Experimental Study on Superconducting Nanomaterials for Energy Applications
Authors: Research Scholar Md Javed, Research Scholar A. Cathirin Preethi
Abstract: Superconducting nanomaterials have tremendous potential for use in energy applications such as zero-loss power transmission, high-energy storage, and cooling of quantum computing hardware. This paper describes the experimental investigation of superconducting phase transition in thin films of niobium and nanostructured nickel chalcogenide compounds for energy applications. Superconducting transition critical temperature (Tc), thermal rectification properties, and electrochemical properties of materials are studied. The Nb-based material is shown to exhibit 20 times reduced radiative heat loss at Tc= 7.4 K, resulting in creation of the first ever cryogenic thermal diode with 70% rectification efficiency. At the same time, the nanostructured S-CdNiS electrode has exhibited a specific capacitance of 2465 F/g with energy density of 101 Wh/kg at a current density of 1 A/g and capacity retention of 91% after 5000 cycles.
Design and Thermal Analysis of High-Efficiency Heat Exchangers for Industrial Applications
Authors: Assistant Professor Dr. Suraj J Patil, Assistant Professor Somashekar R
Abstract: In industries, huge amounts of waste heat are produced; however, gas turbine exhaust heat can reach 400°C, but a heat exchanger can recover merely 40% to 60% of this because of its poor thermal and hydraulic performance. In this study, a design approach is proposed for an efficient heat exchanger using milli-tubes, segmental baffles, and optimized fin designs. Based on the results of experimental studies on six different geometries, heat exchangers incorporating milli-tubes (tube size: 1.4 mm, 8 baffles) were found to have a heat transfer coefficient of 52 W/m²·K, which is an improvement of 41% relative to the baseline case with relatively low pressure losses. According to numerical simulations validated with CFD, the offset fin design increased the Colburn j number up to 150% relative to a smooth tube; however, a pressure loss reduction of 30% to 65% was obtained.
MedGPT-XAI: Explainable Large Language Models for Clinical Decision Support Systems
Authors: Assistant Professor A.Saranya, Assistant Professor Dr. Swarna Surekha
Abstract: The use of large language models (LLMs) in CDSS promises groundbreaking advances but faces challenges owing to the "black box" nature of these models. MedGPT-XAI is proposed in this paper as an innovative framework combining domain-adapted LLMs with multimodal explainability for CDSS. MedGPT-XAI consists of three modules, namely BioBERT, fine-tuned GPT-2 and an explainability module containing ALTI-Logit Decomposition, Attention Manipulation, Evidence-Graph Verification and LightGBM Feature Importance. Experiments conducted on MedQA and MIMIC-III show 89.8% accuracy in decision making with 120ms response time while the metrics for explaining reasoning chains yield 78% evidence support rate and 72% reasoning coherence. Comparison between this framework and baseline models show 15-20% gains in clinician trust scores, thus establishing the viability of MedGPT-XAI framework for clinical applications of AI.
Federated Learning-Based Privacy-Preserving Artificial Intelligence System for Smart Applications
Authors: Assistant Professor Dhanusha Mol K P, Assistant Professor A Lalitha
Abstract: With an increase in the use of smart devices and data-intensive applications, concerns over privacy have increased since centralized machine learning involves sharing users' sensitive data. On the other hand, Federated Learning (FL) is a promising technology that allows collaborative learning without sharing actual data. FedSecure is proposed in this study as an effective framework for privacy preserving FL applied in smart applications, which utilizes efficient parameter freezing, differential privacy, and secure aggregation techniques. Specifically, FedFreeze and FedFreeze+ approaches are used in this approach to improve the efficiency of data transmission by 40%. Moreover, in order to ensure effective privacy protection against inference attacks, a distributed differential privacy scheme using additive secret sharing technique is implemented. Experiments carried out on healthcare and personal assistant applications reveal that FedSecure can efficiently protect privacy in a non-IID environment with an accuracy rate of 94.2% at 60% less communication cost.
