Decision Sciences, Multidisciplinary, General Business, Management and Accounting
30
Scopus Publications
Scopus Publications
Deep Learning-Driven Steering Angle Prediction and Scene Understanding via Harmonic Carpet Weaver Optimization Jayashree Pradip Tamkhade, Sumalatha Bandari, Krishna Murthy Inumula Journal of Field Robotics, 2026 Steering angle prediction refers to forecasting the steering angle as early as possible in advance to ensure smooth and precise vehicle control. Therefore, this article introduces a novel approach, called Quasi Recurrent Neural Network with Harmonic Carpet Weaver Optimization (QRNN_HarCWO), for predicting the steering angles. Primarily, the input video is assimilated from the database, and later, the video is split into multiple frames. Then, the pre‐processing procedure is done by the Gaussian filter, which is exploited to denoise the video frame. Subsequently, the pre‐processed output is fed into the scene understanding module, where semantic segmentation is performed by exploiting BlitzNet with the BiTopK loss function. Here, the BiTopK loss function is a new loss function formulated using binary cross‐entropy and the TopK loss functions. Further, Harmonic Carpet Weaver Optimization (HarCWO) is utilized to train the BlitzNet. Finally, the Quasi‐Recurrent Neural Network with HarCWO (QRNN_HarCWO) is used for performing steering angle prediction, based on which appropriate control action is taken. Moreover, the presented approach gained the minimum values of Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percent Bias (PBIAS) of 0.0368, 0.1918, 0.1766, and 0.0589. Also, the maximum values of 92.877%, 94.887%, 90.766%, and 88.876% are attained by the QRNN_HarCWO model for accuracy, True Positive Rate (TPR), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) metrics. While the results demonstrate high accuracy, potential real‐world challenges such as sensor noise and hardware limitations may affect the deployment of the model in real settings, which need to be addressed in future work.
RNARXNet: recurrent non-linear autoregressive network with exogenous inputs for solar power forecasting using time series data Kiran Prabhakar More, Shweta P. Sondawale, Krishna Murthy Inumula Statistics, 2026 This paper proposes a Recurrent Non-linear Autoregressive Network with Exogenous inputs (RNARXNet) for Solar Power Forecasting (SPF) using time series data. Initially, input time series data is acquired from the Solar Power Generation dataset. Next, technical indicators such as Stochastic Oscillator (STOCH), Aroon (AR), Williams % R (WillR), Time Series Forecast (TSF), Moving Average Convergence Divergence (MACD), and Rainbow Moving Average are extracted. Afterwards, feature selection is done based on Hamming distance. Lastly, SPF is carried out utilizing RNARXNet which is newly modelled by integrating the Recurrent Radial Basis Function network (RRBFN) with a Non-linear Autoregressive Network with Exogenous inputs (NARX). The model obtained a normalized Mean Absolute Error (MAE) of 0.160, normalized Mean Square Error (MSE) of 0.352, normalized R2 of 0.933, and normalized Root Mean Square Error (RMSE) of 0.594. These values show that RNARXNet is reliable and effective for precise SPF.
The Role of Sustainability in Shaping Customer Perceptions at Farmers’ Markets: A Quantitative Analysis Fida Ragheb Hassanein, Sandip Solanki, Krishna Murthy Inumula, Amira Daouk, Nadine Abdel Rahman, Suha Tahan, Samah Ibnou-Laaroussi Sustainability Switzerland, 2025 Purpose—This research paper examines the critical factors in customer satisfaction while purchasing fruits and vegetables at farmers’ markets. Design/methodology/approach—This study was conducted using a prepared questionnaire to collect data on a random sample of 235 customers of farmers’ markets in the state of Maharashtra, India. The research was carried out in the year 2023. Seven hypotheses were tested concerning the relationships between the variables of interest. The variables of convenience, variety, quality, price, health and hygiene, and service conditions were used as independent constructs and were proxied by reflective indicators. Customer satisfaction and customer loyalty were treated as an exogenous variable and an endogenous variable, respectively. Structural equation modeling was used to investigate the model relationships and confirm the theoretical model. Findings—The findings validate all the reflective indicators used in the study. The latent variables of convenience, variety, quality, price, health and hygiene, and service conditions positively and significantly affect customer satisfaction, and customer satisfaction positively and significantly affects customer loyalty toward farmers’ markets. The structural equation explains approximately 55% of the variation in customer satisfaction related to convenience, variety, price, quality, health and hygiene, and service conditions. Significance—The study results provide insights into the factors that influence consumer behavior and attitudes toward farmers’ markets. By identifying these predictors, this study can help farmers’ markets and other stakeholders develop effective marketing strategies to attract and retain customers, ultimately promoting sustainable food production and consumption. Additionally, the results can inform policymakers on how to support and promote farmers’ markets as healthy and sustainable food sources. Practical implication—By implementing the practical suggestions derived from the implications of this research, farmers’ markets can optimize customer satisfaction, boost customer loyalty, and reinforce their position as valuable contributors to local communities’ well-being and sustainability. Originality/value—The acceptance of farmers’ markets in India as an alternative shopping destination for fruits and vegetables is gradually increasing. This exploratory study conducted on farmers’ markets examined several factors, including price, in assessing customer satisfaction and farmers’ markets’ effectiveness at positioning themselves as shopping destinations for consumers in India.
