Ved Srinivas

@tsm.ac.in

Area Chair, General Management
Thiagarajar School of Managment

EDUCATION

BA (Hons) Geography, MBA, MA(Intl Rel), M.Sc. (App Psychology), FDP-M, (IIM Ahmedabad), FDP-RM (IIM Indore), Ph.D. (Management)

RESEARCH, TEACHING, or OTHER INTERESTS

Organizational Behavior and Human Resource Management, Business and International Management, Communication, Business and International Management

Scopus Publications

107

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

The Impact of Green Human Resource Management on IT Employees’ Environmentally Eco-friendly Performance and Behavior: The Mediating Role of Organizational Commitment
KDV Prasad, Shivoham Singh, Ved Srinivas, Hemant Kothari, and Devendra Shrimali
QUBAHAN
This study examined the influence of green human resource management (GHRM) on information technology employees' environmentally friendly performance and eco-friendly conduct. This study also investigated the mediating role of organizational commitment on the nexus between GHRM and employees’ environmentally friendly and eco-friendly behavior. The data were gathered via a structured questionnaire. Data normality and questionnaire internal consistency and reliability were assessed measuring Chronbach’s alpha statistic, which reveals the data were normally distributed, and the questionnaire maintained internal consistency and reliability. 500 valid responses were analyzed and four reflective constructs, organizational commitment, green human resource management, organizations’ environmental performance, and employees’ eco-friendly behavior, were assessed via exploratory, confirmatory factor analysis, and hypotheses were tested via structural equation modeling analysis. The impact of green human resources was positive and statistically significant for all the study variables. Employees’ eco-friendly behavior has a positive and statistically significant impact on IT organizations’ environmental performance. Organizational commitment partially mediated the relationships between green human resource management and organizations’ eco-friendly behavior and environmental performance. The data were gathered using convenience sampling which is fast, cost-effective, easy and suitable for our research. However, the limitations, bias and generalizability were handled through common method bias and the size of 500 is very large for SEM studies and results can be generalizable to some extent. The results have several practical implications for green organizations in terms of recruiting, training, and reducing carbon footprints.

Impact of Workplace Relations on Employee Performance: Mediating Role of Turnover Intentions—An Empirical Study Concerning IT-Enabled Sector Employees in and Around Hyderabad
Ridhi Rani, K. D. V. Prasad, Ved Srinivas, and M. Alfiras
Springer Nature Switzerland

The relationship between work-life balance and psychological well-being: an empirical study of metro rail travelers working in the information technology sector
K. D. V. Prasad, Mruthyanjaya Rao, Rajesh Vaidya, Kottala Sriyogi, Shivoham Singh, and Ved Srinivas
Frontiers Media SA
ObjectivesTo investigate the relationship between work–life balance and the psychological well-being of metro rail travelers working in the information technology sector. The study also examined occupational stress as a pathway between work-life balance and psychological well-being. The study also investigated the impact of occupational stress and work–life balance on the psychological well-being of metro travelers who work in the information technology sector, modeling lower- and higher-order constructs.MethodsA quantitative survey method was used, and the data were gathered from information technology employees who frequently travel on Metro Rail to commute to the office and return home when the COVID-19 pandemic peaked in India in 2022. A structured questionnaire was developed, and a link was provided to the IT sector employees visiting almost all the metro stations in Hyderabad, an Indian Metro, to measure 8 reflective constructs. The data were gathered via random sampling, and the questionnaires were randomly distributed to the different IT sector companies. The valid responses of 500 participants were analyzed for structural equation modeling. The eight reflective constructs in the study are occupational stress, the 3 constructs of work–life balance—“work interference with personal life, personal life interference with work and work–personal life enhancement”—and the four constructs of psychological well-being autonomy, self-acceptance, positive relations, and environmental mastery.ResultsThe SEM results for the lower-order constructs indicate that the impact of occupational stress on psychological well-being was statistically significant (p &lt; 0.005), as were the two constructs of psychological well-being, environmental mastery, and self-acceptance (p &lt; 0.001; p &lt; 0.05). With respect to the impact of the work–life balance constructs, the impacts of the WIPL, WPLE, and PLIW work–life balance constructs were statistically significant (p &lt; 0.05; p &lt; 0.001, respectively) for all four psychological well-being constructs. Occupational stress partially mediated the relationship between work–life balance and psychological well-being, as both the direct and indirect effects were statistically significant when the higher-order constructs work–life balance and psychological well-being were tested. The direct effects of occupational stress and work–life balance on psychological well-being are statistically significant (p &lt; 0.05, p &lt; 0.001).ConclusionThe authors suggest framing policies to mitigate occupational stress and enhance the psychological well-being and work–life balance of employees in the information technology sector.

