Research News

SRM University-AP proudly announces that Dr Bikku R, Assistant Professor at The Department of Liberal Arts has been sanctioned a major research project by the Indian Council of Social Science Research (ICSSR). The research project titled “Religious Deities and Saints/Legends/Heroes Changing Geographic Belongingness and Cultural Economy among the Four Tribal Communities (Chenchus, Kondareddis, Koyas and Lambadi’s) of Telangana and Andhra Pradesh” will receive an outlay of Rs. 10.00 Lakhs.

Congratulations, Dr Bikku on this outstanding achievement!

Research Abstract

India is one of the most diverse countries in the world, where around 705 scheduled tribes are notified with their distinct culture, and as per the 2011 census, they constitute 8.6% of the total population. Out of these, 75 communities belong to Particular Vulnerable Tribal Groups (PVTGs), having declining or stagnant populations, low literacy rates, pre-agricultural technologies and economic backwardness. For tribal communities, culture, economic, political and geographical belongingness are distinct from non-tribals in India. Studying their religious beliefs and practices and changing cultural economies and landscapes is crucial to preserving India’s diverse cultures. The existing studies by social scientists have not emphasised much on tribal saints/ heroes, changing cultural economies and landscapes in India, particularly in the newly bifurcated southern states, Telangana and Andhra Pradesh. The study focuses on religious deities and Saints/Legends/Heroes, changing geographic belongingness and cultural economies among the four tribal communities (Chenchus, Kondareddis, Koyas and Lambadis) of Telangana and AP states of India. Qualitative (ethnographic) and quantitative methods will be used to map their deities and saints/legends/heroes, cultural economy and traditions and record memories of past and contemporary relationships with the belonging to their landscapes and natural resources.

Dr Ravi Kant Kumar, Assistant Professor at the Department of Computer Science and Engineering at SRM University-AP and his research scholar, Ms Gayathri Dhara have recently made a significant contribution to the field of Graph Neural Networks. The teacher-student duo have offered relevant analysis of visual saliency applications using Graph Neural Networks (GNN) in their book chapter titled “Study and Analysis of Visual Saliency Applications Using Graph Neural Networks” in the book, Concepts and Techniques of Graph Neural Networks. The topic of their study will prove to be a key source of reference for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

Description and Significance of the Chapter

The chapter covers the practical applications of GNN in the field of visual saliency. Various applications of computer vision problems implemented using graph neural networks (GNNs) have been studied and analysed in this chapter. It also includes the design approach of GNN, the various computational models used in GNN, its challenges and recommendations. The social significance of GNN in visual saliency extends to various domains like Human attention modelling, Advertising and marketing, Visual content understanding, and so on.

Significance of GNNs

Graph Neural Networks (GNNs) have gained significance in the field of visual saliency due to their ability to model complex relationships and dependencies within visual data. Visual saliency refers to the process of predicting the most visually prominent regions or areas in an image or video that attract human attention. It plays a crucial role in various computer vision applications, such as image understanding, object recognition, and scene understanding. GNNs can learn spatial dependencies and feature representations from visual data. There are many methodologies that address the detection of the salient object using GNN. GNNs can handle multi-modal data, combining visual information with other modalities such as textual or semantic features. This integration allows GNNs to leverage additional cues and contextual information to improve visual saliency prediction. So, studying these different salient object detection methods using GNN and knowing the challenges of GNN will help in meeting our research objectives.

Barath Sivaraj, a research scholar from the Department of Biological Sciences at SRM University-AP has achieved an outstanding feat by receiving the DST INSPIRE Fellowship, a highly regarded programme sponsored and overseen by the Department of Science & Technology.

Sivaraj’s research focuses on exploring how bacteriophages, a type of virus that infects and reproduces inside bacteria, utilises the bacterial system. This research has earned Sivaraj the Fellowship, which runs for a duration of 5 years. The researcher also gets to carry out his research at any national or international laboratories other than their host institute for a period of 18 months.

Barath expresses his immense delight and pride in receiving this fellowship and considers it a significant milestone in his academic journey. Sivaraj explains that applicants must meet several criteria, such as being a top-ranked student in a full-time postgraduate science programme or a related field at the university level. Sivaraj credits his research supervisor, Dr Sutharsan Govindarajan, for guiding him through the application process. As a future prospect, Sivaraj plans to pursue his post-doctoral studies abroad and eventually wishes to offer his professional services at a reputable university.

