A Comparative Analysis of Various Soft Computing Techniques for Indian Stock Market Prediction
Artificial Neural Network; Deep Neural Network; Nifty 50; Principal Component Analysis (PCA); Stock market
Soft computing techniques have been increasingly used for stock market analysis in the past few years because they can capture nonlinear aspects which traditional econometric models do not adequately capture. With different techniques like Artificial Neural Networks, Deep Neural Networks and Stacked Autoencoders available, in this paper, the author tries to determine which of the above methods can model the Indian stock market with higher accuracy. In this study, high-frequency data from Nifty 50 is used, and various feature selection techniques such as PCA and linear regression are used for each of the above machine learning models to predict the Nifty 50 data. Finally, all predictions from the different techniques are compared with the actual index movement and the best method for Nifty 50 is suggested. 2025 Seventh Sense Research Group
Balaji, Lavanya; Anita, H.B.; Kumar, Balaji Ashok
International Journal of Engineering Trends and Technology;Volume;73;Issue;3;pp.95-103
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.14445/22315381/IJETT-V73I3P107 https://www.scopus.com/pages/publications/105001300504?origin=resultslist
Balaji L., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, India; Kumar B.A., Department of Commerce, Government First Grade College Vemagal, Karnataka, India
A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification
Convolutional Neural Networks (CNN); Deep Learning; Generalization; L1 Regularization; L1L2 Regularization; L2 Regularization; Overfitting; Weight Regularization
The research examines how L1, L2, and L1L2 weight regularization methods affect neural network performance, generalization, and sparsity using the CIFAR 10 dataset. A Convolutional Neural Network (CNN) trained with the same environment for each regularization method to evaluate test accuracy, weight sparsity, and computational speed. The study shows that L1 regularization produces sparse weights, which makes models more interpretable, and L2 regularization helps prevent overfitting while improving model generalization. The combination of L1L2 regularization enables individual image classification methods to reach test accuracy. The results indicate that the weight regularization plays a vital role in creating neural networks that are both stable and efficient. They are interpretable, and L2 regularization improves generalization and reduces overfitting. The combined L1L2 regularization achieves the balance between sparsity and performance, leading to higher test accuracy compared to individual techniques for image classification. The research results demonstrate that weight regularization stands as an essential factor for Creating Neural Networks that are robust, efficient, and interpretable, thus helping to enhance Deep Learning model performance. 2025 Seventh Sense Research Group.
Deepa, S.; Siddalingappa, Rashmi; Kalpana, P.; Loveline, Zeema.J.; Vinay, M.; Jayapriya; Suganthi, J.J.; Priya Stella Mary, I.
International Journal of Engineering Trends and Technology;Volume;73;Issue;10;pp.107-116
https://doi.org/10.14445/22315381/IJETT-V73I10P108 https://www.scopus.com/pages/publications/105022820131?origin=resultslist
Deepa S., Department of Computer Science, Christ University, Bengaluru, India; Siddalingappa R., York St Jhon University, London, United Kingdom; Kalpana P., Department of Computer Science, Christ University, Bengaluru, India; Loveline Z.J., Department of Computer Science, Christ University, Bengaluru, India; Vinay M., Department of Computer Science, Christ University, Bengaluru, India; Jayapriya, Department of Computer Science, Christ University, Bengaluru, India; Suganthi J.J., Department of Computer Science, Christ University, Bengaluru, India; Priya Stella Mary I., Department of Computer Science, Christ University, Bengaluru, India
Predictive modeling of mechanical behavior in waste ceramic concrete using machine learning techniques
Artificial Neural Network; Construction Industry; Environmental Sustainability; LightGBM; Machine Learning; Waste Ceramic Concrete
This study identifies the critical demand for a certain approach that aims to predict and ascertain the mechanical behavior of concrete admixed with waste ceramic, a method to overcome and mitigate the related environmental challenges as it pertains to the construction field. Concrete modification with ceramic wastes has received significant attention due to its potential improvement in sustainability. The developed predictive models on waste ceramic concrete (WCC) involved the use of advanced machine learning techniques such as Artificial Neural Network (ANN) and Light Gradient Boosting Machine (LightGBM). Experimental datasets were formulated based on 5% and 20% variability of ceramic waste percentages as input variables for training and testing data for validation of the proposed model. In each case, iterative training improved model performance, with the ANN showing moderate predictability (R = 0.70 and 0.67) and LightGBM demonstrating stronger accuracy. Predictive values ranged between 1.02 MPa and 0.12 MPa for compressive and splitting tensile strengths and had R values of 0.70 and 0.67 for the ANN model, respectively. The established findings will lead to a dependable framework for assessing and improving the performance of ceramic waste-modified concrete. In this regard, these findings have reinforced the potential of machine learning in developing sustainable construction practices. This paper is of value to engineers and decision-makers within the construction industry, providing an informed choice towards environmental sustainability and better risk management. Kamal Upreti et al.
