Authors: Dr.K.Prem Kumar, Dhareddy Rohith Reddy, Varkuti Nitish Reddy, Gummadi Manideep Reddy, Vadla Aakash

 

 

Abstract: Secure Haven – Enhancing Gated Community Management with IoT is an innovative initiative that utilizes advanced Internet of Things (IoT) technologies to improve the overall management and functionality of modern gated communities. By integrating a variety of IoT devices, including sensors and controllers, the system enables real- time monitoring and control of key community operations. This leads to greater efficiency, convenience, and safety for residents. These devices gather critical data related to energy consumption, environmental conditions, occupancy, and security. The collected data is processed using sophisticated algorithms and machine learning techniques to optimize community performance and decision-making. The project focuses on four main areas: energy management, environmental control, security and access control, and predictive maintenance. Energy efficiency is achieved through smart meters and intelligent lighting systems that help reduce energy waste and cut costs. The incorporation of renewable energy sources also supports sustainability. Environmental comfort is maintained using DHT11 sensors that monitor temperature and humidity, ensuring ideal indoor conditions. For security, RFID-based access systems are employed to allow secure and automated entry into the premises, while smart surveillance and real- time alert mechanisms provide an added layer of protection. Predictive maintenance is enabled by IoT-based monitoring systems, such as water level sensors, which facilitate continuous tracking of equipment performance and help prevent failures through early detection.Overall, Secure Haven offers a smart, secure, and sustainable solution for managing gated communities by leveraging the power of IoT to create a more connected and responsive living environment.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Dr. V.K. Srivastava, Ms. Aarti

 

 

Abstract: Generative AI is a huge step forward in technology that allows computers produce text, visuals, sounds, and even complex designs. This technology comes up with new ideas in a lot of different fields by using cutting-edge models like Generative Adversarial Networks (GANs), Large Language Models (LLMs), and diffusion models. This article discusses about how generative AI apps have changed over time and how they might be useful in the arts, business, healthcare, education, and engineering. The study found that generative AI makes creative work easier, speeds up the design process, personalises learning, and improves diagnoses. But generative AI also raises moral challenges, privacy concerns, biases, and areas of the law that aren't clear. The paper talks about these issues and how to fix them, highlighting how important it is for people from different professions to work together to build AI systems that are strong, fair, and open. It also talks about how to make generative AI more aware, adaptable, and accurate. The study highlights how crucial it is to put laws and morals in place so that as many people as possible can utilise AI in a responsible way. Some of the goals for the future are to make AI easier to use, rules easier to obey, and to promote innovation that is culturally and socially diverse. Generative AI can solve these difficulties and help firms in a lot of different industries be more creative, productive, and inventive.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Assistant professor Benasir Begam.F, Agalya.A

Abstract: Machine learning (ML) is transforming material science by shifting the traditional empirical and simulation-driven approaches to a data-centric paradigm. This review presents an integrated overview of how ML methods are applied in microstructure recognition, material property prediction, and alloy design. We discuss key learning paradigms such as supervised, unsupervised, and deep learning, with emphasis on convolutional neural networks (CNNs), autoencoders, and generative models. Representative studies are cited to illustrate applications in predictive modeling and image-based analysis. We highlight challenges related to data scarcity, model interpretability, and integration of physical principles. The review concludes with future directions, including autonomous materials discovery platforms and hybrid physics-informed ML models.

 

 

Published by: vikaspatanker

Authors: Ruselle H. Paano, Jovilyn D. Apigo

Abstract: This study investigates the various factors that influence the academic performance of Grade V pupils in Science. Recognizing the crucial role of science education in shaping critical thinking and problem-solving skills, the research aims to identify both internal and external factors that significantly affect student achievement. The study focuses on variables such as students’ study habits, interest in science, classroom environment, teacher competency, parental involvement, and access to learning resources. Using a quantitative descriptive-correlational design, data were gathered through surveys and academic records from a representative sample of Grade V pupils in selected schools. Statistical analysis revealed that student interest and motivation, teacher teaching strategies, and parental support had the most significant correlation with science performance. Meanwhile, socio-economic status and availability of learning materials also showed notable influence. The findings suggest that improving science performance requires a holistic approach that involves not only curriculum improvements but also increased parental engagement and support for teacher development. The study provides recommendations for educators, administrators, and policymakers to enhance science instruction at the elementary level.

