Research News

The Department of Computer Science Engineering is proud to announce that Dr Sriramulu Bojjagani has published a paper titled “Blockchain-Based Security Framework for Sharing Digital Images using Reversible Data Hiding and Encryption” in the journal Multimedia Tools and Applications (MTAP) having an impact factor of 2.757.

The paper is published in collaboration with D.R Denslin Brabin from the Department of Computer Science and Engineering, DMI College of Engineering, Tamil Nadu and Christo Ananth from the Department of Electronics and Communication Engineering, St. Mother Theresa Engineering College, Tamil Nadu.

Abstract of the Research

Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain-based security framework is proposed for sharing digital images in a multi-user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high-capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards, the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain-based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.

Fig 1: The process of encoding during reversible data hiding

Dr Sriramulu Bojjagani also intends to work on the development of block-chain based solutions to intelligent transport systems and on addressing the challenges of security issues involved in connected and autonomous vehicles.

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The Department of Environmental Science is proud to announce that Dr Lakhveer Singh has published his paper titled, “Progressions in cathodic catalysts for oxygen reduction and hydrogen evolution in bioelectrochemical systems: Molybdenum as the next-generation catalyst” in a prestigious journal Catalysis Review with a high Impact Factor of 20.21.

The article is published in collaboration with NCL Pune, Hong Kong Baptist University, and VITO-Flemish Institute for Technological Research, Belgium.

Abstract of the Research

Oxygen reduction reactions (ORR) are unanimously a key factor of system performances in bioelectrochemical systems (BESs), low-temperature fuel cells, and generally in several electro-chemical platforms. Platinum (Pt)-based catalyst is the finest electrocatalyst for ORR in BESs; however, it is constrained by its low abundance, high price, and poor catalytic durability in an electrochemical setup for cathodic reaction kinetics. Molybdenum (Mo) with its multi-dimensional form as 2D and 3D layers and synergistic combination with other non-metals offers prospects of extraordinary performance as a low-cost metal-based ORR catalyst over the Pt in delivering enhanced ORR potential.

About the Research

This article throws light on the current requirements of sturdier catalyst material and thus provides a comprehensive review of the continuing efforts in exploring the possibility of Mo as a low-cost metal-based ORR catalyst for sustainable energy production.

Mo-based catalysts have been now widely used for their applications in environmental and energy-based catalysis due to the low cost of Mo, high stability, and excellent activity.

In the future, Dr Lakhveer Singh and his collaborators are working on overcoming limitations to fabricate durable, stable, and catalytically active micro/nanoscale two-dimensional MoS2-based cathodes at an industrial scale, commercial bioelectrochemical devices can be obtainable in future.

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Human-like care is difficult to replicate. Due to the lack of a reliable vision-based fall detection AI, it is often more effective to assign a lot of manpower towards vision-based detections that have not been efficiently implemented.

Ms Inturi Anita Rani, Research Scholar in the Department of Computer Science Engineering, working with her supervisor, Dr V. M. Manikandan, has worked on a paper titled, “A Novel Vision-Based Fall Detection Scheme using Keypoints of Human-Skeleton with Long Short-term Memory Network” in the Arabian Journal for Science and Engineering published by Springer with an Impact Factor of 2.33.

Abstract of the research:

Humans are skilled at visually recognizing and classifying actions in videos, but it’s tough to automate this process. Human action detection in videos is useful in applications like automated surveillance, assisted living, human-computer interaction, content-based video retrieval, and video summarization. The ability to recognize atomic actions like “walking,” “bending,” and “falling” is critical for activity analysis when monitoring elderly people’s daily activities. Our paper presents a new promising solution for fall detection using vision-based approaches. In this approach, we analyse the human joint points which are the prime motion indicators. A set of keypoints of the subject are acquired by applying the AlphaPose pre-trained network. These keypoints are inferred to be the joint points of the subject. The acquired keypoints are processed through a framework of convolutional neural network (CNN) layers. Here, the spatial correlation of the keypoints is analysed. The long-term dependencies are then preserved with the help of long short-term memory (LSTM) architecture. Our system detects five types of falls and six types of daily living activities. We used the UP-FALL detection dataset for validating our fall detection system and achieved commendable results when compared to the state-of-the-art approaches. For comparison, we employed the OpenPose network for keypoint detection. It is inferred from the results that the AlphaPose network is more precise in keypoint detection.