Behavioral Analytics in Corporate Finance: Understanding Investor Psychology Through Big Data
Authors: Research Scholar Mr. Surya C L, Assistant Professor Dr. S. Krishnakumar, Assistant Professor Dr. S. Parthiban
Abstract: The emergence of behavioral analysis based on big data has greatly impacted the field of finance by providing a deeper understanding of the role that psychology plays in shaping investors' behavior. The purpose of this paper is to explore the application of various big data techniques such as sentiment analysis, deep learning, and natural language processing as the tools to uncover the psychology behind investors' behavior. Using recently collected empirical evidence, this paper proposes a new model for predicting market movements based on the use of sentiment analysis, employee sentiment, and forecasting using machine learning models. The proposed methodology uses three sources of data: social media content, employees' online reviews, and conventional financial metrics. As shown in quantitative analysis, models based on sentiment provide better predictions than traditional forecasting, as latent profile analysis explains 47% of stock price variability compared to only 10% achieved by the latter.
Emotional Boundary Failure In Conversational AI: When Chatbots Damage Customer Trust
Authors: Rakesh Dondapati, Hari Nagakoteswar Tripurari
Abstract: Conversational AI systems are now a primary interface for customer service across telecommunications, banking, insurance, and healthcare sectors, yet their limitations become most consequential in emotionally sensitive, ambiguous, or high-stakes interactions. This study introduces and operationalizes the concept of emotional boundary failure (EBF) — instances in which AI agents misread customer emotion, fail to recognize escalating frustration, or produce responses that are procedurally correct but socially or relationally inappropriate. Drawing on 184,200 customer service interactions, sentiment trajectory data, escalation records, and post-interaction surveys (n = 26,830) from four service providers spanning telecommunications, banking, insurance, and healthcare, the study compares AI-only, hybrid (AI-with-human-escalation), and human-only service models. Regression results show that issue emotional complexity (IECS) significantly moderates the effect of service model on satisfaction, escalation, complaint filing, retention, and EBF incidence: for low-complexity issues, AI-only service models perform comparably to or better than human agents on efficiency-related outcomes, but for high-complexity issues, AI-only models show significantly worse satisfaction (interaction β = –0.19, p < .001), higher escalation (β = 0.05, p < .001), higher complaint rates (β = 0.014, p < .001), lower retention (β = –0.022, p < .001), and dramatically higher EBF incidence (β = 0.047, p < .001) relative to human agents — a pattern substantially attenuated, though not eliminated, in hybrid service models. Sentiment trajectory analysis reveals that AI-only interactions involving high-complexity issues exhibit a pronounced negative sentiment slope across conversation turns that human and hybrid interactions do not replicate. Thematic analysis of 37 customer and frontline-agent interviews identifies six themes, including a 'sincerity gap' in AI-delivered empathy and a 'context collapse' problem in AI-to-human handoffs. The study develops a theory of socio-emotional fit, proposing that conversational AI performance depends on the alignment between an interaction's emotional complexity and the AI system's socio-emotional capability, and offers a five-principle design framework for responsible conversational AI deployment in emotionally consequential service contexts.
DOI: http://doi.org/10.5281/zenodo.20958732
Evaluating Financial Performance of Axis Bank: A Ratio Analysis
Authors: Karanatak Shruthi, Associate Professor Dr. Ganesh Malla
Abstract: This study focuses on evaluating the financial performance of Axis Bank using ratio analysis. Financial performance is an important indicator of a bank’s operational efficiency, profitability, and stability. The study uses key financial ratios such as Return on Equity (ROE), Return on Assets (ROA), Net Interest Margin (NIM), and Capital Adequacy Ratio (CAR) to analyze the bank’s performance over a period of five years. The data used in this study is secondary in nature and has been collected from the annual reports of Axis Bank. The findings reveal that Axis Bank has shown improvement in profitability and operational efficiency over the years. The study concludes that ratio analysis is an effective tool for evaluating financial performance and provides useful insights for investors, management, and stakeholders.
