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Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods
cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Nae Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. 2024 IEEE. -
Application of distinct motivational types in shaping generative AI (GenAI) adoption behaviour
Differing from AI and GenAI adoption, research on traditional systems emphasised extrinsic factors like utility, social influence and innovativeness as predictors of user behaviour. The role of proximal psychological factors like motivation, however, has been overlooked in this context, which becomes essential with this shift towards AI. In the educational sector, the students use of AI shows the possibility of intrinsic factors like motivation in shaping adoption behaviour. This study uses Self-Determination Theory (SDT) and its Organismic Integration Theory (OIT) extension to propose a conceptual map that examines the role of distinct motivational types in shaping students GenAI adoption behaviour. The adoption behaviour of 348 Indian students pursuing higher education was collected through a cross-sectional survey and analysed using structural equation modelling. Findings indicated that autonomous motivation, including intrinsic, identified, and integrated motivation, significantly predicts students intentions to use GenAI tools. The study further examined the moderating role of perceived compatibility, revealing that alignment between users lifestyles and GenAI usage strengthens the impact of controlled motivations. When students feel that AI fits well with their needs and learning requirements, showing high compatibility, external motivators have a stronger effect on their decision to adopt it. This makes compatibility an important new finding and provides additional insights into the motivational types of GenAI adoption in academic contexts. This study extends the body of knowledge by moving beyond the binary treatment of motivation and empirically distinguishing between specific types of motivation. It emphasises the importance of self-determined motivation while showing how the correlations between various motivation types and GenAI usage intentions are conditioned by perceived compatibility. The study also offers practical insights based on the significant results. The Author(s) 2026. -
Motivation continuum and its effect on electric vehicle acceptance in India
Purpose: The motivation to choose an electric vehicle (EV) is guided by principles of personal values, perceived rewards and preferences. While the benefits of sustainable transportation are known, the acceptance of EVs and the motivation to purchase them is not satisfactory in India. An assessment of the motivation continuum, a range of intrinsic to extrinsic personal and societal drives that encourage specific choices, explains the lack of EV adoption in the country. This study aims to examine the effect of motivation types on EV adoption intentions and also explores the moderating role of gender in this context. Design/methodology/approach: By incorporating constructs from the self-determination theory, the study expands on the technological acceptance model. It uses the structural equation modelling method to test the hypotheses and presents an analysis of responses from 351 participants. Findings: The findings suggest that there are significant relationships between external, identified, integrated motivation and EV buying intentions. The influence of gender on EV adoption is also explored. Originality/value: This study provides an in-depth analysis of varied motivational types on EV buying intentions and the moderating effects of gender on these relationships. 2025, Emerald Publishing Limited. -
Antecedents of Behavioural Intention : Study of Indian Consumer Perceptions Towards P2P Lending Using Technology Adoption Model
Fintech is a rapidly developing area of the financial services business where tech-focused startups and other new players are upending how the sector has historically operated. One of the emerging fintech areas under digital lending is Peer to Peer lending or (P2P) lending; Consumers and authorities are both showing interest in this alternative lending innovation. Results of a literature review show that India is still in the early stages of P2P lending research. The study examines the association between behavioural intention to use P2P lending in India and technological and personal adoption factors. The study model was developed with the help of a literature review and tested using data from 536 respondents who completed an online survey and was tested using covariance-based structural equation modelling (SEM). The results confirm that personal innovativeness, performance expectancy, hedonic motivation, effort expectancy, social influence, and perceived risk are the antecedents of the adoption of P2P lending, except for facilitating conditions and price value. In addition, gender moderates the relationship between performance expectancy, hedonic motivation, personal innovativeness, and intention to adopt P2P lending. The study also throws light on the perceptions of both users and non users in terms of the antecedents. The study's conclusions significantly impact the P2P lending industry and provide practical insights for developers, platforms and regulators to improve and enhance the service. The study suggests looking at other moderating factors like age, voluntariness, experience, and actual usage behaviour for further research. Overall, the research contributes to the academic literature by confirming the predictive power of the extended unified theory of acceptance and use of technology (UTAUT). It highlights personal innovativeness after performance expectancy and motivation as an important factor in predicting the usage of P2P lending. Finally, the study lists managerial implications in the domains of technological adoption, which will assist in the P2P lending long-term success in India. -
Do Investors Tend to Overreact when Investing in Clean Energy Stock Indices?