We wish him all success!

Assistant Professor Dr Vineeth Thomas, from the Department of Liberal Arts, has recently made a publication in the Q2 journal called Economic and Political Weekly. The paper titled Cracking the Code: How Congress Scripted Its Victory In 2023 Karnataka Assembly Elections contributes to comprehending and examining the political landscape of Karnataka, shedding light on the various elements that shape voter behaviour and election outcomes within the context of Indian democracy.

Abstract

The Congress party secured a much-awaited thumping majority in the 2023 Karnataka Assembly elections. The election result gave a much-required lifeline to the existential crisis faced by the Congress party and opened the eyes of the BJP to recognise that the Modi-factor and Hindutva card does not always get converted into votes. In this context, this article provides an in-depth analysis of the factors that led to the Congress party’s victory in the 2023 Karnataka assembly elections.

Explanation of the Research in Layperson’s Terms

Due to multiple reasons, it is worth examining the ingredients of Congress’ magical recipe for winning the Karnataka assembly elections. The Congress’ victory in the Karnataka assembly elections in 2023 is a significant political event, as it marked the party’s return to power in the state after a gap of several years. The result provided a new lease of life for the Indian National Congress and it helped to cement the Congress’ position as a key player in the politics of southern India in general and Karnataka in particular. The victory boosts the spirit of Congress party workers on the ground, who are so used to losing that they sometimes give up before the combat even begins. It also allows the party to generate resources in a situation where the Congress controls only three additional states (Himachal Pradesh, Rajasthan and Chhattisgarh). It also enhances the Congress’s profile within the greater national opposition and may persuade some other parties, who have been sceptical of the party’s political judgement and ability, to consider working together. It may help the Congress, which won only one Lok Sabha seat in Karnataka in 2019, to increase its score in 2024. Understanding the components of the Congress’ electoral strategy in Karnataka might therefore provide light on the party’s overall approach to the future assembly and Lok Sabha elections. Examining the reasons that influenced the Congress’ win in Karnataka can give useful insights into India’s larger political scene and shed light on the anticipated outcomes of future elections in the nation.

Today, biometric systems are widely used across all major domains, but alarmingly these systems are vulnerable to various security attacks. However, Dr Banee Bandana Das and Dr Saswat Kumar Ram, Assistant Professors at the Department of Computer Science and Engineering, SRM University-AP, have jointly come up with a solution that is more efficient and robust. The faculty duo have also  filed and published the patent titled- “A System and A Method For Person Identification” with Application Number: 202341036899.

Abstract

Biometric representation of humans deal with tasks such as identification and verification. It can be done through various methods like fingerprint, face, retina, voice, etc. However, existing biometric systems are vulnerable to various security attacks. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a broadly centred EEG signal exhibiting a subject’s motion intentions without actual actions. This invention proposes an unsupervised framework for feature learning based on autoencoders. It learns sparse feature representations for EEG-based person identification. Autoencoder-CNN exhibits the person identification task for signal reconstruction and recognition. The framework proved to be a practical approach in managing the massive volume of EEG data and identifying the person based on their different tasks in resting state. The experiments have been conducted on the standard publicly available Motor imagery EEG dataset with 109 subjects. This invention proposes an unsupervised framework for feature learning based on autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. The outcomes imply that the implementation of an autoencoder-CNN architecture for person identification was intensely successful with improved recognition performance with the most notable autoencoder architecture. Eye open and closed resting state data as training data is used while four different motor imagery tasks have been considered test data in this biometric model. Training and testing of different state data of the same person have been proved to be the most robust and versatile EEG-based biometric system.

Practical Implementation of the Research

The present invention can be used in smart city applications, considering that the population of cities is increasing by the day, in which case, the security and privacy of people are at high risk in all sectors. The application of this technology will be in areas like:

Smart Office
The workflow and working efficiency of the employees can be enhanced with various innovative features and technology in an intelligent environment like smart offices of smart cities. Biometric-based methods involving fingerprints, retina, voice, and face recognition for secure authentication and identification of employees, clients, and different types of machinery are helpful. Various security attacks are the most severe issue related to these methods. Brain signals (EEG) are more secure and difficult to copy and steal, efficiently used for security needs and authentication.

