Dr Banee Bandana Das,  Assistant Professor in the Department of Computer Science and Engineering, has achieved a remarkable milestone. The invention titled “An Energy Harvesting System for Node Devices and a Method Thereof” has been granted a patent by the Patent Office Journal, under Application Number: 202241066526. This achievement marks a significant leap forward in the realm of energy harvesting systems, promising a brighter and more secure future for IoT applications.

Abstract

The present invention is broadly related to design of secure and Trojan Resilient energy harvesting system (EHS) for IoT end node devices. The objective is to develop a state-of-the-art energy harvesting system which can supply uninterrupted power to the sensors used in IoT. The EHS is self-sustainable. The higher bias voltages are generated on chip. The system is mainly consisting of security module, power conditioning module, Trojan Resilient module, and load controller module. The power failure of the sensors used in IoT may leads to information loss thereby causing catastrophic situations. An uninterrupted power supply is a must for smooth functioning of the devices in IoT. This invention caters secure power requirements with security issues of IoT end node devices.

Practical Implementation:

The IoT end node devices needs 24*7 power supply and are very sensitive to attacks made by adversaries before and after fabrication. This invention takes care of the power requirement of end node devices with green energy and secure the EHS-IC from adversaries and attacks and therefore can be used by individuals, as powering sensors at remote locations and as part of smart agriculture.

Future research plans:

Design more secure and reliable design for making a IoT smart node smarter and self-Sustainable. Exploring more circuit level techniques and find new way to design more power efficient designs.

Dr Banee Bandana Das, Assistant Professor in the Department of Computer Science and Engineering, has achieved a remarkable milestone. The invention titled “An Energy Harvesting System for Node Devices and a Method Thereof” has been granted a patent by the Patent Office Journal, under Application Number: 202241066526. This achievement marks a significant leap forward in the realm of energy harvesting systems, promising a brighter and more secure future for IoT applications.

Abstract

The present invention is broadly related to design of secure and Trojan Resilient energy harvesting system (EHS) for IoT end node devices. The objective is to develop a state-of-the-art energy harvesting system which can supply uninterrupted power to the sensors used in IoT. The EHS is self-sustainable. The higher bias voltages are generated on chip. The system is mainly consisting of security module, power conditioning module, Trojan Resilient module, and load controller module. The power failure of the sensors used in IoT may leads to information loss thereby causing catastrophic situations. An uninterrupted power supply is a must for smooth functioning of the devices in IoT. This invention caters secure power requirements with security issues of IoT end node devices.

Practical Implementation:

The IoT end node devices needs 24*7 power supply and are very sensitive to attacks made by adversaries before and after fabrication. This invention takes care of the power requirement of end node devices with green energy and secure the EHS-IC from adversaries and attacks and therefore can be used by individuals, as powering sensors at remote locations and as part of smart agriculture.

Future research plans:

Design more secure and reliable design for making an IoT smart node smarter and self-Sustainable. Exploring more circuit level techniques and find new way to design more power efficient designs.

The technological advancements in the medical domain have aided in the effective collection of data such as personal information, clinical history, and disease symptoms of patients. However, the accumulation of massive quantity of data may cause errors in the diagnosis of the disease. A chronic disease dataset may be comprised of numerous symptoms and attributes where not all of them are of equal importance in disease diagnosis. A few of those attributes may be less relevant or redundant.

Through her research, Dr Priyanka from the Department of Computer Science and Engineering proposes metaheuristics driven attribute optimization techniques that can be implemented in optimizing chronic disease datasets to achieve optimal efficiency in disease risk prediction which can help in proper medical diagnosis. Her paper titled “A Decisive Metaheuristics Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risks Assessment” was published in the Q1 journal Computational Intelligence and Neuroscience.

This research can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner. The system model may be used to assist medical experts in the efficient diagnosis of chronic disease risks. In future, the research study can be further enhanced to validate the model on more complex heterogeneous datasets with varying sizes and structures. Also, deep learning methods can be employed using image-based real-time datasets.

Abstract of the Research

Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in precise assessment of chronic disorders risks in patients. In this paper, a novel buffer enabled heuristic a Memory based Metaheuristics Attribute Selection (MMAS) model is proposed, which performs local neighbourhood search for optimizing chronic disorders data. Heart disease, breast cancer, diabetes and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall and f-score metric computed were 96.05%, 94.07% and 95.06% respectively. The model also has a least latency of 0.8 sec. Thus it is demonstrated that chronic disease diagnosis can be significantly improved by heuristics based attribute selection coupled with clustering followed by classification.

Figure 1: The proposed Metaheuristics attribute selector based classification model for chronic disorders detection

Figure 2: Accuracy analysis of MMAS method on different disease datasets

SRM University-AP promotes translational research that can add value to society making lives better. Following the tradition, Dr Priyanka Singh, Assistant Professor and her PhD student Ms Jyothsna Devi from the Department of Computer Science and Engineering have published their recent research work “Region-based Hybrid Medical Image Watermarking Scheme for Robust and Secured Transmission in IoMT” in ‘IEEE Access journal’ (Impact Factor of 3.36).

