In the groundbreaking research paper titled “Innovative Web Application Revolutionizing Disease Detection: Empowering Users and Ensuring Accurate Diagnoses,” Dr Pradyut Sanki, Associate Professor at the Department of Electronics and Communication Engineering, along with doctoral scholars Mr P N S B S V Prasad, and Mr Syed Ali Hussain and BTech students Ms Pragya Gupta and Ms Swikriti Khadke introduce a cutting-edge web application that aims to revolutionise disease detection and empower users to understand their health conditions.Their research paper published in the Journal of Electronic Materials has an impact factor of 2.1.

Abstract:

This paper presents an innovative enhancement aimed at revolutionizing disease detection and providing users with a reliable source of information for accurate diagnoses of their symptoms. Our open-source initiative combines a user-friendly interface design with advanced machine learning models, establishing a new benchmark for accuracy and enabling integration with even higher-performing models. We address the pervasive challenges of misinformation and misdiagnosis associated with online symptom searches, presenting a significant advancement in disease detection. Leveraging cutting-edge machine learning techniques.

Practical and Social Implications:

The practical implementation of our research means that people can use our smart tool to get better advice about their symptoms. This could lead to quicker and more accurate diagnoses, helping people get the right treatment sooner. Socially, our research could reduce the spread of false information online about health issues, leading to better-informed decisions and potentially improving overall public health.

Future Research Plans:

As a future research plan the students and faculty together plan to refine and expand their smart tool to make it even more accurate and helpful. They aim to incorporate feedback from users and collaborate with other experts to continually improve the technology. Additionally, they plan to explore ways to make the tool more accessible to a wider range of people and to address any potential biases in the data or algorithms. Overall, they are committed to advancing healthcare technology for the benefit of society.

It is a matter of immense pleasure for the Department of Electronics and Communication Engineering to announce the publication of Dr Duga Prakash, Associate Professor at SRM University-AP. His research paper titled “Analysis of GAA Junctionless NS FET towards Analog and RF Applications at 30 nm Regime”, published in IEEE Open Journal of Nanotechnology, studies how the device can be manufactured with ease and minimal doping, eliminating the need for high-temperature doping processes. The enhanced performance metrics suggest that the device’s potential for faster analog/RF switching circuits paves the way for more efficient analog and RF applications at the 30 nm scale.

Abstract:

A new nanosheet FET is used to generate a quantum model in this research. A Gate-all-around (GAA) Junction-less (JL) nanosheet device with a 1 nm gate dielectric of SiO2 and HfO2 performs according to the standard model. The visual TCAD tool examines ION, IOFF, ION/ IOFF, threshold voltage, DIBL, gain parameters (gm, gd, Av), gate capacitance, and cut-off frequency to evaluate the classical and quantum models of the GAA nanosheet device. Simulation results show that the device’s low gate capacitance of 10–18 makes it suitable for rapid switching applications. Device research reveals a transconductance (gm) value of 21 μS and a remarkable cut-off frequency of 9.03 GHz. Its P-type device response has also been extensively studied. Finally, the inverter model uses the proposed GAA nanosheet device. Despite having larger gate capacitance, the NSFET-based inverter offers the smallest propagation delay helps apply knowledge to real-world situations.

In a remarkable academic achievement, Dr Banee Bandana Das, Assistant Professor in Department of Computer Science and Engineering and Dr. Saswat Kumar Ram, Assistant Professor in Department of Electronics and Communication Engineering, have made significant contributions to the field of biometric security. Their paper, titled “Person Identification using Autoencoder-CNN Approach with Multitask-based EEG Biometric,” has been published in the esteemed ‘Multimedia Tools and Applications journal, which is recognised as a Q1 journal with an impressive impact factor of 3.6.

This pioneering work showcases a novel approach to person identification using electroencephalogram (EEG) data. The research leverages the power of Autoencoder-CNN models combined with multitask learning techniques to enhance the accuracy and reliability of EEG-based biometric systems.

The publication of this paper not only underscores the high-quality research conducted at SRM University-AP but also places the institution at the forefront of innovative developments in biometric technology. It is a testament to the university’s commitment to advancing scientific knowledge and providing its faculty with a platform to impact the global research community positively.

Abstract

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognise, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject’s motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states.

The title of Research Paper in the Citation Format

Person identification using autoencoder-CNN approach with multitask-based EEG biometric. Multimedia Tools Appl (2024).

