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Measuring Financial Inclusion in India: An Approach
In light of the COVID-19-induced financial crisis, the need for robust financial services and networks has become more apparent than ever, which necessitated the accurate measurement of the breadth of financial inclusion in India. First, the study conducted a detailed critical review of the current indices and their construction methodology. Then, we created a financial inclusion index for India by accounting for the flaws existing in the current indices. The primary contribution of this study to the existing literature is the new approach it proposed for the assignment of weights in the financial inclusion index. Based on this new financial inclusion index, the study concluded that Indias Southern states and union territories showed better financial inclusion. In contrast, the traditionally backward BIMARU states of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh, and a few of the North Eastern states of India, lagged. The study also provided a refined and inclusive definition of financial inclusion based on its new approach to index creation. 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Augmented Reality-Enabled Education for Middle Schools
Augmented reality acts as an add-on to teachers while teaching students, and this helps the teachers and students to have an interactive session. Augmented realitys usage in education is cited as one of the major changes in the educational sector. Thus, the work carried out makes a positive impact in the educational industry. Augmented reality provides features like image recogntion, motion tracking, facial recognition, plane detection, etc., to provide interactive sessions. Simultaneous localization and mapping and concurrent odometry and mapping have proved to be efficient algorithms for augmented reality on mobile devices. The work carried out allows students to view interactive newspapers while reading a specific article. It also allows them to view a dynamic three-dimensional model of the solar system on their smartphone using augmented reality. 2020, Springer Nature Singapore Pte Ltd. -
FinTech for Sustainable Financial Market Innovation: The FinTech Transformation of Traditional Finance
Fintech is transforming the traditional banking industry, resulting in creative solutions that enhance financial market sustainability. This section examines how financial technologies like blockchain, AI, mobile banking, and digital payment systems are changing banking operations to make them more transparent, efficient, and accessible. It also examines how Fintech may drive long-term financial innovation by increasing financial inclusion, lowering operating costs, and encouraging green banking activities. This chapter uses case studies and practical examples to investigate the influence of Fintech on the banking system, emphasizing green lending, digital currencies, and sustainable investing platforms. Adopting new technology presents operational and regulatory problems for banks. The global financial system is becoming increasingly dependent on sustainability, as seen by how banks use Fintech to meet the growing demands of ESG standards. This section examines how digital banking platforms, AI risk assessment algorithms, and blockchain green bonds might help banks allocate resources more responsibly. By adopting these technologies, banks can reduce their environmental impact while meeting the rising demand for ethical and accountable financial services. 2026 Nova Science Publishers, Inc. -
Integrating Explainability and Fairness in Credit Risk Prediction: A Hybrid Approach Using Tabnet, LightGBM, SHAP, and Counterfactual Explanations on the FICO Dataset
Transparent and fair credit risk assessment is essential for responsible lending in modern financial systems. This paper presents an interpretable and ethically grounded machine learning framework for loan default prediction using the FICO Explainability Challenge dataset. We combine LightGBM, a high-performing gradient boosting model for tabular data, with TabNet, a deep learning architecture that provides intrinsic interpretability through attentive feature selection. To enhance transparency, SHapley Additive exPlanations (SHAP) are employed for global and local feature attribution, while counterfactual explanations generated using the DiCE framework offer actionable recourse. Fairness is evaluated and mitigated using IBM's AI Fairness 360 toolkit. Experimental results demonstrate that the proposed hybrid approach achieves strong predictive performance while ensuring interpretability and fairness, making it suitable for trustworthy and regulation-compliant credit risk modeling. 2026 IEEE. -
Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing
Quantum-Inspired Soft Computing (QISC) is an advanced concept in computational intelligence in the current era of hi-technology, which has been adapted to principles underpinning quantum mechanics, including superposition and entanglement in the conven tional computing systems. The current chapter aims to identify the growing trends in the market environment concerning QISC for intelligence data processing, specifying the aspects of its applicability, costuse ratio, and adaptability among different industries. The increased need for the utilization of enhanced methods of data handling arising from big data and AI progress has made QISC viable for handling optimization problems, machine learning, and predictive modeling in addition to quantum computing. Major business sectors, such as finance, health care, supply chain, and energy sectors, have benefited from the use of QISC to enhance operational management, decision making, and system reliability. This chapter also discusses how leading players such as Microsoft, IBM, and DWave are in the course of incorporating QISC in cloud environments as well as in hybrid computing systems. Advancements in hardware, such as GPUs and quantum-inspired processors, and in algorithms, such as tensor networks and reinforcement learning, have further extended the usage of QISC. However, there are issues such as standardization, interdisciplinary qualified staff, and computational complexity, which remain important unsolved tasks for further investigation and cooperation. This chapter ends by briefly pointing out new directions for how QISC can work with AI for NLP and real-time analysis. Understanding QISC in terms of its current market and its positive impact on the future of computational intelligence is the focus of this chapter, which focuses on current market trends. Analyzing key trends and the degree of industry adoption, the research findings provide useful perspectives for academic, practical, and policy purposes. 2026 Scrivener Publishing LLC. -
The role of psychological capital in shaping climate anxiety across generations
Background: Climate change has transitioned from a distant environmental issue to an immediate psychological reality that profoundly affects how individuals perceive their future and well-being. This study investigates generational differences in climate anxiety and examines the role of psychological capital (PsyCap) as a potential protective resource through the lens of Environmental Identity Theory (EIT). Methods: A cross-sectional survey was conducted among 384 participants in Kerala, India, comprising Generation X (33.6%), Millennials (29.9%), and Generation Z (36.5%). Climate anxiety was measured using the Climate Anxiety Scale (Clayton & Karazsia, 2020), and PsyCap was assessed through the PCQ-12, encompassing hope, efficacy, resilience, and optimism. Data were analyzed using ANOVA and multiple regression, with effect sizes and confidence intervals reported. Results: Significant generational differences emerged for both cognitiveemotional impairment (F(2,381) = 3.83, p =.023) and functional impairment (F(2,381) = 6.15, p =.002). Gen Z reported significantly higher anxiety levels than Millennials (p =.045, d = ? 0.29*) and Gen X (p =.011, d = ? 0.35*). Regression analyses indicated that PsyCap and generation jointly predicted cognitiveemotional (R = 0.04, p =.008) and functional impairments (R = 0.056, p =.001), with self-efficacy emerging as a significant negatively associated with functional impairment (B = ? 0.12, p =.043). Conclusion: Gen Z experiences greater emotional and functional impacts of climate anxiety compared to older cohorts, while self-efficacy offers a modest buffering effect for functional impairment. These findings underscore the need for interventions that strengthen psychological resources and adaptive coping to mitigate climate-related distress among younger populations. The Author(s) 2026. -
Phytochemicals in Lemongrass (Cymbopogon citratus) Contributing to Growth and Disease Resistance in Goldfish (Carassius auratus Linn. 1758): Integration of Molecular Docking and Statistical Analyses
The ornamental fish industry has experienced significant growth with species like goldfish (Carassius auratus) gaining popularity for their vibrant appearance and ease of care. However, bacterial infections, particularly those caused by Aeromonas hydrophila, pose a significant threat to fish health and market value. In this study, visibly diseased goldfish exhibiting symptoms such as fin rot, black spots, tail rotting and skin lesions were divided into control and treated groups. The treated group was fed lemongrass (Cymbopogon citratus)-coated pellets, while the control group received standard feed. Over a three-week trial, visual improvements, including the healing of fin rot were documented, demonstrating the effectiveness of lemongrass-enhanced feed in promoting recovery and growth. GC-MS analysis of fresh lemongrass leaves identified key bioactive compounds, including citral, tetra decanoic acid, trans-verbenol and 1-undecanol, known for their antimicrobial properties. These findings confirmed the presence of phytochemicals with potential therapeutic applications against bacterial infections. Molecular docking studies further evaluated the interactions of prominent lemongrass phytochemicals: Procyanidin B2, Diosmin, Catechin and Tricin, with A. hydrophila outer membrane protein (3OD9). The results demonstrated strong binding affinities with Procyanidin B2 showing the highest (-8.0 kcal/mol), followed by Diosmin (-7.8 kcal/mol), Catechin (-7.6 kcal/mol) and Tricin (-7.6 kcal/mol), indicating their potential to inhibit bacterial pathogenicity. This study highlights the dual role of lemongrass as a natural growth promoter and antibacterial agent, emphasizing its potential as a sustainable and eco-friendly alternative to antibiotics in aquaculture. By effectively managing bacterial infections and improving fish health, lemongrass offers a promising solution for enhancing sustainability in aquaculture. 2026, World Researchers Associations. All rights reserved. -