Due to climate change, investors are increasingly interested in clean energy stocks attracting many investors due to clean energy prospects. This paper analyses investor overreactions to long-term prices in various clean energy stock indices, such as Clean Energy Fuels (CLNE), Global Clean Energy (GCEI), as well as the Dow Jones Industrials (DJI) stock index, over the period from 24 February 2022 to 23 May 2024. The results show that the Global Clean Energy (GCEI) clean energy stock index rejects H0 at the 16-day lag at a significance level of 1%; similarly, the Clean Energy Fuels (CLNE) index rejects the null hypothesis at lags 8, 9, 10, 11 and 12 days, both indices show negative serial autocorrelation, which means that price movements are not entirely random and are influenced by prior price movements. This evidence could mean that investors overreact to the information that reaches the market. On the other hand, the ETF (PWYF) and the Dow Jones Industrial Stock Index (DJI) show that the random walk hypothesis has not been rejected. In other words, these markets show that they are in equilibrium and that the existence of exaggerated reactions on the part of investors is not significant. The answer to the research question was partially accepted, so the Russian invasion of Ukraine in 2022 led to the partial presence of overreactions in these stock indices. In conclusion, investors operating in these markets should exercise caution and consider their risk tolerance before investing. Investors should, therefore, continue to monitor market trends and adjust their investment strategies accordingly. 2025, Econjournals. All rights reserved. -
Silent subversions and negotiations: An interview with a married effeminate man in the MSM community
Shambu is a member of staff at the MSM branch of a non-profit organisation in urban Bangalore, India, who works on HIV/STI awareness and prevention. His daily life is a careful balance between family responsibilities, his job, multiple sexual partners and sex work. This piece shares Shambus reflections on his early experiences with sexuality, his engagement in cruising sites, gender performance, engagement with male sex workers, non-consensual sex and the financial realities that shape his life, including his involvement in activism. Defying rigid identity boundaries and societal expectations, he openly discusses his relationships with both men and his wife while expressing a desire to be transgender. He recalls a significant relationship with a man he called his panthi, marked by an intricate dynamic of emotions, sex, dominance, economic exchange and silence. His narrative offers a nuanced exploration of identity, gender, sexuality, activism, consent, morality and economic survival in Bangalore. It defies the hegemonic structure of society by challenging the binary construction of gender and sexuality. 2025 The Author(s) -
Living Life Beyond Binaries- Bisexuality in Urban India
This qualitative study aimed to understand the lived realities of bisexual individuals in a society predominantly perceived through a binary lens. Describing the term bisexual by orientation as individuals who engage in same-sex and opposite-sex intimacies, taking into account several factors, that include but are not reducible to the sex and gender of the self and others. The research employed an ethnographic approach with a group of Indian adults. Interestingly, the study revealed that the participants did not consider bisexuality as a central component of their sexual behavior and identity, instead utilizing specific strategies to maintain a heterosexual facade while engaging in same-sex encounters. They regarded these encounters as part of their overall sexual experiences, distinct from a fixed sexual identity and relationships. Contrary to the prevailing notion that bisexuals must suppress their genders attraction to be in monogamous relationships, the participants affirmed being in monogamous relationships. They devised discreet methods to partake in same-sex encounters, safeguarding their monogamous relationships without feeling compelled to openly disclose their same-sex inclinations. 2025 Taylor & Francis Group, LLC. -
Sexual awakening: An autoethnography through tales of sexuality beyond binaries
This autoethnography, structured as a short story, traces my journey from childhood to my current role as a female researcher exploring the subversive possibilities of bisexuality in an urban Indian context as part of my doctoral research. My early beliefs were shaped by the social norms of my convent education, a Catholic upbringing and community, each reinforcing a rigid understanding of morality, relationships and sexuality. Through ethnography, I navigate between personal experiences and academic inquiry, encountering unresolved questions and contradictions that challenged these foundational beliefs. This short story highlights the complexities of living within structures that impose normative ideals, social expectations and moral standards, as well as the realities of everyday deviations. It further interrogates the essence of morality while positioning myself as a woman in society and reflecting on how my socialisation and lived experiences influenced my moral reasoning. By situating my personal journey within broader sociocultural contexts, this narrative ultimately questions the hegemonic patriarchal heteronormative and homonormative structures based on monogamy and monosexuality, offering a critical lens on how sexuality is constructed and negotiated in contemporary society. 2026 The Author(s) -