About the research paper:

In this paper, the author proposes a vision-based system that is capable of detecting various types of falls accurately through video processing with the help of a machine learning approach.

Implementation of the research:

The proposed scheme can be used to monitor the activity of elderly people and if any unusual falls happen, the information can be shared with caretakers to ensure emergency services.

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“Progress in Alternative Strategies to Combat Antimicrobial Resistance: Focus on Antibiotics” is a paper authored by Prof Jayaseelan Murugaiyan, Professor & Head, Department of Biological Sciences at SRM University-AP and his research scholar Ms Saranya Adukkadukkam, in Antibiotics Journal, having an impact factor of 4.639. It is a remarkable achievement that our university served as the first and the corresponding authors of this paper published in association with “Global AMR Insights Ambassadors Network”. A total of 21 co-authors from 13 countries (India, UK, France, The Netherlands, Switzerland, Italy, Spain, Ukraine, Lebanon, Egypt, Uganda, Bangladesh, and Nigeria) and six Indian universities (including SRM AP) participated in this work.

Antimicrobial resistance (AMR) – the ability of microorganisms to survive antimicrobials – is a global healthcare concern. AMR contributes to 1.27 million deaths among the 4.95 million deaths associated with bacterial AMR. If no control measures are taken, it is estimated that by 2050, it will claim the lives of 300 million people. The rise of these “superbug bacteria’s” – means that trivial medical interventions will soon become once again high-risk since no efficient antimicrobial chemotherapy is available. It is, therefore, crucial to understand the current situation and identify alternatives to combat the emergence and spread of antimicrobial resistance. This paper comprehensively discusses the alternative approaches that can be effectively utilised to combat AMR and, at the same time, without inducing further resistance among the pathogens. The paper has great social implications in making society aware of the scenario and encouraging the researchers to focus on alternative strategies to combat AMR.

Abstract of the paper: Antibiotic resistance, and, in a broader perspective, antimicrobial resistance (AMR), continues to evolve and spread beyond all boundaries. As a result, infectious diseases have become more challenging or even impossible to treat, leading to an increase in morbidity and mortality. Despite the failure of conventional, traditional antimicrobial therapy, in the past two decades, no novel class of antibiotics has been introduced. Consequently, several novel alternative strategies to combat these (multi-) drug-resistant infectious microorganisms have been identified. The purpose of this review is to gather and consider the strategies that are being applied or proposed as potential alternatives to traditional antibiotics. These strategies include combination therapy, techniques that target the enzymes or proteins responsible for antimicrobial resistance, resistant bacteria, drug delivery systems, physicochemical methods, and unconventional techniques, including the CRISPR-Cas system. These alternative strategies may have the potential to change the treatment of multi-drug-resistant pathogens in human clinical settings.

Global AMR Insight Ambassador Network: AMR Insights, an international network-based organisation interacting with professionals around the globe: in Human and Veterinary Health, Agri-food and Environment, was set up in 2017 following an in-depth feasibility study towards a new information platform on AMR. It mainly focuses on informing, educating and connecting people with the aim to curb antimicrobial resistance. Prof Jayaseelan Murugaiyan and Ms Saranya Adukkadukkam are members of the Global AMR Insights Ambassador Network.

Ms Saranya Adukkadukkam, the co-author from SRM AP says:

The guidance of Prof Jayaseelan Murugaiyan is the pillar of my research. His support and passion for research always encourage me. He gives importance to translational research and motivates me to stand unique in the field. He shows me a path where I can serve the people through research. Foreseeing my future as a scientist, he guides me to more opportunities to collaborate with international scientists. I feel proud of my mentor Prof Jayaseelan Murugaiyan for letting me fly. Also, I thank SRM University-AP for providing generous fellowship, excellent research facilities and ambience to carry out the research.

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Dr Aqsa Agha, Assistant Professor, the Department of History, has been awarded the South Asia Public Health Fellowship Project by The Institute of Public Health (India) in collaboration with the International Union against Tuberculosis and Lung Disease. As the South Asia Public Health Fellow-India, Dr Agha is expected to conduct research on the ethics of the tobacco industry and submit the report over a period of 8 months, i.e. from December 01, 2021, to July 31, 2022. She has been granted an amount of USD 6660 to carry out the project.