Developing a global sustainable electricity use index using the pressure-state-response framework
This study analyse and compare the sustainable electricity usage in 60 countries listed on the official websites of World Energy Consumption Statistics and Climate Bond Initiative. The study also analyses the impact of increased usage of sustainable electricity on the economies' dependence on non-renewable energy sources in the evaluation system. We used a standard index system based on the Pressure-State Response (PSR) model to measure global sustainable electricity usage. Model results convey that Norway is the best performer in sustainable electricity usage, while several European countries display commendable scores, confirming their commitment to sustainable electricity practices. On the other hand, despite being the leading economies in terms of GDP, major economies such as the United States, China, Japan, and India have underperformed compared to others in the evaluation system. The study employs regression techniques to explain the relationship between sustainable electricity usage and non-renewable energy dependence. Results confirm a negative relationship between the variables, indicating the role of sustainable energy practices in reducing fossil fuel consumption. It emphasizes the urgency of a balanced approach to economic growth and natural resource usage to support a green future. 2024 Elsevier Ltd -
Relative Efficiencies of Farmer Producer Companies in India- Slack-Based Model Approach
The concept of the farmer producer company (FPC) model has received a large momentum especially during the 20202021 farmers' protest in India. This paper examines the relative efficiencies of 46 FPCs in Kerala using non-radial data envelopment analysis (DEA) for the financial year 2018-19. We use a non-oriented slack-based model (SBM) under assumptions of constant and variable returns to scale. The results reveal that 36.96 per cent of the sample FPCs are overall technical efficient and 50 per cent of the FPCs are pure technical efficient. It is found that technical inefficiency is reported for a few FPCs due to scale inefficiency. Among the input and output targets suggested for inefficient FPCs, reduction in the 'number of shareholders' and augmentation of 'profits' reported in most cases to improve their efficiency scores. Based on the findings, we suggest the concerned stakeholders to provide additional financial and non-financial supports to the needy rather than focusing on establishing new FPCs. 2022 IEEE. -
Efficiency study of coconut producer companies in India-A DEA approach
The concept of the Farmer Producer Company (FPC) model has been a hot issue, especially during the 2020-21 Indian farmers' protest. Considering the pioneering initiatives of the Coconut Development Board (CDB) in setting up CPCs, we compare the technical efficiencies of CPCs that focus on coconut and its byproducts in Rural India for two consecutive financial years (2018-19 and 2019-20). Coconut Producer Companies' efficiency scores are estimated using Data Envelopment Analysis (DEA), a mathematical technique to assess technical efficiencies across homogeneous units. The results reveal that 35.11 percent of the sampled CPCs for FY 2018-19 are overall technical efficient, and approximately 76 percent are purely technical efficient. It is found that technical inefficiency is reported for a few CPCs due to scale inefficiency. The overall technical and pure technical efficiency have improved in FY 2019-20 compared to the previous period. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Analysing the impact of oil prices, economic activity, and trade policy uncertainty on CO2 emissions in the US context: A wavelet approach
This study examines the simultaneous co-movements between oil prices, economic activity, trade policy uncertainty, and CO2 emissions in the United States using a series of wavelet methodologies. Unlike traditional approaches, the wavelet approach is appropriate for understanding the time-varying associations at different frequencies and is designed to efficiently handle the non-stationary nature of economic and environmental time series data. The empirical results highlight the potential of a leading relationship where economic activities and trade policy uncertainties drive CO2 emissions in the US during the period from January 1990 to January 2022. Contrarily, the link between oil prices and CO2 emissions is characterized by intricate dynamics, exhibiting both lagging and leading co-movements at different frequencies. Moreover, economic activities have a positive impact on CO2 emissions, while in the high quantile tails, trade policy uncertainty decreases CO2 emissions. This means economic activity is slowing down during the period of high trade policy uncertainty. Our findings highlight the necessity of specific policies that reconcile economic growth with environmental sustainability, manage the effect of oil price changes on CO2 emissions, and match trade policies with emission-minimizing goals. Based on the results, this research offers important implications for policymakers to ensure the equilibrium between economic activity and environmental management within the scope of sustainable development goals. 2025 International Association for Gondwana Research -
Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews
Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. 2024 IEEE. -
A Comparative Study of Pollution Levels in Major Cities of India During Covid-19 in India
This paper aims to study the major pollutants of the four metro cities of India before and after covid 19 first wave. The cities considered for the study are Bangalore, Delhi, Mumbai, and Kolkata. The major pollutants considered for the study are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The basic aim of the study is to find the effect of lockdown and covid restrictions on the level of pollutants across the four major cities of India. We used both parametric and non-parametric tests for the analysis using SPSS. From the study, it is clear that there is a significant decrease in all the major pollutants across India's major cities.6. 2023, University of Wollongong. All rights reserved. -
Cultural quotient: Evolving culturally intelligent business scholar-practitioners
Analytical competency is an essential skill when it comes to the present-day business scenario of the world. However, these days we see a shift in the business needs when it comes to working in a globalized environment. Not only is the intelligence quotient (IQ) looked at but organizations these days are in pursuit of individuals who have another side to their profile - the culturally intelligent side (assessed using the cultural quotient). The need of such a skill can be attributed to the fact that organizations are now churning out their human side of addressing the employees when it comes to ensuring that they blend in the organization with ease. Acquiring a workforce which possesses high cultural intelligence can be a tough task; however, training employees to become culturally competent can be a doable task. Like any other personality trait which can be imbibed over time through constant analysis and observation, cultural competency is one such area which may be cultivated through various methodologies and practices. 2018, IGI Global. -
Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India
Coronavirus is airborne and can spread easily. Air pollution may have an impact on breathing and also keep the virus airborne. The levels of air pollution were impacted by the lockdown measures, restricting the vehicular and industrial pollutants. Therefore, there is a need to understand the relation between air pollution levels and the Coronavirus infection rate. The study aims to find the effect of various pollutants across major cities of India on the R-value. The pollution data was collected from the Governments official portal. The major pollutants on which the data was collected are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The data on air pollution levels were also collected for the selected cities from April 2020 to April 2021. The spread is measured as the reproduction number at time t (Rt), which is an estimate of infectious disease transmissibility throughout an outbreak, or it is the rating of Coronavirus or any diseases ability to spread. The data is analysed using MS Excel and R Programming. Descriptive statistics and regularisation are performed on the data. The study results reveal that some pollutants positively and negatively affect the infection rate. However, the effect is very low, and it concluded that the pollution might not directly affect infection rates. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
A Comparative Analysis of Machine Learning Algorithms for Image Classification: Evaluating Performance
Image classification plays a crucial role in various applications, and selecting the most effective machine learning algorithm is essential for achieving accurate results. In this study, we conducted a comparative analysis of several well-known supervised machine learning techniques, including logistic regression, support vector machine (SVM), k-nearest neighbours (kNN), nae Bayes, decision trees, random forest, AdaBoost, and artificial neural networks (ANN). To assess the performance of these algorithms, we utilised different fonts of the English alphabet as our dataset and performed the analysis using the R programming language. We evaluated the algorithms based on standard performance criteria, such as the area under the Receiver Operating Characteristic curve (ROC), accuracy, F1 score, precision, and recall. Our research findings demonstrated that the classification performance