Innovation Characteristics, Personality traits and their impact on Fintech Adoption-P2P Lending
This paper investigates moderating influence of innovation attributes on the perceptions of Peer-to-Peer or P2P lending users and the influence of innovativeness traits on instrumental beliefs regarding the adoption of P2P lending. Two technology adoption theories were combined to develop the conceptual map denoting antecedent factors. Using 464 responses, structural equation modeling analysis was used to test the hypotheses. Performance expectancy, effort expectancy, social influence, and perceived compatibility were salient antecedents of P2P lending adoption. Perceived compatibility moderates the relationship between performance expectancy, facilitating conditions, and buying intentions. Innovativeness trait predicts performance expectancy and effort expectancy of P2P lending users. 2024 IEEE. -
Antecedents of Adoption of Peer to Peer (P2P) Lending-A Fintech Innovation in India
This study examines the association between adoption variables and behavioural intention (BI) to adopt Peer to Peer (P2P) lending technology platform in India. A critical review of literature on technological and personal adoption factors led to development of the theoretical framework using multiple technology adoption models. Results support the generalizability of technology adoption readiness (AR), a parsimonious higher-order construct for the use and acceptance of technology context In addition, a personal antecedent, personal innovativeness (PIIT) was shown to positively affect behavioural intentions and technology adoption readiness. 2022 IEEE. -
Delving into the Exchange-Traded Funds (ETFs) Market: Understanding Market Efficiency
Exchange-traded funds (ETFs) are the most popular products in the financial sector today. There is extensive literature on the multifractal analysis of some stock markets, but not about the multifractal behaviour of the ETF market. This study examines the efficiency of stock index ETFs worldwide from an Efficient Market Hypothesis (EMH) perspective, using the ETFs: Ishares Msci World ETF (URTH), Ishares Russell 1000 ETF (IWB), SPDR S&P 500 ETF TRUST (SPY), Ishares Global Clean En. ETF (ICLN), Ishares USD Green Bond ETF (BGRN), from 1 January 2021 to 24 May 2024. It analyses a pre-conflict and a geopolitical conflict to uncover distinct patterns of behaviour reflecting significant changes in market conditions. Before the conflict, the Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500 and Ishares USD Green Bond ETFs showed signs of anti-persistence in returns, indicating a lack of strong relationship or predictability between short-term price movements. The Ishares Global Clean Energy ETF did not reject the random walk hypothesis, suggesting that returns follow a pattern closer to random, where market prices already efficiently reflect all available information. During the conflict, there was a transition in the ETFs' behaviour patterns, as evidenced by the increases in slope values for Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500, Ishares Global Clean Energy and Ishares USD Green Bond. Thus, the possible transition from anti-persistence to long-term memories in ETF returns during the conflict. For portfolio managers, these findings highlight the need to continually adapt investment strategies to manage risks better and take advantage of opportunities in a dynamic and complex investment environment. 2024, Creative Publishing House. All rights reserved. -
Exploring the Relationship between Clean Energy Indices and Oil Prices: a Ten-Day Window approach
This paper aims to assess the comovements between clean energy indices, namely the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO), West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. We used 10-day windows to analyse the duration and nature of the shocks. Granger causality tests revealed that 20 of the 30 possible pairs showed significant movements, with the WTI influencing all the clean energy indices, highlighting its global importance. CELS also showed a robust influence on all pairs, while SPGTCLEN had a significant but less far-reaching influence. The CLNE and ECO indices showed limited influences, suggesting the potential for diversification, the TCEC.CN proved to be independent and a determining factor for portfolio diversification. The Impulse Response Functions (IRF) confirmed significant movements between CELS, SPGTCLEN and WTI, reflecting the market's response to policies and adjustments in expectations. Fluctuations in oil prices substantially affect clean energy indices, highlighting the interconnectedness and volatility of these markets. In conclusion, these results indicate that despite the growth of clean energy, the sector is still influenced by fluctuations in the fossil fuel market. 2024, Creative Publishing House. All rights reserved. -
Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price