Advanced IoT and Machine Learning Techniques for Effective Heart Disease Diagnosis
Dankan Gowda V, M. Sathyanarayanan, Kirti Rahul Kadam, Ved Srinivas, Venkatesan Hariram, and KDV Prasad
IEEE
The worldwide statistics of heart diseases are increasing; therefore, new strategies for the prevention and effective treatment are becoming important. The findings of this study seeks to develop complex solutions involving IoT and ML in increasing the efficiency of diagnosis of heart diseases. The study establishes an IoT based system for health monitoring to collect essential cardiovascular data using wearable sensors. The data is processed using more sophisticated methods of machine learning, and the forecasted results are potential heart diseases. The effectiveness of the proposed system is assessed by using the large dataset yielded by such diagnostic measures as accuracy, sensitivity, and specificity enhanced when compared to conventional approaches. The results stress the importance of the synergy between two often-discussed buzzwords, IoT and ML, to transform the diagnosis of heart diseases and hence, help create the future intelligent healthcare systems.

Big Data-Driven Strategies for Growth in 3PL with Advanced IT Solutions
Kottala Sri Yogi, Suganthi. N, Dankan Gowda V, Attru HanumanthaRao, Ved Srinivas, and R. Kavitha
IEEE
The focus of this paper is to examine how different big data initiatives support the development and operations of third party logistics providers. Focusing on the use of advanced technologies like IoT, AI, and ML in the context of 3PL, the research has the objective to show how these innovations enhance efficiency, operating cost, and customer satisfaction. Information gathered from live monitoring, use of algorithms or predictive techniques, and integration of logistics optimization techniques are used to highlight performance enhancement. According to the proposed model, the processes related to the utilisation of big data and IT solutions are divided into several categories: big data acquisition, cloud computing, and subsequent constant evaluation. This study reveals insights that 3PL providers can create a sustainable competitive advantage to the clientele firms by adopting accurate demand forecasting, improvement of routes, and optimization of supply chain visibility. Thus, the findings of this study add to the current literature and practice by enumerating effective big data technologies to apply in logistics management and showcasing how these advancements can catalyze sustainable development.

A Novel Approach to Social Sustainability with Machine Learning and the Best Worst Method in Manufacturing
Kottala Sri Yogi, Franklin Jino R E, Dankan Gowda V, C. Ravindra Murthy, Veduri Veera Prasad, and Ved Srinivas
IEEE
This paper is aimed at presenting a novel approach for enhancing the measurement of social sustainability in manufacturing domain with the help of ML with BWM. Social sustainability when dealing with manufacturing is part of the four sustainability pillars that seeks to address aspects such as; welfare of workers, social organization, and practice within the firm. This is why the conventional tools may not sufficiently inform on the right combination of the said aspects. The information from the big data is then processed by the means of ML to disclose the patterns illuminating the stance influences and BWM is integrated to analyze specialists’ insight the power of evolving influential aspects of stance. They are useful in minimizing the time as well as the energy required in the formulation of the decisions because the details and correlations needed are already provided. It is illustrated with the help of an example in the manufacturing setting and significant improvements in the values of the social sustainability indices compared to the classical approach. From these conclusions, it is possible to indicate the prospects for the integration of ML and BWM as the authors’ contribution to tackling the problem of social sustainability in the manufacturing sector.

Scalable Machine Learning Frameworks for Large-Scale Multimodal Image and Speech Signal Processing
Dankan Gowda V, Disha Pathak, K D V Prasad, Ved Srinivas, Manu Y M, and N Sudhakar Reddy
IEEE
The machine learning algorithm proposed in this paper is suitable for Big Data multimodal datasets and in particular for integrating image and speech data. Preliminary feature extraction is based on convolutional neural network (CNN) for image analysis and recurrent neural network (RNN) for speech processing, while the fusion was done in a fusion layer. For assessment, the system’s performance is compared on the aspect of accuracy and latency, computational overhead, and resource consumption. The above experiment results prove that the proposed framework reduces the latency to 120ms and the required computational power, but the training and validation set accuracies as high as 90% and 87% are higher than the corresponding values of basic models such as CNN and hybrids of CNN+LSTM. It also illustrates the system’s scalability: inference time of less than 2s in the range of moderate batch size, and lower memory consumption (110 MB) in comparison with the baseline models. The characteristics of the model mean that it takes less resources to process data, making the model ideal for use in applications where there is a need for processing of data in real time.

Modeling Occupational Stress on Employee Performance with Mediating and Moderating Roles of Social Support: Structural Equation Modeling and Multivariate Analysis
KDV Prasad, Shivoham Singh, Rajesh Vaidya, Sripathi Kalavakolanu, and Ved Srinivas
Universal Wiser Publisher Pte. Ltd
Purpose: This empirical study investigated the relationship between occupational stress and employee performance and the mediating and moderating effects of social support on the relationship between occupational stress and the performance of IT sector employees in Bangalore city. Methodology: A quantitative methodology was used. The data were collected via a questionnaire to measure the three reflective constructs of the study: occupational stress, employee performance, and social support. Factor loadings > 0.5 for the items of all three constructs were considered for analysis. The questionnaire's internal consistency was measured by assessing Cronbach's alpha and the split-half correlation coefficient. SEM analysis was carried out on the valid responses of 500 responses via AMOS version 28. Findings: The results of the Shapiro-Wilk test for normality indicated normally distributed data. Excellent model fit was observed, as indicated by the model fit statistics. A statistically significant direct effect between occupational stress and employee performance and social support was observed, with both the variable performance of occupational stress and social support explaining 28% of the variance in the dependent variable. performance This study also examined the moderating role of social factors in the relationship between occupational stress and employee performance. Social support also moderated the performance of the IT sector employees. Positive and statistically significant moderating effects of social support on the relationship between occupational stress and employee performance were observed. The slope analysis revealed that social support strengthens the relationship between occupational stress and employee performance. The authors suggest that organizations adopt social support strategies, such as breaks, meditation, and yoga, to relieve stress and increase social support among employees. Originality: This study assessed the effects of modeling occupational stress on employee performance with mediating and moderating roles of social support via structural equation modeling analysis and multivariate analysis.

