Authors: Dr.K.ChandraSekhar, Sathi Sudharshan Reddy, Anakapalli Bhargavi, Ulli Sri Satyasai Ramcharan Teja, Gubbala Y V Ganesh Kumar, Kakara Vivek

Abstract: Heart disease remains one of the leading causes of death worldwide, making early detection and accurate diagnosis essential for improving patient outcomes. Traditional diagnostic approaches often rely on clinical examinations and expensive medical tests, which may not always be accessible in all healthcare environments. In this research, we explore the use of machine learning techniques to develop an intelligent system for predicting the presence of heart disease using clinical parameters such as age, gender, blood pressure, cholesterol level, and heart rate. The dataset used in this study contains labelled medical records that are pre-processed, balanced, and divided into training and testing sets to ensure reliable model evaluation. Several supervised machine learning algorithms, including Logistic Regression, Support Vector Machines, Naïve Bayes, Decision Trees, K-Nearest Neighbors, and Linear Discriminant Analysis, are implemented and compared to identify the most effective model for heart disease diagnosis. Feature selection techniques are applied to determine the most influential clinical attributes contributing to disease prediction. To evaluate model performance, we employ a 5-fold cross-validation approach along with evaluation metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).Experimental results demonstrate that the Logistic Regression and Linear Discriminant models achieve the highest prediction accuracy, showing strong capability in identifying heart disease risk from clinical data. In addition, the integration of optimized feature selection methods improves the overall diagnostic performance while reducing computational complexity. The proposed machine learning framework provides an effective and scalable approach for supporting early heart disease detection and assisting healthcare professionals in clinical decision-making.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.167

 

Published by: vikaspatanker

Authors: Mr.G.Vijay Kumar, Pathi Krishna Kanth, Srikakolapu Chandi Mohana Manjusha, Palacharla Vidhatri, Makineedi Hari Gangadhar Satya Sairam, Bathula James

Abstract: Water pollution has become a major environmental concern due to the increasing discharge of industrial and domestic contaminants into wastewater systems. Continuous monitoring of wastewater quality is essential to detect harmful pollutants and prevent environmental damage. This study proposes an intelligent wastewater pollution detection system that integrates low-cost multisensor technology with deep learning techniques. The system collects environmental data using multiple sensors capable of measuring chemical characteristics present in wastewater. The acquired sensor data is pre-processed and transformed into structured textual representations, enabling advanced machine learning models to analyse patterns associated with different pollutants. A deep learning model based on transformer architecture is then employed to classify and identify contaminants present in the wastewater. The proposed approach improves detection accuracy while maintaining computational efficiency. Experimental evaluation demonstrates that the system achieves higher classification performance compared to conventional machine learning methods. The developed framework provides a cost-effective and scalable solution for real-time wastewater monitoring and environmental protection. Future improvements may include integration with IoT-based monitoring platforms and deployment in large-scale environmental monitoring systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.166

 

Published by: vikaspatanker

Authors: Mrs.P.Lakshmi Satya, Dadala Aksha, Pandrangi Sri Venkata Arya, Akula Raja, Pithani Hemalatha, Thota Venkata Subha Santosh

Abstract: Rice quality assessment plays a crucial role in the food industry as it directly affects consumer satisfaction, market value, and food safety. Traditional rice inspection methods rely mainly on manual observation and mechanical tools, which are time-consuming, labour-intensive, and prone to human error. To address these limitations, this study proposes an intelligent computer vision framework for automated rice quality assessment using deep learning and explainable artificial intelligence techniques. The system captures high-resolution images of rice grains and applies image preprocessing techniques such as grayscale conversion, edge detection, and segmentation to extract important visual features. Deep learning models, including VGG16 and ResNet50, are used to learn complex feature representations and classify rice grains based on their physical attributes such as size, shape, texture, and colour. To improve transparency and interpretability of the model predictions, Explainable AI (XAI) techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) are integrated into the framework. Experimental results demonstrate that the proposed approach significantly improves classification accuracy and reliability compared to traditional inspection methods. The developed system provides an efficient, scalable, and automated solution for rice quality evaluation in agricultural and food processing industries.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.165