A Study On Customer Awareness and Usage of E-Banking Services with Reference to Axis Bank – Hyderabad
Authors: Gorre Sairam, Dr.M.P. Suri Ganesh
Abstract: The rapid growth of digital technology has transformed the banking sector by enabling customers to access banking services through electronic platforms E-banking services provide convenience, speed, security, and round-the-clock access to financial transactions without requiring customers to visit bank branches. This study aims to evaluate customer awareness and usage of e-banking services offered by Axis Bank in Hyderabad. The research analyses customer awareness, usage patterns, security perceptions, and satisfaction levels regarding various e-banking services. Primary data were collected through a structured questionnaire from 500 respondents in Hyderabad, while secondary data were obtained from books, journals, websites, RBI publications, and Axis Bank reports. Statistical tools such as tables, graphs, and percentage analysis were used for interpretation. The findings reveal that customers are increasingly adopting e-banking services due to convenience, faster transactions, and accessibility. However, concerns regarding cyber security, fraud, and privacy continue to influence customer behaviour. The study concludes that Axis Bank should strengthen customer awareness programmes, enhance cyber security measures, and continuously improve digital banking services to increase customer confidence and satisfaction.
DOI: http://doi.org/10.5281/zenodo.21126897
A Study On Portfolio Management Practices at Punjab National Bank, Hyderabad
Authors: Vadthyavath Shiva, Dr. M. P. Suri Ganesh
Abstract: The Portfolio management plays a vital role in helping financial institutions achieve an optimal balance between risk and return while ensuring efficient utilization of available funds. Punjab National Bank (PNB), one of India's leading public sector banks, adopts various portfolio management practices to maintain profitability, improve asset quality, and meet regulatory requirements. This study examines the portfolio management practices followed by Punjab National Bank, Hyderabad, with a focus on analyzing the risk and return characteristics of selected securities during the period 2021–2026. The study is based entirely on secondary data collected from Punjab National Bank annual reports, Reserve Bank of India (RBI) publications, National Stock Exchange (NSE), Bombay Stock Exchange (BSE), company annual reports, journals, books, and other financial websites. Six securities—Gujarat Ambuja Cement Ltd., Larsen & Toubro Ltd., Ranbaxy Laboratories, Cipla Ltd., Karur Vysya Bank Ltd., and ICICI Bank Ltd.—have been selected for analysis. Statistical tools such as return analysis, standard deviation, correlation, diversification analysis, and portfolio weighting have been used to evaluate portfolio performance and investment risk. The findings indicate that effective portfolio diversification helps reduce unsystematic risk while improving overall portfolio performance. The analysis also reveals that selecting securities from different sectors enhances investment stability and enables better risk-adjusted returns. Punjab National Bank follows systematic investment and risk management practices that support efficient allocation of funds and long-term financial sustainability. The study concludes that a well-diversified portfolio supported by continuous monitoring and sound investment strategies contributes significantly to improving portfolio efficiency and achieving organizational financial objectives.
DOI: http://doi.org/10.5281/zenodo.21127662
A Study on Work Life Balance of Employees with Reference to Ispatial Techno Solutions Pvt. Ltd., Madhapur
Authors: Naini Rajasai, Associate Professor Dr. Girija Shri
Abstract: Work and life remain the two most principal areas within the lifetime of a second user single individual. There’s a developing readiness in today's workplaces that employees don't surrender their lives simply because they work. With the increasing differences of family structures spoke to in today's workforce, especially with the creating standard of twofold profession families, the imperativeness of managing an employee's work-life balance have expanded prominently in recent years. Managements understand that the chance of an employee's near home and family life effects work quality which there are solid business motivations to advertise work and non-work coordination. during this project, we battle that helping employees to realize a work-life balance should transform into an important little bit of HR policy and system if it's to actually get the most effective from the association's kin without forsaking them unsatisfied, exhausted and unfulfilled.