Relationship between employer branding and employee performance: Mediating and moderating effects of supportive work environment and compensation and benefits
Ved Srinivas, KDV Prasad, Ridhi Rani, and M Nisa
Conscientia Beam
Employer branding is a complex idea that is important in many different industries. It represents a company's ability to attract, engage and retain talent as well as its status as an employer. A strong employer brand transcends industry boundaries to impact the success and sustainability of businesses in today's competitive job market. The authors examined the effect of employer branding on employee performance by mediating and moderating roles of a supportive work environment and compensation and benefits respectively. The authors followed the quantitative methodology with a set of strategies and hypotheses and gathered numerical data. The data were gathered by surveying employees working in information technology sector companies in the city of Bangalore. The data analysis includes descriptive statistics, factor analysis and structural equation modeling analysis using SPSS and IBM AMOS version 28. The Cronbach’s alpha values of the reliability statistics ranged from 0.920 to 0.930 indicating the questionnaire’s internal consistency and reliability. The model fit indices CMIN/DF 1.389, CFI 0.966, GFI 0.952, TLI 0.961, IFI 0.966, NFI 0.904, SRMR 0.060, RMSEA 0.051 and PClose 0.448 indicate an excellent fit of the model. The constructs of compensation and benefits and a supportive work environment partially mediate employee performance through employer branding. The moderator’s supportive work environment strengthens the positive relationship between employee branding and employee performance. Strong employer branding is important for attracting top talent and improving employee performance in various industries but it is especially important in the IT sector.

Effect of organizational citizenship behavior on psychological well-being: Mediating and moderating effects of emotional intelligence: An empirical study of it-enabled industry employees in Hyderabad
KDV Prasad, Sripathi Kalavakolanu, Rajesh Vaidya, Santosh Aghav, and Ved Srinivas
Conscientia Beam
The purpose of this study is to investigate the relationship between organizational citizenship behavior and psychological well-being and assess the moderating and mediating effects of emotional intelligence (EI) on the psychological well-being of Information Technology-enabled employees in Hyderabad. Survey research with quantitative study methodology was applied, and a structured questionnaire was used to collect the data. The effects of organizational citizenship behavior and emotional intelligence on psychological well-being of IT-enabled employees were assessed. The mediating and moderating effects of emotional intelligence on the relationship between organizational citizenship behavior and psychological well-being were assessed. The assessed Cronbach’s alpha coefficient, which ranged from 0.79 to 0.91, indicates the questionnaire maintained its reliability and internal consistency. The SEM analysis results revealed excellent model fit, and the impact of OCB and EI on psychological well-being was statistically significant (p<0.001) on the IT-enabled industry employees. Furthermore, EI partially mediated psychological well-being through the OCB of information technology employees. The slope analysis reveals that emotional intelligence strengthens the positive association between the OCB and the PWB of IT-enabled sector employees. EI and OCB enhance PWB and employee performance. The study's conclusions can be utilized to create employee promotion plans for OCB and EI, which have several significant ramifications for IT companies. Therefore, the organizations should try to enhance the EI and OCB of employees and develop a supportive culture within the organizations. There are some subjectivity and cultural issues that were elaborated at the end.

Optimizing IoT Device Networks with Edge Computing to Address Latency and Bandwidth Constraints
Naziya Hussain, Dankan Gowda V, Chitta Shyamsunder, Ved Srinivas, Ridhi Rani, and M Balaji
IEEE
The enormous interconnected devices or the ‘Internet of Things' are one of the main concerns when it comes to dealing with the network latency and bandwidth. Regular cloud computing offerings are limited because of the relatively large latency and the high amount of bandwidth needed to send data to central points. This research study is focusing on the utilization of the edge computing for managing the challenges concerning the IoT device networks. Edge computing is a form of data processing that is done closer where the data is generated thus reducing latency issues and the bandwidth consumed in the system. The novel architecture that has been suggested entails utilization of edge devices where the processing tasks are offloaded from the cloud; descriptions of the simulations that have been run to analyze the proposed architecture as well as the result that show significant enhancement on the latency and bandwidth. The presented results reveal that edge computing can be used to improve the efficiency of the IoT network and its applicability across different domains.