Smart Healthcare
The laboratory and the data related to the health sectors are always important. The privacy and restricted access are must to secure it. By using the proposed invented model, the security can be enhanced as compared to the traditional biometric traits.

Smart Defence
The use of biometric in defence is not discussed in public but is undoubtedly an essential parameter in this domain. Many countries rely on biometric data like faces, irises, fingerprints, etc., for identification. A unique and trusted database is a must in defence for identifying persons involved in various operations. These include authenticating scientists, pilots, engineers etc., and to identify criminals in specific places. EEG-based person identification can be a secure alternative in this domain.

As a future prospect, Dr Das and Dr Ram are planning to develop a more secure and reliable biometric authentication system that will be based on Multimodal Techniques using Machine learning Methods.

Socially relevant and community-centric research is of primal focus at SRM University-AP leading to innovations that transpire into transforming society. Dr Vimal Babu and his research scholar Ms Rukma R, Paari School of Business, have undertaken creative research to understand employee attrition in organisations critically. The research cohort employed a modified Random Forest Algorithm for assessing employee attrition and analysing its findings and benefits to Human Resource departments of corporate houses. Their groundbreaking invention and research titled “A System to generate a Model Predicting an Employee Attrition Rate and a Method Thereof” with Application No: 202341031320 has recently been filed and published under the Patent Office Journal.

Abstract

The problem of employee attrition in every organization concerns the employee turnover ratio, thereby increasing the cost of investment in human resources. Various factors are reasonable for the rapid attritions at different phases. The purpose of the current study is to predict the employees who are likely to leave the organization. The factors that lead to attrition are identified using the Random Forest algorithm. Random Forest algorithm is one of the widely used supervised machine learning technique for both classification and prediction. However, the random forest algorithm has specific issues, such as it is too slow and ineffective for real-time predictions. i.e., the large number of trees can make the algorithm, which results in a slower model. Therefore, the study proposes a new alternative for choosing the appropriate decision trees based on the concept of fractional factorial design of experiments. The different performance criteria were compared across the modified random forest algorithm, existing random forest algorithm, Support Vector Machine (SVM), Logistic Regression (LR), Navie Bayes (NB), K – Nearest Neighbour (K-NN), Decision Tree, XG Boost tree and Neural Network (NN). It was found that the modified random forest algorithm excelled in all criteria and performed better than the existing ones.

The findings of this study offer valuable assistance to human resources departments by providing necessary insights into potential employees’ decisions to leave the company. By analysing the identified factors or reasons for leaving the organisation, HR professionals can better understand attrition patterns and make informed decisions to improve employee retention. The proposed method not only assists in identifying the factors contributing to employee attrition but also predicts the likelihood of attrition based on employee signals. This predictive capability enables organisations to proactively address potential attrition cases and take preventive measures to retain valuable employees.

Practical implications of the research

Overall, the practical applications include designing retention strategies, predicting attrition cases, tailoring employee engagement initiatives, developing HR policies, optimising recruitment and selection processes, benchmarking attrition rates, and implementing continuous improvement measures. These applications can collectively contribute to reducing employee attrition and enhancing overall employee retention in organizations.

Collaborations and Future research plans

The applicants, viz. Ms Rukma, R.; Dr Vimal Babu and Dr Vijaya Prabhagar, M. plan to collaborate in the intersecting area of HR and analytics to arrest employee attrition by employing sophisticated analytics models and explore the future scope of our research to come up with impactful research outputs benefitting the industry.

Phases of prediction of employee attrition

During the Two-day National Conference on Youth Development titled Youth in the 21st Century: Prospects and Psychosocial Challenges, organised by Rajiv Gandhi National Institute of Youth Development (RGNIYD), Sriperumbudur, Tamil Nadu, in collaboration with ICMR – NIRT, Chennai, Dr Dhamodharan, Assistant Professor, Department of Psychology, presented a lecture on Status of the Youth in Fishing Community in Terms of Education, Health, and Violence.