Upreti, Kamal; Pandey, Adesh Kumar; Kushwah, Virendra Singh; Kshirsagar, Pravin R.; Sharma, Kamal Kant; Singh, Jagendra; Parashar, Jyoti; Jain, Rituraj
International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.124-135
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.14419/ywwvvd04 https://www.scopus.com/pages/publications/105005015895?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Pandey A.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Kushwah V.S., School of Computing Science & Engineering, VIT Bhopal University, Highway, Kothrikalan, Madhya Pradesh, Sehore, India; Kshirsagar P.R., Department of Electronics & Telecommunication Engineering, J D College of Engineering &; Management, Maharashtra, Nagpur, India; Sharma K.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Parashar J., Bharati Vidyapeeths Institute of Computer Applications and Management, New Delhi, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Does financial inclusion and ICT influence the economic growth: an evidence from select emerging economies using panel data estimates
Digital Financial Inclusion; Financial Inclusion; ICT; Panel Data; Sustainable Economic Growth
Information Communication Technology (ICT) 's contribution to financial services and economic growth is extensively researched. With technological development, the financial systems are converging with ICT platforms, thereby leading to digital financial systems, creating opportunities that bridge the gap between affluent and disadvantaged sections in emerging economies. In this study, we employ the panel data estimation method to evaluate the impact of ICT in providing financial services that would lead to sustainable economic growth in 12 emerging economies from 2010 to 2024. The findings emphasize the critical role of digital financial services and ICT infrastructure in boosting financial inclusion and driving inclusive economic growth. The Random Effect model has inferred that there exists a moderate effect of fixed telephone lines and mobile cellular subscribers on GDP by simplifying digital applications, integrating regional languages. By ensuring secure systems in places, the regulators and service providers can contribute to the sustainable economic growth of both the country and the underserved communities. Shylaja H N et al.
Shylaja, H.N.; Pachiyappan, Sathish; Arjun, B.S.; Chandrakala, G.; Ravinagarajan, Janani
International Journal of Accounting and Economics Studies;Volume;12;Issue;1;pp.41-49
All Open Access; Bronze Open Access; Green Open Access
https://doi.org/10.14419/xsnv7178 https://www.scopus.com/pages/publications/105004243580?origin=resultslist
Shylaja H.N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Pachiyappan S., School of Business and Management, CHRIST University Bannerghatta Road Campus, Karnataka, Bengaluru, 560076, India; Arjun B.S., School of Business and Management, CHRIST University Bannerghatta Road Campus, Karnataka, Bengaluru, 560076, India; Chandrakala G., School of Commerce and Management studies, Dayananda Sagar University, Karnataka, India; Ravinagarajan J., Shiv Nadar University, Tamilnadu, Chennai, India
A Sustainable Business Model for Converting Construction and Demolition Waste to Wealth
Construction and Demolition Waste; Processing Plant; Recycled Products; Resource Efficiency; Sustainable Business Model
India's rapid urbanisation necessitates a planning approach that ensures the sustainability of its cities through efficient waste management. This swift urban growth has significantly accelerated modern construction and demolition of older infrastructure or structures within Indian cities. C&D (Construction and Demolition) waste is accountable for approximately 30 percent of urban municipal waste within metropolitan areas. Managing C&D waste and transforming it into valuable resources presents considerable challenges for all urban local bodies (ULBs). Recycling C&D waste offers dual benefits: it reduces pressure on the extraction of virgin construction materials and helps mitigate environmental pollution. Recycled C&D waste can produce various valuable products, including aggregates of different sizes, manufactured sand, paver blocks, concrete bricks, double-tee precast panels for boundary walls, manhole covers, water tanks, and more. These products are durable and eco-friendly building materials that contribute to the conservation of natural resources. However, a sustainable business model is essential for understanding the volume of C&D waste produced and for addressing current challenges and opportunities at the city, regional, and state levels. The current research aims to gather information about the overall scenario of C&D waste management procedures in India, relying on secondary resources. It proposes a sustainable business model for C&D waste handling that transforms this specific waste into a valuable resource, identifying possible advantages and the resource efficiency of recycled items. permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
International Journal of Accounting and Economics Studies;Volume;12;Issue;4;pp.508-514
https://doi.org/10.14419/wkkc6k22 https://www.scopus.com/pages/publications/105014641570?origin=resultslist
Sahu A., Anant National University, Ahmedabad, India; Dash M., CHRIST (Deemed to be University), Bangalore, India
Workforce Transformation and Value Creation in the Era of Industry 4.0, 5.0 and 6.0: Challenges and Enablers
Enablers; Implementation Challenges; Technological Adoption; Value Creation; Workforce Transformation
Industry 4.0, Industry 5.0, and Industry 6.0 have been empowered by the acceptance of several advanced technologies, including the Internet of Things (IoT), artificial intelligence, robotics, and human-centric innovation in releasing industries. Comprehensive studies that can include barriers as well as enablers are difficult to conduct. This study employs a mixed-methods research approach, integrating of AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to evaluate key challenges and enablers in workforce transformation. The Findings indicate that leadership vision, digital investment, and employee up-skilling play a crucial role in transformation navigation. Additionally, automation and AI adoption present both opportunities and challenges for workforce adaptability. The study provides strategic insights for organizations to enhance their workforce resilience, competi-tiveness in the evolving industrial landscape. The study offers actionable advice for businesses, policymakers, and educators, and suc-cessfully adaptation the paradigm and sustainable development in the modern era. Authors.