A Novel Approach to Enhancing Manufacturing Efficiency and Quality Control with Industrial IoT
V Dankan Gowda, G Dakshayini, Shivoham Singh, Ved Srinivas, Ridhi Rani, and Namineni Gireesh
IEEE
The application of the IIoT system has appeared as a relatively innovative method to optimize manufacturing effectiveness in the last several years. Therefore, this study presents a new framework that incorporates IIoT services for the enhancement of manufacturing processes and quality assurance. The key components include the use of real-time data capture, analysing and decision supporting systems with the goal of enhancing production operations, reducing excessive time and decline in product quality. It also showed that the effectiveness in manufacturing and the establishment of quality control proved to be much effective in comparison to conventional procedures. The above approach is capable of enriching techniques in smart manufacturing and can be implemented at scale across the industries.

Machine Learning Applications in Azure for Enhanced E-commerce Customer Sentiment Analysis
Tanmoy De, Dankan Gowda V, Pooja Thirani, KDV Prasad, Ved Srinivas, and Namineni Gireesh
IEEE
This study performs both qualitative and quantitative examination of sentiment analysis applied to the E-Commerce platforms with machine learning technologies being the main component which are run on Microsoft Azure cloud Through a high focus on customers’ emotional targets and feedback, it is assumed that this study will follow the effects of sentiment analysis on e-commerce businesses in general. According to our analysis from the leading e-commerce A-to-Z and grocery companies in India for the past three years through the power of machine learning on Azure we are able to weave intelligence into your solutions. The work describes the final research that globalizes sentiment analysis in the process of preceding customers’ interaction. Moreover, another point of discussion revolves around possibility of the advantages, including weaknesses, that come from conducting reviews of online customers. The research which encompasses this provides the significant contribution on exploiting machine learning for doing the improvement of e-commerce experience with the in depth analysis of sentiment.

Accelerating Sustainability through Leveraging Machine Learning to Analyze CSR Spending in the Indian Automobile Industry
Kottala Sri Yogi, Dankan Gowda V, Anwesha Pati, Ved Srinivas, Santosh Aghav, and Ibrahim Abdullah
IEEE
This paper uses machine learning to assess Corporate Social Responsibility spending trends on the Indian automobile sector between 2016 and 2021. Simply put, the aim of the research is to recognize trends and patterns in sustainability expenditures. More specifically, the study examines how businesses allocate money across the three important aspects of the sustainability report: People, Planet, and Profit Purcell, 2020. The research is based on a largely industry-based database obtained from public companies’ records. The use of advanced statistical techniques, such as machine learning and random forest modeling, enables one to examine the impact of financial allocations on sustainability achievements. Increments in CSR spending are related to enhanced company sustainability performance in terms of correlation outcomes. The paper also recognizes areas where cost cuts have the best returns, as well as shortfalls in the existing CSR approach, offering a strategy for possible development’s.

Impact of Machine Learning on Applying the Best Worst Method for Social Sustainability in Manufacturing Supply Chains
Kottala Sri Yogi, Dankan Gowda V, Atul Kumar Sahu, Madan Mohanrao Jagtap, Ved Srinivas, and Ibrahim Abdullah
IEEE
This research is carried out merging ML with BWM to intensify decision making in manufacturing supply chain using social sustainability under light. As companies become more focused on environmental sustainability, the capability of machine learning to effectively assess and prioritize sustainability criteria should be highly valued. Through a team of domain experts, the research clearly identifies the most vital social sustainability factors and, afterwards, deploys ML algorithms to analyze and optimize the provided decision-making framework by BWM. The selected approach gives possibility to dynamic weighting of the criteria which is based on real-time data. This way we are able to come up with the more precise and effective sustainability strategies. The results show that the hybrid ML-BWM model is not only an effective tool to enhance the validity and accuracy of evaluations but also one that provides the prudent directions that help the manufacturing sector shape its choice and responses. This paper analyzes the possible outcome of the integration of AI and traditional approach to decision-making process on social aspects of sustainability within supply chain.

