Authors - Uttam Patole, Manish Shrivastava, Archana Ugale, Abhishek Deshmukh Abstract - Poor water use and late illness detection force farmers to struggle, which hurts their crops and squanders resources. This study presents a computerized precision farming system combining machine learning, humidity sensors, and robot automation to improve irrigation and disease control. The technology operates in two phases: first, humidity sensors calculate the ideal watering amount by assessing soil moisture and applying a Random Forest model. Second, disease detection sensors identify agricultural diseases and forecast disease outbreaks using a Gradient Boosting Regressor (GBR), thereby guiding robot pesticide spraying. By integrating machine learning with real-time environmental monitoring, the technology minimizes human interaction while assuring exact irrigation and tailored pesticide administration. This strategy avoids the usage of pesticides and excessive water consumption while enhancing resource efficiency and crop health. The proposed system is scalable and adaptive to varied farming situations, making it a good alternative for modern precision agriculture. By combining predictive analytics and automation, the model enables data-driven decision-making in farming, supporting sustainable agricultural practices and boosting overall output.
Authors - Rahul Dhaigude, Ruby Chanda, Shrikant Ghadge Abstract - The healthcare industry is a complex and vital sector that plays a fundamental role in society, focusing on the well-being and health of individuals. In recent years, the integration of Artificial Intelligence (AI) into hospital logistics has emerged as a transformative force in the healthcare industry. This study investigates the application of AI in hospital logistics to enhance tracking and overall operational efficiency. A questionnaire was designed to gather quantitative and qualitative data on various aspects of hospital logistics, AI integration, challenges, and potential improvements. Qualitative data from open-ended questions and interviews were analyzed thematically to extract key themes and insights. The results show that AI has become an essential tool for optimizing logistics processes, improving patient outcomes, and enhancing the overall patient experience. However, careful planning, implementation, and consideration of data privacy and staff readiness are critical for successful AI adoption in hospital logistic.
Authors - T. Sruthi, Sheshikala Martha Abstract - Extracting meaningful knowledge & actionable comprehensions from complex, multi-dimensional, and heterogeneous biomedical data residues a significant challenge in healthcare. Modern health care systems generate various types of medical data that are often intricate, diverse, and typically unstructured. These large datasets are often difficult to interpret and process. Traditionally, data mining techniques have been employed to extract features from such data, with prediction or clustering models built on top of those features. However, this approach faces numerous challenges, particularly when dealing with complex data and limited domain expertise. Recent advancements in deep learning, however, have introduced new and effective methods for building learning models from these complex datasets. In this paper, we discuss various clinical data types and their relevant features that can serve as inputs to deep learning net-works, contributing to the creation of a more reliable and sustainable healthcare system.
Authors - T. Aruna, Dhanya Kulkarni, Riya Javali, Rakshan Kulkarni, Suneeta V. Budihal, Shamshuddin K Abstract - In 5G networks, dynamic slicing is a major improvement, which makes it possible to allocate specific resources to satisfy the various quality-of-service (QoS) requirements of applications including ultra-reliable low-latency communications, enormous IoT, and enhanced mobile broadband. However, managing these slices in response to dynamic and varied traffic patterns requires real-time flexibility, which poses considerable hurdles. The methods for effective dynamic network slicing are examined in this research, with an emphasis on resource allocation optimization, QoS adherence, resource waste reduction, and network stability. To accommodate upcoming developments, the suggested solutions seek to improve 5G network performance, scalability, and adaptability.
Authors - Ajay Talele, Madhav Jagtap, Radhika Gadewar, Akanksha Katore, Sufiyan Sajan, Deepika Sidral, Yash Shinde, Gaurav Desale, Saburi Nikam, Shubham Landge, Chetan Channa Abstract - Farm trade is a key component of economic viability, but farmers usually struggle with issues of market access restriction, price volatility, and dependency on middlemen. This article introduces a Java-based mobile market that is capable of empowering farmers by creating direct links to customers, thus ensuring transparency and profitability. The new platform incorporates real-time price feeds, demand forecasts, and electronic secure transactions to form a highly efficient and consumer-friendly trading system. Further, the system incorporates IoT-based weather updates and agricultural advisory services to enable farmers to make appropriate decisions. The system utilizes a recommendation engine employing machine learning to maximize prices and predict demand trends. The system is highly secure because transactions are encrypted and involves a strong mechanism for user verification. Fair trade is promoted while minimizing post-harvest losses; the marketplace helps farmers make electronic payments, which enhances financial inclusion and sustainable agriculture. System analyses and case studies attest to its capability to revolutionize agricultural commerce, close the urban-rural digital divide, and enhance farmers’ economic performance.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Hemender Sai, Bipin Sai Bhaskar, P.Saranya Abstract - Tuberculosis continues to be a major global health concern, particularly in remote regions with limited access to healthcare. Early and precise diagnosis is essential to prevent its spread. This project utilizes high-performance computing (HPC) to tackle two major challenges: handling inconsistent medical data and enhancing TB detection. Due to the disproportion between healthy and TB-positive samples, Generative Adversarial Networks (GANs) are employed to create synthetic images, expanding the dataset and improving its diversity. This enriched dataset is then used to train Convolutional Neural Networks, which are highly effective in medical image processing. By leveraging HPC, we accelerate the CNN training process on large-scale, augmented datasets, significantly cutting down computation time while preserving accuracy. This approach enhances TB detection by integrating GAN-based data augmentation with CNN models, ensuring a quicker and more reliable diagnosis.