Abstract

Over sixty percent of the fisher population in India is Below Poverty Line (BPL). For traditional fishers, fishing is their primary source of income, and they have no other options. Hence, families in the coastal village are socially, financially, and educationally disadvantaged and frequently face financial difficulties. Additionally, the literacy level of the fishing community is deficient. Fishers have met an unbalanced diet, tension, excessive drinking, tobacco usage, and harmful behaviours. Fisher communities are often underprivileged and constitutionally and communally isolated from other communities. Limited research has been conducted on the fishing community youth, particularly in Tamil Nadu and Pondicherry. Hence, the study needs to be understood and conducted for the fishing community youth regarding education, health, and violence. The empirical study combined quantitative and qualitative approaches to Tamil Nadu and Pondicherry fishing community youth. The study tools used for the data collection are a semi-structured questionnaire for youth for their perspective on health care, school, violence, and family support and a semi-structured questionnaire to the parents regarding their perspective on their children’s education, health, violence, and family support. Twenty case studies were conducted in the selected clusters. Also conducted were key informant interviews with fishing community leaders in four clusters. The results showed that more than 50 % of participants face verbal violence, more than 80 % face physical violence, and nearly 25% face sexual violence. The fishing community is an under-educated, predominantly nuclear family, and the family income is less than ten thousand rupees monthly. Physical abuse had significantly associated with place of residence, Puducherry residing youth had more physical abuse than Tamil Nadu. The social-economic condition of the family and the parental education seemed to be better in the state of Tamil Nadu as compared to Puducherry. Youths from Puducherry had a greater risk of experiencing physical and verbal abuse. Parents had a relatively low level of awareness and understanding of child rights and laws. The study’s results helped to understand the problem of fisher community youth and their perception of parental care, education, health, and violence.

Practical Implementation of the Findings

• Social welfare department and local NGOs to take up need-based intervention programmes for the welfare of the fishing community and youth.
• The youth welfare department understands the problems of youth in the fishing community.
• Policymakers to develop policies and legislation for the youth of fishers in the education and health sectors.
• Understanding the problems and violence against the youth of the fisher community, as well as helping the Non-Government Organisations for making intervention programs at the community level.
• Assist the school administration in better understanding the Fisher community students’ situation and creating counselling centres in school settings.

As the world grapples with the escalating challenges of urbanisation and environmental degradation, the concept of green smart cities has emerged as a promising solution. Green smart cities integrate advanced technologies, sustainable practices, and innovative urban planning to create environmentally friendly, resource-efficient, and liveable urban spaces. However, implementing green smart cities poses numerous multidisciplinary challenges that require careful consideration and collaborative efforts from various fields.

Assistant Professors Dr Dhamodharan M and Dr Aehsan Ahmad Dar from the Department of Psychology have published a chapter titled Multidisciplinary Challenges in Green Smart Cities Implementation in the book Green Blockchain Technology for Sustainable Smart Cities in Elsevier, which is Scopus Indexed.

Focusing on challenges in green smart cities implementation will help the policymakers, government, and public to get aware of the problems related to all aspects. Implementing green smart cities may provide difficulties in the previously described dimensions of governance, economics, social interaction, technology, and ethics. Therefore, as the world’s population grows, there is a need to adapt to the changes, such as green smart cities. Government, policymakers, and the general public should adopt solutions to societal issues supported by science and research. The next generation will find it convenient and necessary to develop green smart cities. The world will prosper with green smart cities if policymakers, the government, and the people simultaneously identify the issues and begin the work properly with the right strategy and support.

Abstract

Villages are the pride of the nation. Nevertheless, cities reflect the nation’s growth and prospects. Department of Economic and Social Affairs in the United Nations explained that the universe’s people will be nearly 70 % in urban areas by 2050. Cities worldwide are facing important issues with increasing urbanisation, environmental sustainability, unemployment, slums, and mitigation of climate variation. So, policymakers and researchers focused on the concept of a smart city to manage these challenges. Consequently, the concept of “Smart Green Cities” came into the picture. Smart green cities are the collaborative hub linked with business, government, education, and the public to generate comfortable living in urban environments by encouraging change with scientific-based problem-solving. In simple terms, Green smart cities are technologically advanced in solving problems without harming the environment. For Implementing green smart cities, developed and developing countries are collaborating and signing a memorandum of understanding with one another. Government representatives, educators, and the public should cooperate to make a green smart city successful.