 

Published by: vikaspatanker

Authors: Ms.A.Harini, Battina Sai Mounika, Kondeti Sushan Niharika, Mummidi Rajesh, Sodasani Hari Veera Narasimha Manikanta, Kalla Vinod

Abstract: Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.164

 

Published by: vikaspatanker

Authors: Mr.KVV. SubbaRao, Neyigapula Jayakrishna, Meesala Venkata Sai Gnana Prakash, Pinninti Lakshmi Prasanna, Kallepalli Ramesh

Abstract: Traffic congestion has become a major challenge in urban transportation systems due to the increasing number of vehicles on roads. Conventional traffic signal systems generally operate on fixed timers, which often results in inefficient traffic management and unnecessary waiting time at intersections. To address this issue, an intelligent traffic control system based on image processing techniques is proposed. The system captures real-time traffic images using surveillance cameras and processes them to estimate vehicle density. The captured images undergo preprocessing operations such as grayscale conversion and noise reduction before applying the Canny edge detection algorithm to identify vehicle edges. The density of vehicles is determined by calculating the number of edge pixels in the processed image and comparing them with a reference image. In addition, the You Only Look Once (YOLO) object detection algorithm is used to identify emergency vehicles such as ambulances and provide them with priority signal allocation. Based on the estimated traffic density, the system dynamically adjusts traffic signal duration for each lane. The proposed approach improves traffic flow efficiency, reduces waiting time, and enhances emergency vehicle movement at intersections. This intelligent system can serve as a practical solution for modern smart city traffic management.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.163

 

Published by: vikaspatanker

Authors: Mrs.K.Sham Sri, Repuri P S S Chaitanya, Karibandi Manasa, Indraganti Sai Teja, Dulam Shiva, Kona Venkata Satya Sai Kumar

Abstract: The rapid growth of the Industrial Internet of Things (IIoT) has led to the continuous generation of large volumes of time-series data from sensors and industrial devices. These data streams are commonly stored and processed in cloud platforms to enable scalability, remote monitoring, and advanced analytics. However, storing sensitive industrial data in cloud environments introduces significant privacy and security risks, including unauthorized access and data breaches. To address these challenges, a secure data storage and sharing framework for time-series data streams in IIoT environments is proposed. The system employs a symmetric homomorphic encryption technique that enables analytics to be performed directly on encrypted data without revealing the original information. Additionally, the framework introduces fine-grained access control mechanisms that allow data owners to selectively share encrypted data streams with authorized third-party services. A verification mechanism based on message authentication ensures data integrity and authenticity during data processing and sharing. The proposed SmartCrypt-based approach enhances data confidentiality while maintaining efficient query processing and analytics capabilities. Experimental analysis demonstrates that the system improves query performance and throughput compared to existing encrypted data stream processing solutions, making it suitable for secure and scalable IIoT data management.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.162

Published by: vikaspatanker

Authors: Bekzod Tursunov

Abstract: The evolution of Enterprise Resource Planning (ERP) systems has reached a pivotal stage where the integration of Artificial Intelligence (AI) and cloud computing is enabling the transition toward the autonomous enterprise. This review article analyzes the technical and strategic frameworks required to integrate SAP systems with AI for automated decision-making. We explore the role of the SAP Business Technology Platform as the orchestration layer for agentic AI, moving beyond traditional predictive models to autonomous digital agents that plan and execute cross-functional workflows. The article examines the transition from Joule-powered generative support to multi-agent systems capable of self-healing supply chains and autonomous financial operations. We further discuss the technical imperatives of a clean core strategy and the mitigation of risks such as AI hallucinations and data sovereignty. By grounding AI in business semantics through retrieval augmented generation, these systems ensure that autonomous actions remain compliant with corporate logic and global regulations. The review highlights how the synergy between SAP AI Core and hyperscaler infrastructure facilitates the scaling of these models across global enterprises. Furthermore, we evaluate the shift in the human role from manual data processing to the strategic governance of intelligent agents. This transition promises to redefine operational agility, allowing businesses to react to market fluctuations with unprecedented speed and precision. By synthesizing current architectural trends, this review provides a comprehensive roadmap for organizations to leverage AI-integrated SAP ecosystems to achieve proactive business resilience in a volatile digital economy.