Machine Learning and Secure Image Transmission for Disease Forecasting Angotu Saida, Mallareddy Adudhodla, Niladri Maiti, Krishna Murthy Inumula, G. Kalaiarasi, Lakshmi Chandrakanth Kasireddy Advanced Secure Transmission of Telemedicine Based Bio Medical Images, 2025 The transformative potential of machine learning (ML) in disease forecasting is emphasizes how crucial secure transmission techniques are to maintaining the integrity of patient data. With the growing reliance of healthcare on sophisticated data analytics, machine learning (ML) models have become indispensable instruments for forecasting disease outbreaks and enhancing patient results. ML algorithms are able to detect patterns and trends that help with early detection and intervention in a variety of health conditions by examining large datasets that include clinical data, medical images, and environmental factors. But considering the increasing frequency of cyberattacks and data breaches in the healthcare industry, it is critical that this sensitive data be transmitted securely.
Role of Secure Image Transmission in Agricultural Health Monitoring Daneshwari A Noola, Chinnem Rama Mohan, Shailendra M. Pardeshi, Krishna Murthy Inumula, P. Asha, Bhawna Janghel Rajput Advanced Secure Transmission of Telemedicine Based Bio Medical Images, 2025 Digital technology adoption for health monitoring has become essential in the quickly changing agricultural landscape. In order to protect sensitive data that is gathered from a variety of sources, including drones, sensors, and satellites, this chapter examines the crucial role that secure image transmission plays in agricultural health monitoring. In order to preserve data integrity and guarantee that agricultural stakeholders receive timely and accurate information for efficient decision-making, secure transmission techniques such as encryption and end-to-end protocols are crucial. This chapter outlines the many advantages of secure image transmission, such as improved data integrity, prompt decision-making, heightened stakeholder trust, and defense against cyberattacks. Strong security measures are also required for compliance with data privacy regulations and efficient resource management as agricultural data becomes more digitalized.
IoT-Driven Secure Data Sharing in Telemedicine Systems Megha Mudholkar, Pankaj Mudholkar, S. Prince Chelladurai, Krishna Murthy Inumula, M. Selvi, Joshuva Arockia Dhanraj, A. S. Hovan George Advanced Secure Transmission of Telemedicine Based Bio Medical Images, 2025 The crucial part that connected devices play in the process of transforming the delivery of healthcare through telemedicine, with an emphasis on the potential of these devices to improve patient care, increase access to services, and make it easier to monitor health conditions in real time. The need for robust security measures to protect sensitive patient data has emerged as a primary concern as the integration of these technologies in healthcare facilities becomes increasingly widespread. There is an immediate need for ongoing research and development in the field of secure telemedicine systems. As the state of technology continues to advance, healthcare organizations are required to remain one step ahead of new dangers by continuously enhancing their security protocols and compliance measures.
Mitigating Environmental Impacts Through Modal Shifts: A Life Cycle Assessment of India's Freight Transport Infrastructure Krishna Murthy Inumula, Misbah Hayat International Journal of Transport Development and Integration, 2025 This research emphasizes analyzing existing transport logistics systems of the state, detecting problems within every mode of transport, and proposing solutions for them to advance towards the sustainable development of multimodal logistics.It also looks into how the nation's logistic infrastructure can be optimized, and challenges associated with shifting from one mode of transport to another within the Indian transport system are considered as such changes are deemed necessary to remedy the structural imbalance.Exante and ex-post evaluations of the funding strategies were carried out as life cycle assessments using OpenLCA.The software and eco-invent database concluded that the new modal infrastructure would be less damaging when utilized than the available one.Building rail shipments' share of the total to 45% would significantly mitigate the adverse effects on the environment that the current structure of the modalities of freight transport.In addition, it was found that, hence why the changes were made, the displacement of transportation brought down global warming impacts by a commendable 9%, as well as the effects of emissions in ecotoxicity in the land, ocean and freshwater by 20% on average.These results highlight the need to boost rail traffic and build railway infrastructure as the most efficient strategy towards positive outcomes.The research admits some data-sourced weaknesses, but it contributes to appreciating the need to put in place an appropriate transport system that is environmentally sound for the country's anticipated development.
