Volume 13 | Issue 11 |

 Abstract

The integration of artificial intelligence (AI) in healthcare promises data-driven clinical insights, yet raises critical concerns around privacy, compliance, and ethical governance. Federated Learning (FL) enables multi-institutional model training without direct data exchange, addressing these privacy challenges. This study presents a Federated Learning framework that integrates Differential Privacy (DP) and an adaptive noise calibration mechanism to dynamically balance data protection with predictive performance. Using simulated multi-hospital datasets for diabetes and cardiovascular prediction, the proposed Privacy-Utility Engine (PUE) maintained accuracy above 91% with a privacy budget ? <= 3. The framework demonstrates how privacy-preserving AI can be responsibly deployed in healthcare without compromising diagnostic effectiveness. Future research directions and challenges in clinical translation are also discussed.

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 Keywords

Federated Learning, Privacy-Preserving AI, Healthcare Analytics, Differential Privacy, Adaptive Noise Calibration

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Crop diseases significantly affect agricultural productivity and can lead to severe economic losses for farmers. Early identification and treatment of these diseases are crucial for maintaining healthy crop growth and ensuring a stable food supply. Traditional methods of disease detection rely on manual observation, which is time-consuming, labor-intensive, and often inaccurate. To overcome these challenges, machine learning techniques are used to automatically detect and classify crop diseases from image and environmental data. In this project, a machine learning-based model is developed using algorithms such as Convolutional Neural Networks (CNN) for image-based disease identification and Random Forest for environmental condition analysis. The system is trained using datasets of healthy and infected plant leaves, enabling it to recognize disease patterns with high accuracy. Once trained, it can predict the type of disease and suggest suitable fertilizers or treatments. This intelligent approach helps farmers take timely preventive actions, reducing the spread of diseases and improving overall yield. The model provides a user-friendly interface that allows easy access and quick predictions. By integrating technology with agriculture, the proposed system supports precision farming, resource optimization, and sustainable agricultural growth. This project demonstrates how machine learning can transform traditional farming into a more efficient and data-driven practice, ensuring better productivity and food security.

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Crop Disease Prediction, Machine Learning, Convolutional Neural Network (CNN), Smart Farming, Sustainable Agriculture

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Abstract This study explores the philosophical and pedagogical implications of teacher development and ethical practice through a hermeneutic and interpretive examination of Hanuman's life and deeds as depicted in the Ramayana. Grounded in qualitative philosophical methodology integrating textual analysis and pedagogical reflection, the study aims to derive a framework for ethical teacher formation based on Hanuman's attributes--devotion, humility, service, courage, and wisdom. The research is guided by four objectives: identifying Hanuman's ethical virtues, interpreting their relevance to contemporary teacher formation, examining their applicability in modern education, and exploring pedagogical implications for teacher education programs. Findings suggest that Hanuman's life provides a profound archetype for educators, embodying principles of selfless service, lifelong learning, ethical integrity, and learner-centered devotion. The study concludes that integrating such philosophical insights into teacher education can enhance professional identity, moral consciousness, and the cultivation of pedagogical virtue. Implications for modern teacher education curricula and ethical pedagogy are discussed.

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Keywords: teacher development, pedagogical ethics, Hanuman, philosophical pedagogy, teacher formation, moral education

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The metaphor of "Fire on the Mountain" has long been associated with destruction, chaos, and danger. Rooted in cultural narratives across various societies, fire has symbolized an uncontrollable force that devastates everything in its path. However, such representations often overlook the multifaceted role fire plays in ecological, psychological, and societal contexts. . Literature depicts reality through the prism of human imagination and perception, using language as its medium. While learning a language, children are exposed to the culture's preconceptions and ideals, which they naturally embrace as adults. According to Kenyan author Ngugi wa Thiong'O, 'Language conveys culture and culture carries especially via orature and literature the full set of values by which we learn to view us and others.' This paper aims to challenge and deconstruct the stereotype of fire as merely a destructive element by exploring its dual nature both as a force of renewal and transformation. Drawing on historical, ecological, and psychological perspectives, the research highlights how fire is essential for environmental regeneration, cultural resilience, and personal growth. By critically examining the portrayal of fire in literature, media, and cultural narratives, this paper proposes a more nuanced and holistic understanding of fire's role in nature and human life.

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Stereotype, Fire, Mountain, human imagination, culture

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(The representation of the human psyche and behavioral complexity in the English literary tradition offers a profound exploration of the inner workings of the mind, emotions, and motivations. This paper examines how English literature, from the early modern period to the contemporary era, has depicted psychological depth and multifaceted human behavior. Through close readings of canonical texts, the study highlights the evolution of psychological realism, the use of narrative techniques such as stream of consciousness and interior monologue, and the influence of psychological theories on literary characterization. The paper also investigates how literature reflects and critiques societal norms, existential anxieties, and the interplay between individual identity and collective experience. By analyzing works by authors such as Shakespeare, Woolf, Dostoevsky, and Plath, the study demonstrates that literature serves as both a mirror and a lens for understanding the complexities of the human condition. The findings underscore the enduring relevance of psychological themes in literature and their capacity to foster empathy, provoke critical reflection, and illuminate the nuances of human behavior.)