Along with producing a quality case study on India focusing on industry interferences promoting the tobacco epidemic and undermining public policies related to tobacco control, the project entails drafting the regional report on South Asia. It will bring together country-level case studies and implications between December-May, 2022, engaging in the dissemination of the country-level case studies with relevant stakeholders in June-July 2022.

Article 5.3 of the World Health Organization Framework Convention on Tobacco Control (WHO FCTC) acts as a treaty for ratified countries to protect their citizens against the commercial and vested interests of the tobacco industry as per guidelines laid down in the said article. Countries of South Asia, including India, have signed and fully ratified this treaty in the early 2000s. However, the lack of robust national policies and continued influence of the tobacco industry perpetuates the tobacco epidemic, as established in the available literature. Given each country’s mandate to protect the health of its citizens, there is a growing need to understand issues of tobacco industry interferences that undermine public policies meant to protect its population. The South Asian Public Health Fellowship is an initiative to generate knowledge regarding issues of tobacco industry interference not only in India but in other countries of South Asia (i.e., Nepal, Sri Lanka and Bangladesh) to better understand such issues from a regional perspective.

Dr Aqsa Agha holds a PhD in History from the Centre for Historical Studies, JNU. She is also the Project Head of Unnat Bharat Abhiyan at SRM University-AP. Before joining SRM University-AP, Dr Agha was a Consultant with Partners in Change, New Delhi and prepared a report titled “Status of Corporate Responsibility in India, 2020”, focusing on corporate responsibility and ethical business practices. Prior to that, she worked as a Research Officer for National Research Study on Human Trafficking in India at TISS, Mumbai. Before TISS, she worked with the Human Rights Defenders’ Alert- India on human rights violations on the India-Bangladesh border in West Bengal. Along with teaching, she has consulted with organisations, including the Centre for Equity Studies, New Delhi, where she conducted sessions with grassroots activists to effectively observe, analyse and document reality through participatory research. Her broader research interest lies in historical processes and their impact on the social locations of class, caste and gender.

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.

Persistent dry coughing and abnormally high body temperature are identified as more relevant risk factors associated with the COVID-19 crisis. Besides these, maintaining 6 feet social distancing norm was also recognized as a crucial factor. By taking these three features into consideration, the research group consisting of Dr Kshirasagar Sahoo, Assistant Professor, Department of Computer Science and Engineering at SRM University-AP have designed a smart, reliable and efficient COVID-19 tracking device model to monitor suspected infected people in public places. The device named “Smart COVID-Shield” utilising the IoT technology is equipped with a suspender and a belt to be placed over the clothes of the user which constitutes a cough detect unit, temperature detects unit and distance computing unit. Coughing and temperature patterns can be detected through the PIR sensor of the suspender while a belt with an ultrasonic sensor can be used to track people who violate the 6 feet social distancing norms in a real-time environment. A research paper titled “Smart COVID-shield: An IoT Driven Reliable and Automated Prototype for COVID-19 Symptoms Tracking” is published in Computing Journal as a part of the project.

Abstract of the paper:

IoT technology is revolutionizing healthcare and is transforming it into more personalized healthcare. In the context of the COVID-19 pandemic, IoT’s intervention can help to detect its spread. This research proposes an effective “Smart COVID-Shield” that is capable of automatically detecting prevalent symptoms like fever and coughing along with ensuring social distancing norms are properly followed. It comprises three modules which include Cough Detect Module (CDM) for dry cough detection, Temperature Detect module (TDM) for high-temperature monitoring, and Distance Compute Module (DCM) to track social distancing norm violator. The device comprises a combination of a lightweight fabric suspender worn around the shoulders and a flexible belt wrapped around the waist. The suspender is equipped with a passive infrared (PIR) sensor and temperature sensor to monitor persistent coughing patterns and high body temperature and the ultra-sonic sensor verify 6 feet distance for tracking an individual’s social distancing norms. The developed model is implemented in an aluminium factory to verify its effectiveness. Results obtained were promising and reliable when compared to conventional manual procedures. The model accurately reported when body temperature rises. It outperformed thermal gun as it accurately recorded a mean of only 4.65 candidates with higher body temperature as compared to 8.59% with the thermal gun. A significant reduction of 3.61% on social distance violators was observed. Besides this, the latency delay of 10.32 s was manageable with a participant count of over 800 which makes it scalable.

