Paper for Issue

Big Data in Data Science for Real-Time Stock Market Analysis

Abstract

The growth of big data has transformed stock market analysis, especially in real-time environments, by enabling the aggregation and processing of diverse data sources, including stock prices, financial news, and sentiment from social media. This paper examines the role of big data in real-time stock market analysis and proposes an integrated approach utilizing YOLOv5, a high-performance object detection model. By analyzing visual data streams for events impacting stock values, we aim to create a system that enhances traditional data-driven analysis. Key contributions include the integration of visual and textual data sources, the implementation of machine learning for prediction, and the use of big data tools to ensure low-latency, real-time processing.

“Al Driven Natural Language Processing Using Transformer Models”

Abstract

The integration of Al-driven natural language processing (NLP) transformer models fundamentally transformed the way businesses analyze and engage with customer-generated text data. This convergence of Al and advanced language models allows organizations to process and extract valuable insights from large volumes of unstructured textual data, such as customer reviews, social media interactions, support tickets, and email correspondence.

Predictive Maintenance in Manufactuaring with AI and Data Science

Abstract

The integration of Artificial Intelligence (AI), Data Science, and big data technologies is revolutionizing predictive maintenance in the manufacturing sector. This study investigates the role of IoT-enabled sensors, machine learning models, and advanced analytics in optimizing equipment reliability and operational efficiency. Leveraging technologies such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and YOLOv5, the proposed system predicts failures, detects anomalies, and minimizes downtime. By addressing challenges related to real-time data processing, model scalability, and integration costs, this research outlines a comprehensive, phased approach to implement predictive maintenance. The findings demonstrate significant improvements in cost efficiency, safety, and scalability, positioning AI-driven predictive maintenance as a cornerstone for the future of Industry 4.0.

A NOVEL BASED TRANSLATION MODEL FROM ENGLISH TO TELUGU

Abstract

This project explores an adaptive rule-based machine translation system designed for translating English sentences into Telugu. The proposed approach utilizes a combination of rule-based methodologies, including if-then logic for optimal rule selection, probability-based word choice, and rough set theory for sentence classification. The system relies on a set of production rules, a comprehensive training set, and a bilingual dictionary for both English and Telugu. The translation process begins with tokenizing the input English sentence into individual words, which are then tagged with their respective parts of speech (POS). Words not present in the predefined database are tagged using formulated grammatical rules. By leveraging these POS tags, the system retrieves appropriate word translations from the database and concatenates them to form the final translated sentence in Telugu. The motivation for developing this translation system stems from several key factors: the scarcity of robust translation systems from English to Indian languages and the specific linguistic complexities of Telugu, which features intricate phrasal, word, and sentence structures. Additionally, while direct machine translation (MT) is often used for related languages, this work applies it to the more challenging Telugu-to-English translation, aiming for simplicity, rapid development, and enhanced accuracy.

Urban Heat Islands and Mitigation Strategies

Abstract

This study about the phenomenon of Urban Heat Islands (UHIs) presents significant environmental challenges, exacerbating urban temperatures and affecting energy use, human health, and sustainability. This paper explores the UHI concept, identifying key contributors like non-permeable materials and anthropogenic heat sources. The study provides an in-depth analysis of mitigation strategies, focusing on permeable materials, green infrastructure, and reflective surfaces. Case studies-Bosco Verticale, The Edge, and IIM Ahmedabad highlight the role of sustainable materials and urban planning in minimizing UHI impacts. The findings underscore the importance of integrated approaches in mitigating UHI effects and promoting urban resilience.

Ai-Driven big data and deep learning for healthcare resource optimisation

Abstract

The integration of Artificial Intelligence (AI) and Big Data analytics in healthcare has emerged as a transformative force, enhancing the efficiency and effectiveness of resource allocation. This research paper investigates the role of AI-driven big data and deep learning techniques in optimizing healthcare resources. By employing advanced algorithms and data processing capabilities, we aim to address the critical challenges faced by healthcare systems, including resource scarcity, patient management, and operational inefficiencies. Our study proposes a framework that leverages deep learning models to analyze vast datasets, enabling predictive analytics and informed decision-making. Through rigorous experimentation, we seek to validate our proposed methodologies in real-world healthcare settings, ultimately contributing to improved patient outcomes and resource utilization.

Data science and deep learning for image classification and recognition

Abstract

Image classification and recognition are integral components of computer vision, with applications spanning healthcare diagnostics, autonomous vehicles, facial recognition, and retail analytics. The confluence of data science and deep learning has dramatically enhanced the accuracy and scalability of these tasks, marking a paradigm shift from traditional machine learning approaches that relied heavily on handcrafted features and shallow models. Data science plays a pivotal role in managing the data pipeline—spanning data acquisition, preprocessing, augmentation, and analysis—while deep learning leverages advanced architectures like Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models to automate feature extraction and decision-making. This paper explores the interplay between data science and deep learning in building robust image classification and recognition systems. We highlight key advancements such as transfer learning, real-time object detection, and self-supervised learning, which have enabled these systems to excel in handling complex real-world challenges. Despite these advancements, significant challenges remain, including the need for large annotated datasets, high computational requirements, and addressing ethical issues like bias and fairness. Finally, the paper discusses emerging trends such as explainable AI, edge computing, and lightweight models, which are set to shape the next generation of image recognition technologies.

Data science and deep learning for real-time financial market prediction

Abstract

Predicting financial markets has long been a challenging risk due to their inherent volatility, complexity and high frequency nature. Traditional method such as statical models have limited capacity to handle the vast amounts of structured and unstructured data produced in real-time. This paper explores the application of data science and deep learning techniques for real-time financial market prediction, focusing on stock price forecasting, volatility prediction, focusing, and high-frequency trading. We highlight the role of time series, analysis, sentiment analysis and the integration of alternative data sources in enhancing predictive accuracy.