A Decade of Online Learning Research: Insights from a Bibliometric Study Krishna Murthy Inumula, Sandip Solanki Proceedings of the 2025 International Conference on Technology Enabled Economic Changes Intech 2025, 2025 This paper presents a bibliometric analysis of research on “online learning” spanning from 2016 to 2023. The analysis was conducted using the Bibliometrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex> package in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}$</tex> Studio, focusing on journals indexed in the Scopus database. A total of 19,781 publications were reviewed, with IEEE Access emerging as the most prominent publication source, contributing 788 papers. Among all authors, Y. Zhang was identified as the most prolific researcher, with 192 papers, the highest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$h$</tex>-index (35), g-index (75), and a total citation count of 6205. The term “learning” emerged as the most commonly used title keyword throughout the research corpus. Additionally, the paper includes author keywords co-occurrence mapping and collaboration network analysis. This study provides valuable insights into the key trends, contributors, and research topics within the field of online learning over the last decade, highlighting critical areas of growth and collaboration in the discipline.
Technological Advancement of Big Data Analytics in Enhancing Financial Decision-Making and Trade Performance in Emerging Markets Sandeep N K, Ramya Lakshmi Bolla, Hrishikesh Satyakumar Kakde, Casanova-Villalba César Iván, Meera K L, Krishna Murthy Inumula 1st International Conference on Advances in Computer Science Electrical Electronics and Communication Technologies Ce2ct 2025, 2025 The revolutionary potential of big data analytics (BDA) is causing a dramatic transformation in the field of financial risk management (FRM). Financial companies are now leveraging massive databases as more than just historical information to change risk management practices. The financial industry has seen a paradigm shift as a result of machine learning's (ML) convergence, which has enabled businesses to gain deeper insights and make well-informed decisions. This abstract examines the various connections between these developments and demonstrates their effects on risk management, financial operations, predictive analytics, and customer-focused services. Proactive risk assessment, accurate forecast models, and customised financial plans are made possible by the integration of large databases and complex technologies. It has problems with interpretability, data privacy, and ethical use even though it is revolutionising the sector. The transformational potential and related ramifications of ML in the financial arena are examined in this paper.
Digital Transformation in Management: Leveraging Emerging Technologies for Enhanced Business Financial Operations Pramod Kumar Patjoshi, Bushra Majaz Ahmad Khan, Manjunatha S, Krishna Murthy Inumula, Kuldeep Sharma, Prakhar Mittal 2025 6th International Conference for Emerging Technology Incet 2025, 2025 Digital transformation is transforming the management of business by utilizing new technologies to streamline operations, improve decision-making, and enhance efficiency. This paper suggests an integrated framework that combines artificial intelligence (AI), the Internet of Things (IoT), cloud computing, and blockchain to establish an end-to-end digital ecosystem. AI-enabled analytics facilitate automated decision-making, IoT supports real-time monitoring, cloud computing provides scalable data management, and blockchain offers greater security and transparency. The suggested method expunges inefficiencies present in fragmented digital transformation strategies through offering an overarching data-driven management framework. The findings from experimentation depict dramatic improvement areas such as the reduction of 30% of operational expenditures, a 40% gain in the efficacy of real-time decision-making, and a decrease by 50% in security risks. The paper indicates the potential for agility, resilience, and competitiveness gained when firms apply the integrated digital transformation framework. This study acts as a guidebook for companies in search of a strategic plan towards digital innovation to promote sustainable growth and adaptability in an increasingly technological marketplace.
Financial inclusion: Scale modification and validation of socio-economic indicators Scms Journal of Indian Management, 2019
Causal nexus between electricity consumption and GDP in India International Journal of Applied Business and Economic Research, 2017
A study on the impact of shape of package of cereals on consumers' buying behavior and their perception about the product liking International Journal of Applied Business and Economic Research, 2017
Exploring causal nexus between crude oil price and exchange rate for India International Journal of Economic Research, 2017
Customer satisfaction, the need of the hour for low cost airlines in India International Journal of Economic Research, 2017
A study on consumer behaviour with reference to indian domestic airlines in Pune International Journal of Economic Research, 2017
Forecasting gold prices using geometric random walk growth model Indian Journal of Finance, 2012
