Volume 13 | Issue 10 |

 Abstract

The accelerating threat of climate change demands innovative and data-driven solutions. Artificial Intelligence (AI), powered by machine learning and deep learning, has emerged as a transformative tool to address both climate change mitigation and adaptation. This research paper explores how AI-enabled "smart models" can analyze massive and complex datasets, enhance predictive capabilities, and optimize resource utilization for sustainable development. Applications range from renewable energy forecasting and precision agriculture to disaster prediction and carbon monitoring. The paper highlights both existing work and future opportunities, while also addressing key challenges such as energy costs, algorithmic bias, and data inequities. By proposing an integrated framework that emphasizes inclusivity, energy efficiency, and explainability, this work contributes toward shaping a sustainable and equitable AI-driven climate future.

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 Keywords

Artificial Intelligence (AI), Climate Change, Smart Models, Machine Learning, Climate Mitigation, Climate Adaptation, Renewable Energy, Precision Agriculture, Sustainable Development.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kazuo Ishiguro's fiction persistently interrogates the tensions between home, identity, and displacement, rendering exile not merely as a geographical dislocation but as a psychological and moral condition. This article explores the thematic and narrative dimensions of exile in When We Were Orphans (2000) and The Remains of the Day (1989), examining how Ishiguro reimagines the concepts of home and self through fragmented memory, emotional repression, and moral introspection. Both novels reveal that exile is as much internal as external, a state of estrangement from one's past, identity, or emotions. The protagonists, Christopher Banks and Stevens, embody the exilic consciousness that negotiates belonging in the absence of rootedness, illustrating Ishiguro's preoccupation with the ethical complexities of remembering and forgetting. Through his distinctive narrative minimalism and introspective prose, Ishiguro transforms exile into a site of moral inquiry where silence, memory, and selfhood intersect. The paper argues that Ishiguro's characters do not merely experience exile; they inhabit it as a mode of being, thereby redefining the modern literary imagination of home and identity.

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 Keywords

Kazuo Ishiguro; exile; memory; home; identity; displacement; silence; When We Were Orphans; The Remains of the Day; reimagined self.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Family businesses are the foundation of economic stability, are instrumental in long-term value creation, and contribute to 70-90% of global GDP, comprising over 60% of employment base. In India, they account for more than 70% of GDP, driving growth across sectors and creating enduring social impact. From small enterprises to global conglomerates such as Tata, Reliance, Samsung, and Walmart, these organizations combine entrepreneurial spirit with intergenerational vision, making them critical to national and global prosperity. Their success stems from long-term orientation, strong governance, and shared family values that promote stability during economic uncertainty. Yet, succession planning, generational transition, and governance complexity remain persistent challenges. To address these, many businesses have institutionalized family councils and created governance frameworks that balance family unity and professional management. The modern evolution of family offices marks a significant shift--transforming traditional family enterprises into institutional investors and diversified wealth managers. With rising interest in private equity, technology, ESG, and impact investing, next-generation leaders are redefining growth through innovation and sustainability. This transition ensures not only business continuity but also broader economic resilience. Family businesses and offices today stand at the intersection of legacy and modernization--serving as economic powerhouses and custodians of enduring values. Their ability to combine trust, stewardship, and professional governance makes them principal catalysts in shaping the future of both the Indian and global economy. Future Trends : Family offices are professionalizing and scaling rapidly. Over the next 3-10 years the biggest shifts will be: heavier allocations to private/real assets and direct deals; widescale adoption of technology, data & AI for investment and ops; meaningful entry into digital assets and tokenization; growth in impact / ESG and mission-aligned strategies; and consolidation via multi-family platforms, plus stronger governance and talent models. These forces reshape risk, access, reporting and operational models for families and office teams.

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 Keywords

FamilyBusiness FamilyOffice Investment FutureTrend ArohiTiwary

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Handwritten digit recognition is an important application in computer vision, often utilized in banking, postal services, and educational tools. This paper presents a deep learning-based method that makes Convolutional neural network (CNN) use to appropriately classify the handwritten numbers. MNIST is one of the reference datasets used to train the model, which contain diverse samples of handwritten numerals. The CNN architecture includes multiple convolutional and pooling layers,Then come layers that are entirely connected and Regularization of dropouts to avoid overfitting. Techniques like data augmentation and transfer learning are applied to improve generalization and reduce computational load. Performance was assessed by comparing and implementing several deep learning models and machine learning models. With precision, recall, and F1-score values of 0.99%, the CNN model also earned the maximum accuracy of 99.25%. These outcomes show how reliable and efficient CNNs are in recognizing handwritten digits. The system exhibits enormous potential for Empirical world uses that require precise and quick digit categorization.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Computer Vision,Deep Learning,MNIST Dataset ,Data Augmentation, Model Generalization, Digit Categorization, Precision ,Recall ,Classification Accuracy.