Authors - Shankar Lingam. M, Raghavendra GS, Sakthi Kamal Nathan Sambasivam Abstract - In the digital era, the integration of ICT policies and e-Governance has emerged as a critical driver for public sector modernization. ICT policy sets the groundwork for effective e-Governance systems by enabling the digital transformation of government functions and services. However, the realization of e-Governance goals faces significant challenges, particularly in fostering inter-governmental cooperation. These challenges arise from varying policy frameworks, technological disparities, and governance structures across different levels of government. In this context, navigating the intricacies of inter-governmental relationships is essential to ensure seamless information exchange and collaborative governance. The evolving digital landscape introduces both opportunities and complexities, particularly in terms of data privacy, cybersecurity, and digital inclusion. This paper presents a comprehensive review of ICT policies and e-Governance frameworks, with a focus on overcoming inter-governmental challenges in the digital era. Our methodology includes a scoping review of key studies and case analyses, such as the work of Obi (2007) on global perspectives of e-Governance [Obi, T. (2007). E-Governance: A Global Perspective on a New Paradigm], Prasad (2012) on India’s ICT policy for digital democracy [Prasad, K. (2012). E-Governance Policy for Modernizing Government through Digital Democracy in India], and Manda (2017) on South Africa’s smart governance approach [Manda, M. I. (2017). Towards "Smart Governance" through a Multidisciplinary Approach to E-Government Integration]. The findings highlight the importance of fostering interoperable and inclusive e-Governance systems to navigate the inter-governmental challenges posed by ICT adoption. Key implications for policy include the need for harmonized digital policies, the development of interoperable infrastructure, and the emphasis on inclusivity to bridge the digital divide. The results underscore the need for collaborative frameworks that enable effective governance across multiple levels of government, thus ensuring that the digital transformation of public services benefits all stakeholders.
Authors - Manisha Mane, Gargee Nitin Rangnekar, Gayatri Kishore Kshirsagar, Adarsh Suresh Nikam Abstract - Monitoring and prediction of space weather have gained tremendous significance with the increasing reliance of the telecommunication and aviation sectors on satellite communication and navigation systems. The two sectors are very vulnerable to space weather occurrences because they can always interfere with the functioning of satellites, high-frequency radio communication, and GPS accuracy. To mitigate these exposures, we recommend that telecommunication and aviation companies utilize a machine learning Space Weather Dashboard to facilitate real-time data visualization and predictive analytics assistance. The proposed architecture employs Azure Workspace for data storage and management, Unreal Engine 5 for the production of high-fidelity graphics, and machine learning models developed in Python. Our approach is based on the utilization of Long Short-Term Memory networks (LSTMs) for historical space data, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Various types of weather data points like solar X-ray flux, solar wind speed, coronal mass ejections, interplanetary magnetic field measurements, solar energetic particles, ionospheric data, and auroral data are utilized to enhance prediction precision. The dashboard allows for actionable insights to be built for industry professionals and real-time monitoring of critical space weather parameters. Additionally, in consideration of how it can improve their contribution to operation safety, the research here addresses the forecasting power of some of the machine learning architectures used for space weather. Our comparative research affirms that improved forecasting results in effective warning and risk assessment. This is a wonderful benchmark for the aviation and telecommunications industries, improving situation awareness and round-the-clock operating continuity.
Authors - Surya K, Rohit Kumar Abstract - The work deals with conceptual representation of sensitive issues in society as images in media using generative AI. Conceptual representation of sensitive information creates an overall societal impact. Conceptual processing involves understanding the represented information and the way how people respond and interact to the displayed information. Some of the issues like menstrual cycle representation, sex education, domestic violence, sex abuse and mental health awareness are difficult to represent conceptually and respectfully in the media. The goal of this paper is to use generative AI and represent these issues clearly to the society using media. Our work aims at understanding the challenges in representation of these images conceptually as well as providing an overview of the generative AI tools that can be used for implementing the solutions. A case study of a generative AI tool is used to understand the underlying problem of conceptual and respectful image generation for representing it in media. There are many generative AI tools for image generation, and we have chosen the Google Gemini AI as it gives more creative images compared to other tools [13]. In this paper, some of the sensitive issues are taken into consideration for representation in media in a conceptual and respectful manner.
Authors - T. A. Alka, M. Suresh, Aswathy Sreenivasan Abstract - This study aims to explore recent trends in rural entrepreneurship (RE). 439 documents available in the Scopus database are used for the trend analysis by using the R programming Biblioshiny package for bibliometric analysis. The result shows that rural entrepreneurship has been trending since the 1970s, and there is scope for further research. Rural entrepreneurs contribute to the society's upliftment, and development and ultimately result in the growth of the nation. The study is focused on the Scopus database; other databases are not considered. The study identified major themes, such as the rural agriculture entrepreneurs' contribution to developing the rural economy and the need for rural entrepreneurship education among college students in developing countries. Trend topics in this field highlight the rural entrepreneurship contribution to the development of the rural economy, agriculture, rural development, fostering innovation, promoting sustainable development, developing a sound entrepreneurial ecosystem, the role of rural entrepreneurship in the growth of developing countries, etc. The study identified North America and Asia connection, intra-European and transatlantic collaboration, Europe-Middle East, and Asia-North America regional collaboration. The study is relevant even when comparing recently published papers to map the trends; the themes used in research-related articles are always changing. This study gives insights to policymakers to help them with planning, policy formulation, program support, etc., to the development of rural entrepreneurs and also offers future research direction through thematic analysis.
