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Agriculture Sustainability: A Comprehensive Review

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Agriculture Sustainability: A Comprehensive Review/strong>
Authors:-Rajat Kumar, Gurshaminder Singh

Abstract-Agricultural sustainability is essential for meeting global food demands, safeguarding the environment, and ensuring economic stability. This review delves into the various dimensions of sustainable agriculture, covering practices, technologies, policies and their collective effects on biodiversity, soil heath, and climate resilience. A central focus is on blending traditional agricultural knowledge with contemporary innovations to create sustainable practices that support biodiversity and soil vitality while adapting to climate challenges. The role of agroecology, which emphasizes ecological principles in agricultural settings, is highlighted as a key approach in promoting biodiversity and minimizing environmental impact. Additionally, the review stresses the importance of robust policy framework that support sustainable practices, ensure resource management, and address climate impacts. The paper also examines the main challenges hindering sustainable agriculture, such as resource depletion, land degradation, water scarcity, and economic pressures. These issues are interconnected with socio-economic factors, including access to resources, income stability, and social equity, all of which shape agricultural sustainability and impact communities reliant on farming. Resource depletion and land degradation are particularly emphasized, as they reduce productivity and soil health, leading to less resilient agricultural systems. To combat these challenges, the review suggests innovative solutions aimed at fostering resilience and sustainability. These include precision agriculture, which leverages data and technology for efficient resource use, crop diversification to reduce vulnerability to climate shifts, and regenerative farming practices that enhance soil health and sequester carbon. The potential of agroecology and regenerative practices is especially emphasized for their ability to restore ecosystems while boosting productivity. Policy interventions, particularly those that support sustainable practices, incentivize research and development in agro-innovations, and provide farmers with training and resources, are crucial for advancing sustainable agriculture.

DOI: 10.61137/ijsret.vol.10.issue5.321
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Adoption of Artificial Intelligence: Benefits, Challenges, and Future Prospects

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Adoption of Artificial Intelligence: Benefits, Challenges, and Future Prospects/strong>
Authors:-Malvika Singh

Abstract-Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, reshaping industries, driving operational efficiencies, and fostering innovation. The adoption of AI spans numerous sectors, such as healthcare, finance, retail, and manufacturing, where it is optimizing processes, enhancing decision-making, and delivering personalized services. However, while AI adoption holds significant promise, it also presents notable challenges, including ethical concerns, data privacy issues, skills gaps, and high implementation costs. This paper explores the advantages of AI adoption, the barriers it faces, and future trends that could shape its progression. By examining case studies and identifying key trends, this paper aims to provide a comprehensive overview of the adoption of AI and its potential for transforming industries worldwide.

DOI: 10.61137/ijsret.vol.10.issue5.320
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Energy Theft Detection in Smart Grids Using Graph Neural Networks (GNNs)

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Energy Theft Detection in Smart Grids Using Graph Neural Networks (GNNs)/strong>
Authors:-Assistant Professor Dr. Pankaj Malik, Himisha Gupta, Anoushka Anand, Siddhesh Bhatt, Devansh Gupta

Abstract-Energy theft poses significant challenges to smart grid operations, leading to substantial financial losses and grid instability. Traditional machine learning approaches often fall short in detecting energy theft due to the complex and interconnected nature of smart grid systems. This paper proposes a novel approach to energy theft detection using Graph Neural Networks (GNNs), leveraging the inherent graph structure of smart grids. By representing the grid as a graph, where nodes correspond to smart meters and transformers, and edges represent electrical connections, GNNs capture both the local consumption patterns and the relationships between grid components. The proposed model aggregates node and edge features to identify anomalous consumption behaviors indicative of energy theft. We apply both Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to enhance detection accuracy by considering both the structural and consumption-related features of the grid. The model is trained and evaluated on real-world and simulated smart grid datasets, showing improved performance over traditional classification models such as support vector machines and random forests. Evaluation metrics including precision, recall, and F1-score demonstrate the model’s robustness, even in the presence of noisy data and imbalanced class distributions. This research highlights the potential of GNNs to enhance energy theft detection in smart grids, providing a scalable and interpretable solution that can adapt to evolving grid conditions. Future work includes expanding the model to incorporate temporal data for real-time detection and exploring reinforcement learning for adaptive theft prevention strategies.

