Category Archives: Uncategorized

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

Authors: Harish M, Dr.M.D.Chinnu

Abstract: This study focuses on strategies for inclusive digital rural development, aiming to bridge the digital gap between rural and urban areas. It examines the availability of digital infrastructure, access to technology, and the level of digital literacy in rural communities. The study also explores the challenges faced by rural people in using digital services, especially women, elderly, and marginalized groups. It highlights the role of digital tools in improving education, healthcare, financial services, and rural livelihoods. Both primary and secondary data are used to understand the current situation and identify gaps. The research emphasizes the importance of government support, policy planning, and skill development programs. Based on the findings, effective strategies are suggested to promote inclusive and sustainable digital growth. Overall, the study aims to support balanced development and empower rural communities through digital inclusion.

Published by: vikaspatanker

Authors: Zarina Iskandarova

 

Abstract: The modern industrial landscape is witnessing a fundamental shift in Enterprise Resource Planning (ERP) as organizations transition from static data collection to dynamic, self-optimizing business processes. This review article investigates the integration of Machine Learning (ML) within SAP ecosystems, specifically focusing on the deployment of real-time cloud analytics pipelines. By leveraging the SAP Business Technology Platform (BTP) as a connective tissue between the SAP S/4HANA digital core and hyperscaler cloud services, enterprises can now process transactional data with sub-second latency to drive proactive decision-making. The article evaluates key ML methodologies, including regression-based demand forecasting, unsupervised anomaly detection for financial fraud, and reinforcement learning for autonomous supply chain tuning. Central to this transformation is the architecture of the real-time pipeline, which utilizes technologies such as Change Data Capture (CDC) and streaming frameworks like Apache Kafka to eliminate the "latency gap" inherent in traditional batch processing. We analyze how these pipelines create a closed-loop system, where analytical insights are automatically translated back into operational actions within the SAP environment. Furthermore, the review addresses the technical hurdles of data gravity, the necessity for Explainable AI (XAI) in corporate governance, and the emerging role of generative agents in 2026. Ultimately, we conclude that the convergence of ML and real-time cloud analytics is no longer an optional enhancement but a strategic imperative for the "Intelligent Enterprise" seeking resilience and efficiency in a volatile global economy.

DOI: https://doi.org/10.5281/zenodo.19427984

 

Published by: vikaspatanker

Authors: Dilshod Rahmonov

Abstract: The escalating complexity of modern distributed systems—characterized by microservices architectures, cloud-native deployments, and dynamic resource scaling—has rendered manual performance tuning nearly obsolete. Traditional methods, which rely heavily on human intuition and static rule-based configurations, fail to account for the non-linear interactions between distributed components. This review article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) as a paradigm shift in system optimization. By leveraging techniques such as Reinforcement Learning (RL), Bayesian Optimization, and Deep Learning, researchers are developing autonomous "self-tuning" systems capable of managing memory allocation, query execution, and network latency in real-time. We examine the transition from black-box modeling to transparent, interpretable AI frameworks. This review synthesizes current methodologies, highlights the challenges of training overhead and data drift, and outlines the future trajectory of AI-based tuning, emphasizing the move toward proactive, workload-aware orchestration that ensures high availability and cost-efficiency in large-scale environments.

DOI: https://doi.org/10.5281/zenodo.19427937

Published by: vikaspatanker

Authors: Javlon Ismailov

Abstract: Modern cloud computing and distributed networks face unprecedented traffic volatility, rendering traditional, static load-balancing algorithms—such as Round Robin or Least Connections—increasingly inefficient. Intelligent load balancing, driven by machine learning (ML), has emerged as a transformative solution to manage these dynamic workloads. By leveraging historical data and real-time metrics, ML models can predict traffic surges, identify resource bottlenecks, and autonomously redistribute tasks to optimize Quality of Service (QoS). This review explores the paradigm shift from reactive to proactive traffic management. We examine various ML architectures, including supervised learning for resource estimation, unsupervised clustering for traffic classification, and reinforcement learning for real-time decision-making. The article synthesizes current research on multi-objective optimization, focusing on the trade-offs between energy efficiency, latency reduction, and throughput maximization. Finally, we discuss the challenges of implementing these models in edge and fog computing environments, providing a roadmap for future developments in self-healing, autonomous network infrastructures.

DOI: https://doi.org/10.5281/zenodo.19427890

Published by: vikaspatanker