Kuwar, Vishakha; Sonawaney, Vandana; Shankar, Uma; Yadav, Amit; Radhakrishnan, V.; Upreti, Kamal; Gupta, Komal; Jain, Rituraj
International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.314-324
https://doi.org/10.14419/r61ag136 https://www.scopus.com/pages/publications/105006780598?origin=resultslist
Kuwar V., Symbiosis International University, Pune, India; Sonawaney V., Symbiosis International University, Pune, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq; Yadav A., Banarsidas Chandiwala Institute of Professional Studies, Dwarka, New Delhi, India; Radhakrishnan V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Upreti K., Department of Computer Science, CHRIST University, Uttar Pradesh, Delhi-NCR Ghaziabad, India; Gupta K., Accenture, Bangalore, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Structural Modelling of Homebuyer Behavior in The Residential Housing Market
Consumer behavior; Homebuyers; Purchase Intention; Real Estate
Rapid urbanization has significantly increased the demand for residential housing, underscoring the need for real estate retailers to comprehend the factors that drive homebuyers' purchasing behavior. This study employs a Structural Equation Modelling (SEM) framework to examine the critical determinants of purchase intention within the residential real estate sector. Key constructs analyzed include housing attributes, financial and economic considerations, location and service quality, environmental concerns, brand image, and information sources. The SEM analysis elucidates the strength and significance of the relationships among these variables, providing a comprehensive understanding of consumer decision-making processes. The findings yield actionable insights for real estate practitioners, offering guidance for the development of targeted marketing strategies, enhanced customer engagement practices, and improved service delivery. By aligning their offerings with buyer expectations, real estate firms can foster stronger client relationships and enhance their competitive positioning in a dynamic housing market. Authors.
International Journal of Accounting and Economics Studies;Volume;12;Issue;6;pp.814-822
https://doi.org/10.14419/gqh4w175 https://www.scopus.com/pages/publications/105021214195?origin=resultslist
Kavya K.M., Department of commerce, Christ (Deemed to be University), India; Almeida S.M., Department of commerce, Christ (Deemed to be University), India
Predicting financial asset prices with neural network: a comparative study of neural networks effectiveness in financial decision-making
Financial Decision Making; GARCH; LSTM; Neural Networks: ARIMA; Prediction
Investing requires deep knowledge of complex financial markets, making it incredibly tedious to predict inflation and deflation. Predictive conventional models like ARIMA and GARCH do not accurately capture the non-linearity and volatility presented in financial datasets. This research examines the different forms of predictive assets, real estate, stocks, commodities, bonds, and cryptocurrency using Long Short-Term Memory (LSTM) Neural networks. The primary focus of this research is to assess the valuable prediction capabilities of LSTM across assets and its integration with financial decision-making. According to the empirical results, deep learning LSTM models give better outcomes with equities and gold, with the R2 indicator reaching over 99% alongside a low RMSE. LSTMs had an over 100% MPE prediction error rate for other assets during the test phase, making it harder to predict intensely volatile assets. The model's verification transfers residual autocorrelation, showing that it can enhance forecasting performance with detailed macroeconomic indicators and sentiment analysis data. Studies show that LSTMs are effective in high-frequency markets with non-linear price changes, but they require special attention to balance interpretability and overfitting. Despite the progress that has been achieved in utilizing neural networks for financial forecasting, hybrid models integrated with XAI are recommended to improve efficiency and real-world applicability. These results contribute to the growing domain of AI-powered finance by offering additional means for many investors, analysts, and decision-makers who wish to utilize data for market speculation. Dr. Aishwarya Nagarathinam et al.
Nagarathinam, Aishwarya; Padhi, Pritesh Kumar; Rangasamy, Sangeetha; Hemalatha, M.; Chellasamy, Aarthy
International Journal of Accounting and Economics Studies;Volume;12;Issue;1;pp.131-145
https://doi.org/10.14419/f221hb82 https://www.scopus.com/pages/publications/105007191899?origin=resultslist
Nagarathinam A., Christ University, Bangalore, India; Padhi P.K., Christ University, Bangalore, India; Rangasamy S., Christ University, Bangalore, India; Hemalatha M., Christ University, Bangalore, India, Model Institute of Engineering and Technology, Jammu, India; Chellasamy A., Christ University, Bangalore, India
Sustainable Marketing and Green Finance: Integrating ESG Metrics into Financial Reporting and Strategic Branding
Brand Trust; ESG Metrics; Financial Reporting; Green Finance; Strategic Branding
As sustainability gains significance in the global business landscape, an increasing number of companies are adopting Environmental, Social, and Governance (ESG) frameworks to enhance transparency and strengthen stakeholder relationships. This study looks at how using ESG metrics in branding and financial reporting affects the creation of long-term corporate value. The main goal is to find out how combining ESG initiatives with marketing plans and financial disclosures affects brand equity and financial credibility. There are both qualitative and quantitative parts to the study. This involves a qualitative analysis of ESG reports from 150 multinational corporations and the utilization of quantitative regression methods to examine the impact of ESG integration on brand performance and financial metrics. The Global Reporting Initiative (GRI) set the rules for ESG scores. Return on Assets (ROA) and Tobin's Q were the most important financial measures. The results show that companies with high levels of ESG integration saw a 12.4% increase in ROA and a 0.38 average increase in Tobin's Q compared to companies with low levels of ESG activity. Both changes were statistically significant at p < 0.01. Survey data also showed that companies that closely linked their ESG disclosures to their branding had a brand trust index that was 22% higher. These results show that strategically branded, ESG-focused reporting not only improves financial performance but also makes consumers feel better about the company. Ultimately, the study offers a framework for integrating ESG metrics into financial and marketing strategies, emphasizing ESG's function as both a moral obligation and a source of competitive advantage in the context of responsible capitalism. Sireesha Nanduri et al.