This is a collaborative work of H. K. Tripathy, S. Mishra from School of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar, Odisha, India and A. Nayyar from Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam.

Early detection of the coronavirus symptoms is one feasible means to restrict the spreading of coronavirus. The IoT enabled “Smart COVID-Shield” is developed and implemented in this study to monitor social distancing violators in crowded places. In future, all other COVID symptoms can be incorporated into the model to make it more effective and real-time. An emergency alert module can also be included as part of the model to create awareness among people. An enhanced security mechanism can be further embedded in the working model to prevent any data compromise and dilution in data availability.

Read the full paper here.

Being a researcher demands commitment, sustained effort, and a high level of inspiration. Ms Lakshmi Bhargavi from 3rd-year Computer Science Engineering has presented a paper titled “Application of distributed back propagation neural network for dynamic real-time bidding” at the 11th International Advanced Computing Conference (IACC-2021), hosted by the University of Malta, Europe. It is a reputed conference indexed in Scopus and DBLP, having H-index 25.

The research is based on the backend of ad placement on websites which involves finances. The process involves finding the best deal between the dealer and the supplier. In the present system, the bid is prefixed, thereby reducing the possibility of optimal budget utilisation. In comparison, Ms Lakshmi’s research uses an ML algorithm, which is dynamic and learns from the previous bids. This research has resulted in 15% lower costs for the suppliers, thereby saving a lot of money and resulting in a better system.

Abstract — Programmatic buying, popularly known as real-time bidding (RTB), is a key ascendancy in online advertising. While data has become essential for targeting and ad performance, data businesses have become difficult to differentiate due to their proliferation, as well as limitations of attribution. This provides an opportunity for Big Data practitioners to leverage this data and use machine learning to improve efficiency and make more profits. In such an opportunity, the research came up with an application of a machine learning algorithm, distributed back propagation neural network, d-bpnn, to predict bid prices in a real-time bidding system. This paper depicts how d-bpnn is used to achieve less eCPM for advertisers while preserving win rate and budget utilisation.

The 11th International Advanced Computing Conference (IACC-2021) was hosted by the University of Malta, Europe, with an H-index of 25. The conference is indexed in Scopus and DBLP and in collaboration with Springer. A few selected papers will be published in SCOPUS/SCI Indexed journals. The presentation was held on 19th December 2021. It was an ideal platform for people to share views and experiences in Futuristic Research Techniques.

Let’s hear from Ms Lakshmi:

My university has been with me in every step taken towards this conference. I would like to thank the mentoring of Dr Priyanka throughout the writing and presentation of the paper. The immense support of SRM AP management, my professors, HOD, Pro VC sir and VC sir made me reach the level to write a paper confidently and show my knowledge to the world.
I feel honoured to present a good paper at a global conference. The experience and connections I made through this conference are priceless. It gave me new insights into several other technical domains. I believe I gave my best at the unique opportunity given to me and hopefully will continue to deliver good work in future too.

SERB-DST projects aim to build up the best systems that would match the best global practices in the area of promotion and funding of basic research. Dr Murali Krishna Enduri, Assistant Professor, Department of Computer Science Engineering at SRM University-AP is yet another faculty member who has obtained a project with a total outlay of ₹18 lacs for a duration of three years. The project is sanctioned under the scheme of Teachers Associateship for Research Excellence (TARE) of SERB-DST, Government of India.

In the duality theory, the dual problem is the problem of checking the duality of a pair of monotone Boolean expressions in disjunctive normal form. Problem: DUAL Input: The complete DNF of two monotone Boolean functions, f and g. Output: If f is dual of g. Whether the problem DUAL admits a polynomial-time algorithm has been one of the challenging open problems in the field of Duality theory of Boolean function for the last 35 years. It is one of the few problems whose polynomial-time solvability is still unknown. So, this problem is important in complexity theory due to its unknown complexity status and it plays a central role in various applications arising in computational logic, data mining, reliability theory, artificial intelligence and game theory etc. The project goal is to solve the dual problem for an interesting class of Boolean functions. Improving the existing complexity results of the DUAL problem for a particular class of Boolean functions is a challenging task.

