial language of users on social media remains a challenging problem, access to users’ medical perspectives that conversationally divulge healthcare-related experiences and insights can help reshape healthcare ecosystems like chronic disease management, pandemics, public health, pharmacovigilance and more. Most existing models are constrained to a particular dataset while neglecting model adaptability across data sources and domains. Model generalization across variable data sizes also has received very little research attention. Conventional foundation models can be fine-tuned by adding additional model heads or by appending contributing network layers, however, there has been very little focus on effective parameter calibration for adapting neural foundation models to a specific task. In this study, an Adaptive Learning mechanism for Socio-Medical data (AL4SM) built on generic foundation neural models with efficient parameter learning is proposed, to categorize users’ perspectives on prescription drug-related experiences and adapt to diverse socio-medical data sources of variable sizes. AL4SM aims to lighten the over-parameterized mechanisms adopted by existing foundational techniques by efficiently learning latent medical information based on optimized parameter calibration and weight reinitialization techniques. Comprehensive cross-domain and cross-data analyses are undertaken to explore specific user perspectives related to prescription effectiveness and side effects. Validation experiments conducted on standard datasets obtained from Drugs.com and Druglib.com revealed that the proposed AL4SM outperformed state-of-the-art models, achieving an improvement of 6.06% in accuracy and 7.62% in F1-score for 3-class and 2% in F1-score for 10-class drug perspective categorization. The cross-data experiments further emphasized the superiority of the proposed model, with improved accuracy of 17% on Drugs.com and 9% on Druglib.com datasets, respectively. © 2023 Elsevier LtdItem FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments(Institute of Electrical and Electronics Engineers Inc., 2024) Sachin, D.N.; Annappa, B.; Hegde, S.; Abhijit, C.S.; Ambesange, S.The advent of the Internet of Medical Things (IoMT) devices has led to a healthcare revolution, introducing a new era of smart applications driven by Artificial Intelligence (AI). These advanced technologies have greatly influenced the healthcare industry and have played a crucial role in enhancing the quality of life globally. Federated Learning (FL) has become popular as a technique to create models that can be shared universally using the vast datasets collected from IoMT devices while maintaining data privacy. However, the complex variations in IoMT environments, including diverse devices, data characteristics, and model complexities, create challenges for the straightforward application of traditional FL methods. Consequently, it is not well-suited for deployment in such contexts. This paper introduces FedCure, a personalized FL framework tailored for intelligent IoMT-based healthcare applications operating within a cloud-edge architecture. FedCure is adept at addressing the challenges within IoMT environments by employing personalized FL techniques that can effectively mitigate the impact of heterogeneity. Furthermore, the integration of edge computing technology enhances processing speed and minimizes latency in intelligent IoMT applications. Lastly, this research showcases several case studies encompassing IoMT-based applications, such as Eye Retinopathy Detection, Diabetes Monitoring, Maternal Health, Remote Health Monitoring, and Human Activity Recognition. These case studies provide a means to assess the effectiveness of the proposed FedCure framework and showcase exceptional performance with accuracy and minimal communication overhead, especially in addressing the challenges posed by heterogeneity. © 2013 IEEE.Item Federated learning for digital healthcare: concepts, applications, frameworks, and challenges(Springer, 2024) Sachin, D.N.; Annappa, B.; Ambesange, S.Various hospitals have adopted digital technologies in the healthcare sector for various healthcare-related applications. Due to the effect of the Covid-19 pandemic, digital transformation has taken place in many domains, especially in the healthcare domain; it has streamlined various healthcare activities. With the advancement in technology concept of telemedicine evolved over the years and led to personalized healthcare and drug discovery. The use of machine learning (ML) technique in healthcare enables healthcare professionals to make a more accurate and early diagnosis. Training these ML models requires a massive amount of data, including patients’ personal data, that need to be protected from unethical use. Sharing these data to train ML models may violate data privacy. A distributed ML paradigm called federated learning (FL) has allowed different medical research institutions, hospitals, and healthcare devices to train ML models without sharing raw data. This survey paper overviews existing research work on FL-related use cases and applications. This paper also discusses the state-of-the-art tools and techniques available for FL research, current shortcomings, and future challenges in using FL in healthcare. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.Item Smart client selection strategies for enhanced federated learning in digital healthcare applications(Springer, 2025) Sachin, D.N.; Annappa, B.; Ambesange, S.Federated Learning (FL) trains AI models in healthcare without sharing patient data. FL computes client models locally and combines them to create a global model. However, involving all clients is impractical due to resource limitations. Random selection of a subset of clients in each FL round can pose challenges for resource-limited devices, leading to longer processing times and potential training failures. To tackle these obstacles, this research proposes a novel strategy for FL that treats each training round as a client selection process to improve the efficiency and effectiveness of FL in healthcare applications, where data privacy is paramount. The approach begins by calculating the uncertainty value for each client, which quantifies the contribution of the client’s data to the overall model. Clients are then ranked based on their uncertainty values, and those with higher loss values are given a higher probability of participating in the training process. The experimental outcomes clearly show that the proposed strategy effectively makes 1.3x training faster, and 30% lowers communication expenses, conserves computational resources, and enhances model performance when contrasted with random client selection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.