Nanduri, Sireesha; Chacko, Elizabeth; Dasaraju, Srinivasa Rao; Moorthygari, Sreelakshmi; Bommisetti, Ravi Kumar; Mahadevan, M.; Nallamalli, Venkata Raghu Babu; Pathuri, Mohan
International Journal of Accounting and Economics Studies;Volume;12;Issue;6;pp.438-448
https://doi.org/10.14419/9pp6rt15 https://www.scopus.com/pages/publications/105020296923?origin=resultslist
Nanduri S., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Karnataka, Bengaluru, 560009, India; Chacko E., School of Business and Management, Christ University, Bengaluru, India; Dasaraju S.R., Finance and Accounting, IBS Hyderabad, (Under IFHE, Telangana, Hyderabad, India; Moorthygari S., Department of Business Management, Mahatma Gandhi University, Telangana, Nalgonda, India; Bommisetti R.K., Department of Commerce, Akal University, Talwandi Sabo, Punjab, Bathinda, 151302, India; Mahadevan M., Department of Management Studies, School of Commerce and Management, Mohan Babu University, Andhra Pradesh, Tirupati, India; Nallamalli V.R.B., Department of Management studies, RISE Krishna Sai Prakasam Group of institutions, Andhra Pradesh, India; Pathuri M., SKIM, Khammam, India
Spatiotemporal analysis and intensity prediction of forest fires using cuckoo search hybrid models
Fire Intensity Classification; Forest Fire Prediction; Kernel Density Estimation; Spatiotemporal Analysis; Uttara Kannada
Forest fire forecasting is a critical aspect of environmental conservation and ecological risk management, particularly in biodiversitysensitive areas like Uttara Kannada, India. In this research, this article suggests a new hybrid modeling ap-proach that combines Cuckoo Search Optimization (CSO) with ensemble machine learning techniques, namely Random Forest (RF) and XGBoost (XGB), for forecasting fire intensity levels. Known as CSORF and CS-XGB, the hybrid models were trained and validated against a spatiotemporally dense dataset from 2009 to 2024, with primary environmental, topographic, and anthropogenic predictors. Aside from classification modeling, spatiotemporal analyses such as Kernel Density Estimation (KDE), seasonal fire patterns, and influence studies on features were performed to determine high-risk seasons and areas. CSO was used to automate the hyperparameter tuning process for both classifiers, yielding a significant boost in performance. The CS-XGB model registered the top accuracy of 99.49%, better than CSORF's 98.99%. Feature importance testing confirmed ecological significance, and humidity, temperature, and rainfall were the top-ranked variables. The work adds a scalable and precise prediction model that can assist in early warning systems and forest manage-ment practices. Kamal Upreti et al.
Upreti, Kamal; Hundekari, Sheela; Date, Saroj; Tiwari, Akhilesh; Radhakrishnan, Ganesh V.; Shankar, Uma
International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.181-190
https://doi.org/10.14419/9pk3h895 https://www.scopus.com/pages/publications/105005538496?origin=resultslist
Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Hundekari S., School of Computer Applications, Pimpri Chinchwad University, Pune, India; Date S., Department of Artificial Intelligence and Data Science, CSMSS Chh. Shahu College of Engineering, Chh. Sambhajinagar, Maharashtra, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR, Ghaziabad, India; Radhakrishnan G.V., Department of Economics and Finance, KIIT Univeristy, Bhubaneswar, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq
Mapping Road Traffic Injury in India: Causes, Prevention, Economic Impact, and Role of Public Health Governance
Disability; Economic Burden; Mortality; Prevention; Public Health; Road Traffic Injury; Trauma Care
Road Traffic Injuries (RTIs) represent the main reason for fatalities globally and are acknowledged as a significant national health problem. RTIs affect the victims and also have a profound impact on the family and relations. The socio-economic conditions of families, and consequently society and the nation, are negatively influenced by these incidents. A significant number of deaths on the roads involve cyclists, motorcyclists, and pedestrians. The prevalence of RTIs is particularly high in African and other middle-income countries, while developed nations experience comparatively fewer incidents. Each year, RTIs result in approximately 1.2 million deaths worldwide (WHO, 2018), marking them as a primary, preventable cause of mortality. Global attention has shifted towards the critical need for road safety, particularly with the endorsement of the 2030 Agenda for Sustainable Development Goals (SDGs). Implementing robust legal enforcement could lead to behavioural changes among road users. India ranks among the highest in the world for road accident-related fatalities. Ad-hering to safety measures such as using helmets, wearing seat belts, maintaining appropriate speeds, and following traffic regulations would significantly reduce Road Traffic Injuries. Stakeholders in road safety must be made aware of the economic costs, Disability Adjusted Life Year (DALY), and human losses associated with RTIs and their repercussions. This paper aims to outline the existing RTI situation, its causes, preventive strategies, magnitude of economic burden, costs involved in RTI, catastrophic health expenditure, Return on Investment (RoI) in Trauma Care Systems, Financing mechanisms, Governance, and the Health sector's role in addressing RTIs. The findings indicate that road accidents are the predominant reason for mortality in India (with many incidents being underreported or undocumented), and the state must take a proactive approach to tackle this issue by fostering strong connections among various stakeholders, while the health sector should implement a multifaceted strategy to manage RTIs. Authors.
