Blockchain-Based Framework for Secure OTA Updates in Autonomous Vehicles
Authors:- Siranjeevi Srinivasa Raghavan

Abstract- – This paper presents a blockchain-based framework designed to enhance the security of Over-the-Air (OTA) updates in autonomous vehicles. By leveraging the decentralized, immutable, and transparent nature of blockchain technology, the framework ensures the authenticity and integrity of software updates. A smart contract-driven approval mechanism prevents unauthorized modifications while addressing critical challenges such as latency, scalability, and energy efficiency. The study evaluates the trade-offs in blockchain adoption for vehicular systems, offering a detailed analysis of its impact on operational performance. Results demonstrate that the proposed framework significantly improves OTA update security without compromising real-time requirements or resource constraints, making it a viable solution for secure vehicular ecosystems.

DOI: 10.61137/ijsret.vol.8.issue6.426

Published by: Kajal Tripathi

Unlocking Success: Integrating AI in Traditional Banking Operations
Authors:- Kinil Doshi

Abstract- – This article reviews the practical application of Artificial Intelligence in the framework of traditional banking, focusing on three major vectors – efficiency increase, customer service and compliance strengthening. Acknowledges that AI is an opportunity for banks to keep up with the times and improve business processes, adapt services to users, optimize workflow and ensure the purity of the market and adherence to procedures. In particular, the work considers options for using AI, identifies the benefits of its application and the challenges that must be addressed, taking into account the regulatory framework and the need for impeccable data governance. Thus, the provision of strategies for successful introduction and reflection on the experience of successful banks creates a fundamental basis for banks that still need to gamify their business in terms of AI.

DOI: 10.61137/ijsret.vol.8.issue6.537

Published by: Kajal Tripathi

Deep Learning Algorithms for Crop Analysis by Agricultural Experts to Enhance Crop Management and Health
Authors:- Dr.C.Saravanabhavan, Janesh M, Kathirvel T, Dhivagar R

Abstract- – In India, agriculture is an essential industry that makes a substantial economic contribution. However, most of conventional crop monitoring techniques are still done by hand, which makes the procedure time-consuming and ineffective. On the other hand, wealthy countries adopt cutting-edge technologies to increase the productivity of crops and enhance resource utilization. We suggest an integrated strategy for crop health monitoring that makes use of aerial drones, IoT, machine learning, and deep learning in order to close this gap. Several sensory modalities are used in our approach for generating varied information with different accuracy in space, temporal fidelity, and character. While drone-based multispectral imagery collects precise information to create vegetation indices like the Normalized Difference Vegetation Index (NDVI), which calculates crop health based on chlorophyll content, IoT sensors provide real-time environmental data that influences crop development.To obtain a comprehensive analysis, variable-length time-series data from IoT sensors and multispectral images were converted into a fixed-sized representation to generate crop health maps. Several machine learning and deep learning models were applied, with a deep neural network (DNN) with two hidden layers achieving the highest accuracy of 98.4%. Due to the absence of reference data, the health maps were validated through ground surveys and expert evaluations. This technology-driven solution enhances real-time decision-making, optimizing large-scale agriculture in India.

DOI: 10.61137/ijsret.vol.8.issue6.540

Published by: Kajal Tripathi

Neural Network-Based Advanced Cancer Prediction and Classification for Enhanced Diagnosis and Prognosis Accuracy
Authors:- Valarmathi P, Rubadharshini A K, Subashini P, Arullakshmi A

Abstract- One of the main areas of contemporary machine learning and data mining research is medical diagnostics. Since single nucleotide polymorphisms (SNPs) contribute significantly to the variability of the human genome, they have been linked to a number of illnesses, including cancer. The most prevalent malignant growth in women, breast cancer, has become much more prevalent during the last 20 years. Several methods have been used on Genetic data to make distinctions between these tumorous and benign data. The large amount of features in SNP data, which makes classification difficult, is one of the main issues.The dimensionality problem for the diagnosis of cancer in women is addressed in this research by an innovative blended intelligence technique based on Association Rules for Harvesting (ARM) and neural network technology (NN) who employs the Evolutionary Computation (EA). While NN is employed to achieve successful classification, ARM optimized by Grammatical Evolution (GE) is used to obtain relationships between SNPs, diminish dimension, which and find the most useful features. The NCBI GEO (Gene Expression Omnibus) website’s carcinoma SNP dataset was used to test the suggested NN-GEARM technique. Up to 90% consistency has been achieved by the developed model.

DOI: 10.61137/ijsret.vol.8.issue4.467

Published by: Kajal Tripathi

Development of Paver Block from Textile Dye Waste
Authors:-M.P. Iniya, K.Dhanush, G.Jagan

Abstract- Textile sludge management is a huge problem for its disposal from the textile industry. It has tremendous applications, such as walking paths, street road, and fuel stations, etc. In this manner, an innovative step has been towards the manufacture of paver blocks blended with textile effluent treatment plant sludge to use of it in reasonable extends. A different percentage of sludge starts from 50% to 100% to be taken for this study for the effective utilization of sludge to the construction industry. This thinks about looked for to experience the potential use of as a binding fabric for paving blocks generation. Conventional paver block is cast with full replacement of sand by using M-sand as fine aggregate. Paver blocks consist of textile waste in addition to distinctive proportions was casted according to the recommendation of Indian Standards (IS) 15658 (2006), also the various results were obtained through experimentally and it was compared to the conventional paver block. The different mix combinations outcome reveals that 50% of fine aggregate replacement by effective utilization of textile sludge and waste water from the textile industry. The density and compressive strength of paver blocks were decreased with increase in the percentage of textile sludge and water absorption capacity was increased.

