Leaveraging AI for Public Health Management
Authors:-Ganesh Ramalingam

Abstract-This whitepaper examines the use of artificial intelligence (AI) in managing population health. It discusses how AI can analyze population health data to identify trends, predict outbreaks, and optimize resource allocation. The paper covers the ethical considerations of using AI in public health and the regulatory measures needed to protect patient data. A novel algorithm called Geo Health AI is presented, along with Python code, to demonstrate how AI can be applied to geospatial population health analysis. Case studies and outcomes from AI implementations in population health management are reviewed. Finally, recommendations are provided for healthcare organizations looking to leverage AI for population health initiatives.

DOI: 10.61137/ijsret.vol.10.issue4.215

Published by: Kajal Tripathi

Implementing Artificial Intelligence in Thermoelectric Generators: A Review of Data Science Applications in Enhancing Efficiency and Security
Authors:- Arvind Malhotra, Rohit Bedi

Abstract:- The integration of Artificial Intelligence (AI) into thermoelectric generator (TEG) technologies offers a groundbreaking approach to improving both energy efficiency and cybersecurity within the rapidly evolving Internet of Things (IoT) ecosystems. This review delves into the diverse applications of AI-driven methodologies—including machine learning, big data analytics, and predictive modeling—to enhance the operational performance of TEGs, with a particular focus on systems utilizing advanced thermoelectric materials such as bismuth telluride (Bi2Te3) and lead telluride (PbTe). By conducting an extensive examination of the existing literature, this paper identifies and analyzes key AI techniques that have been instrumental in optimizing energy conversion processes, thereby significantly boosting the efficiency of TEG systems. Moreover, it explores how AI can be leveraged to fortify the security of IoT ecosystems, addressing vulnerabilities and safeguarding interconnected devices against potential cyber threats. The review also discusses the synergistic potential of integrating AI with TEGs to create intelligent, adaptive systems capable of responding dynamically to varying conditions and threats. The findings underscore AI’s pivotal role in not only advancing TEG efficiency and IoT security but also in shaping future research trajectories aimed at overcoming persistent challenges. Ultimately, this review highlights the transformative impact of AI on developing resilient and sustainable energy solutions, emphasizing its importance in meeting the growing demands of modern energy systems and securing digital infrastructure in an increasingly interconnected world.

DOI: 10.61137/ijsret.vol.6.issue6.214

Published by: Kajal Tripathi

Getting Around the Blockchain Technology Landscape: A Comprehensive Study of Risks and Solutions
Authors:-Vijay kumar

Abstract-Overview: The included report focuses into a number of areas of blockchain development, from the Bitcoin whitepaper to recent advances in growth, con- fidentiality, and consensus processes. It seeks to define blockchain architecture, investigate protocol improvements, solve important security issues, and debate blockchain integration with upcoming technologies such as the Internet of Things (IoT) Findings: Blockchain is built on decentralized peer-to- peer networks and cryptographic proofs, which provide trust and security without relying on a central authority. Ethereum and Hyperledger Fabric are protocols that ex- pand the capabilities of blockchain to smart contracts and corporate solutions. New consensus algorithms, such as Del- egated Proof-of-Stake and Bitcoin-NG, increase scalability and efficiency. Objectives: To demonstrate basic blockchain ideas like decentralization and cryptographic proofs. Investigate the evolution of blockchain protocols and consensus tech- niques. To examine significant security concerns and pri- vacy solutions in blockchain technology. To investigate the convergence of blockchain with IoT and its potential consequences. Results: Clarification of key blockchain ideas, with an emphasis on decentralization and cryptographic proofs. Insights on blockchain protocol and consensus mechanism improvements. A comprehensive review of security flaws and privacy remedies. Exploration of blockchain-IoT syn- ergies, emphasizing blockchain’s revolutionary influence on developing technologies.

DOI: 10.61137/ijsret.vol.10.issue4.213

Published by: Kajal Tripathi

Assessing the Ecological and Socioeconomic Ramifications of Climate Change on Fisheries: A Scientific Review
Authors:-Scholar Soro Nabintou, Professor Dr Hitesh A. Solanki

Abstract-Climate change poses a significant and escalating threat to global ecosystems, with profound implications for the fisheries sector. This comprehensive review aims to elucidate the underlying causes and multifaceted consequences of climate change on fisheries. The impacts are categorized into three primary dimensions: physical, biological, and geographical transformations. Physical alterations encompass rising temperatures, changes in oxygen levels, ocean acidification, and shifts in salinity, all of which directly influence marine environments. Biological shifts manifest as species extinctions, morphological alterations, population declines, and heightened susceptibility to diseases among fish populations. Elevated temperatures exacerbate mortality rates and disrupt fundamental physiological processes. Geographical transformations disrupt fish habitats and alter the distribution patterns of various species, thereby reshaping marine ecosystems on a global scale. Through synthesizing the latest scientific evidence, this review underscores the urgent need for proactive measures to mitigate the adverse effects of climate change on fisheries, safeguarding both ecological integrity and socioeconomic stability.