Raja Meenakshi, J.; Akkayasamy, Vigneshwaran Subbiah; Panneer, Sigamani; Sangeetha, R.; Saha, Soma
International Journal of Accounting and Economics Studies;Volume;12;Issue;5;pp.901-907
https://doi.org/10.14419/9mnsdr18 https://www.scopus.com/pages/publications/105017922704?origin=resultslist
Raja Meenakshi J., Department of Social Sciences, School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, India; Akkayasamy V.S., Department of Social Work, School of Psychological Sciences, Education and Social Work, Christ (Deemed to be University), Bangalore Central Campus, Bangalore, India; Panneer S., Centre for the Study of Law and Governance, Jawaharlal Nehru University, New Delhi, India; Sangeetha R., Department of Commerce, School of Social Sciences and Languages, Vellore Institute of Technology, Tamil Nadu, Vellore, India; Saha S., Department of Social Sciences, School of Social Sciences and Languages, Vellore Institute of Technology, Tamil Nadu, Vellore, India
Enhanced Wavelet Block Shrinkage Technique For Mammogram Denoising Using K-Means Clustering And Neural Networks
Breast Cancer; Convolutional Neural Network; Digital Mammogram Denoising; Enhanced Wavelet Block Shrink; K-Means Clustering; Wavelet Shrinkage
The proposed research study introduces a novel approach for denoising digital mammograms by improving the existing wavelet block shrinkage filtering method with K-Means clustering and a convolutional neural network. This approach involves decomposing both the original and noisy mammograms into frequency subbands using 2D discrete wavelet transformation. The resulting subbands are then grouped into multiple clusters based on similar features of the wavelet coefficients, employing K-Means clustering. This represents an improvement over the traditional block shrinkage method, which uses fixed-size blocks. These clusters from the original and noisy mammograms are paired to train a convolutional neural network, which serves as an optimal shrinkage function. This neural network-based thresholding mechanism replaces traditional hard and soft thresholding methods that rely on a universal threshold. Test results demonstrate that the proposed enhanced wavelet block shrinkage mechanism achieves a 20% improvement in peak signal-to-noise ratio and a 5% increase in structural similarity index score compared to traditional wavelet block shrinkage. Authors.
International Journal of Basic and Applied Sciences;Volume;14;Issue;4;pp.648-660
https://doi.org/10.14419/8psp9843 https://www.scopus.com/pages/publications/105014807521?origin=resultslist
Sahoo A.K., School of Computer Science and Engineering, Christ University, Bangalore, India; Rajavel R., Department of AI, ML and Data Science, Christ University, Bangalore, India
Financial Literacy and Fintech Exposure As Determinants of Investment Decisions: The Mediating Role of Investment Interests A Study of Individual Investors in Hyderabad, India
Behavioural Finance; ETC; Financial Literacy; Financial Proficiencies; Fintech Adoption; Prudential Investments
The evolution of the financial sector, particularly the rise of financial technologies (Fintech), has reshaped how individual investors make investment decisions. This study investigates the influence of financial literacy and fintech exposure on individual investment decisions in the underexplored emerging markets like Hyderabad, emphasising the mediating role of investment interest. Drawing on behavioural finance theory and empirical studies, the research explores how informed financial understanding and engagement with digital financial platforms shape investment behaviour. The data is collected using a structured survey method from 311 individual investors in Hyderabad, India. For the study, Structural Equation Modelling (SEM) was employed to test relationships among the constructs. Unlike prior studies that have often examined financial literacy or fintech exposure in isolation, this paper uniquely integrates these two determinants with the mediating mechanism of investment interest, providing a comprehensive model of investment decision-making in the Indian context. The study contributes to the understanding of investment psychology within the Indian context and offers insights for policy-makers and financial institutions aiming to foster inclusive and informed investment ecosystems. It can be concluded that financial literacy positively influences investment decisions by providing investors with the necessary knowledge and skills to evaluate options critically and confidently. Dr. Syed Jaffer et al.