Nexus between organizational citizenship behavior and psychological wellbeing: emotional intelligence as a pathway
KDV Prasad, Shivoham Singh, Ved Srinivas, Rajesh Vaidya, and Krishna Kant Dave
Frontiers Media SA
Aim/purposeThe aim of this study is to investigate the nexus between organizational citizenship behavior and psychological wellbeing and assess the moderating and mediating effects of emotional intelligence (EI) on the relationship betwem psychological wellbeing of IT-enabled Sector employees in Hyderabad.Design/methodology/approachTo measure the study variables of organizational citizenship behavior (OCB) and emotional intelligence (EI) on psychological wellbeing (PWB) data were gathered using a questionnaire. The mediating and moderating effects of emotional intelligence on the relationship between organizational citizenship behavior and psychological wellbeing was also assessed. The was reliable as indicated by the Cronbach's alpha coefficient statistic that between 0.79 to 0.91.FindingsThree hundred valid responses were considered for SEM analysis using AMOS, version 28. The model fit indices indicate excellent fit: CMIN/DF 2.788 CFI 0.935, IFI 0.937, TLI 0.921, NFI 0.923, RMSEA 0.054, SRMR 0.077 and PClose 0.092. The SEM analysis revealed that the impact of exogenous variables OCB and EI were statistically significant (p &lt; 0.001) on endogenous variable psychological wellbeing of IT-enabled industry employees. Furthermore, EI partially mediates psychological wellbeing through the OCB of information technology employees. This empirical study also examined the moderating effects of EI on the psychological wellbeing of information technology-enabled employees through OCB. The slope analysis reveals that emotional intelligence strengthens the positive association between OCB and the PWB of IT-enabled sector employees. EI and OCB enhance PWB and employee performance.Research implications/limitationsThe findings of this study have several important implications for organizations in the IT sector and can be used to develop strategies for promoting OCB and EI among employees. The structural relationships between PWB and OCB in the context of hotel employees and reported positive effects of OCB on hotel employees are well documented. The limitations are the data were collected from the Information Technology employees of Hyderabad Metro. There are some subjectivity and cultural issues which were elaborated at the endContribution/OriginalityThis empirical study helps to clarify the relationship between organizational citizenship behavior, psychological wellbeing, and the mediator and moderator variable emotional intelligence. The study also comprehends the available literature and adds value to the existing theoretical knowledge and behavioral studies.JEL classificationM10 M12, M19.

Remote learning and exploring the factors affecting students' adoption of behavioral intentions toward conference applications
K.D.V. Prasad, Shivoham Singh, and Ved Srinivas
Emerald
PurposeThe authors investigated whether remote learning and its associated factors affect students’ adoption of Zoom, Microsoft Teams, Blue Jeans and other conference applications.Design/methodology/approachThe study used a quantitative design; data were collected by surveying B-school students in Hyderabad using a questionnaire prepared adopting the validated scales. About 33 items were used to measure nine reflective constructs: remote learning, performance expectancy, adoption behavioral intention, institutional support, ecological acceptance, habit formation, hedonic motivation, attitude towards conference apps and social influence. The exploratory and confirmatory factor analyses were carried out, and hypotheses were tested using IBM SPSS and AMOS version 28.FindingsA 61% variance in students’ adoption behavioral intentions and a 37% variance in students’ attitude towards conference apps are accounted for by remote learning, performance expectancy, institutional support, ecological acceptance, habit formation, hedonic motivation and social influence. The exogenous constructs of institutional support, environmental acceptance, habit formation and social influence are statistically significant and influence students’ adoption and behavioral intentions toward conference applications. The attitude towards conference apps fully mediated the relationship between students’ adoption behavioral intentions and performance expectancy. However, the constructs of environmental concern, social influence and habit formation are partially mediated. This study provides empirical evidence that attitude towards conference apps, environmental acceptance, performance expectancy, institutional support, habit formation and social influence are the key predictors of remote learning and students’ adoption of and conference applications.Research limitations/implicationsThis study was limited to the B-schools of Hyderabad city, an Indian metro. To encourage students to adopt remote learning through conference apps, academicians should appropriately illustrate the idea of remote learning. To enable students to learn while on the go, educational institutions should offer intuitive applications with enhanced reading layouts. Second, since internet access is required for remote learning, this study is crucial for service providers. To make it simpler to obtain educational resources, the internet should be more widely accessible. Third, since technology is linked to remote learning, this type of study is essential for the education sector since devices need to be developed.Practical implicationsThe pandemic has caused restructuring of the educational system, necessitating new strategies for distance and virtual learning for teachers. In the future, teachers will adopt techniques centered around the use of virtual platforms, social media and video production. The government should establish sufficient infrastructure to facilitate online education and assist instructors in becoming more knowledgeable and proficient in the use of technology, especially when creating, executing and assessing online instruction.Originality/valueThe purpose of this study is to determine how beneficial it is to use online/remote learning with Zoom, BlueJeans, Microsoft Teams and other conference software in particular. Both the online/remote learning method itself and the learners' capacities and capabilities for adjusting to new normal scenarios should be developed in educational environments.

Advanced Machine Learning Approaches to Evaluate User Feedback on Virtual Assistants for System Optimization
Disha Pathak, Dankan Gowda V, K. Manivannan, Santosh Aghav, Ved Srinivas, and Namineni Gireesh
IEEE
ChatGPT, a language model developed by OpenAI based on GPT (Generative Pre-trained Transformer) technology, generates coherent and contextually relevant responses from a diverse range of web content. Its influence on Generation Z extends beyond education, posing significant implications for writing skills during formative years, potentially leading to dull, voiceless prose. This research study examines the multifaceted impact of ChatGPT on Gen Z, focusing on its role in shaping learning, fostering creativity, and addressing ethical considerations such as bias and privacy concerns. The study emphasizes the importance of responsible use and digital literacy skills. It finds that ChatGPT has become a critical tool for Gen Z, enhancing information access, transforming communication patterns, and enriching learning experiences. The research recommends equipping Gen Z with essential digital literacy skills to navigate the ethical challenges posed by AI technologies like ChatGPT.