Practical implementation/social implications of the research

1. To develop a personal identification system using MI-EEG data.
2. This work is about an Autoencoder-CNN-based biometric system with EEG motor imagery inputs for dimensionality reduction and denoising (extracting original input from noisy data).
3. The designed Autoencoder-CNN-based biometric architecture to model MI-EEG signals is efficient for cybersecurity applications.

Collaborations

1. IIITDM, Kurnool, India
2. National Institute of Technology, Rourkela, India
3. University of North Texas, Denton, USA

Future Research Plan

In the future, different deep learning and machine learning methods can be merged to explore better performance in this EEG-based security field and other signal processing areas. We will investigate the robustness deep learning architectures to design a multi-session EEG biometric system.

Link to The Article

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The advent of scientific research and technologies in the domain of medicine has recently taken limelight due to its immense benefit on humankind and the medical community. The expert faculty and scholars have recently published a patent, “A System and a Method for Early and Accurate Diagnosis of Cataracts” with Application no.: 202341072058 has put forth an engaging invention on utilising technology for early cataract recognition. Hearty congratulations to Prof. Siva Sankar Yellampalli, Professor, Dr Ramesh Vaddi, Associate Professor, and their Ph.D. Scholars Ms P L Lahari, Mr P Rahul Gowtham, and Mr A Vinod Kumar from the Department of Electronics and Communication Engineering for this groundbreaking achievement!

Abstract

Cataracts are a common eye condition in which the lens of the eye gets clouded, impairing vision. Early cataract detection is crucial for prompt treatment and vision preservation. We have classification and prediction algorithms like VGG, ResNet, DenseNet, Xception, Inception, and other object detection techniques like Yolo, Fast R-CNN, and SSD. Various pre-trained models are employed for cataract categorisation and prediction. Several attempts to detect cataracts have been made, but none have proven effective. A clinical examination by eye specialists is used to diagnose cataracts. An edge board can be used instead of a clinical examination to diagnose cataracts.

We created a method for real-time cataract recognition using the present pre-trained weights of the object detection model YoLoV5. We employ pre-trained YoLo V5 weights for model training, testing, and validation. Connect the Jetson Nano board and Lenovo HD USB camera to the CPU, which serves as the CPU. The monitor is used for programming, and the output is presented on the monitor owing to the board communicating with the camera. The result shows the image with an eye labelling box that tells if the eye is normal or cataract.

The Department of Electronics and Communication Engineering is proud to announce the publication of a research paper by Dr Sunil Chinnadurai, Assistant Professor and Research scholar Shaik Rajak titled “Novel Energy Efficient IRS-relay Network for ITS with Nakagami-m Fading Channels” in the Q1 journal ICT Express, having an Impact Factor of 5.4. The paper focused on developing an energy-efficient network for ITS that utilises Nakagami-m fading channels to improve communication reliability and efficiency.

In this work, the research duo introduced a cooperative system involving relay technology and an IRS (Intelligent Reflective Surface) with passive elements. Evaluating energy efficiency and achievable rates, they found that the cooperative relay-IRS system outperformed individual relay and IRS setups. The study also compared multi-IRS setups, highlighting their effectiveness in reducing power consumption and deployment costs for improved ITS development.

Abstract

The research paper investigates the performance of energy efficiency (EE) for Intelligent Transportation Systems (ITS) using a cooperative IRS-relay network. The proposed cooperative IRS-relay-aided ITS network integrates an IRS block with a number of passive reflective elements to improve EE. The research analyses the ITS in terms of EE and achievable rate under Nakagami-m fading channel conditions. The research aims to reduce power consumption over long distances and operate the system faster and safer.

Practical implementation/ social implications of the research

The proposed cooperative IRS-relay network for Intelligent Transportation Systems (ITS) has practical implications for improving energy efficiency and achieving higher data rates in ITS networks. Integrating an IRS block with passive reflective elements in the relay model enhances the coverage area and reduces power consumption in ITS. The research highlights the significance of cooperative IRS-relay and multi-IRS-aided networks in the development of ITS, which can contribute to safer and faster transportation.

Dr Chinnadurai and Mr Rajak Future will continue to work on their research focusing on optimizing the design of the cooperative IRS-relay network for ITS to improve energy efficiency and achievable data rates further in real-world scenarios.

Link to the article