Volume 14 | Issue 8 |

 Abstract

Agriculture plays a vital role in ensuring food security and economic stability worldwide. Plant diseases significantly affect crop productivity and quality, leading to substantial financial losses for farmers. Traditional disease diagnosis methods rely heavily on manual inspection by agricultural experts, which can be time-consuming, subjective, and inefficient for large-scale farming applications. This paper presents an intelligent plant leaf disease detection framework based on Ensemble Learning and Explainable Artificial Intelligence (XAI). The proposed system combines the predictive capabilities of multiple deep learning models, including CNN to enhance classification accuracy and robustness. Ensemble learning integrates the strengths of individual models while minimizing their weaknesses, resulting in improved disease recognition performance. Furthermore, Explainable AI techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) are incorporated to visualize and interpret the decision-making process of the ensemble model, thereby increasing transparency and user trust. Experimental evaluation is conducted using a publicly available plant leaf disease dataset comprising multiple crop species and disease categories. The results demonstrate that the proposed ensemble framework achieves superior classification accuracy, precision, recall, and F1-score compared to individual deep learning models. The integration of explainability further assists farmers and agricultural experts in understanding disease symptoms and validating model predictions.

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 Keywords

Plant Disease Detection, Ensemble Learning, Explainable Artificial Intelligence (XAI), Deep Learning, Convolutional Neural Networks, Grad-CAM, Precision Agriculture, PlantVillage Dataset, Crop Health Monitoring, Image Classification.

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ABSTRACT: The present study titled "an assessment of asset quality and financial performance of the shimoga arecaunut mandi merchants cooperative bank ltd. Shimoga" aims to assess the financial performance and asset quality of the bank. Cooperative banks play an important role in mobilizing savings and providing credit facilities to various sections of society. Therefore, evaluating their asset quality and financial performance is essential for understanding their operational efficiency and financial stability. The study primarily focuses on two objectives. First, it assesses the asset quality and Non-Performing Asset (NPA) position of Shimoga Gramin Credit Merchants Cooperative Bank Limited, Shimoga, by analyzing the Gross Non-Performing Asset (GNPA) Ratio, Net Non-Performing Asset (NNPA) Ratio, and Provision Coverage Ratio (PCR). Second, it analyzes the bank's financial performance with reference to net profit, shareholders' equity, dividend percentage, and Return on Equity (ROE). The study adopts a descriptive and analytical research methodology and is based mainly on secondary data collected from the bank's annual reports, financial statements, and other relevant published sources. Appropriate financial ratios and analytical techniques are used to evaluate the bank's asset quality, profitability, and shareholders' returns over the selected study period. The study is expected to provide insights into the bank's NPA management, asset quality, profitability, dividend performance, and efficiency in utilizing shareholders' funds. The findings may be useful to the management of the bank, members, depositors, researchers, and other stakeholders in understanding the financial strengths and areas requiring improvement. The study also contributes to a better understanding of the financial performance and operational challenges of cooperative banking institutions.

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 Keywords

Key constructs: Financial performance, efficiency, Asset quality, profitability, dividend performance, return on equity and risk management practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Determinants, Finance, Pay-out, Decisions, Analysis, Industries,

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Watershed; Geology; Deccan Trap; Basalt; Hydrogeology; Groundwater; Remote Sensing; GIS; Aquifer Recharge; Watershed Management.

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In the era of globalization, live-in relationships are a form of non-marital cohabitation form of marriage. It is distinct from the traditional concept of "marriage". In contemporary India, such relationships have emerged as a product of socio-legal phenomena. And the very concept of live-in relationships is greatly influenced by western cultural dynamics and global exposure. Despite the increasing prevalence of live-in relationships, there is a sharp existence of a sensitive and ambiguous space due to the absence of a robust statutory legal framework. Therefore, this journal critically examines the historical, constitutional, statutory and judicial dimensions and safeguards backed by the constitution of India. This journal minutely analyzes the defining roles of Article 14, 19 and 21 of the Constitution of India . By analyzing the articles, it safeguards the individual autonomy, dignity and privacy and at the same time it also evaluates the limited recognition under the Protection of Women from Domestic Violence Act, 2005 . Furthermore, it also examines the provisions under Family Law, Inheritance Law, Evidence Law, and Code of Criminal Procedure, 1973. Through this journal, a critical assessment is being conducted to examine the landmark judgements, highlighting the proactive role of the judiciary in extending the legal protection of the women and children born within such relationships . In addition, it also undertakes a comparative analysis of legal frameworks of foreign countries to bring the much-needed reforms in the Indian context. The journal also identifies the acute gaps, relating in maintenance and recognition of same sex marriage, and argues for framing an inclusive and comprehensive legal framework that finely balances individual dignity and autonomy with social recognition and protection, aligning with constitutional values and morality.

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 Keywords

Era of Globalization, Live-in relationship, non-marital cohabitation, Individual Dignity, Protection of Women from Domestic Violence Act 2005, Same-Sex Marriage, Article 14, 19 and 21 of the Constitution of India, Family Law, Inheritance Law, Evidence Law and Code of Criminal Procedure 1973, Constitutional Values, Constitutional Morality.