DOI: https://doi.org/10.5281/zenodo.19437889

Published by: vikaspatanker

Authors: Dr. Geetika

Abstract: The inter-linkages between environmental contamination, animal health, and food safety have emerged as critical concerns in the context of rapid industrialization and agricultural intensification. This study develops a policy integration model grounded in the One Health framework, using empirical evidence from Haryana, India. Heavy metals and pesticide residues originating from industrial and agricultural activities were traced across soil, water, livestock feed, and milk, demonstrating systemic transfer through the food chain. Health risk assessment indices, including Estimated Daily Intake (EDI), Hazard Quotient (HQ), and Cancer Risk (CR), indicate potential human health implications. The findings highlight the inadequacy of fragmented governance systems and propose an integrated, multi-sectoral policy model aligned with global sustainability goals. This research contributes to bridging the gap between environmental science and policy design, offering actionable insights for developing economies.

 

 

Published by: vikaspatanker

Authors: Dr. Ekata Singh

Abstract: Sustainable development needs to adopt a new way to interact with the environment and not only technological advances but also culturally. Environmental awareness and education are major components of this change and it is all to do with ecological literacy, ethical responsibility and sustainability-oriented decisions. This paper provides the background on environmental education and how it is used in higher education and chemistry for a sustainable future. Through the integration of sustainability theories into the curriculum it will enable students to develop a cognitive approach and a behaviour towards environmental protection. Green chemistry is a key source of knowledge in chemical science as it can help reduce pollution and resource use to make chemicals safer. Teaching methods which are concerned with environmental issues (problem-based learning, experiential training, interdisciplinary education and collaboration) are recognised as the best way to link theoretical knowledge with the real-world ecological problems. This research also demonstrates the role universities and research agencies play in the promotion of sustainability through curriculum reform, policy alignment and cooperation. But problems such as lack of uniformity in curriculum, poorly trained teachers and lack of resources continue to prevent the implementation of environmental education in much of the world including in developing countries. The research shows that environmental consciousness and education are the key sources of sustainable development and they are the drivers of ethical citizenship and work ethics. Systems-based education and interdisciplinary integration, as a result, are the key to solving the environmental challenges and achieving sustainability goals long term.

DOI: https://doi.org/10.5281/zenodo.19437118

Published by: vikaspatanker

Authors: Dr. Ekata Singh

 

Abstract: Fluoride contamination in drinking water is a major environmental and public health problem, especially in rural areas where groundwater is the main source of water. In this study Iaim to assess fluoride levels in drinking water in rural communities and assess the risk of skeletal fluorosis in the exposed population. Icarried out a systematic field-based survey in selected villages, sampling groundwater sources such as hand pumps, bore wells, open wells and so on. Fluoride levels were analyzed in the usual way and compared with the standard levels of international health authorities. In addition, Icarried out a formal health survey to establish the prevalence of skeletal fluorosis symptoms in the different age groups (joint stiffness, bone deformity, and restricted mobility). The study also looked at demographic, dietary, and socioeconomic characteristics to identify potential risk factors for fluoride toxicity. The water samples were found to be above safe levels of fluoride, and deeper aquifers were more strongly associated with the water samples. In the same way, skeletal fluorosis was found to be high, and high levels of skeletal fluorosis were observed in the case of long exposure and poor nutritional status, which shows an association with high levels of fluoride and skeletal fluorosis, and immediate action is needed to address the issue. Defluoridation techniques are suggested to be sustainable, safe alternatives, and community education programs should be introduced to prevent illness. This study offers a better understanding of fluoride contamination dynamics and can be used to construct region-specific water management and health policy.

DOI: https://doi.org/10.5281/zenodo.19436761

 

Published by: vikaspatanker