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Author Archives: Kajal Tripathi

Predicting Migration Trends Using AI Models on Geopolitical and Climate Data

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Predicting Migration Trends Using AI Models on Geopolitical and Climate Data
Authors:-Ashwini.M

Abstract-:Migration trends have always been influenced by a variety of factors, including political, economic, and environmental conditions. In recent years, the role of artificial intelligence (AI) in predicting migration patterns has garnered increasing attention. This paper explores the application of AI models in predicting migration trends by incorporating geopolitical and climate data. With the rapid advancements in machine learning and data analytics, AI models have proven to be powerful tools in analyzing complex, multidimensional datasets, providing insights into the potential movements of populations under various scenarios. This research aims to combine geopolitical factors such as conflict, political instability, and governance with climate-related data, including temperature changes, natural disasters, and resource scarcity, to generate more accurate migration forecasts. By applying machine learning algorithms, especially supervised and unsupervised techniques, the study integrates a wide range of datasets, including real-time geopolitical shifts and projected climate patterns, to create predictive models. The paper discusses the methodology of integrating AI algorithms with spatial and temporal data, while also evaluating the reliability and robustness of these models in forecasting migration flows across different regions. Furthermore, it addresses the challenges and limitations of using AI in this context, including the availability of high-quality data, ethical considerations, and the uncertainties inherent in predicting human behavior. The findings of this study will offer valuable insights for policymakers, international organizations, and humanitarian agencies in planning for future migration scenarios and managing related risks. By leveraging AI’s potential, migration forecasting can be more nuanced, timely, and context-aware, ultimately enabling better-informed decision-making in the face of global challenges.

DOI: 10.61137/ijsret.vol.11.issue2.418

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AI-Powered Zero Trust Architectures for Secure Government Cloud Systems

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AI-Powered Zero Trust Architectures for Secure Government Cloud Systems
Authors:-Arun Kumar

Abstract-:AI-powered Zero Trust architectures are emerging as a pivotal approach for securing government cloud systems, addressing the increasing complexity and sophistication of cybersecurity threats. This paper explores the concept of Zero Trust Architecture (ZTA), its integration with Artificial Intelligence (AI), and how these combined technologies can bolster the security of cloud environments within government sectors. Zero Trust is grounded in the principle that trust should never be implicit, even within trusted networks, and demands continuous authentication, authorization, and monitoring to ensure secure access to resources. When AI is embedded within Zero Trust models, it enhances threat detection, risk assessment, and response capabilities by enabling automated, data-driven security decisions. The dynamic nature of cloud environments necessitates robust, adaptive security frameworks. Traditional perimeter-based defenses, such as firewalls and intrusion detection systems, no longer provide sufficient protection against modern cyber threats, including insider attacks, data breaches, and advanced persistent threats. As government organizations increasingly adopt cloud services to store and manage sensitive data, ensuring the security of these systems becomes paramount. AI offers the ability to analyze vast amounts of data in real time, predict potential vulnerabilities, and respond to incidents faster and more accurately than manual methods. This paper discusses the principles behind Zero Trust, the role of AI in its implementation, and examines several use cases within the government sector. It also highlights the challenges faced when adopting AI-powered Zero Trust frameworks and offers solutions to mitigate these challenges.

DOI: 10.61137/ijsret.vol.11.issue2.417

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Cloud-Based ETL Pipelines for Social Media Analytics

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Cloud-Based ETL Pipelines for Social Media Analytics
Authors:-Parth Yangandul, Sakshi Soni

Abstract-:The rapid expansion of social media has resulted in massive volumes of user-generated content, offering valuable insights for businesses, researchers, and policymakers. However, extracting, processing, and analysing this data presents challenges in scalability, efficiency, and cost. This research proposes a cloud-based ETL (Extract, Transform, Load) pipeline designed for handling large-scale social media data, ensuring efficient extraction, transformation, and structured storage for further analysis. The study will explore data extraction techniques using the Reddit API, optimizing for rate limits and scalability. The transformation process will involve text cleaning, metadata structuring, and sentiment classification to enhance data quality. For storage, AWS S3, Redshift, and NoSQL databases will be evaluated based on performance, query speed, and cost efficiency. To handle real-time and batch processing, the research will implement Apache Spark, comparing their effectiveness in different analytics scenarios. Orchestration tools like Apache Airflow and Docker will automate ETL workflows, while Terraform will enable infrastructure provisioning. Performance will be assessed through processing speed, cost, scalability, and accuracy. Additionally, Power BI and Google Data Studio will be used for visualization and reporting. This research aims to provide a scalable, cloud-native ETL solution that enhances social media data analytics, benefiting data engineers, businesses, and researchers. Index Terms—ETL, Cloud Infrastructure, Social Media Analytics, Data Pipelines, Automation.