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Artificial Intelligence (AI) has rapidly transformed economic activity, public administration, healthcare, financial services, education, employment, commerce, transportation, and legal services. The increasing deployment of AI systems creates significant opportunities for innovation and efficiency but also raises complex legal and ethical questions concerning liability, accountability, transparency, privacy, discrimination, intellectual property, consumer protection, and human rights. Traditional legal frameworks are generally based on identifiable human actors and organizations, whereas advanced AI systems may generate outputs through complex, probabilistic, and partially autonomous processes. This creates difficulties in determining responsibility when AI systems cause financial loss, physical injury, discrimination, misinformation, privacy violations, or other forms of harm. This research article examines the emerging challenges of legal regulation of Artificial Intelligence, with particular emphasis on liability, accountability, and ethical governance. It analyzes whether existing principles of tort law, contract law, consumer protection, product liability, data protection, intellectual property law, criminal law, and corporate responsibility are sufficient to address AI-related risks. The study also examines the importance of transparency, explainability, human oversight, algorithmic fairness, risk management, and corporate accountability. Particular attention is given to the Indian regulatory environment, including the Information Technology Act, 2000, the Digital Personal Data Protection Act, 2023, consumer protection legislation, constitutional principles, and emerging AI-governance initiatives. Comparative reference is made to international approaches, including the European Union's risk-based AI regulatory framework and developments in the United States and other jurisdictions. The article argues that AI systems should not be treated as independent legal persons merely because they exhibit autonomous characteristics. Instead, responsibility should generally be allocated among developers, deployers, providers, users, and other actors according to their respective roles, control, knowledge, and contribution to risk. The study proposes a human-centred, risk-based, technology-neutral, and accountability-oriented regulatory framework capable of balancing innovation with fundamental rights and public interest.

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 Keywords

Artificial Intelligence, AI Regulation, Legal Liability, Accountability, Ethical Governance, Algorithmic Bias, Data Protection, Human Rights, AI Governance, Technology Law, Product Liability, India.

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In this paper, we introduce fuzzy mildly Minimal-open and fuzzy mildly Minimal-closed functions in fuzzy topological spaces and obtain certain characterization of the mildlyMinimal closed and mildly Minimal open functions.

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Fuzzy mildly Minimal-open functions, fuzzy mildly Minimal-closed functions , fuzzy mildly Minimal-closed sets, fuzzy mildly Minimal-open sets.

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Investment decisions are often assumed to be based on careful analysis, available information and rational expectations. However, in real-world financial markets, investors do not always behave rationally. Emotions, past experiences, personal beliefs, fear, greed and the behaviour of other investors often influence the way people invest their money. Behavioural finance attempts to understand these psychological factors and explains why investors sometimes make decisions that differ from traditional financial theories. This article examines the relationship between investor psychology and behavioural finance, with particular attention to common behavioural biases such as overconfidence, loss aversion, herd behaviour, anchoring and mental accounting. The study is descriptive in nature and is primarily based on secondary information obtained from academic literature, books, journals and financial studies. The article highlights how psychological influences can affect investment choices and suggests measures that can help investors make more disciplined and informed financial decisions

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Behavioural Finance, Investor Psychology, Investment Decisions, Behavioural Biases, Loss Aversion, Herd Behaviour, Financial Markets

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Retention of language skills remains a critical challenge in English as a Second Language (ESL) education. Learners often achieve short-term gains in vocabulary and grammar but struggle to sustain this over time, leading to high attrition rates. This Study proposes the Gamified AI Retention Pathway Model, a conceptual framework that uniquely integrates gamification mechanics, AI personalization, and Self-Determination Theory to explain long-term retention in ESL learning emphasizing autonomy, competence, and relatedness as fundamental psychological needs that sustain motivation over time. Gamification contributes mechanics such as points, badges, challenges, and leaderboards, which are strategically aligned with these needs. AI adds adaptive and personalized feedback that ensure learners receive timely, individualized guidance while maintaining control over their learning pathways. The Study synthesizes existing research to construct a conceptual framework that explains how learners move from short-term engagement to sustained participation and ultimately long-term retention. The framework contributes theoretically by connecting gamification and AI to retention through motivational alignment, practically by offering design principles for educators and developers, and politically by informing policy on evidence-based EdTech adoption. At its core, the framework recognizes retention as an active process supported by motivational scaffolding. By satisfying autonomy, competence, and relatedness through gamified AI environments, learners can internalize language skills and sustain their learning efforts. Future research should empirically validate this model through longitudinal qualitative studies, capturing learner experiences and teacher perspectives.

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Keywords: Gamification, Artificial Intelligence, Self-Determination Theory, ESL retention, motivation, educational technology.

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Financial fraud has existed for as long as financial transactions themselves, but the way fraud is committed has changed significantly with digitalisation. Online banking, digital payments, mobile applications and technology-driven financial services have made transactions faster and more convenient, while also creating new opportunities for fraudulent activities. Traditional fraud-detection methods often depend on predefined rules, manual verification and investigation after suspicious activity has already occurred. These approaches remain important, but they may struggle to deal with the speed and volume of modern financial transactions. Artificial Intelligence (AI) is increasingly being used to strengthen fraud detection by analysing large volumes of data, identifying unusual patterns and assessing suspicious transactions in near real time. Machine learning, predictive analytics, natural language processing and behavioural analytics can help financial institutions recognise patterns that may otherwise remain unnoticed. However, AI is not a complete replacement for human judgement. Concerns relating to data privacy, false alerts, algorithmic bias, transparency and the increasing use of AI by fraudsters themselves remain important. This article examines the role of Artificial Intelligence in detecting financial fraud, discusses its major applications and advantages, identifies the challenges associated with its adoption and highlights the need for a balanced combination of technology, internal controls and human expertise

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 Keywords

Artificial Intelligence, Financial Fraud, Fraud Detection, Machine Learning, Banking, Financial Technology, Data Analytics

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Creative Commons Attribution 4.0 and The Open Definition