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Creative Commons Attribution 4.0 and The Open Definition

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In recent years, the recognition of mental health as a critical component of overall employee well-being has surged. Companies worldwide are increasingly implementing mental health initiatives to foster a supportive work environment, enhance productivity, and reduce absenteeism. This report analyzes the impact of such initiatives on employee well-being by exploring various dimensions including employee satisfaction, productivity, turnover rates, and overall mental health outcomes. Through comprehensive data analysis and visualization, this report aims to provide insights on the effectiveness of mental health programs and their correlation with employee performance and organizational success.

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Mental Health,Employee Well Being,Workplace Wellness Supportive Work Environment

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This project focuses on applying an end-to-end data analytics workflow to solve a key business problem providing sales teams with a clear, data-driven understanding of regional sales performance to optimize resource allocation and identify growth opportunities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data Analytics, Python (for EDA and Data Wrangling),Power BI, MySQL, Exploratory Data Analysis (EDA),Data Cleaning / Data Wrangling ,Feature Engineering

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Digital Natives, Millennials, Generation Z and Alpha, Technology adoption

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bhartiya sarvochnyalay Ka Mahilao Ev Kishoro Ke Prati Sakriyata Ev Savedhanshilta Ka Vishleshan

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Creative Commons Attribution 4.0 and The Open Definition

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Bhartiya sarvochnyalay Ka Mahilao Ev Kishoro Ke Prati Sakriyata Ev Savedhanshilta Ka Vishleshan

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of Artificial Intelligence (AI) into healthcare is reshaping the delivery of medical services, enabling early disease detection, precise treatment planning, and real-time monitoring. This research proposes an AI-driven framework for intelligent healthcare and predictive diagnostics that leverages machine learning, deep learning, and natural language processing to extract actionable insights from heterogeneous medical data, including electronic health records, imaging, and sensor-based monitoring systems. The framework emphasizes predictive modeling to forecast disease progression, support preventive interventions, and personalize treatment strategies while ensuring scalability across diverse clinical scenarios. Key contributions include the design of adaptive algorithms capable of handling high-dimensional data, mechanisms for explainable decision-making to enhance trust among clinicians, and integration with cloud-edge infrastructures for timely and resource-efficient deployment. Experimental validation highlights improved diagnostic accuracy, reduced latency in decision support, and enhanced patient outcomes compared to conventional approaches. This work underscores the transformative potential of AI in advancing predictive diagnostics, fostering proactive healthcare, and paving the way toward sustainable, patient-centered medical ecosystems.

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 Keywords

Artificial Intelligence, Predictive Diagnostics, Intelligent Healthcare, Machine Learning, Deep Learning, Clinical Decision Support.

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 Abstract

The proliferation of space debris poses an existential threat to the sustainability of operations in Low Earth Orbit (LEO). While classical Artificial Intelligence (AI) solutions have improved tracking and localized debris capture, they encounter significant computational intractability when planning large-scale, multi-target Active Debris Removal (ADR) missions. This paper proposes a Hybrid Quantum-Classical (HQC) framework specifically designed to overcome these combinatorial optimization bottlenecks. The framework leverages Quantum Annealing (QA) to efficiently solve the optimal routing problem (ORP), formulated as a high-fidelity Quadratic Unconstrained Binary Optimization (QUBO) model. This optimization is integrated with Quantum Machine Learning (QML) for accelerated Space Situational Awareness (SSA) and real-time collision risk assessment (Pc). Simulation results benchmarking the HQC optimizer against classical metaheuristics, such as Genetic Algorithms (GA) and Simulated Annealing (SA), demonstrate a superior solution quality (98% near-optimal fuel consumption) and a substantial reduction in time-to-solution (a 10-fold speedup for N=50 targets). Furthermore, the application of Variational Quantum Algorithms (VQAs) for quantum-enhanced anomaly detection improves sensor data fidelity and strengthens autonomous decision-making robustness, validating the critical role of nascent quantum technologies in preserving the orbital environment against the escalating threat of Kessler Syndrome.

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 Keywords

Hybrid Quantum-Classical (HQC) Framework, Quantum Annealing (QA), Quantum Machine Learning (QML), Active Debris Removal (ADR), Space Situational Awareness (SSA), Low Earth Orbit (LEO), Optimal Routing Problem (ORP), Quadratic Unconstrained Binary Optimization (QUBO), Variational Quantum Algorithms (VQAs), Quantum Neural Networks (QNNs), Quantum Autoencoders (QAEs), Quantum K-Nearest Neighbor (QkNN), Space Traffic Management (STM), Collision Probability (Pc), Multi-Target Optimization, Combinator

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Creative Commons Attribution 4.0 and The Open Definition