Dr Priyanka’s research focuses on the healthcare industry that is rapidly transforming medical images into ones that operate in real-time environments (IoMT, IoT, Cloud and so on). The research is proposed to address security and integrity issues in medical image transmission on IoT and edge healthcare applications with a lossless reversible region-based MIW scheme.

In this era of technological advancement, medical images and patient information are widely transmitted through a public transmission channel on the Internet of Medical Things (IoMT) applications. While sharing medical images or electronic patient records (EPR) through a public network, they can get tampered with or manipulated, leading to the wrong diagnosis by the medical consultants. Confidentiality of the patient record is also a major concern. Thus, it is very important to ensure the authenticity, authorisation, integrity, and confidentiality of the information during transmission.

ABSTRACT:

With the growth in Internet and digital technology, the Internet of Medical Things (IoMT) and Telemedicine have become buzzwords in healthcare. A large number of medical images and information are shared through a public network in these applications. This paper proposes a region-based hybrid Medical Image Watermarking (MIW) scheme to ensure the authenticity, authorisation, integrity, and confidentiality of the medical images transmitted through a public network in IoMT. In the proposed scheme, the medical image is segmented into Region of Interest (RoI) and Region of Non-Interest (RoNI).

RoI tamper detection and recovery bits are embedded in RoI to ensure the integrity of the medical image. RoI is watermarked using adaptive Least Significant Bit (LSB) substitution with respect to the hiding capacity map for higher RoI imperceptibility and accuracy in tamper detection and recovery. Electronic Patient Record (EPR) is compressed using Huffman coding and encrypted using a pseudo-random key (secret key) to provide higher confidentiality and payload. Encrypted EPR, QR code of hospital logo and RoI recovery bits are embedded in RoNI using Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) hybrid transforms to achieve a robust watermark.

The proposed scheme is tested under various geometric and non-geometric attacks such as filtering, compression, rotation, salt and pepper noise and shearing. The evaluation results demonstrate that the proposed scheme has high imperceptibility, robustness, security, payload, tamper detection, and recovery accuracy under image processing attacks. Therefore, the proposed scheme can be used in the transmission of medical images and EPR in IoMT. The relevance of the proposed scheme is established by its superior performance in comparison to some of the popular existing schemes.

A research paper titled “Detection of Diabetic Retinopathy (DR) severity from fundus photographs: An Ensemble Approach using weighted average”, has been published by Dr Mahesh Kumar Morampudi, Assistant Professor of Computer Science and Engineering, in the Arabian Journal for Science and Engineering.

Diabetic Retinopathy is a common diabetic disease that affects the retina and can result in blindness if not treated initially. Deep learning (DL) based models are proposed to detect the blood abnormalities in the retinal tissue due to diabetes mellitus obtained from fundus camera. The drawback of these models is the lack of performance. To address this, we propose to automate the process of detection of the severity of Diabetic Retinopathy (DR) using ensembles of pre-trained models, thus exploring the power of transfer learning in the field of automated diagnosis. Deep learning models perform well when the model is trained on a large amount of data. In this regard, we also put forth data augmentation and preprocessing techniques to generate synthetic images and to improve image quality. Extensive experimental results on publicly available databases illustrate that the proposed ensemble model achieves fair accuracy when compared to existing models. Thus, the proposed model shows good scope for deployment in real-time diagnosis.

Every year multiple people are diagnosed with diabetes. Diabetes is a chronic disease that affects several organs of the human body namely the eyes, kidneys, heart etc. Diabetic Retinopathy (DR) is a situation induced by diabetes in which severe loss happens to the retinal blood vessels that can ultimately lead to blindness. Regular diabetic retinopathy screening is hence needed for detecting it in advance. In the present situation, a trained clinician or an ophthalmologist is required to identify diabetic retinopathy (DR) by the existence of lesions related to the vascular abnormalities induced by the disease. The ophthalmologist needs to evaluate and examine digital colour fundus images of the retina. So, it is a very tedious and sluggish process. He/She needs more time to diagnose DR. The diagnosis of the disease by any manual means seems to be tiresome and usually results in errors. To overcome this limitation, we propose a model to automate the process of detection of the severity of DR using ensembles of pre-trained models, thus exploring the power of transfer learning in the field of automated diagnosis.

The research group believes that the study helps ophthalmologists to identify diabetic retinopathy at its early stage accurately, as a result, the chance of losing the vision due to diabetic retinopathy can be reduced. The work is done in collaboration with Dr Mulagala Sandhya, Assistant professor, NIT-Warangal. In the future, Dr Mahesh Kumar plans to work on a project related to Privacy-preserving Biometric Authentication.