DOI: 10.61137/ijsret.vol.10.issue5.319
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Dietary Interventions for Speech Delay and Hyperactivity in a Child with Machine Learning and AI Applications

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Dietary Interventions for Speech Delay and Hyperactivity in a Child with Machine Learning and AI Applications/strong>
Authors:-Sujatha Mudadla

Abstract-This study investigates the role of specific dietary changes in addressing speech delay and hyperactivity symptoms in my son. Recognizing nutrition and maternal health as influential factors in child development, I explored how targeted dietary adjustments might enhance speech clarity, attention, concentration, and behavior. The study also explores maternal influences, including anemia and stress during conception, and their potential impacts on gut health and speech development. Additionally, I examined the effectiveness of repeated oral teaching methods, such as memorizing rhymes and vocabulary, for reinforcing neural pathways. To extend the research, I explore how machine learning (ML), deep learning (DL), computer vision, and generative AI can be applied to monitor, predict, and enhance the intervention’s effectiveness.

DOI: 10.61137/ijsret.vol.10.issue5.318
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AI-Augmented Platform Engineering: Redefining Developer Experience through Autonomous, Self-Optimizing Enterprise Systems

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Authors: Shravan Kumar Reddy Padur

Abstract: The evolution of enterprise software delivery has entered a transformative era where artificial intelligence (AI) and platform engineering unite to revolutionize the developer experience (DX). Traditional DevOps pipelines, though effective at accelerating releases, often introduced cognitive overload, toolchain sprawl, and inconsistent governance. The advent of internal developer platforms (IDPs) exemplified by Spotify’s Backstage, Humanitec, and CNCF’s platform engineering models has redefined developer productivity through unified, self-service abstractions that reduce operational friction while preserving control and compliance. Concurrently, AI’s influence has permeated every layer of the development lifecycle: AI-assisted coding enhances ideation and reduces context switching, AI-driven operations (AIOps) enable proactive detection and self-healing, and predictive analytics frameworks like DORA and SPACE translate delivery data into actionable performance insights. Together, these advances are ushering in an era of adaptive, intelligence-augmented platforms where automation, observability, and developer empathy converge—elevating enterprise software delivery from procedural execution to a continuously learning, self-optimizing ecosystem.

DOI: http://doi.org/10.5281/zenodo.17679655

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Heart Disease Prediction Using Machine Learning Techniques in Python: A Review

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Heart Disease Prediction Using Machine Learning Techniques in Python: A Review/strong>
Authors:-Tanmay Deshmukh, Supriya Kharatmol, Professor Nishant Patil

Abstract-As the global incidence of heart disease escalates daily, there is an urgent imperative to accurately predict and diagnose these conditions efficiently. Heart illness, also referred to as cardiovascular disease, is a broad category of conditions that affect the heart, including congenital abnormalities, vascular problems, and cardiac arrhythmias. In recent decades, it has emerged as one of the world’s top causes of death. Thus, it is imperative to create accurate and trustworthy techniques for early disease detection .Heart illness, also referred to as cardiovascular disease, is a broad category of conditions that affect the heart, including congenital abnormalities, vascular problems, and cardiac arrhythmias

DOI: 10.61137/ijsret.vol.10.issue5.317
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Space Debris Tracking and Prediction Models

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Space Debris Tracking and Prediction Models/strong>
Authors:-Sakshi Khedekar, Jayesh Jadhav, Jiya Mokalkar, Pratik Patil, Professor Manisha Mali

Abstract-In a growing risk for space activities intentionally located or accidentally resulting from the creation of space debris, monitoring and forecasting are indispensable for the protection of both crewed and uncrewed space missions. The paper presents the assessment of eight most widespread space debris tracking and prediction models: TLE based SGP4, ORDEM, MASTER, Debrisat, SDebrisNet, SDTS, CARA, SSN. For every model, a multi-faceted approach with respect to its various characteristics, accuracy, complexity, data requirement, adaptability, reliability, and usability is employed. This appraisal provides the benefits and associated drawbacks of each methodology in tackling the major issues of data, computation and construction of the complete system. The research further considers the progress of tracking devices and existing systems as well as possibilities of their improvement for the realtime challenges. The comparative assessment of the models presented in this paper will help to strategically improve current approaches to space debris control instruments, thus supporting safety and long-term operating trends in outer space. This study has been carried out in order to devise strategies that will fit the growing and dynamic endeavors of exploring space, by tracking debris with the utmost efficiency and precision.

