Developing an Integrated Framework for Ensuring Patient Privacy and Confidentiality in the Age of Social Media: A Case Study and Research Approach in the Healthcare Sector of India
Authors:-Sanika Satish Lad, Shifa Siraj Khan, Sanika Rajan Shete, Anant Singh, Devesh Amlesh Rai

Abstract- The rapid development of the use of communication technologies and social media among healthcare providers raises potential privacy issues for patients. Health Information can be easily transmitted using platforms like Facebook, Instagram, Twitter, Snapchat and TikTok that allow users to communicate electronically with friends and family all over the world. Thus, offering means of spreading sensitive healthcare information—as well as an easy way to compromise patient privacy. the purpose of this research is to shed light on how medical information shared on social media by healthcare providers poses risks to patient’s privacy in the India. To better explain and understand the research problem, both qualitative and quantitative methods were used. These include scientific facts, statistics, surveys, interviews, social media posts and studies conducted from 2000 to 2023. Also, all quantitative data used were taken from the existing literature done by other researchers. Before conducting the research, the expectations were to use primary data to better show the existing gap and explain the research problem. However, due to some limitations, many assumptions and analyses were made based on secondary data. This research paper will help healthcare professionals to improve in ways that are “privacy-respecting and privacy reinforcing.

DOI: 10.61137/ijsret.vol.10.issue3.160

Published by: Kajal Tripathi

Design and Fabrication of Pedal Operated Shredder Machine
Authors:-Chandan sahoo, Ajay vikash kumar behera, Abinash mallick, Ananta Prasad sethi, Dr. Mamata kumari padhy

Abstract- The scope of this project was to design and development of Shredder machine focus on chopping of vegetables, areca leaves, this chopped powder to prepare the vermin compost. The project began with collection of information and data on user lifestyle and current process by which they perform their job. Concepts were developed with reference of four different shredder machine and operating processes. Concept was developed considering the safety factor users operating environment and maintenance. Considering the users’ needs and buying capacity, spur gear, bearings, structural frame, cutter and dual shaft. The machine frame is built using mild steel and tungsten carbide is used for cutter tip preparation. Two Blade are mounted on Singal shafts, which rotate parallely driven by a spur gear. The power from the by cycle is transmitted to cutter shaft through a chain drive. Cut is made inside the chopping house due to the effect of tensile, friction, and impact effect in chopping process. The vegetables get chopped and powder is collected at the bottom.

DOI: 10.61137/ijsret.vol.10.issue3.159

Published by: Kajal Tripathi

A Deep Learning Structure for Forecasting Cyclone Intensity
Authors:-Assistant Professor Kavitha, Abhineet Raj, Tanmay Tiwari, Ayush Madurwar

Abstract-In a world where cyclone frequency and intensity pose major risks to people living along the shore, there has never been a more urgent need for accurate and early forecast. The paper “Cyclone Intensity Prediction,” aims to advance forecasting techniques by developing and implementing a novel approach. This research, which embraces cutting-edge technologies, uses advanced modelling approaches, machine learning algorithms, and meteorological data analytics to establish a solid foundation for predicting cyclone intensity with previously unheard-of accuracy. The combination of these elements enables a thorough comprehension of the intricate dynamics affecting the development and evolution of cyclones. The research attempts to find patterns and connections in historical cyclone data that were previously missed by performing in-depth data analysis and feature engineering. Modern deep learning algorithms make it possible to extract insightful information that helps build a predictive model that can predict cyclone intensity more accurately and with more advance warning. Furthermore, the paper focuses on real-time data integration to guarantee that the prediction model adapts dynamically to changing meteorological conditions. The integration of satellite imaging, oceanic data, and atmospheric factors increases forecast abilities, resulting in a more complete and nuanced knowledge of cyclone dynamics. This study not only advances the scientific community’s understanding of cyclone dynamics, but it also has far-reaching societal ramifications. Improved cyclone intensity forecasts can empower disaster response organizations, governments, and vulnerable communities by allowing them to take proactive measures to reduce potential damage and save lives.

DOI: 10.61137/ijsret.vol.10.issue2.155

Published by: Kajal Tripathi

Smart Hire – An Intelligent Hiring Platform
Authors:-Nikhil Kumar Thakur, Aakriti Chowdhary, Ujjwal Bhattarai, Professor Geetha Rani K, Dr. Shivakumar C