DOI: 10.61137/ijsret.vol.11.issue2.416

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Design and Development of Iot Prototype for Real-Time Theft Detection and Optimization of Electricity Using Machine Learning Techniques

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Design and Development of Iot Prototype for Real-Time Theft Detection and Optimization of Electricity Using Machine Learning Techniques/strong>
Authors:-Assistant Professor Lakshmi G, Associate Professor Dr. M Charles Arockiaraj

Abstract-The pervasive issue of electricity theft poses a substantial challenge to power utilities globally, resulting in significant financial losses and operational inefficiencies. This paper presents the plan and growth of an IoT-based prototype for real-time electricity theft detection and optimization of electricity distribution using advanced machine-learning practices. By integrating smart meters and IoT sensors, the system continuously monitors electricity consumption, providing accurate, real-time data. Utilizing Deep Neural Networks (DNNs), the prototype identifies anomalous usage patterns indicative of theft, ensuring swift and precise detection. Additionally, the structure influences machine-learning procedures to optimize electricity distribution, enhancing overall efficiency and reducing waste. This complete method not only mitigates the risk of theft but also improves the dependability and sustainability of electricity supply. The proposed solution demonstrates important possibilities for enhancing the operational effectiveness of power utilities, offering a scalable, robust, and efficient framework for modern energy management.

DOI: 10.61137/ijsret.vol.11.issue2.404

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Deep Learning for Liver Segmentation

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Deep Learning for Liver Segmentation
Authors:-Amarnath Chigurupati. Madhuri Sirasanagandla. Ankit Kommalapati. Siddique Ibrahim Peer Mohammed, Madhuri Sirasanagandla

Abstract-Liver cancer is becoming a huge threat to global health health, where early detection and accurate diagnosis are crucial for effective treatment [1]. Our research on deep learning- A based learning system for automatic segmentation of the liver and The tumor from computed tomography (CT) images is highlighted, using a U-Net model integrated with ResNet-34, which acts as a backbone [2]. This model is trained on the Liver Tumor Segmentation Challenge (LiTS) dataset, which is a standard for This type of problem [2]. Training a high-performance model, The project itself differentiates with the development of a user- friendly GUI with the help of the Python package PyQt5, making It is possible to achieve real- time visualization and user-friendly interaction for the end users like radiologists, students, and researchers [10] . This interface helps in taking input as an image in the form of a JPG, predicts segmentation tasks, and compares the results With the grayscale liver anatomy structures. Our model delivers high accuracy in segmentation, obtaining a high accuracy Dice coefficient of 98.20% with an extraordinary precision, recall, and f-score up to 99.89%, making it usable for real-time scenarios like clinical and research purposes Index Terms—Liver Segmentation, Deep Learning, U-Net, ResNet-34, FastAI, PyQt5.

DOI: 10.61137/ijsret.vol.11.issue2.375/a>

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Work –Life Balance of Women Employees: Challenges and Strategies

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Work –Life Balance of Women Employees: Challenges and Strategies
Authors:-Greeshma Muraly

Abstract-Women across the world encounter difficulties in managing what their professional work and personal life. These research paper exams the difficulties of workload and life balancing in women particularly among working women and entrepreneurs. It analysis the key factors affecting work life balance, the impact of overload on mental and Physical health and Strategies for achieving stability. This paper also highlights policies and support systems that can help woman in managing the responsibilities effectively.

