Research News

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.

Today, we live in a world where sustainability and sustainable development are the need of the hour and amidst this, Associate Professor, Dr A Lakshmana Rao and Research Scholar, Mr Akhil Pasupuleti from the Department of Commerce have come up with their pacesetting research publication titled – “A Thematic Study of Green Finance with Special Reference to Polluting Companies: A Review and Future Direction.” The research work gives an impetus to many polluting companies to adopt green finance as an option to combat environmental pollution, this can be in the form of business strategy, energy saving, green credit, and innovation.

Abstract

The objective of the study was to understand the phenomenon of green finance in polluting companies through a systematic literature review. The methodology involves the search, selection, classification, and categorisation of thirty-five articles on green finance in polluting companies which were analysed for the time span of eleven years, i.e., 2011–2022. The outcome of the review identified the following five themes: (i) green credit and environmental protection; (ii) green finance and green innovation; (iii) green innovation and environmental protection; (iv) green finance and investment; and (v) green innovation and firm performance. The review has put forward recommendations for further advancement in policy strengthening and the utilisation of extensive data analysis, indicating potential avenues for future research and development. The findings of the study provide insights to researchers, practitioners, and policymakers about the status of green finance in polluting companies.

Dr Lakshmana Rao Ayyagari and his student Mr Akhil Pasupuleti are working on developing future prospects of Green Finance such as ESG disclosure and reporting practices, its application and the relationship of CSR and sustainability.

Dr Ghanshyam Kumar Pandey, Assistant Professor, Department of Economics has published a paper titled “Market Integration of Chickpea Crop: An Evidence of India”, in the esteemed Q1 Journal, Journal of Agribusiness in Developing and Emerging Economies having an impact factor of 3.50. Through this paper, Dr Pandey analyses the integration and direction of causality of prices of chickpea produce in the markets of India.

Abstract

Purpose – The purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for chickpea legumes in major chickpea markets in India.

Design/methodology/approach – In this paper, the authors have employed the Johansen co-integration test, Granger causality test, vector autoregression (VAR), and vector error correction model (VECM) to examine the integration of markets. The authors use monthly wholesale and retail price data of the chickpea crop from select markets in India spanning January 2003–December 2020.

Findings – The results of this study strongly confirm the co-integration and interdependency of the selected chickpea markets in India. However, the speed of adjustment of prices in the wholesale market is weakest in Bikaner, followed by Daryapur and Narsinghpur; it is relatively moderate in Gulbarga. In contrast, the speed of adjustment is negative for Bhopal and Delhi, weak for Nasik, and moderate for retail market prices in Bangalore. The results of the causality test show that the Narsinghpur, Daryapur, and Gulbarga markets are the most influential, with bidirectional relations in the case of wholesale market prices. Meanwhile, the Bangalore market is the most connected and effective retail market among the selected retail markets. It has bidirectional price transmission with two other markets, i.e., Bhopal and Nasik.

Research limitations/implications – This paper calls for forthcoming studies to investigate the impact of external and internal factors, such as market infrastructure; government policy regarding self-reliant production; product physical characteristics; and rate of utilisation indicating market integration. They should also focus on strengthening information technology for the regular flow of market information to help farmers increase their incomes. Very few studies have explored market efficiency and direction of causality using both linear and nonlinear techniques for wholesale and retail prices of chickpeas in India.

Dr Rajapandiyan Panneerselvam, Assistant Professor, Department of Chemistry, and his team have developed a method using a portable Raman spectrometer to quickly identify six common pathogenic Vibrio species that can contaminate seafood. His latest research paper Intelligent convolution neural network-assisted SERS to realise highly accurate identification of six pathogenic Vibrio, has been published in the Q1 Nature Index journal Chemical Communications, having an Impact Factor of 6.0.

By using gold-silver nanoparticles, the study was able to accurately detect these harmful microorganisms. The new deep learning model called a convolutional neural network (CNN), outperformed traditional machine learning methods with a classification accuracy of 99.7%. The entire identification process only took 15 minutes. The researchers also discovered that the Raman signals emitted by Vibrio species are similar to signals from certain substances found in purine degradation, such as uric acid and adenine. This knowledge helps them explain why different Vibrio species produce slightly different Raman signals. Overall, the CNN-assisted Raman spectroscopy method offers a fast and accurate way to diagnose and identify harmful microorganisms responsible for food contamination.

Abstract

The utilisation of label-free Surface-Enhanced Raman Spectroscopy (SERS) technology enabled a comprehensive analysis of the connection between Raman signals emitted by pathogenic Vibrio microorganisms and purine metabolites. Through extensive research, a sophisticated Convolutional Neural Network (CNN) model was developed, demonstrating exceptional performance with an accuracy rate of 99.7% in the rapid identification of six common pathogenic Vibrio species within a mere 15-minute timeframe. This breakthrough offers a groundbreaking approach to pathogen identification, introducing a novel and efficient method to the field.

Practical Implementation of the Research

The practical implementation of label-free SERS technology combined with a deep learning CNN model enables rapid and accurate identification of pathogenic Vibrio microorganisms. This has important social implications, including improving public health and safety by quickly identifying and controlling outbreaks, enhancing food safety measures, and enabling real-time pathogen detection in resource-limited areas. The method’s speed and accuracy contribute to more informed decision-making, mitigating the spread of infectious diseases and ultimately creating a safer society.

Future Research Plans

Moving forward, future work in the field of label-free SERS technology for pathogen identification could focus on expanding the coverage to include a wider range of Vibrio species, increasing the diversity of the dataset used for training, conducting rigorous cross-validation and external validation studies, exploring integration with portable SERS devices for on-site detection, optimising the deep learning model for speed and efficiency, and investigating clinical and environmental applications. By pursuing these avenues, the research can further enhance the versatility, reliability, and real-world applicability of the method, leading to improved methods for rapid and accurate pathogen identification in various domains.

Collaborations

• Dr Jianfeng Li (College of Materials, State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China)
• Dr Lin Zhang (State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China)
• Dr Zehui Chen (Xiamen City Center for Disease Control and Prevention, Xiamen 361005, China)