The Use of Smart Materials in Building Design

Abstract

Smart building materials are revolutionizing architectural design by making structures more sustainable, functional, and adaptable. This study delves into innovative materials like bio-concrete, smart windows, and hygromorphic composites, exploring their unique applications and the challenges they face. Bio-concrete uses bacteria to self-heal cracks, cutting down on maintenance needs and environmental impact. Smart windows, with thermochromic and electrochromic technologies, adjust to light and heat levels, boosting energy efficiency. Inspired by nature, hygromorphic materials respond to humidity changes, creating adaptive and dynamic facades. While issues like cost, durability, and integration remain, these materials hold immense promise for greener, smarter buildings. The research underscores the importance of collaboration and innovation in advancing these technologies for a more energy-efficient and sustainable future.

Spatial Configurations and Layouts in Smart Institutional Campuses

Abstract

Smart institutional campuses are transforming into spaces that foster innovation, collaboration, and efficient use of resources. This research explores how flexible spatial configurations, coupled with smart technologies like IoT and AI, can enhance creativity and productivity in these environments. By examining the design of adaptable spaces and their impact on human interaction, the study highlights how modern campus layouts and technology integration can support learning, teamwork, and innovation. While limited by available data and rapidly changing technologies, this research emphasizes the need for ongoing adaptation in campus design to meet future demands.

Ai-enabled predictive analytics for smart cities

Abstract

In the rapidly evolving landscape of e-commerce, AI-driven personalization has emerged as a pivotal strategy for enhancing customer engagement and driving sales. This paper explores the integration of big data analytics and artificial intelligence to create personalized shopping experiences tailored to individual consumer preferences and behaviors. By leveraging vast datasets from various sources, including browsing history, purchase patterns, and social media interactions, e-commerce platforms can utilize machine learning algorithms to predict customer needs and deliver targeted recommendations in real-time. The study highlights key methodologies for implementing AI-driven personalization, such as collaborative filtering, content-based filtering, and deep learning techniques. Furthermore, it examines the ethical considerations surrounding data privacy and consumer consent in the context of personalized marketing. The findings underscore the significant impact of AI-driven personalization on customer satisfaction and loyalty, ultimately contributing to increased conversion rates and revenue growth in the competitive e-commerce sector. This research aims to provide insights into best practices for e-commerce businesses seeking to harness the power of big data and AI to enhance their personalization strategies.

How to develop positivity in college or university students ?

Abstract

This paper examines the potential benefits of enhancing positivity and resilience in students at colleges and universities through structured interventions. Many students struggle to keep their academic, social, and personal responsibilities in line, which causes them distress, procrastination, and undesirable mental health outcomes. Literature review shows that for well-being among students, those skills of time management, stress management, and an academic-life balance are extremely crucial. In addition, the use of positive psychology principles, including resilience building and growth mind-sets, has been proven to increase the resiliency and self-confidence in students. Campus-based support systems, including mentorship programs, community-building activities, as well as mental health resources, also have a significant impact on nurturing an environment that supports and cares for students. Analysing the results reveals that students who experience high institutional support and are given resources both practically and emotionally display better academic performance, life satisfaction, and stronger coping abilities. Surprising results indicate that the perceptions of institutional care play a more significant role in student engagement and motivation. In short, the present role of colleges and universities in relation to managing stress, challenges, or view constructively with regard to growth, academic or personal, requires integrated support, which is discussed within the study.

Quick: Strategies Composition of Strong Descriptive Paragraphs for Improvement

Abstract

Descriptive writing is an essential skill across various domains, which requires both precision and creativity. This research investigates methods for composing a strong descriptive paragraph efficiently. By looking into techniques such as imagery creation, audience adaptation, and structured coherence, this study identifies effective strategies and addresses common challenges faced by writers. Data gathered from feedback indicates that writers often struggle with emotional expression and consistent tone, which blocks their ability to convey vivid imagery. By deeply analyzing these aspects, practical guidelines are suggested to help writers write detailed and engaging descriptions instantly without compromising with the quality. The findings aim to contribute to the development of instructional materials and resources that enhance the efficiency and effectiveness of descriptive writing in diverse contexts, ultimately encouraging better communication skills among various writing professionals.

SALIENT FACTORS WHICH CONTRIBUTE TO LANGUAGE SHIFT

Abstract

The purpose of this research paper is to investigate the salient factors contributing to language shift. It particularly focuses on the impact of globalization on language dynamics in India. The highlights include the interrelation between cultural, social, and economic forces that motivate individuals and communities towards increasingly using dominant languages. This often takes place at the expense of regional languages. The paper also discusses the implications of these shifts for cultural identity and linguistic diversity. It highlights that proficiency in global languages is highly associated with economic advancement and social mobility however there is a movement towards revitalizing native languages which has been gaining momentum. Effective solutions for promoting this balance in education are those that combine global material with instruction in the local tongue. The role of digital media and technology is also examined, with a focus on how they might support linguistic variety and improve language acquisition. The results highlight how crucial it is to continue speaking fluently in one's home tongue in order to protect cultural identity in the face of increasing challenges from globalization. In the end, the study recommends focused interventions and all-encompassing educational policies that encourage bilingualism, guaranteeing that local and global languages can coexist peacefully. The study intends to advance knowledge of the opportunities and difficulties globalization presents in influencing language practices in India by examining these dynamics.