Green smart cities make global connectedness, productivity, efficiency, and revolution possible. Meanwhile, poorly regulated green smart cities will result in environmental difficulties like socioeconomic inequalities, poor public safety, and conservational destruction. Furthermore, implementing green smart cities is not a simple strategy. This chapter discusses multidisciplinary challenges in green smart cities’ implementation. People face challenges through green smart cities implementation in the following sustainability dimensions such as Blockchain challenges, Governance challenges, Economic challenges, Social challenges, Technology challenges, Environmental challenges, and Ethical challenges.

Collaborations

• Dr Dhamodharan M, Assistant Professor, Department of Psychology, School of Liberal Arts and Social Sciences, SRM University-AP.
• Mr Vimalkumar, Research Scholar, Department of Mechanical Engineering, Indian Institute of Technology, Palakkad, Kerala
• Dr Aehsan Ahmad Dar, Assistant Professor, Department of Psychology, School of Liberal Arts and Social Sciences, SRM University-AP.

The Department of Civil Engineering is glad to announce that Dr Raviteja KVNS, Assistant Professor, has received the Best Paper Award for his paper titled “Compressibility Characteristics of Bentonite Amended Fly Ash Liners Exposed to Phosphate Contamination” at the 2nd International Conference RAISE 2023 (Recent Advances in Sustainable Environment) held on May 15-16, 2023. Dr Raviteja collaborated with Dr Janga Jagadeesh Kumar and Dr Krishna R Reddy, Civil, Materials and Environmental Engineering, University of Illinois Chicago for the research paper. Their cutting-edge research focused on developing alternate liner materials for waste containment systems.

Abstract

Waste containment systems like landfills, and impoundments are often lined with low permeable clays of hydraulic conductivity less than 10-7 cm/s. However, it is often challenging to get large volumes of low permeable clays near the project site. Conveyance of large volumes of clay from distant locations can be unsustainable due to the associated carbon emissions and energy costs, and not viable financially. Hence, there is a need to identify alternative liner materials without compromising on the containment capabilities. This study proposes the use of fly ash, amended with bentonite, as an alternative to the traditional liner systems. From preliminary studies, it is understood that a mixture of 80% fly ash amended with 20% bentonite is optimal to function as an effective liner material. However, the hydro-mechanical stability of liners needs to be investigated under different contamination scenarios. The present study reports the compressibility behavior of the proposed liner system under phosphate contamination, typically found in landfill leachate, impoundments, and stormwater retention ponds. One-dimensional consolidation tests were conducted on pure bentonite and fly ash with water to study the compressibility characteristics of individual materials. The optimum mix (80-20) was then tested at three different solutions exposure: water, 3.2 mg/L PO4-3-P and 12 mg/L PO4-3-P. It is depicted that the addition of 20% bentonite to fly ash did not affect the compressibility significantly, while the presence of phosphate contamination did not compromise the hydro-mechanical stability of the proposed liner system.

Covid-19, has wreaked havoc in ordinary life, health and finance have taken the worst hit. As for finance, people saw the loss of their livelihood, businesses collapsed and for several mid-scale and large-scale businesses, the working capital and firm performance diminished to a great extent. In the given context, Dr Pradeep Rathore Assistant Professor from The Paari School of Business (PSB) published a compelling research paper titled, “Working Capital and Firm Performance: Role of COVID-19 Disruption” in International Journal of Productivity and Performance Management” where he addresses the issue and analyses the cause.

Abstract

This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic. This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns. The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations. This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

The study implies that organisations need to have higher working capital during an economic downturn such as COVID-19 as it takes care of present and future financing needs, to facilitate their day-to-day operational activities, and to enhance performance of both working capital and firm performance, operational and financial. The study also suggests that Managers should understand the value of working capital and advocate for higher working capital investment to address supply chain disruptions during economic downturns.

Dr Pradeep Rathore is presently working on topics related to sustainable development
goals, sustainability, and solid waste management.