Green Pearl Education and Management Corporation (GPEMC) has recently sanctioned a significant consultancy project to SRM University-AP, titled “Multilingual Minutes of the Meeting Generation.” This project aims to develop innovative solutions for generating multilingual minutes of meetings, ensuring effective communication, and understanding across language barriers.

The Consultancy Project has been assigned to Dr. Ashu Abdul and Dr. Dinesh Reddy Vemula, Assistant Professors for a period of 7 months from the Department of Computer Science and Engineering (CSE). Their expertise in language processing and computational linguistics will play a crucial role in the successful execution of this project.

Joining them in this endeavour is Mr Phanindra Kumar, a dedicated PhD scholar, and a team of talented B.Tech. students, including Mr Shivansh Goel, Mr Md Ahmad Raza Khan, Ms Subrabala Dash, Mr Nithish Sri Ram, Mr Hadi Mahmood, and Mr Phanidra Kumar. Their combined skills and enthusiasm will contribute to the development of innovative solutions for multilingual meeting documentation.

The Consultancy Project has been sanctioned with an outlay of Rs 48,91,786/- as consultancy charges for a period of 7 months. This funding will support the research and development efforts required to deliver efficient and accurate multilingual minutes of meetings.

The objective of this project is to create an automated system that can generate minutes of meetings in multiple languages, ensuring accurate and comprehensive documentation. By overcoming language barriers, this project aims to facilitate effective communication and collaboration in diverse organizational settings.

The collaboration between SRM University-AP and GPEMC highlights the commitment of both institutions to research and innovation. This consultancy project presents an exciting opportunity for the faculty members and students to contribute to real-world problem-solving and make a substantial impact in the field of language processing.

Abstract

The project ‘Multilingual Minutes of the Meeting Generation’ aims to develop an innovative application that automates the creation of meeting minutes, action plans, and summaries in multiple languages. Leveraging advanced research in natural language processing and speech recognition technologies, our application seeks to streamline the tedious and time-consuming task of minute-taking. It will do so by accurately identifying, associating, and documenting statements with their respective speakers during meetings using speech diarization techniques.

The primary objective is to enhance the efficiency and accuracy of meeting minutes while supporting diverse linguistic needs and eliminating human error, thereby catering to global business environments. The system will improve the meeting outcomes for all participants by identifying the action items from the meeting. This project intends to deliver an effective solution for enterprises by combining the latest technological advances with a user-centric design.

Explanation of the Research in Layperson’s Terms

The project called “Multilingual Minutes of the Meeting Generation” is about creating a new tool that automatically writes down what happens in meetings—like the minutes, action items, and summaries—and can do this in several languages. This tool uses the latest technology in understanding and processing human speech, which helps make the task of recording meeting details much faster and easier.

The special feature of this tool is its ability to figure out who is speaking and accurately attach their words to them in the meeting notes. This makes sure the meeting records are both correct and detailed. The main goal here is to make creating and keeping track of meeting records more efficient and accurate, help people who speak different languages, and reduce mistakes that can happen when humans take down notes.

This system is especially useful in today’s global business setting, where people from different parts of the world need to work together smoothly. It aims to lessen the work involved in manually writing meeting notes and make the results easier for everyone to use and understand. Overall, the project promises to blend cutting-edge technology with designs that focus on the needs of the users to provide a practical tool for businesses.

Practical Implementation or the Social Implications Associated with the Research

The practical implementation of this project is particularly beneficial for enterprises, institutions, and professional settings where meetings are frequent. By automating minute-taking, it allows participants to focus more on discussion and decision-making, rather than note-taking. This leads to more productive meetings and ensures that important details are not lost or misinterpreted. Ultimately, this technology enhances communication and documentation accuracy across diverse linguistic and cultural landscapes in business and professional environments.

Collaborations

This project is being developed in collaboration with the members of the Next Tech Lab AP at SRM University-AP

Future Research Plans

The future plans for this project involve integrating the application with widely used platforms such as Zoom, Google Meet, and others that support video/audio conferencing. This integration aims to seamlessly incorporate the automated minute-taking functionality into these platforms, enhancing accessibility and usability for users across various companies and institutions. By expanding compatibility with popular conferencing tools, the project seeks to further streamline meeting documentation processes and cater to a broader user base in diverse professional settings.

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.