Volume 13 | Issue 12 |

 Abstract

A healthy body and peaceful mind are essential for a meaningful life. Yoga and meditation are ancient practices that promote balance between the body, mind, and spirit. They help relieve stress, enhance concentration, and improve the quality of sleep. In the modern world, where anxiety and insomnia are increasing, yoga and meditation serve as effective tools for relaxation and mental stability. This paper explains how regular practice of yoga and meditation promotes mental calmness, supports emotional well-being, and enhances sleep quality through both physical and psychological mechanisms.

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 Keywords

Yoga, Meditation, Relaxation, Sleep, Mental Health

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This paper analyzes the economic associations between artificial intelligence (AI) adoption and agricultural outcomes across six districts in Tamil Nadu's Cauvery Delta region from 2018 to 2023. Using comprehensive secondary data from 12 official sources, including Tamil Nadu Agricultural University reports and NABARD assessments, we estimate significant positive correlations between AI adoption intensity and key performance metrics. Our multivariate regression models, controlling for district and farm characteristics, indicate that districts with higher AI adoption show correlations with a 28% increase in net returns per hectare (95% CI: 24-32%), a 32% reduction in irrigation water requirements (95% CI: 28-36%), and a 24% decrease in fertilizer consumption (95% CI: 20-28%). Economic analysis reveals benefit-cost ratios of 2.0-2.8 across technology packages, with sensitivity analysis confirming robustness. The findings highlight AI's potential contribution to Sustainable Development Goals 2 (Zero Hunger) and 6 (Clean Water) through climate-smart agricultural intensification. Findings suggest policy interventions to scale AI-based precision systems under India's Digital Agriculture Mission.

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 Keywords

Artificial Intelligence, Precision Agriculture, Rice Farming, Economic Outcomes, Sustainability, Secondary Econometric Analysis, Cauvery Delta, Agricultural Policy

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Plant taxonomy, the foundation of botanical science, is entering a transformative era driven by the integration of artificial intelligence (AI) and multidisciplinary data. Traditional taxonomy has relied heavily on morphological traits, but the complexity of plant diversity and cryptic species often challenges human-based identification. Integrative taxonomy, which combines morphological, molecular, ecological, and geographical data, provides a more holistic framework for species delimitation. However, handling and interpreting such heterogeneous data demand computational methods capable of recognizing complex patterns and relationships. Here, we present an overview of how AI particularly machine learning and deep learning can revolutionize plant taxonomy by automating data analysis, detecting hidden diversity, and accelerating species identification. We highlight the integration of image-based recognition of plant organs, DNA barcoding classification, and ecological niche modelling through AI algorithms. Additionally, we discuss recent advances in multimodal data fusion that enable the synthesis of molecular and phenotypic datasets for more robust taxonomic decisions. The study emphasizes the potential of AI to enhance reproducibility, reduce human bias, and enable rapid biodiversity assessment in the face of global environmental change. We conclude that the synergy between integrative taxonomy and artificial intelligence represents a paradigm shift in plant systematics, paving the way for a new era of automated, data-driven taxonomy and biodiversity discovery.

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 Keywords

Integrative taxonomy, plant systematics, artificial intelligence, machine learning, DNA barcoding, deep learning, biodiversity.

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Artificial Intelligence (AI) is reshaping educational practices in the life sciences through adaptive systems, virtual laboratories, generative content tools, and data-driven feedback mechanisms. This review critically synthesizes literature from 2015-2025 to evaluate how AI is transforming teaching and learning in the life sciences. It identifies key teaching methods, summarizes empirical evidence of learning outcomes, and assesses the ethical, technical, and institutional risks involved. Responsible integration practices centered on ethical literacy, transparency, faculty training, and equitable access are discussed as essential to sustainable adoption. The review concludes with recommendations for aligning AI innovation with pedagogical and ethical standards to ensure that technology enhances rather than replaces the human elements of scientific education.

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Teaching with AI in the Life Sciences: A Review of Methods, Risks and Responsible Practice

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The emergence of Artificial Intelligence (AI) in networking has transformed the design and operation of modern communication infrastructures. AI routers, enhanced with Machine Learning (ML) algorithms, enable intelligent decision-making, predictive analysis, and dynamic optimization of network resources. Unlike conventional routers that rely on static protocols, AI routers continuously learn from network data to predict congestion, reroute traffic, and ensure optimal performance. Machine learning techniques such as supervised learning, reinforcement learning, and deep neural networks have been effectively applied for traffic prediction, congestion control, anomaly detection, and energy-efficient routing. In Software-Defined Networking (SDN), AI-based routing enhances scalability and adaptability by enabling proactive flow control. Similarly, in Internet of Things (IoT) and Wireless Sensor Networks (WSN), ML-powered routers improve energy efficiency and reliability in dense environments. AI routers are also crucial in data centers, UAV-based communication, and 5G/6G systems, where real-time adaptability and low-latency routing are vital. Reinforcement learning models like Deep Q-Networks (DQN) and actor-critic algorithms are used to learn optimal paths dynamically under changing network conditions. Additionally, AI routers enhance network security by detecting malicious traffic patterns through anomaly-based learning models. Despite their advantages, challenges persist in scalability, computational complexity, and explainability of ML models. Future research aims to integrate explainable AI (XAI), federated learning, and edge intelligence to build autonomous, self-healing, and energy-aware routing systems. AI routers thus represent a pivotal step toward the realization of fully intelligent, adaptive, and resilient communication networks for next-generation systems.