Abstract-In the rapidly evolving landscape of human resources and talent acquisition, traditional methods of hiring are proving increasingly inadequate in meeting the demands of modern organizations and job seekers. The inefficiencies inherent in manual resume screening, subjective evaluations, and disjointed recruitment processes contribute to extended timelines, suboptimal candidate selections, and diminished candidate experiences. Recognizing these challenges, this research endeavors to introduce a paradigm shift in recruitment practices by harnessing the power of micro-service architecture. Through the development of a sophisticated web application, this study aims to revolutionize the hiring process by integrating cutting- edge technologies such as Natural Language Processing (NLP) for resume screening, examination management, and interview scheduling. By adopting a modular micro- service architecture, the system promises to streamline recruitment workflows, enhance decision-making accuracy, and elevate the overall candidate experience. The primary objective of this research is to create a comprehensive solution that not only addresses the immediate pain points of recruiters and job seekers but also lays the foundation for a more agile and responsive recruitment ecosystem. By automating repetitive tasks, minimizing bias in candidate evaluation, and facilitating transparent communication between stakeholders, the proposed system seeks to transform recruitment into a strategic advantage for organizations. Furthermore, this research explores the potential benefits of the micro- service based approach, including improved efficiency, enhanced accuracy, better candidate experiences, and greater scalability. Through meticulous design and rigorous testing, the system aims to deliver tangible outcomes that align with the evolving needs of the talent market and contribute to organizational success in an increasingly competitive landscape.

DOI: 10.61137/ijsret.vol.10.issue2.154

Published by: Kajal Tripathi

Solar Based Green Hydrogen Production with Header and Riser Tube Arrangements
Authors:-Mr. E.Sivaprakash., Mohammed Imthiyas.A, MukesVarma.K, Kesavan. N

Abstract- The growing demand for sustainable energy solutions has spurred research into solar-based green hydrogen production systems. This study proposes an innovative approach integrating solar photovoltaic (PV) panels with paraffin wax-enhanced copper tubes to facilitate efficient hydrogen generation through electrolysis. The system design incorporates header and riser tube arrangements to optimize water flow and heat transfer. Paraffin wax, when spattered on the surface of the copper tubes, acts as a phase change material, enhancing heat absorption and retention. The hot water generated by the solar- heated copper tubes is directed to an electrolyzer where hydrogen and oxygen are separated. To ensure accurate measurement of hydrogen output, specific equipment tailored for hydrogen quantification is employed. This comprehensive system not only harnesses renewable solar energy but also capitalizes on the thermal properties of paraffin wax to achieve higher energy efficiency and greener hydrogen production. The findings of this study contribute to the advancement of sustainable energy technologies, paving the way for a cleaner and more sustainable future.

DOI: 10.61137/ijsret.vol.9.issue4.343

Published by: Kajal Tripathi

Age and Gender Prediction from Facial Images Using Deep Learning Approach
Authors:-Associate Professor Dr. A. Selva Reegan, Adan C Benedict, Jeevithan S, Hari B L, Raghul Babu J

Abstract- significant attention due to its wide range of applications in various facial investigations. This research paper presents a comprehensive approach utilizing convolutional neural networks (CNN) and deep learning methodologies to develop a gender and age detection system. The paper explores the underlying algorithms and techniques employed in CNN models for gender classification and age estimation, highlighting their synergy and integration. The primary objective of this study is to leverage deep learning techniques to create an accurate gende and age detector capable of providing approximate predictions for human faces in images. Additionally, the paper discusses the significance of this technology and its potential impact on improving everyday lives. Further- more, the research paper emphasizes the diverse range of applications where such technology can be effectively utilized. These applications span across various domains, including intelligence agencies, CCTV cameras, policing, and matrimony websites. The potential benefits and implications of implementing gender and age detection systems in these areas are explored. Overall, this research paper provides insights into the development of a gender and age detection system using deep learning and highlights its potential applications in different sectors, showcasing the value and impact it can have on society .]Automatic age and gender prediction from facial images has gained significant attention due to its wide range of applications in various facial investigations. This research paper presents a comprehensive approach utilizing convolutional neural networks (CNN) and deep learning methodologies to develop a gender and age detection system. The paper explores the underlying algorithms and techniques employed in CNN models for gender classification and age estimation, highlighting their synergy and integration. The primary objective of this study is to leverage deep learning techniques to create an accurate gender and age detector capable of providing approximate predictions for human faces in images. Additionally, the paper discusses the significance of this technology and its potential impact on improving everyday lives. Further- more, the research paper emphasizes the diverse range of applications where such technology can be effectively utilized. These applications span across various domains, including intelligence agencies, CCTV cameras, policing, and matrimony websites. The potential benefits and implications of implementing gender and age detection systems in these areas are explored. Overall, this research paper provides insights into the development of a gender and age detection system using deep learning and highlights its potential applications in different sectors, showcasing the value and impact it can have on society .