varied depending on the training size of the dataset. Notably, as the training size increased, neural networks exhibited superior performance compared to other machine learning techniques. Consequently, we conclude that neural networks and SVM are the most effective algorithms for image classification based on our study. By conducting this comprehensive analysis, we contribute valuable insights into selecting appropriate machine learning algorithms for image classification tasks. Our findings emphasise the significance of considering the training dataset size and highlight the advantages of neural networks and SVM in achieving high classification accuracy. This study provides valuable guidance for practitioners and researchers in choosing the most suitable machine learning algorithm for image classification, considering their specific requirements and dataset characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Classifying voice-based customer query using machine learning technique
Timely attention to issues raised by customers is critical. It is imperative that the average handling time is lesser, which in turn contributes to productivity. It was found from the data from the banking industry in the US that, on average, a customer service call last for seven minutes. The first two minutes are for the call to get redirected to the respective team. This study investigates a method using machine learning to classify and redirect the customers into the respective department directly based on their initial voice response or voice message. It will substantially reduce the service time. CRISP-DM methodology is being used to design the process of the study. The most frequently occurring issues and the department to which they are associated are created through machine learning from the dataset that contained product reviews and metadata of different issues. The programming languages that are used in this study are Python, HTML and Java. An interface is created by using HTML, which makes it quite user-friendly. The study tests the effectiveness of converting voice to text and interprets which department the call should be transferred to address the issue. A support vector machine and a logistic regression model were used for the prediction, and it was found that the models provided an accuracy of 83 and 84 percent, respectively. The study proves that using ML and voice recognition reduces the average handling time. 2021 Ecological Society of India. All rights reserved. -
Using Academic Performance Indicator to Evaluate the Cost to Company of Management Graduates
As the placement season hits CBS Business School, India, the pressure to get placed is at its peak. As the placement season draws to a close, the unplaced students storm the Directors office complaining about unfair treatment in the process. They lay blame on the random shortlisting followed by the Placement co-ordinator. Concerned with these allegations, the Director calls on faculty to investigate the situation. During the conversation one of the students, Rachit, expresses regret in not focusing solely on academics and instead on developing a more well-rounded profile. He feels that that is the reason for his failure to get placed. A fundamental question arises of how closely academic performance and Cost to Company (CTC) are related. Data is collected to examine the validity of the long-held belief that higher academic performance leads to higher paying job placement. 2022 NeilsonJournals Publishing. -
Unsupervised Feature Selection Approach for Smartwatches
Traditional feature selection methods can be time-consuming and labor-intensive, especially with large datasets. This studys unsupervised feature selection approach can automate the process and help identify important features preferred by a particular segment of users. The unsupervised feature selection method is applied for smartwatches. Smartwatches continue to gain popularity. It is important to understand which features are most important to users to design and develop smartwatches that are more engaging, user-friendly, and meet the needs and preferences of their target audience. The rapid pace of technological innovation in the smartwatch industry means that new features and functionalities are constantly being developed. Multi-cluster feature selection, Laplacian score, and unsupervised spectral feature are used. Conjoint analysis is done on the most common features in all three selection methods. The unsupervised feature selection technique is used for identifying the relevant and important features of new smartwatch users.The practical implication of the research is in the application of the technique in the new product design of smartwatches. The result of the study also informs smartwatch manufacturers and developers on the features they need to prioritize and invest in. This can ultimately result in better and more user-friendly smartwatches and a good overall experience for the user. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Predicting Consumer's Brand Switching Behaviour for Cell Phones
The IUP Journal of Marketing Management, ICFAI, Vol. XV, Issue 4, ISSN No. 0972-6845