Jaffer, Syed; Zaheeruddin, Mohamed; Roopalatha, N.; Sultana, Farhana; Babu, G.N.P.V.; Kumar, Pinninti Siva
International Journal of Accounting and Economics Studies;Volume;12;Issue;5;pp.542-549
https://doi.org/10.14419/87y3za88 https://www.scopus.com/pages/publications/105016845184?origin=resultslist
Jaffer S., School of Business, GITAM-Deemed to be University, Hyderabad, India; Zaheeruddin M., Accounting and Finance Unit, Department of Business Administration, University of Technology and Applied Sciences-Shinas, Oman; Roopalatha N., School of Business, GITAM-Deemed to be University, Hyderabad, India; Sultana F., Accounting and Finance Unit, Department of Business Administration, University of Technology and Applied Sciences-Shinas, Oman; Babu G.N.P.V., School of Business and Management, Yeswanthpur Campus Christ University, Bengaluru, India; Kumar P.S., Department of Commerce, Indian Institute of Management and Commerce, Hyderabad, India
Strategic Decision-Making in The Era of Artificial Intelligence: A Multi-Dimensional Evaluation of Opportunities, Challenges and Ethical Concerns
Artificial Intelligence (AI); Decision-Making Outcomes; Ethical Considerations; Healthcare; Strategic Business Decisions
This paper explores the impacts of Artificial Intelligence (AI) on strategic business decisions, focusing on the opportunities, hurdles, and ethical aspects associated with embedded AI in organizations. With a mixed-methodology, the study does not rely solely upon quantitative data to assess decision-making consequences influenced by AI and machine technologies, but includes qualitative feedback from business leaders on the ground. The findings imply that while AI significantly enhances data analysis and informs strategic decisions, it also raises issues about transparency, accountability, and whether there may be bias embedded in decisions driven by algorithms. Nowhere is this more apparent than in the healthcare sector, the implications of which are significant as greater reliance on AI can facilitate improving operational efficiencies and patient results, but it also demands robust frameworks to confront the ethical issues around patient privacy and informed consent. Ultimately, this work fuels the debate regarding the transformative effect of AI on business strategy and highlights that the incorporation of ethical considerations cannot be compromised if we are to develop guidelines in this area. These recommendations can help to mitigate AI-related risks and enable responsible AI adoption in the healthcare and other contexts, and pave the way for future research on the emergent technology-strategic management connection. Neeraj Kumar et al.
Kumar, Neeraj; Yadav, Swati; Charitha, Bondili Naga Sai Bhavya; Retd, Chacko Jose P.; Khound, Kaveri; Khandhadiya, Ravi Vijaybhai; Rupani, Jigar; Yadav, Nikita
International Journal of Accounting and Economics Studies;Volume;12;Issue;5;pp.950-955
https://doi.org/10.14419/7jyx1a07 https://www.scopus.com/pages/publications/105017889400?origin=resultslist
Kumar N., School of Computer Science & Engineering (SCSE), Galgotias University, Uttar Pradesh, India; Yadav S., Graduate School of Business, Tulas Institute, Dehradun, India; Charitha B.N.S.B., Department of GITAM School of Business, GITAM University, Bangalore, India; Retd C.J.P., St Aloysius College, Elthuruth, Kerala, Thrissur, India; Khound K., Department of Economics, K.R. Mangalam University, Gurugram, India; Khandhadiya R.V., Faculty of Management Studies, Marwadi University, Gujarat, Rajkot, India; Rupani J., School of Business and Management, Christ University, Bangalore, India; Yadav N., Jagran Lakecity Business School, Jagran Lakecity University, Bhopal, India
Enhancing Mobile DeFi Transactions Through Blockchain Adoption: A Pythagorean Fuzzy AHP Study
Blockchain; Cross-Border Transactions; Defi; Digital Finance; Fintech Strategy; PF-AHP
Blockchain technology has emerged as a pivotal enabler for innovation in mobile decentralized finance (DeFi) ecosystems. This study ememploys the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) to identify and rank critical enablers driving blockchain adoption for enhancing mobile DeFi transactions. A structured three-stage methodology evaluates twenty-four sub-criteria across operational, managerial, and strategic dimensions. Results emphasize managerially focused factorssuch as reduced foreign exchange (FX) transfer costs and open-source adaptabilityas the most critical enablers, followed by operational drivers like transparency. Sensitivity analysis confirmed the ro-robustness of these findings. The results offer actionable insights for fintech practitioners, digital strategists, and policymakers seeking to optimize blockchain-based mobile financial platforms. By contributing to improved financial inclusion, operational efficiency, and regulatory alignment, the study supports broader welfare enhancement in the digital economy. The proposed PF-AHP framework provides an empirical decision-making tool to guide innovation and strategic planning in financial technology services. Yogesh Kumar Jain et al.
Jain, Yogesh Kumar; Tak, Tan Kuan; Upreti, Kamal; Kumar, Nishant; Kshirsagar, Pravin R.