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AI routers, Machine learning, Intelligent routing, SDN, IoT, 6G, WSN, Reinforcement learning

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Artificial Intelligence (AI) has become a transformative tool in managerial decision-making, offering data-driven insights that enhance strategic, operational, and tactical efficiency. Managers today face increasing complexities due to dynamic market conditions, vast data generation, and the need for real-time decisions. AI-driven systems, through predictive analytics, machine learning (ML), and natural language processing (NLP), enable managers to optimize processes, forecast trends, and mitigate risks. This paper explores the integration of AI into managerial decision-making, its impact on efficiency, accuracy, and innovation, along with challenges such as ethical concerns, bias, and data privacy. The study concludes by highlighting future directions and the evolving human-machine collaboration in management.

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 Keywords

Artificial Intelligence, Managerial Decision-Making, Predictive Analytics, Machine Learning, Business Strategy, Data-Driven Management

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) has emerged as a transformative force in the education sector, reshaping teaching, learning, and administrative processes. The integration of AI tools such as adaptive learning platforms, intelligent tutoring systems, and automated assessments has significantly improved learning outcomes, accessibility, and engagement. This paper explores the opportunities presented by AI in education, such as personalization, inclusivity, and efficiency, alongside challenges including ethical dilemmas, data privacy concerns, dependence on technology, and inequality in access. It emphasizes the need for responsible AI implementation, digital literacy, and policy frameworks to ensure equitable and effective use of AI in education.

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 Keywords

Artificial Intelligence, Education, Digital Learning, Machine Learning, Ethical Challenges, Personalized Learning

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Creative Commons Attribution 4.0 and The Open Definition

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The increasing complexity of global agricultural systems, coupled with the challenges of population expansion, climate variability, and diminishing natural resources, necessitates the adoption of advanced technological interventions. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools in fostering smart and sustainable agricultural practices. This paper critically examines the role of AI and ML in optimizing various dimensions of farming, including soil fertility assessment, precision irrigation, crop health monitoring, pest and disease detection, and yield forecasting. Through the integration of IoT-enabled sensors, unmanned aerial vehicles (UAVs), and remote sensing data, AI-driven analytics facilitate real-time decision-making and automation, thereby enhancing both efficiency and productivity. The study further explores how intelligent systems contribute to environmental sustainability by minimizing excessive input usage, mitigating greenhouse gas emissions, and promoting adaptive responses to climatic fluctuations. Economic implications such as cost reduction, risk mitigation, and improved value-chain management are also addressed. Despite their potential, the diffusion of AI and ML technologies remains constrained by factors including data scarcity, inadequate digital infrastructure, high deployment costs, and limited technical literacy among smallholders--particularly in developing economies such as India. The paper concludes that the successful realization of AI-enabled sustainable agriculture requires a multi-stakeholder framework encompassing policy support, capacity building, open-data ecosystems, and context-specific algorithmic design. Future research should emphasize the development of explainable, inclusive, and resource-efficient AI systems that align technological innovation with the imperatives of ecological balance and food security.

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 Keywords

Artificial Intelligence (AI); Machine Learning (ML); Precision Agriculture; Smart Farming; Sustainable Agriculture; IoT; Data Analytics; Crop Monitoring; Climate Adaptation

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Artificial Intelligence (AI) and Machine Learning (ML) are transforming the life sciences sector by revolutionizing research, diagnostics, drug discovery, and personalized medicine. Their ability to analyse vast datasets and recognize complex patterns enables innovations that were previously unimaginable. From accelerating genomic sequencing to optimizing clinical trials, AI and ML are now integral components of modern biological research and healthcare. This paper explores the applications, benefits, challenges, and future directions of AI and ML in the life sciences, highlighting real-world advancements from 2023 to 2025 that demonstrate their growing impact.

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 Keywords

Artificial intelligence, Machine learning, Drug Discovery and Development Genomics and Precision Medicine Medical Imaging and Diagnostics

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 Abstract

Classification of plant disease is important in reducing losses of yields, but the traditional diagnosis method is inaccessible to a large number of farmers. This paper assesses Plantix, which is a smart mobile application that employs image recognition using deep learning to detect diseases and pests in plants, nutrient deficiencies, etc. The backend is trained on huge annotated datasets, which allows it to classify 30+ crops and 400+ disorders using CNN-based models. Inference outputs are a disease classification, severity estimation, and the cause of the disease after image acquisition. The app also gives the treatment plans, chemical, biological, and cultural plans, as well as nutrient control, weather forecast, and crop calendar by season. Plantix has a diagnostic accuracy of over 90% but is affected by light, image sharpness, type of crop, and position of the symptoms. Although it has such merits as quick inference, multilinguality support, and sharing of the community, there are also obstacles, such as rare-disease coverage, the reliance on connection, and not being much integrated with local soil or sensor data. Altogether, Plantix has strong potential with regard to the applicability as an AI-powered and scalable crop-advisory system.

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 Keywords

Plantix, deep learning, convolutional neural networks, computer vision, precision agriculture.

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Creative Commons Attribution 4.0 and The Open Definition