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Keywords: Human psyche, behavioral complexity, psychological realism, stream of consciousness, interior monologue, existential angst, character motivation, narrative technique, literary analysis, mental health, identity, social norms

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This prospective observational pilot study evaluated the prescribing pattern of antihypertensive drugs and assessed medication adherence among hypertensive chronic kidney disease patients undergoing maintenance hemodialysis. Ten patients were included, and adherence was measured using the ARMS scale before and after pharmacist-led counselling. The results showed a significant improvement in adherence scores following counselling, indicating that structured education and patient counselling play a crucial role in enhancing medication adherence and overall blood pressure management in hemodialysis patients.

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Hypertension, Hemodialysis, Antihypertensive Drugs, Medication Adherence, ARMS Scale, Patient Counselling.

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This paper explores the themes of "enhousement" and "dehousement" in the works of four prominent authors of the postcolonial and diasporic literary tradition: V.S. Naipaul, Salman Rushdie, Anita Desai, and Bharati Mukherjee. The terms "enhousement" and "dehousement" are used to explore the idea of rootedness and dislocation within the context of colonialism, migration, and the search for identity. By examining the novels and short stories of these writers, the paper demonstrates how their protagonists wrestle with the complexities of belonging, alienation, and identity across different spaces, often being torn between the inherited traditions of their homeland and the unfamiliar, sometimes hostile, cultures they encounter abroad. The paper critically assesses how these authors engage with issues of home, memory, displacement, and cultural hybridization through both narrative strategies and thematic concerns.

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enhousement, dehousement, dislocation, belonging, alienation

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The rapid digital transformation of education has positioned Artificial Intelligence (AI) as a pivotal force in reshaping teaching and learning practices worldwide. Among its applications, AI-powered chatbots have attracted significant attention for their potential to facilitate communication, provide personalized learning, and enhance administrative efficiency. However, their role in inclusive education, particularly in the Indian context involving Children with Special Needs (CWSN), remains underexplored. This study addresses this gap by examining the ethical and cultural implications of chatbot integration in Indian inclusive classrooms. Chapter 1 introduced the background, problem statement, and objectives, highlighting the need for ethically grounded and culturally responsive AI practices. Chapter 2 reviewed existing literature, identifying key benefits of chatbots, ethical challenges such as bias and privacy concerns, and India-specific cultural factors, including linguistic diversity and socio-economic disparities. Chapter 3 developed a conceptual framework informed by the Technology Acceptance Model, Universal Design for Learning, and Sociocultural Theory, integrating ethical guardrails, cultural adaptability, and stakeholder engagement. Chapter 4 operationalized the framework, proposing applications such as personalized support, multilingual assistance, and parental collaboration, alongside policy recommendations emphasizing ethics guidelines, teacher training, and inclusive design. Finally, Chapter 5 synthesized findings, underscored theoretical and practical implications, acknowledged limitations, and proposed directions for future research. Overall, this study concludes that AI chatbots hold promise for advancing inclusive education in India when deployed with ethical safeguards, cultural responsiveness, and human-centered design. By addressing these considerations, AI technologies can contribute meaningfully to equity, access, and inclusion in education.

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Keywords: Artificial Intelligence, AI Chatbots, Inclusive Education, Ethics in Education, Cultural Responsiveness

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Abstract: Duel personality in literature refers to the study of a character performing the two various identities at two different situations. Robert Stevenson, a late Victorian Period author, brings social reforms, industrial growth throughout his writings. His works moreover are blended with supernatural elements with modernist influences leading to inner state of a person with psychological realism. The present study intends to describe the duel personality of the characters in the novel. Robert Stevenson brings this duel personality with the help of psychoanalysis. The present study analyses the duel personality in the novel entitled Dr. Jekyll and Mr. Hyde on the basis of the study on structural elements as well as on the psychoanalytical study of the characters. Duality believes that the human mind and human body are juxtaposed separate entities. The present study highlights the duel personality of Dr. Jekyll who represents good side of human nature while Mr. Hyde represents the darker side of human thinking.

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Duel personality, psychoanalysis, human behaviour

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Drowsiness detection plays a critical role in preventing road accidents and ensuring driver safety. This paper explores the use of Convolutional Neural Networks (CNNs) for real-time drowsiness detection by analyzing facial features such as eye closure and yawning. The proposed system processes live video frames, detects driver fatigue, and generates real-time alerts. The model is trained on labeled datasets of drowsy and non-drowsy facial expressions and achieves high accuracy through deep learning techniques. The system is designed for the integration in vehicles, mobile applications, and cloud-based monitoring platforms to enhance road safety.

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 Keywords

Drowsiness detection, CNNs (Convolutional Neural Networks), Real-time monitoring, Facial features, Driver safety

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