International Journal of Basic and Applied Sciences;Volume;14;Issue;2;pp.678-690
https://doi.org/10.14419/6726x143 https://www.scopus.com/pages/publications/105011030185?origin=resultslist
Jain Y.K., School of Management, IILM University, Greater Noida, India; Tak T.K., Engineering Cluster, Singapore Institute of Technology, Singapore; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Kumar N., School of Business Management, Christ University, Bengaluru, India; Kshirsagar P.R., J D College of Engineering & Management, Maharashtra, Nagpur, India
Exploring The Dynamic Relationship between Macroeconomic Variables on Indias Premier Benchmark Sensex 30 Index
Cointegration; Econometrics; Multicollinearity; OLS; VECM
The purpose of the study is to examine the impact of macroeconomic indicators on the closing prices of the BSE Sensex 30, a key benchmark index in India known for its volatility in response to economic conditions. This research is particularly relevant in the context of economic shocks, as it aims to recommend the adoption of appropriate economic policies that could benefit the stock market index, ultimately advancing growth in the capital market. Using the ordinary least squares (OLS) method, the study analyzes the effect of various macroeconomic variables on the BSE Sensex. Additionally, the complex relationship between these variables is explored using the Johansen Cointegration test and evidenced through the Vector Error Correction (VECM) model. The findings reveal that GDP, the Index of Industrial Production (IIP), Indias foreign trade, gold prices, Foreign Direct Investment (FDI), and money supply significantly influence the BSE Sensex. However, External Commercial Borrowing, the Consumer Price Index (CPI), exchange rates, and foreign exchange, which showed the highest Variance Inflation Factors (VIF), were excluded from the study based on OLS results. In conclusion, the study advocates for the implementation of suitable economic policies that support the stock market, thereby aligning with investors interests and promoting capital market growth. Vishweswarsastry V. N. et al.
Vishweswarsastry, V.N.; Guruprasad Desai, D.R.; Manjushree; Narasimha Murthy, H.; Kantharaju, N.P.
International Journal of Accounting and Economics Studies;Volume;12;Issue;4;pp.598-606
https://doi.org/10.14419/4jzk6k89 https://www.scopus.com/pages/publications/105014985477?origin=resultslist
Vishweswarsastry V.N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Guruprasad Desai D.R., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Manjushree, Presidency University, Karnataka, Bangalore, India; Narasimha Murthy H., Department of Professional Studies, School of Commerce, Finance and Accountancy, CHRIST University, Bangalore, India; Kantharaju N.P.
Contextual Marketing Insights from Literature Review and Implications
Contextual Marketing; Implications; Marketing
The proliferation of new technologies, such as personal digital assistants, or PDAs, and interactive television, amongst other things, has opened a wealth of potential for effectively targeting customers in real time while they are present in a virtual environment. This opens a lot of opportunities for businesses. Customers have access to the World Wide Web and the Internet through their wireless devices, no matter where they are or when they want to use them. Contextual marketing, often referred to as CM and commonly abbreviated as CM, is a strategy that entails providing customers with information that is both individualized and pertinent to their present position at the precise moment when customers need such information. This strategy is essential for luring in new clients and keeping the ones you already have. Clients in an economy that is already oversaturated with information demand not only knowledge, but also products and services that are relevant, personalized, and contextual at the time of purchase. The purpose of this research is to conduct a comprehensive review of the existing literature on contextual marketing in the preceding 20 years, focusing on both the global and the Indian contexts. The development and expansion of contextual marketing, as well as its contemporary consequences regarding earlier published works, are investigated. In the study, the systematic literature review approach of searching for relevant material is used. This research was carried out with the intention of conducting a comprehensive assessment of the existing literature with the purpose of ensuring that the fullest possible list of relevant studies is taken into consideration. To compile the papers comprising the systematic review, an algorithmic search strategy was utilized. An analysis of the previous research was carried out with the use of online databases such as Elsevier, Routledge, Emerald Group, Springer Nature, Sage, Directory of Open Access Journals, Semantic Scholar, Wiley, Academia, JSTOR, and Guildford Press. The study reaches its conclusion by providing a tangible theoretical foundation for contextual marketing while also outlining the ramifications that contextual marketing has in the modern digital world. Authors.
International Journal of Accounting and Economics Studies;Volume;12;Issue;sI-1;pp.186-191
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.14419/12jyyh39 https://www.scopus.com/pages/publications/105016790866?origin=resultslist
Clifford M.J., Department of Commerce, Christ (Deemed to be University), Bengaluru, India; Kavitha R., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Bengaluru, India
Mechanical Characterization of Cu-Al-based Shape Memory Alloys: Influence of Mn, Be and Fe on Tensile Strength, Yield Stress, Yield Strain, Ductility and Hardness
Composite variants; Crack strength; Materials; Residual strength; Shape memory alloy; Shape recovery
The pursuit of cost-effective and robust Shape Memory Alloys (SMAs) continues to expand, especially for applications in adaptive and smart structural systems, while Ni-Ti-based SMAs remain prevalent due to their superior pseudoelasticity and longevity. However, the limitations of NiTi alloys, including the high processing costs and fabrication difficulties, prompt the exploration of alternatives. This study investigates Cu-Al-based SMAs alloyed with Mn, Be, and Fe as cost-effective alternatives to NiTi systems. In the present work, Cu-Al-based alloy wires with Mn, Be, and Fe were betatized at 850 C and water-quenched to achieve martensitic structures, followed by evaluation of tensile strength, yield behavior, ductility, and hardness. Mn addition significantly enhanced tensile strength (up to 425 MPa), while Be and Fe improved ductility through grain refinement. Hardness increased with Mn due to solid solution strengthening. Thus, the current work provides a comparative analysis of Cu-Al-Mn, Cu-Al-Be-Mn, and Cu-Al-Fe-Mn alloys, linking alloying strategies to microstructural evolution and mechanical performance, demonstrating their potential for advanced engineering applications. 2025 King Mongkuts University of Technology North Bangkok. All Rights Reserved.