Impact of Drone and Big Data Integration on Supply Chain Efficiency and Operations
Chitta Shyamsunder, Dankan Gowda V, Hariprasad Soni, Ved Srinivas, Santosh Aghav, and Ibrahim Abdullah
IEEE
This paper focuses on analyzing the effects that are characterized by the use of drones in supply chain management and the role played by big data integration. The analysis of the contemporary trends that take place in the supply chain industry has demonstrated that considering the use of drones and IoT the companies have attainable opportunities to become more effective and transparent. Depending on what type of sensors and cameras are mounted onto an unmanned aerial vehicle, it can collect tremendous amounts of over the airspace. When used with big data analytics, this information can be used for logistics, observing shifts in users' behavior and last-mile delivery. In this paper, the importance of the big data analytics in procurement and supply chain management decision making process will be evaluated, usage of drones in the last mile delivery will be discussed, and dependence of both the drone and big data technologies in supply chain management innovations will be established.

Smart Urban Ecosystems with IoT-Based Strategies for Traffic Optimization and Pollution Control
V Dankan Gowda, Swati Patil, Ved Srinivas, Kdv Prasad, Madan Mohanrao Jagtap, and Mandeep Kaur
IEEE
The current research is directed toward the usage of Internet of Things (IoT) solutions for the improvement of urban environment by joining traffic optimization and pollution management. Through the utilization of data-driven analysis and MATLAB simulations, research evaluates the effects of implemented IoT technologies on traffic management in urban areas and emission reduction. The research concludes that the IoT-related initiatives can make a great difference in improvements of traffic flow, reducing the levels of principal pollutants, and inspiring the move towards more environmentally-friendly modes of transport. Moreover the paper investigates the system’s resilient nature and energy efficiency making the case for a technological paradigm shift that will transform cities planning and governance. In addition to these challenges like privacy, data security, access of equitable technologies is also examined. The results of the survey strongly point out to the transformative power of IOT in the creation of cities which are pragmatic, sustainable and liveable moreover showing a future where smart technologies are indispensable part in the solving of the urban complexities and environmental management.

Scaling up "sustainability development": Analyzing the intricacies and application of blockchain technology vis-à-vis financial markets
Ridhi Rani, Ved Srinivas, and Anita Sable
IGI Global
Blockchains are currently recognized as a new area of digitization with difficulties in attaining sustainability. The research has not yet produced a unified framework for understanding the characteristics of blockchains that allow the development of financial trading markets over the long term. The topic of how blockchains support the growth of sustainable financial trading platforms and services still exists. A qualitative and iterative investigation was done to compile the basic concepts found in the literature to provide a conceptual framework with four sustainability themes and eleven types of affordances. The results are significant because they show how closely related these categories are, leading to conflicts between various sustainability-related concepts.

Wi-Fi Router Signal Coverage Position Prediction System using Machine Learning Algorithms
Rajalaxmi Hegde, Sandeep Kumar Hegde, Kdv Prasad, Ved Srinivas, Tanmoy De, and V Dankan Gowda
IEEE
As a popular topic, indoor positioning has gradually drawn the attention of both academia and business. Numerous location based services including healthcare, repository tracking, and security call for accurate estimation. An accurate estimation might be achieved by using additional location-sensing equipment, but this is not generally done because it would result in expensive brand specialization. A flexible and affordable location determination technique that exploits the already-existing WLAN infrastructure in indoor spaces has been designed without incurring additional costs, among all suggestions in the literature that include hardware and highly complex computations.This positioning strategy is becoming more popular. Soon in actual surroundings, WLAN will be able to be employed as part of an indoor positioning system. In comparison to similar systems, it is a good option in terms of accuracy, precision, and cost. It has also become the most user-friendly way, particularly with the widespread use of smartphones and tablet computers. In the literature, many machine learning algorithms such as cluster-filtered KNN and fuzzy c means algorithms have been proposed for the WiFi router signal coverage position prediction system but the disadvantage of these approaches is that it will consume much pre-processing time for reference points data and also these approaches found to be less accurate. In the proposed study, adaptive KNN-based machine learning model has been proposed for the WiFi signal coverage prediction system. The proposed method adjusts the value of K for each position by examining the relationship between the K value and the intensity of the received WiFi signal. This technique improves positioning accuracy by more than 30% when compared to the existing approach. The experimental results are conducted by applying various machine learning algorithms. The experimental finding demonstrates that the proposed approach obtained better results compared to traditional algorithms.