DOI: 10.61137/ijsret.vol.10.issue5.316
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ECG Signal Classification Using Fine-Tuned MobileNetV2 for Cardiovascular Disease Detection

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ECG Signal Classification Using Fine-Tuned MobileNetV2 for Cardiovascular Disease Detection/strong>
Authors:-Assistant Professor Nadikatla Chandrasekhar, Chennapragada Tarun, Gorle vassudeva rao, Burada Jeevan

Abstract-Cardiovascular disease, otherwise referred to as heart disease, represents one of the most common and fatal illnesses that entails injuring the heart as well as the blood vessels. These, in turn, can cause a range of complications such as myocardial ischemia, for instance, coronary artery disease, or heart failure. The appropriate and timely identification of heart conditions. Clinical practice is determined by the relevance of the illness. The sickness known as heart disease, also known as cardiovascular disease, is common and, sadly, harmful. This condition deals with the morbidity and mortality associated with the heart and blood vessels. This can cause numerous issues such as myocardial ischemia, coronary heart problems and heart failure. A timely and correct identification of heart ailments. clinical practice is guided by the relevance of the disease. Being able to identify those at risk allows for preventative measures, preventative actions, and individualized treatment plans to lessen the negative effects and slow the disease’s course. The identification of cardiac disease has seen significant growth in recent years. major improvements as a result of the incorporation of the complex. Technology and methods based on computation. Among them is the machine. predictive modeling, data mining methods, and learning algorithms frameworks that make extensive use of physiological and clinical data information.

DOI: 10.61137/ijsret.vol.10.issue5.315
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A Cost-Benefit Analysis of Material Handling on the Productivity of Food and Beverage Manufacturing Industries

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A Cost-Benefit Analysis of Material Handling on the Productivity of Food and Beverage Manufacturing Industries/strong>
Authors:-Ms. Krupa Shetty

Abstract-Food and Beverage manufacturing companies face challenges today due to their high competitiveness, poor working conditions, and more stringent regulations. Growing demand for high quality products and frequent changes in the variety of products by the consumers had an impact on the viability of the food and beverages manufacturing sector. Working conditions for many food and beverages operatives are difficult, as it requires large number of labours for handling. In the present scenario, the production cost increases due to the handling of material from one place to another inside the factory by using the unskilled labour. Due to shortage of labour majority of the manufacturing industries are facing problem and there is a drastic reduction in total output and not achieving the required target is a common weakness In most of the small scale food and beverage manufacturing companies manual handling is adopted to transfer the raw material from one place to other, transfer of semi- finished material from one equipment to other and finally transfer the final finished products to the packing section and storage division. In all these stages movement of material takes place with help of semi-skilled workers. Because of this required quality is not achieved. Finally cost of the product increases, which they are not in a position to match the competitive market. One of the major reasons for slow growth of the Indian Economy is the improper handling of materials and unnecessary costs incurred. This research paper focuses on the benefits of utilising the material handling system with properly planed plant layout and automation, there is a drastic reduction labour cost and it avoids the damage caused by manual movement of material, which results in better- quality product with less cost of production. Good handling system also improve inventory control, less fatigue of workers, greater industrial safety with less accident potential and disruption of work, improved morale of workers.

DOI: 10.61137/ijsret.vol.10.issue5.314
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Student Voting Election Portal

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Student Voting Election Portal/strong>
Authors:-Professor Swati Shinde, Vaishnavi Borse, Resham Umale, Shraddha Borate

Abstract-With advancements in technology, traditional voting methods are evolving, offering more advanced solutions like online voting portals. A Student Voting Election Portal provides a modern and secure way for students to participate in elections from any location with internet access, eliminating the need for physical polling stations. This online system offers several benefits, such as improved accessibility, time and resource efficiency, greater accuracy, and transparency, making the voting process more democratic. Critical to the success of such a platform are proper voter verification and the accurate management of student information. While online voting systems have been implemented successfully in various contexts, there are still challenges and limitations to overcome for widespread adoption. This paper will explore different types of electronic voting, examine successful implementations of student election portals, and compare them to traditional voting methods, highlighting current trends and potential future developments.

DOI: 10.61137/ijsret.vol.10.issue5.313
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