DOI: 10.61137/ijsret.vol.10.issue2.312

Published by: Kajal Tripathi

A Blockchain-based Approach for Drug Traceability in Healthcare Supply Chain
Authors:-Assistant Professor Mrs.J.Sunanthini, Ashina.R, Priskila.B, Pushpa Lincy.J, Sanju.P

Abstract- Counterfeit drugs are an immense threat for the pharmaceutical industry worldwide due to limitations of supply chain. Our proposed solution can overcome many challenges as it will trace and track the drugs while in transit, give transparency along with robust security and will ensure legitimacy across the supply chain. It provides a reliable certification process as well. Fabric architecture is permissioned and private. Hyperledger is a preferred framework over Ethereum because it makes use of features like modular design, high efficiency, quality code and open-source which makes it more suitable for B2B applications with no requirement of cryptocurrency in Hyperledger Fabric. QR generation and scanning are provided as a functionality in the application instead of bar code for its easy accessibility to make it more secure and reliable. The objective of our solution is to provide substantial solutions to the supply chain stakeholders in record maintenance, drug transit monitoring and vendor side verification.

DOI: 10.61137/ijsret.vol.10.issue2.311

Published by: Kajal Tripathi

Stabilization of Black Soil Using Lime and Jute Fibre
Authors:-P.Prabhu, S.Kamaleshwaran, B.Yogeshvaran, R.Ajith

Abstract- Black cotton soil is a type of expansive soil that exhibits high swelling and shrinking behavior due to changes in moisture content, causing damages to structures built on it. This study aims to improve the engineering properties of black cotton soil by stabilizing it with lime and jute fibre, which are natural and eco-friendly materials. The soil samples were prepared with different proportions of lime (10%, 20%, and 30%) and jute fibre (10%, 20%, and 30%) and tested for shrinkage limit, unconfined compressive strength, and California bearing ratio. The results showed that the addition of lime and jute fibre reduced the shrinkage limit, increased the unconfined compressive strength and the California bearing ratio of the soil, indicating an improvement in the soil stability and bearing capacity. Soil is a base of structure, which actually supports the structure from beneath and distributes the load effectively. If the stability of the soil is not adequate then failure of structure occurs in form of settlement, cracks etc. Expansive soil also known as black cotton soil is more responsible for such situations and this is due to presence of montmorillonite mineral in it, which has ability to undergo large swelling and shrinkage. To overcome this, properties of soil must be improved by artificial means. Soil reinforcement technique is one of the most popular techniques used for improvement of poor soils. Metal strips, synthetic geotextiles, geogrid sheets, natural geotextiles, randomly distributed, synthetic and natural fibres are being used as reinforcing materials to soil. The study concluded that lime and jute fibre can be effectively used as soil stabilizers for black cotton soil.

DOI: 10.61137/ijsret.vol.10.issue2.306

Published by: Kajal Tripathi

Design and Performance Analysis of Electrochemical Micro Machining on GI Sheet
Authors:-M. Maniyarasan, A. Premkumar, S. Vigneshwaran, S. Surya

Abstract-Galvanized iron is manufactured and used for wide variety of purposes but its primary use is for sheet metal roofing and other building materials, such as metal framing studs, metal roof shingles and fencing. It may be used in future as micro level applications on the field of science and Nano technology. The current techniques for micro manufacturing mostly are silicon based. These manufacturing techniques are not suitable for use in demanding applications like aerospace and biomedical industries. Electrochemical micromachining (ECMM) can machine hard metals and alloys at micrometer scale. So, we developed a cost efficient electrochemical micromachining with feed control setup and conduct a performance analysis of electrochemical micromachining on GI sheet to find out the efficient parameters value required for GI sheet to perform a micro hole in electrochemical micromachining.

DOI: 10.61137/ijsret.vol.10.issue2.299

Published by: Kajal Tripathi

Predictive Analytics: Mitigating Risks in Fintech Products with AI
Authors:-Chintamani Bagwe

Abstract-In Fintech, predictive analytics play an important role in dealing with increasing complexity in the financial sphere, radically altering risk assessment and hence the nature of financial choices. This paper examines the interaction between cutting-edge predictive analytics and artificial intelligence in promoting corporate risk assessment, fraud detection, and operational productivity in solution providers. It discusses a number of predictive analytics models, such as time set classification and neural networks and uses them to think through market trends, customer clustering behaviour, and anomaly identification. The identification links are then used in many financial service scenarios to consider their impact on risk solvency development and customer experience and to predict market patterns. The paper looks at difficulties in data administration, illustration, and ethics, and suggests a solid data management approach and an ethical concept. It concludes with thoughts and ideas that would lead to more risk awareness and AI-driven decisions in the future, and highlights the predicted growth in competitively elegant predictive forecasting and situation control. The sense-the essay makes is to remind Fintech managers and specialists of the importance of keeping their learning up-to-date to take full advantage of the most recent advances in artificial intelligence and predictive analytics to make sensible decisions and policy thinking in their career.

DOI: 10.61137/ijsret.vol.10.issue2.265

Published by: Kajal Tripathi