A Framework for Smart City Implementation using IoT-Cloud Based System Architecture
V Dankan Gowda, KDV Prasad, Tanmoy De, Ved Srinivas, N Anil Kumar, and Tejashree Tejpal Moharekar
IEEE
The Internet, a key participant (world wide web) in worldwide information exchange and media sharing, has experienced cumulative upgrades, developments, and given up consequent product IOT, making technology affordable and practical for all uses. Research development includes smart sensors, a communication channel and protocols for exchanging data, and peripherals like an Arduino, a Texas Instruments MSP430G2553, a Weather-proof Tx. Rx. Kit (for exchanging data), a Data Receptor and Decoder Shield for Lab View Kit (for exchanging data), an addressing and de-addressing data module, a communication shield, etc. It is now possible to communicate from a desktop computer or an Android smartphone to a portable device that has an embedded device, such as a microcontroller, a microprocessor, or a sensor. In other words, IOT combines always-on networking, always-on computation, and always-on intelligence. The Internet of Things facilitates communication between the central hub (fitted with a Lab-View interface) and the distributed modules (found in places like parks, subways, and highways) that control the lights. The Internet of Things is also useful for keeping the overall cost of setup and maintenance within reasonable economic bounds. The planned development takes into account IoT on two main scales: city-wide automation (also known as smart city) and home-level automation (also known as covering up).

Approaches for Advanced Spectrum Sensing in Cognitive Radio Networks
Abhay Chaturvedi, Kdv Prasad, Sudhanshu Kumar Jha, Ved Srinivas, N Anil Kumar, and V Dankan Gowda
IEEE
Intelligent learning and adaptation allow cognitive radios (CRs) to maximise available spectrum while maintaining stable connections. Cognitive radio technology may detect unused channels of radio frequency space via a process called “spectrum sensing," and then get rapid and easy access to such channels. An important aspect of CR technology is spectrum sensing, which allows CRs to detect gaps in the spectrum. It’s the practise of keeping tabs on a certain radio spectrum band on a regular and changing basis to see if there are any interference issues that might prevent its utilisation. Noise power and signal fading in a wireless channel have a significant impact on the efficiency of spectrum sensing techniques in noisy situations. Another difficulty for sensing algorithms involving a single secondary user is the dilemma of a hidden main user in shadowed areas. Algorithms for cooperative spectrum sensing must take into account a number of aspects, including sensing time, speed, cooperation overheads, and decision fusion methods. In order to provide efficient and adaptable IoT networking, many wireless technologies have emerged in recent years. One of the primary technologies that provides opportunistic connection to a wide variety of interconnected IoT devices is cognitive radio (CR), which makes software-defined radio possible. An unmanaged and unregulated invasion of privacy through low-powered wireless sensors into the Internet of Things (IoT). The output of such networks is dependent on the main consumer’s observed spectrum pattern because of the networks’ opportunistic nature.

Analysing the Mental Health and Well Being of Entrepreneurs

RECENT SCHOLAR PUBLICATIONS

The impact of mobile-wallet factors on customer satisfaction and customer loyalty: A study of B-schools in Hyderabad
H Soni, C Shyamsunder, S Singh, V Srinivas
Qubahan Academic Journal 5 (1) 2025

The relationship between work-life balance and psychological well-being: an empirical study of metro rail travelers working in the information technology sector
KDV Prasad, M Rao, R Vaidya, K Sriyogi, S Singh, V Srinivas
Frontiers in Psychology 15, 1472885 2025

Leveraging the FreeTTS Library in ATM Voice Over-A Java-based System for Enhanced Accessibility for Visually Impaired Bank Users
J Benita, YVV Satish, M GopiChandu, NS Reddy, VRM Srinivas
2025 6th International Conference on Mobile Computing and Sustainable 2025

Impact of Workplace Relations on Employee Performance: Mediating Role of Turnover Intentions—An Empirical Study Concerning IT-Enabled Sector Employees in and Around Hyderabad
R Rani, KDV Prasad, V Srinivas, M Alfiras
Business Sustainability with Artificial Intelligence (AI): Challenges and 2024

Scalable Machine Learning Frameworks for Large-Scale Multimodal Image and Speech Signal Processing
D Gowda, D Pathak, KDV Prasad, V Srinivas, M YM, NS Reddy
2024 8th International Conference on I-SMAC (IoT in Social, Mobile 2024

Remote learning and exploring the factors affecting students' adoption of behavioral intentions toward conference applications
KDV Prasad, S Singh, V Srinivas
Journal of Applied Research in Higher Education 2024

Big Data-Driven Strategies for Growth in 3PL with Advanced IT Solutions
KS Yogi, D Gowda, A HanumanthaRao, V Srinivas, R Kavitha
2024 1st International Conference on Advanced Computing and Emerging 2024

Advanced IoT and Machine Learning Techniques for Effective Heart Disease Diagnosis
D Gowda, M Sathyanarayanan, KR Kadam, V Srinivas, V Hariram, ...
2024 1st International Conference on Advanced Computing and Emerging 2024

A Novel Approach to Social Sustainability with Machine Learning and the Best Worst Method in Manufacturing
KS Yogi, FJ RE, D Gowda, CR Murthy, VV Prasad, V Srinivas
2024 1st International Conference on Advanced Computing and Emerging 2024

Nexus between organizational citizenship behavior and psychological wellbeing: emotional intelligence as a pathway
KDV Prasad, S Singh, V Srinivas, R Vaidya, KK Dave
Frontiers in Psychology 15, 1389253 2024

Machine Learning Applications in Azure for Enhanced E-commerce Customer Sentiment Analysis
T De, D Gowda, P Thirani, KDV Prasad, V Srinivas, N Gireesh
2024 7th International Conference on Circuit Power and Computing 2024