DOI: 10.61137/ijsret.vol.8.issue2.298

Published by: Kajal Tripathi

Supervisory Control and Monitoring of IoT Enabled Paddy Cultivation
Authors:-Dr.R. Shankar, S. Karthika, M.Suwathy, J.Vidhya

Abstract- The Internet of Things (IOT) is changing agriculture in which farmers enable a wide range of techniques. The IOT technology helps to collect information about conditions such as climate, humidity, temperature, soil fertility, water level, pest detection, animal penetration in the field, and crop growth. Sensor networks are used to monitor the terms of the farm, and the ESP32-CAM is used for controlling and automating the processes of the farm and combining all the information into the cloud. To monitor the cultivated field in the form of images and videos with wireless cameras from a far. Image processing techniques are used to protect the field from birds and animals by creating noises when they are recognized in the rice field. IOT technology can reduce the maintenance costs and improve the productivity of traditional agriculture.

DOI: 10.61137/ijsret.vol.8.issue2.297

Published by: Kajal Tripathi

Deep Learning Algorithms for Crop Analysis by Agricultural Experts to Enhance Crop Management and Health
Authors:-Dr.C.Saravanabhavan, Janesh M, Kathirvel T, Dhivagar R

Abstract- In India, agriculture is an essential industry that makes a substantial economic contribution. However, most of conventional crop monitoring techniques are still done by hand, which makes the procedure time-consuming and ineffective. On the other hand, wealthy countries adopt cutting-edge technologies to increase the productivity of crops and enhance resource utilization. We suggest an integrated strategy for crop health monitoring that makes use of aerial drones, IoT, machine learning, and deep learning in order to close this gap. Several sensory modalities are used in our approach for generating varied information with different accuracy in space, temporal fidelity, and character. While drone-based multispectral imagery collects precise information to create vegetation indices like the Normalized Difference Vegetation Index (NDVI), which calculates crop health based on chlorophyll content, IoT sensors provide real-time environmental data that influences crop development.To obtain a comprehensive analysis, variable-length time-series data from IoT sensors and multispectral images were converted into a fixed-sized representation to generate crop health maps. Several machine learning and deep learning models were applied, with a deep neural network (DNN) with two hidden layers achieving the highest accuracy of 98.4%. Due to the absence of reference data, the health maps were validated through ground surveys and expert evaluations. This technology-driven solution enhances real-time decision-making, optimizing large-scale agriculture in India.

DOI: 10.61137/ijsret.vol.8.issue2.296

Published by: Kajal Tripathi

Enhancing Diabetes Mellitus Prediction with Machine Learning Techniques
Authors:- Mrs. S. Sangeetha, P Kowsika, M Swathihasree, K.Shalini, K.Kokila Lakshmi

Abstract- Diabetes is a chronic metabolic condition that affects millions of individuals globally and can have serious long-term health effects if left untreated. Diabetes is a fast growing worldwide health issue that needs to be identified early and managed well to avoid serious consequences. Conventional diagnosis techniques depend on intrusive blood testing and clinical assessments, which can be costly, time-consuming, and unavailable to many. In order to identify at-risk individuals in a non-invasive, economical, and real-time manner, this project intends to create an AI-driven diabetes prediction system utilizing machine learning techniques. The technology improves patient outcomes and lowers the risk of complications by utilizing predictive algorithms to enable early diagnosis. Due to a lack of early symptoms or restricted access to medical facilities, many people go untreated. By providing automated, data-driven predictions that help patients and medical professionals assess risk, machine learning algorithms can close this gap. This device can revolutionize diabetes screening and enable people to take preventative action before the disease progresses.

DOI: 10.61137/ijsret.vol.8.issue1.198

Published by: Kajal Tripathi

Future of Supply Chains: Trends in Automation, Globalization, and Sustainability
Authors:- Lakshmi Kalyani Chinthala

Abstract- The future of supply chains is being reshaped by a combination of technological advancements, globalization, and an increasing focus on sustainability. This paper examines the current trends that are influencing supply chain management and how businesses are adapting to these changes to remain competitive. Automation is playing a pivotal role in enhancing the efficiency and flexibility of supply chains, reducing costs, and improving accuracy. The integration of technologies such as artificial intelligence (AI), robotics, and the Internet of Things (IoT) is transforming traditional supply chain models, enabling real-time tracking, predictive analytics, and autonomous operations. At the same time, globalization has led to more complex supply chains, with companies sourcing materials and products from across the globe. While this has resulted in cost efficiencies, it has also introduced new challenges related to supply chain visibility, risk management, and geopolitical factors. Sustainability is another key trend, as businesses are under increasing pressure to reduce their environmental impact, adopt responsible sourcing practices, and promote ethical labor standards. The paper explores how companies are navigating these trends and highlights the importance of integrating automation, globalization, and sustainability into supply chain strategies to build resilience and achieve long-term success.

DOI: 10.61137/ijsret.vol.10.issue5.795

Published by: Kajal Tripathi

Data Warehouse Modernization for Insurance: Integrating AI and Cloud Technologies
Authors:- Srinivasa Chakravarthy Seethala

Abstract- The insurance sector faces mounting challenges from regulatory changes, competitive pressures, and the demand for real-time data insights. Traditional data warehouses, essential for data storage and retrieval, often lack the flexibility, speed, and scalability required by modern insurance operations. This article examines how integrating Artificial Intelligence (AI) and cloud technologies can drive data warehouse modernization for insurers, delivering real-time decision-making capabilities, optimized data management, and enhanced operational efficiency. We explore methodologies, technologies, and case studies that demonstrate the transformative impact of AI and cloud in modernizing legacy data warehouses in the insurance sector.

DOI: 10.61137/ijsret.vol.10.issue5.794

Published by: Kajal Tripathi