A Study on Performance of Mutual Funds at Net Worth Stock Broking Limited
Authors: Chennoju Manikiran, Professor Dr. S. Narender
Abstract: Mutual funds have emerged as one of the most preferred investment avenues by offering investors the benefits of professional fund management, portfolio diversification, liquidity, and risk reduction. This study aims to evaluate the performance of selected mutual fund schemes at Net Worth Stock Broking Limited, Hyderabad, over the five-year period from March 2020 to March 2025. The study focuses on analyzing the risk-return relationship of selected mutual fund schemes and comparing their performance with the benchmark market index (Nifty). The research is based entirely on secondary data collected from reliable sources such as the Association of Mutual Funds in India (AMFI), company reports, journals, and financial websites. Five mutual fund schemes—UTI, SBI, Axis, Reliance, and Aditya Birla—were selected for analysis. Standard financial performance measures, including the Sharpe Ratio, Treynor Ratio, Beta, Average Returns, Variance, and Standard Deviation, were used to evaluate the risk-adjusted performance of the selected schemes. The findings reveal that the mutual fund schemes exhibited varying levels of returns and risk over the study period. Equity-oriented schemes generally generated higher returns but were accompanied by greater market volatility, while diversified and income-oriented funds demonstrated relatively stable performance. The analysis indicates that risk-adjusted performance measures provide valuable insights for investors in selecting suitable investment options based on their risk appetite and return expectations. The study concludes that mutual funds continue to be an effective investment alternative for both retail and institutional investors. The application of risk-adjusted performance measures enables investors to make informed investment decisions, while financial intermediaries such as Net Worth Stock Broking Limited play a significant role in guiding investors toward appropriate mutual fund schemes.
A Study on Customer Satisfaction Towards Dominos
Authors: B Durga Sandeep, Associate Professor Dr. Vellala Subramanya Rama Murty
Abstract: Happiness among buyers shapes how well quick-service eateries perform over time. Looking closely at Domino’s Pizza, this work checks what people think about the meals and support they get. Different things come into play when deciding if someone feels good about their order – flavour matters, so does freshness, cost plays a role too. Speed counts just as much as how polite staff are during pickup or drop-off. Special deals might sway opinions, yet spotless spaces leave strong impressions. Each part adds up to shape the full picture of being pleased – or not. What happens behind the counter often shows up in how folks rate their visit. From those answers, patterns began to emerge. Information came straight from Domino's customers via a detailed survey form. Books, academic papers, online sources, and earlier work about fast food and what people like helped add context later. Instead of just counting numbers, the team used visual graphs, simple math, and organized layouts to spot trends in how satisfied buyers really were. Most people say they like what Domino’s offers, especially the taste of their pizzas, how many choices there are on the menu, how easy it is to order online, also how quickly orders arrive. Still, some parts could be better – prices feel high to some, waits get longer when busy, customizing orders isn’t always smooth. Staying consistent with service while really listening to what customers want turns out to matter a lot if the brand wants repeat buyers and an edge over others selling quick meals.
A Study on Modern Marketing Approaches in Healthcare Sector with Reference to Apollo Pharmacy
Authors: Kasula Bhanuprasad, Assistant Professor Dr. Sivaji Jinka
Abstract: The healthcare sector is undergoing a rapid transformation in the way it markets its services, moving away from traditional print and broadcast media towards digital, data-driven, and patient-centric approaches. This study examines modern marketing approaches adopted in the healthcare sector, with specific reference to Apollo Pharmacy, India's first and largest branded pharmacy chain. The study explores the shift towards social media engagement, search engine optimization, content marketing, telehealth promotion, and mobile health applications, and evaluates how healthcare providers use data analytics to personalize marketing and improve patient engagement. Primary data was collected through a structured questionnaire administered to 130 respondents from the general public in Siddipet, selected through simple random sampling, and supported by secondary data from published articles, company records, and industry reports. The findings indicate that a majority of respondents are aware of modern marketing techniques in healthcare and consider them at least somewhat effective, with budget constraints identified as the principal barrier to implementation. Social media engagement emerged as the most preferred channel for both collecting patient feedback and measuring campaign effectiveness, while AI-driven marketing and mobile health applications were identified as the leading future trends. The study concludes that patient-centric, technology-enabled marketing strategies enhance patient satisfaction, trust, and engagement, and offers suggestions for healthcare providers such as Apollo Pharmacy to strengthen their digital marketing efforts while maintaining regulatory compliance and data privacy.