Published by: vikaspatanker

Authors: Vadla Aakash, Gummadi Manideep Reddy, Dr.K.Prem Kumar Associate, Dhareddy Rohith Reddy,, Varkuti Nitish Reddy

 

 

Abstract: Secure Haven – Enhancing Gated Community Management with IoT is an innovative initiative that utilizes advanced Internet of Things (IoT) technologies to improve the overall management and functionality of modern gated communities. By integrating a variety of IoT devices, including sensors and controllers, the system enables real- time monitoring and control of key community operations. This leads to greater efficiency, convenience, and safety for residents. These devices gather critical data related to energy consumption, environmental conditions, occupancy, and security. The collected data is processed using sophisticated algorithms and machine learning techniques to optimize community performance and decision-making. The project focuses on four main areas: energy management, environmental control, security and access control, and predictive maintenance. Energy efficiency is achieved through smart meters and intelligent lighting systems that help reduce energy waste and cut costs. The incorporation of renewable energy sources also supports sustainability. Environmental comfort is maintained using DHT11 sensors that monitor temperature and humidity, ensuring ideal indoor conditions. For security, RFID-based access systems are employed to allow secure and automated entry into the premises, while smart surveillance and real- time alert mechanisms provide an added layer of protection. Predictive maintenance is enabled by IoT-based monitoring systems, such as water level sensors, which facilitate continuous tracking of equipment performance and help prevent failures through early detection.Overall, Secure Haven offers a smart, secure, and sustainable solution for managing gated communities by leveraging the power of IoT to create a more connected and responsive living environment.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Mahboob Ansari, Dr. Shahnwaz Alam, Dr.Mohd Faizan Hasan

Abstract: The welding of metals is a critical process in various industries, and the choice of welding technique can significantly influence the quality of the weld. This study presents a comparative analysis of microstructural and mechanical properties of MS1018 steel welded using Friction Welding (FW) and Tungsten Inert Gas (TIG) Welding operations. The MS1018 steel, a widely used medium carbon steel, was chosen for its applications in construction and automotive industries. The study evaluates the influence of welding processes on the microstructure, hardness, tensile strength, and impact toughness of the welds. The research concludes by comparing the outcomes of both welding methods and discussing their advantages and limitations in terms of welding quality, performance, and suitability for specific applications.

Published by: vikaspatanker

Authors: NIKITA RATHORE

 

 

Abstract: Global superstores have emerged as a cornerstone of the modern retail industry, offering diverse products and competitive prices that cater to a wide consumer base. This study examines the operations, challenges, and societal impacts of global superstores, focusing on their role in international commerce and their influence on local economies. By employing a mixed-methods approach, including customer surveys, interviews with industry stakeholders, and secondary data analysis, the research provides a comprehensive understanding of superstore dynamics. Key findings highlight the advantages of enhanced consumer access and operational efficiency, juxtaposed with concerns about sustainability and labor practices. The study underscores the need for strategies that balance profitability with social and environmental responsibilities, contributing valuable insights for academia, practitioners, and policymakers in navigating the evolving retail landscape

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Shubhangi Murukate, Professor Dr. Ayesha Siddiqui

Abstract: Healthcare emergencies require rapid, coordi- nated, and intelligent responses. This paper proposes an AI- enabled emergency response optimization system integrated with blockchain-based smart contracts to enhance efficiency, transparency, and automation in critical healthcare scenarios. The system leverages machine learning for incident prediction and prioritization, while smart contracts ensure secure, real- time collaboration among hospitals, ambulances, and government agencies. Experimental simulations show improved response time and reduced resource allocation errors. The proposed system can significantly strengthen emergency preparedness and crisis response in healthcare.

Published by: vikaspatanker

Authors: Shubhangi Murukate, Professor Dr. Ayesha Siddiqui

Abstract: Healthcare emergencies require rapid, coordi- nated, and intelligent responses. This paper proposes an AI- enabled emergency response optimization system integrated with blockchain-based smart contracts to enhance efficiency, transparency, and automation in critical healthcare scenarios. The system leverages machine learning for incident prediction and prioritization, while smart contracts ensure secure, real- time collaboration among hospitals, ambulances, and government agencies. Experimental simulations show improved response time and reduced resource allocation errors. The proposed system can significantly strengthen emergency preparedness and crisis response in healthcare.

Published by: vikaspatanker

Authors: Akshita verma

 

Abstract: Purpose: To make calcium carbonate nanoparticles (CaCO₃NPs) and test their fluorescent and cytotoxic properties utilising Tradescantia pallida (Commelinaceae). Method: T. pallida aqueous leaf extract is used to make CaCO₃ NPs according to a straightforward technique (TPALE). Determine their crystalline nature and functional groups, Fourier transform infrared spectroscopy (FTIR) investigations are used. Photoluminescence characteristics of CaCO₃ Nanoparticles are evaluated using fluorescence spectroscopy. Following confirmation of CaCO₃ NP synthesis, the 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide (MTT) assay is used to assess cytotoxicity against HeLa cell line. Conclusion: On the basis of TPALE, a simple method for synthesising CaCO₃ NPs has been successfully established. The generated CaCO₃ NPs have good luminescent characteristics and are cytotoxic to a cancer cell line.

DOI: http://doi.org/ijsret.vol.11.issue3.152

 

Published by: vikaspatanker