DOI: 10.61137/ijsret.vol.10.issue4.212

Published by: Kajal Tripathi

Fake Social Media Profile Detection: A Hybrid Approach Integrating Machine Learning and Deep Learning Techniques
Authors:-Anila S, Meenakshi Mohan, Mariya Jacob, Najiya Nasrin

Abstract-In the contemporary era of rapid information dissemination through social platforms, the proliferation of fake content undermines the trust and integrity of online communities. Existing detection algorithms exhibit limitations in terms of accuracy and adaptability, necessitating the creation of an innovative hybrid model. Our goal is to integrate the strengths of traditional machine learning approaches, such as k- Nearest Neighbors or Support Vector Machines, with the power of deep learning methods. By combining these techniques, we aim to enhance the accuracy and efficiency of fake profile detection beyond current state-of-the-art methods, providing a robust and effective solution for distinguishing between genuine and deceptive profiles in the dynamic landscape of social media.

DOI: 10.61137/ijsret.vol.10.issue4.211

Published by: Kajal Tripathi

Development and Implementation of Python Applications for 2d Geometry Learning
Authors:-By. Quyen Vo Truong Ngoc

Abstract-In the contemporary educational landscape, integrating technology with traditional learning methods has shown to enhance comprehension and engagement among students. This project explores the application of Python programming to facilitate the learning of 2D geometry. Python, known for its simplicity and powerful libraries, is utilized to create interactive tools and visual aids for understanding fundamental geometric concepts. This study details the development and implementation of a Python-based application designed to assist students in visualizing and computing various 2D geometric shapes, including points, lines, triangles, squares, and circles. The application leverages libraries such as Matplotlib, Pygame, and Turtle to render shapes and perform calculations related to area, perimeter, and other geometric properties. Preliminary results indicate that students using the application show improved understanding and retention of geometric principles compared to traditional methods. This paper discusses the methodology, key features of the application, and its potential impact on enhancing geometry education. Future directions include expanding the application’s capabilities and adapting it for different educational levels.

DOI: 10.61137/ijsret.vol.10.issue4.210

Published by: Kajal Tripathi

Detection of Landuse Land Cover Changes by using Maximum Likelihood Algorithm Application on Landsat Satellite Images
Authors:-Priyanka Gupta, Sharda Haryani, V.B. Gupta

Abstract-The identification of the LULC classes for the Mandsaur district Madhya Pradesh, India is the main objective of this research. The satellite images used in the analysis. Based on pixel-by-pixel supervised categorization of Landsat satellite images taken between 2003 and 2023 using the Arc-GIS tool across 20 year period, the work makes use of maximum likelihood approach. Various classifications of land use and land cover features are considered to predict overall changes, including populated areas, water bodies, agricultural land, forests and desert terrain. Landsat 8 photos from 2023 and remotely sensed Landsat 5 images from 2003 were used to detect changes inorder to accomplish this goal. The five LULC classes for the Mandsaur region are explained in this paper. The maximum likelihood algorithm is used in this work to compare the LULC classes for the Mandsaur region. The validation of the results for the supervised classification using MLC yielded kappa coefficients of 0.8263 and 0.7841 for 2023 and 2003 respectively. Land cover classification should benefit greatly from the application of MLC algorithms.

DOI: 10.61137/ijsret.vol.10.issue4.209

Published by: Kajal Tripathi

An Analyze the Trends for GST Revenue Collection in Uttar Pradesh
Authors:-Research Scholar Mani Shanker Lal Dwivedi, Assistant Professor Dr. Nancy Gupta

Abstract-GST is an Indirect Tax which has replaced many Indirect Taxes in India. The Goods and Service Tax Act was passed in the Parliament on 29th March 2017. The Act came into effect on 1st July 2017; Goods & Services Tax Law in India is a comprehensive, Multi- stage, destination-based tax that is levied on every value addition. In simple words, Goods and Service Tax (GST) is an indirect tax levied on the supply of goods and services. This law has replaced many indirect tax laws that previously existed in India. GST is one indirect tax for the entire country. This article deals with Analysis of GST Collection of India.

DOI: 10.61137/ijsret.vol.10.issue4.208

Published by: Kajal Tripathi

Question Answering for Low Resource Languages Using Natural Language Processing
Authors:-Nirav A. Baldha

Abstract- Recent advancements in Question Answering (QA) systems have significantly improved their performance, predominantly benefiting high-resource languages. However, low-resource languages, which lack extensive linguistic resources and data, face substantial challenges in developing effective QA systems. This paper provides an in-depth review of methodologies and advancements in QA systems for low-resource languages using Natural Language Processing (NLP) techniques. We discuss various approaches, including transfer learning, multilingual models, and cross-lingual embeddings. Additionally, we highlight case studies and experimental results, aiming to offer a comprehensive overview and suggest future research directions.

DOI: 10.61137/ijsret.vol.8.issue2.207

Published by: Kajal Tripathi

Impact of AI-Powered Investment Algorithms on Market Efficiency
Authors:-RB Nitish Kumar

Abstract-The integration of artificial intelligence (AI) into investment strategies has transformed financial markets by enhancing trading algorithms and decision-making processes. This research paper explores the impact of AI-powered investment algorithms on market efficiency, focusing on how these advanced technologies influence the speed, accuracy, and stability of financial markets. AI algorithms, including machine learning and deep learning models, have the potential to process vast amounts of data at unprecedented speeds, leading to more precise market predictions and trading decisions. However, the rapid adoption of AI also raises concerns about market volatility and the potential for new forms of systemic risk. This study evaluates empirical evidence on the performance of AI-driven trading systems, comparing their impact on market efficiency with traditional investment methods. Additionally, it addresses regulatory and ethical considerations related to the deployment of AI in finance. The findings aim to provide insights into the benefits and challenges of AI in investment strategies and its implications for future market dynamics and financial stability.

DOI: 10.61137/ijsret.vol.10.issue4.206

Published by: Kajal Tripathi