Accelerating Sustainability through Leveraging Machine Learning to Analyze CSR Spending in the Indian Automobile Industry
KS Yogi, D Gowda, A Pati, V Srinivas, S Aghav, I Abdullah
2024 7th International Conference on Circuit Power and Computing 2024

Impact of machine learning on applying the best worst method for social sustainability in manufacturing supply chains
KS Yogi, D Gowda, AK Sahu, MM Jagtap, V Srinivas, I Abdullah
2024 7th International Conference on Circuit Power and Computing 2024

Optimizing IoT Device Networks with Edge Computing to Address Latency and Bandwidth Constraints
N Hussain, D Gowda, C Shyamsunder, V Srinivas, R Rani, M Balaji
2024 5th International Conference on Electronics and Sustainable 2024

A Novel Approach to Enhancing Manufacturing Efficiency and Quality Control with Industrial IoT
VD Gowda, G Dakshayini, S Singh, V Srinivas, R Rani, N Gireesh
2024 5th International Conference on Electronics and Sustainable 2024

Neuromarketing's Impact on Buying Intentions: The Mediating Influence of Ethics
S Singh, KDV Prasad, D Shrimali, V Srinivas, D Hiran
Pacific Business Review International 17 (2) 2024

Effect of self-consciousness on e-learning attitudes among high school students, Hyderabad, India
R Rani, KDV Prasad, V Srinivas
Cadernos de Educao Tecnologia e Sociedade 17 (se2), 68-78 2024

Impact of Drone and Big Data Integration on Supply Chain Efficiency and Operations
C Shyamsunder, D Gowda, H Soni, V Srinivas, S Aghav, I Abdullah
2024 2nd International Conference on Sustainable Computing and Smart Systems 2024

MOST CITED SCHOLAR PUBLICATIONS

Wi-Fi Router Signal Coverage Position Prediction System using Machine Learning Algorithms
R Hegde, SK Hegde, K Prasad, V Srinivas, T De, VD Gowda
2023 International Conference on Sustainable Computing and Smart Systems 2023
Citations: 16

A study on marketing practices followed by Tomato growers and source of market Information
MV Srinivas, YB Venkatareddy, BSL Reddy
International journal of marketing and human resource management 5 (4), 1-5 2014
Citations: 12

Student stress and its association with student performance and psychological well-being: an empirical study on higher academic education students in and around Hyderabad metro
K Prasad, R Mookerjee, R Rani, V Srinivas
International Journal of Professional Business Review: Int. J. Prof. Bus 2022
Citations: 11

Web Based Hospital Management System
AC Babu, VNCS Teja, AD Reddy, EN Kumar, V Srinivas
2023 9th International Conference on Advanced Computing and Communication 2023
Citations: 10

Detection of traffic congestion from surveillance videos using machine learning techniques
SG Rao, R RamBabu, BSA Kumar, V Srinivas, PV Rao
2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile 2022
Citations: 10

Marketing behaviour of vegetable growers.
MV Srinivas, BSL Reddy, YBV Reddy
2016
Citations: 7

Approaches for advanced spectrum sensing in cognitive radio networks
A Chaturvedi, K Prasad, SK Jha, V Srinivas, NA Kumar, VD Gowda
2023 7th International Conference on Intelligent Computing and Control 2023
Citations: 6

Modelling and performance analysis of aero piston engine
AK Kumar, V Srinivas, C Murthy
Journal of Advanced Research in Dynamical and Control Systems 10 (7), 344-348 2018
Citations: 5

Smart Urban Ecosystems with IoT-Based Strategies for Traffic Optimization and Pollution Control
VD Gowda, S Patil, V Srinivas, K Prasad, MM Jagtap, M Kaur
2024 2nd International Conference on Advancement in Computation & Computer 2024
Citations: 4

Analysing the Mental Health and Well Being of Entrepreneurs
M Nisa, V Srinivas, R Rani, KDV Prasad, T De
Journal for ReAttach Therapy and Developmental Diversities 6 (4), 369-377 2023
Citations: 2

Scaling up “sustainability development”: analyzing the intricacies and application of blockchain technology vis--vis financial markets
R Rani, V Srinivas, A Sable
Corporate sustainability as a tool for improving economic, social, and 2023
Citations: 2

Predict the Performance Analysis of Supervised Learning Techniques Using Heart Disease Database
SG Rao, PC Reddy, V Srinivas, BSA Kumar
2021 2nd International Conference for Emerging Technology (INCET), 1-7 2021
Citations: 2

VLSI Implementation of Signed Multiplier using Quaternary Signed Digit Number System
G Rajkumar, V Srinivas
International Journal of Scientific Engineering and Technology Research 4 2015
Citations: 2

Neuromarketing's Impact on Buying Intentions: The Mediating Influence of Ethics
S Singh, KDV Prasad, D Shrimali, V Srinivas, D Hiran
Pacific Business Review International 17 (2) 2024
Citations: 1