DOI: 10.61137/ijsret.vol.11.issue2.374/a>

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Speech Emotion Recognition Using CNN

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Speech Emotion Recognition Using CNN
Authors:-Pratiksha Sathe, Dr. Jasbir Kaur, Assistant Professor Suraj Kanal

Abstract-Speech Emotion Recognition (SER) is an evolving and critical field in human-computer interaction, aimed at identifying and interpreting human emotions through speech signals. The ability to recognize emotions accurately from speech has applications in various domains, including mental health diagnostics, customer service, and adaptive learning systems. This paper focuses on leveraging Convolutional Neural Networks (CNN) for SER, emphasizing their capability to perform robust feature extraction and accurate classification. CNNs excel in capturing both spatial and temporal characteristics of audio signals, making them particularly well-suited for processing speech data. By converting speech signals into Log-Mel spectrograms, which effectively represent the spectral and temporal properties of audio, the proposed model achieves high accuracy in recognizing a diverse range of emotions. The study demonstrates the practical application of CNNs for SER, highlights their advantages over traditional machine learning models, and evaluates their performance on benchmark datasets such as RAVDESS and IEMOCAP. The results underscore the potential of CNN-based approaches to advance the field of speech emotion recognition, paving the way for more sophisticated and empathetic human-computer interaction systems.

DOI: 10.61137/ijsret.vol.11.issue2.373/a>

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Silent Voice -The Sign Language Recognition Android Application Using Machine Learning Algorithm

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Silent Voice -The Sign Language Recognition Android Application Using Machine Learning Algorithm
Authors:-Ms Mitali Pawar, Dr. Jasbir Kaur, Assistant Professor Ms.Sandhya Thakkar

Abstract-Sign language is an essential communication tool for people with speech and hearing impairments.”[4] The creation of an Android application for sign language recognition using machine learning methods is presented in this study. The program facilitates communication between sign language users and non-sign language users by using OpenCV and a machine learning model to process images and transform hand gestures into text. Real-time alphabetic sign recognition from live video input is possible with the suggested approach. The application guarantees effective gesture detection with low resource consumption by using TensorFlow Lite for model inference on mobile devices, which makes it appropriate for Android devices with low processing power. The system’s user-friendly interface facilitates quick and precise translations between sign language and other languages, encouraging inclusion and assisting in the removal of barriers to communication.

DOI: 10.61137/ijsret.vol.11.issue2.372/a>

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Developing an AI Based Interactive Chatbot for the Department of Justice’s Website

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Developing an AI Based Interactive Chatbot for the Department of Justice’s Website
Authors:-Sulake Bhavya Sri Bai, Kamboja Akshith Swamy

Abstract-addition to the Department of Justice website to enhance the virtual experience. The new website upgrade is centered around an artificial intelligence-enabled chatbot that uses Natural Language Processing (NLP) to become more conversational and easier for visitors to interact with when they just speak to it. The DOJ website enhancement also offers a multilingual capability for all citizens, irrespective of their ability. It is powered by a scalable cloud-based infrastructure that ensures high availability and round-the-clock access. Long legal procedures that, regrettably, impede the ability of many regular people to obtain simple information or services are the main goals of this efficiency improvement. Key words: Natural Language Process (NLP), AI chatbot, voice assistant, legal technology, accessibility, public service automation, department of justice, and legal query resolution.

DOI: 10.61137/ijsret.vol.11.issue2.371/a>

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An Evaluation of Key Factors Influencing the Productivity of Plywood Shuttering in Construction Projects

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An Evaluation of Key Factors Influencing the Productivity of Plywood Shuttering in Construction Projects
Authors:-Sachin, Dr. Amit Moza

Abstract-This research paper examines the critical factors that influence the productivity of plywood shuttering in construction projects—a fundamental element in modern formwork systems. Although plywood shuttering is favored for its cost-effectiveness, reusability, and ease of handling, practical productivity often falls short of theoretical benchmarks due to issues related to material quality, labor proficiency, adverse site conditions, and management practices. By integrating a detailed literature review, rigorous field observations, in-depth case studies, and quantitative productivity measurements (including time–motion studies and benchmark comparisons with IS 7272 and CPWD DAR 2021), this study establishes realistic productivity standards and proposes actionable strategies to enhance on-site efficiency. The findings provide valuable insights for optimizing resource allocation, reducing material wastage, and ultimately improving construction performance.

DOI: 10.61137/ijsret.vol.11.issue2.370/a>

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