Hanumantharayappa, Naresh; Sanikere, Prashantha; Kodandappa, Ramesha; Nagaraja, Santhosh
Applied Science and Engineering Progress;Volume;19;Issue;2;Article No.;7981;
Academic Enhancement Department, King Mongkut's University of Technology North Bangkok
https://doi.org/10.14416/j.asep.2025.11.006 https://www.scopus.com/pages/publications/105033938304?origin=resultslist
Hanumantharayappa N., Department of Mechanical Engineering, Siddaganga Institute of Technology, Karnataka, Tumakuru, India; Sanikere P., Department of Mechanical Engineering, Siddaganga Institute of Technology, Karnataka, Tumakuru, India; Kodandappa R., Department of Mechanical and Automobile Engineering, Christ University, Kengeri, Bangalore, India; Nagaraja S., Department of Mechanical Engineering, MVJ College of Engineering, Near ITPB, Whitefield, Bangalore, India
Optimising Education for Sustainable Development through Secondary School Teachers with Relevant Subjects, Standards and Training: Quantitative Review
curriculum; education for sustainable development; quantitative; secondary school teachers; subjects
India aims to become a developed nation by 2047, emphasizing the role of Education for Sustainable Development (ESD) in achieving Sustainable Development Goals. This study examines the beliefs of Kerala secondary school teachers regarding ESD, investigating how teaching standards, subjects, and prior ESD training shape their perspectives. A survey of 400 teachers utilized the revised ESD Belief Scale, incorporating demographic considerations. The research examined demographic variables, including teaching level, subject specialization, and previous ESD training. Quantitative analysis encompassed descriptive statistics, t-tests, and ANOVA to evaluate beliefs across various groupings. Findings reveal that educators predominantly recognise the significance of ESD in fostering sustainable decision-making and lifelong learning. The discipline taught, especially social sciences in contrast to science, technology, engineering, and mathematics, is the primary determinant of educators' beliefs towards ESD. Teachers recognise the benefits of ESD, although they encounter obstacles, including restricted curricular integration and implementation. This research addresses ESD within the secondary curriculum in a unique manner, filling a notable gap in both theoretical and empirical literature. The implementation of an updated belief scale and subgroup analyses provides policymakers and curriculum developers with novel perspectives. It is recommended that curriculum reform incorporate ESD throughout all courses, accompanied by specialized teacher training to enhance awareness, skills, and pedagogical techniques for the effective implementation of ESD. Secondary educators in Kerala predominantly advocate for the integration of ESD, particularly within the social sciences. Future policy and research must emphasise curricular innovation and longitudinal assessment to further Indias sustainable development objectives. 2026 Sijo VARGHESE & P.M. MATHEW. Published by the Asian Society of Human Services (ASHS).
Asian Journal of Human Services;Volume;30;Issue;2;Article No.;e3002.1.023;
https://doi.org/10.14391/ajhs.e3002.1.023 https://www.scopus.com/pages/publications/105039313067?origin=resultslist
Varghese S., Department of Social Work, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Mathew P.M., Department of Social Work, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India
Navigating the Metaverse: TCCM Approach for Comprehensive Review of Avatar Marketing Strategies
Avatar Marketing; Brand Promotion; Immersive Advertising; Metaverse
Despite the growing importance of avatars in reshaping consumer interactions, there remains a discernible gap in the literature necessitating a comprehensive synthesis of existing knowledge. In response, this study conducts a systematic-literature-review employing the Theory-Context-Characteristics-Methodology (TCCM) framework. Study aims to delineate the current state of the field by addressing key research foci and conducting a meticulous TCCM analysis. Drawing from the TCCM-framework, our investigation encompasses an exploration of (1) dominant theories guiding research endeavours, (2) contexts within which avatar marketing is situated, (3) key characteristics characterizing the studies, (4) diverse study methodologies employed, and (5) the overall trajectory of research in this domain. Study unfolds a panoramic view of avatar marketing in the metaverse, synthesizing existing knowledge, and illuminating the key dimensions that shape this dynamic field. Insights gained from systematic-literature-review contribute to a deeper understanding of the theories, contexts, characteristics, and methodologies that have underpinned research endeavours thus far. 2013 The Korean Society of Management Information Systems. All rights reserved.
Dhanya, Debora; Gupta, Gaurav; Gakhreja, Shweta; Joshi, Mahesh Chandra; Kumar, Satinder
Asia Pacific Journal of Information Systems;Volume;35;Issue;2;pp.289-323
Korean Society of Management Information Systems
https://doi.org/10.14329/apjis.2025.35.2.289 https://www.scopus.com/pages/publications/105016801374?origin=resultslist
Dhanya D., Christ (Deemed to be University), Bangalore, India; Gupta G., Amity University, Greater Noida, India; Gakhreja S., Centre for Distance & Online Education, Manipal University Jaipur, India; Joshi M.C., Centre for Distance & Online Education, Manipal University Jaipur, India; Kumar S., School of Management Studies, Punjabi University Patiala, India