In theory, geopolitical risk and political uncertainty can directly affect energy markets. Fluctuations lead to the cost of clean energy sources as they compete with traditional energy. The purpose of this study is to analyse financial integration and test the diversifying asset hypothesis between clean energy indices, specifically the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO) and West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. Analysing the results reveals a scenario where most of the clean energy indices show cointegration with each other, indicating long-term relationships that reflect common trends in the clean energy sector. However, the relative independence of the WTI suggests that Oil still acts as an important and potentially diversifying external factor for investors focused on sustainable energy. Structural breaks in 2021 and 2022 in several indices point to significant events that have altered market dynamics, possibly including changes in environmental policies, technological innovations and the impacts of the COVID-19 pandemic. The cointegration evidence and structural breaks provide valuable information for building investment portfolios. Investors can consider the WTI to diversify portfolios dominated by clean energy assets, taking advantage of Oils relative independence. On the other hand, the high correlation between clean energy indices suggests that, within this sector, diversification options are more limited, requiring careful analysis of the specific characteristics of each index and the macroeconomic forces affecting them. 2024, Econjournals. All rights reserved. -
A Fog-Based Retrieval of Real-Time Data for Health Applications
Fog computing is an emerging technology that offers high-quality cloud services by providing high bandwidth, low latency, and efficient computational power and storage capacity. Although cloud computing is an efficient solution so far to store and retrieve the huge data of IoT devices, it is expected to limit its performance due to low latency and storage capacity. Fog computing addresses these limitations by extending its services to the cloud at the edge of the network. In this paper, we use a fog computing network approach for efficiently retrieving the real-time patient data. The performance of our proposed approach has been compared with the cloud computing approach in terms of retrieval time of real-time data. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint
Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors. -
AI Based Non-invasive Glucose Detection Using Urine
This proposed device uses urine to predict the glucose level present in the patient using non-invasive technique with a high level of accuracy for detection of diabetes. The paper presents a urine glucose level diagnosing and prediction using a computer-based polarimeter held in a portable device, to provide a fast and accurate on-field result. The instrument consists of an LCD screen, optical sensor, Benedicts reagent, a detachable tank, and an embedded system-on-chip (SoC). Springer Nature Singapore Pte Ltd 2020. -
Handwritten tibetan character recognition using hidden markov model
The Tibetan language which is one of the four oldest and most original languages of Asia is elemental to Tibetan identity, culture and religion and it convey very specific social and cultural behaviors, and ways of thinking. The annihilation of the Tibetan language will have tremendous consequences for the Tibetan culture and hence it is important to preserve it. Tibetan language is mainly used in Tibet, Bhutan, and also in parts of Nepal and India. Tibetan script is devised based on the Devanagari model and Sanskrit based grammars. In this paper, a method for Tibetan handwritten character recognition based on density and distance feature detection is presents. To get a better classification result, images are converted into binary and noise removal is done by using Otzsos method. Features are extracted by normalizing the image based on distance and density of the pixel in the image. Finally, Hidden Markov Model is used for character classification. BEIESP. -
Skin lesion classification using decision trees and random forest algorithms
Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Friction stir welding of aluminum alloy 1100 and titanium-al alloy
A intercalating joint between Al and Ti alloy is friction stir welded using a high speed steel tool. The material mixing occurs mainly in the shoulder region while the pin region shows nominal mixing. Microscopy and hardness experiments indicate sporadic formation of intermetallic compounds. The joint region near the shoulder and to some extent below it shows increase in hardness compared to the base Ti alloy. Copyright 2016 by ASME. -
Influence of Digital Voice Assistance on Consumer Purchase Intention
The focus of this research is to analyze how digital voice assistants (DVAs) influence the buying decisions of consumers when it comes to online shopping in the Delhi-NCR region. The study involves surveying 225 online shoppers using a purposive sampling method to collect data on their perceptions and experiences with DVAs in online shopping. To study consumer purchase intention, we conducted a data analysis that involved a structural equation model, which focused on the perceived ease of use and perceived usefulness of DVAs. The paper highlights the crucial role of DVAs in shaping consumer purchase intention and influencing purchase decisions in the context of e-commerce/online shopping. The study has significant implications for e-commerce companies and marketers. To promote their adoption, they should think about integrating DVAs into their online purchasing platforms, grow DVA trust, and explain their advantages. Additionally, businesses ought to spend money creating more complex DVAs that can offer tailored advice and recommendations based on client preferences and previous purchases. 2025 by Apple Academic Press, Inc.