Few applications of the project are as follows:
Type error diagnosis: Type error diagnosis is the task of generating an explanation for some error. It requires finding all minimal unsatisfiable subsets of a given set of constraints (representing the error) which can be managed via solving the computational variant of Dual in its minimal transversal formulation.

Computational medicine: Optimal vaccination strategies are given a subset of initially infected individuals from a population of individuals and assumptions about disease transmission. The task of computing inclusion minimal vaccination strategies can be solved using the computational variant of Dual in its transversal hypergraph formulation.

The project will be carried out in collaboration with IIT Madras (Dr Jayalal Sarma, Associate Professor, Department of Computer Science & Engineering, Indian Institute of Technology Madras, Chennai, India.)

Nowadays, the energy demand of the present electrical power industry is increasing exponentially, and most of the electricity production depends on fossil fuel resources. A research paper titled “Dynamic Economic and Emission Dispatch with Renewable Energy Integration Under Uncertainties and Demand Side Management”, published by Dr B Lokeshgupta, Assistant Professor, Department of Electrical and Electronics Engineering, SRM University-AP, Andhra Pradesh, answers some of the pertinent questions regarding reducing the environmental pollution level.

Integration of renewable energy resources (RERs) along with demand-side management (DSM) is almost inevitable in the present scenario to meet the growing energy demand with minimum environmental pollution. This work proposes a combined model of dynamic economic and emission dispatch (DEED) and DSM to integrate renewable energy resources (RERs). In this analysis, the DSM load-shifting scheme is incorporated with the DEED problem to obtain the generation side operational benefits as well as the reduction in environmental pollution level. In this study, various smart home appliances and their complex constraints are included in the DSM load shifting process. The variability and stochastic nature of the load demand and RERs such as solar, wind are modelled with Normal, Beta, and Weibull distribution functions, respectively. The proposed model is implemented in both deterministic and stochastic approaches with the help of the non-dominated sorting genetic algorithm (NSGA-II) and the Monte Carlo Simulation (MCS) approach. In the stochastic model, the MCS approach appropriately handles the uncertainties of system load demand and RERs. Four different case studies are carried out in the simulation analysis to show the impacts of RERs and DSM integration on the traditional DEED problem.

Meeting the excessive energy demand with the minimum environmental pollution is a challenging task. The integration of RERs such as wind and solar into the grid is one of the superior solutions for this issue. However, the variability and uncertainty of the RERs bring challenges to the power system operation. Energy management schemes such as demand-side management (DSM) methods can help the power industry address the challenges of RERs integration. That is why the combination of renewable energy integration and DSM is one of the key solutions in the smart grid environment to meet the increased energy demand with the lowest possible energy cost and minimum pollution level. The RERs and DSM combination gives several financial, environmental, and technical benefits to the power industry along with a better system operation.

The dynamic economic and emission dispatch (DEED) is one of the widely adopted tools in the operation and planning of power systems. Both DEED and DSM are the essential tools in the smart grid environment for efficient energy management with the concern of economic and environmental aspects. The DEED’s primary task is to obtain the optimal scheduling of generators with minimum cost and emission for the given load demand. At the same time, the DSM’s primary goal is to improve the optimal values of system objective functions by shifting or managing the controllable loads of consumers. This work introduces a combined stochastic optimisation model of DEED and DSM scheme with the integration of solar and wind energy to show how DSM and RERs bring benefits to a generation company, and also to get better optimal operation cost and emission values simultaneously. The DSM load-shifting scheme is implemented in this study with the help of 10,000 active residential consumers. The effectiveness of the proposed combined model has been tested on a system of six thermal generating units, one wind-powered generator, and one solar-powered generator. The MCS approach and NSGA-II method are used in this paper to solve the proposed stochastic combined DEED and DSM optimisation model.

From the overall analysis, it is recognized that the implementation of the DSM load-shifting scheme along with RERs integration is essential for future smart grids to improve the financial savings of generation companies as well as to reduce the environmental pollution level. The paper is written in collaboration with Dr S Sivasubramani, Associate Professor, Department of Electrical Engineering, Indian Institute of Technology, Patna.

In future, the proposed DSM optimisation can be extended with the inclusion of a neighbourhood power-sharing model in the environment of multiple smart home consumers and prosumers. The proposed DSM model can also be integrated with the distribution network planning and operation problems to enhance the financial and technical benefits of distribution companies.