Sai Teja Boppiniti | Pace University (original) (raw)

Papers by Sai Teja Boppiniti

Healthcare data governance has emerged as a critical priority in the digital age, driven by the p... more Healthcare data governance has emerged as a critical priority in the digital age, driven by the proliferation of electronic health records (EHRs), wearable devices, and data-intensive healthcare solutions. This study delves into the intricacies of healthcare data governance, identifying key challenges such as data silos, privacy concerns, and regulatory compliance. It proposes a framework combining robust strategies like role-based access control, data lineage tracking, and advanced encryption techniques to ensure data integrity, security, and accessibility. The research underscores the importance of stakeholder collaboration and the integration of emerging technologies, including blockchain and AI, to address governance complexities. By focusing on strategies tailored to the healthcare industry, the study offers actionable insights for improving decision-making, fostering innovation, and safeguarding patient trust in a rapidly evolving digital landscape.

International Journal of Sustainable Development in Computing Science, 2019

The healthcare sector is witnessing an unprecedented surge in data volume and complexity, driven ... more The healthcare sector is witnessing an unprecedented surge in data volume and complexity, driven by the adoption of electronic health records (EHRs), telemedicine, and precision medicine. However, the lack of a cohesive master data architecture hampers the efficient utilization of this data for informed decision-making and improved patient outcomes. This study introduces a novel master data architecture tailored for the healthcare industry, emphasizing interoperability, scalability, and data governance. By leveraging cutting-edge technologies such as AI, blockchain, and cloud computing, the proposed architecture enables seamless data integration across diverse systems and stakeholders. Real-world implementation scenarios demonstrate its ability to streamline operations, enhance data quality, and foster a patient-centric approach to healthcare delivery. This research underscores the transformative potential of advanced data management frameworks in driving efficiency, compliance, and innovation within the digital healthcare landscape.

International Journal of Sustainable Development in Computing Science, 2016

Big Data Analytics has emerged as a critical tool for extracting value from large, complex datase... more Big Data Analytics has emerged as a critical tool for extracting value from large, complex datasets in the modern digital era. This study delves into the fundamental standards that underpin big data analytics, including data collection, processing, storage, and analysis techniques. It highlights the diverse applications of big data analytics across industries such as healthcare, finance, retail, and logistics, demonstrating its capacity to drive informed decision-making, optimize processes, and foster innovation. The paper also addresses the challenges associated with data privacy, security, and scalability, emphasizing the need for robust frameworks to fully realize the potential of big data analytics in transforming businesses and societies.

International Journal of Sustainable Development in Computing Science, 2014

Robotics is revolutionizing the modern world by blending advanced engineering and artificial inte... more Robotics is revolutionizing the modern world by blending advanced engineering and artificial intelligence to replicate human capabilities in precision-driven tasks. This review delves into the fundamental principles of robotics, outlining their operational characteristics, advantages, and current implementations across industries such as healthcare, manufacturing, and service sectors. With rapid technological advancements, robotics is poised to become an integral part of various domains, driving innovation and efficiency. The study also provides insights into emerging trends and explores the potential for robotics to reshape the future, addressing challenges and unlocking new possibilities.

International Journal of Sustainable Development in computer Science Engineering, 2015

Machine learning (ML) stands at the forefront of technological innovation, driving transformative... more Machine learning (ML) stands at the forefront of technological innovation, driving transformative change across industries worldwide. This paper provides a global overview of cutting-edge ML applications, showcasing its impact in sectors such as healthcare, finance, autonomous systems, natural language processing, and beyond. By leveraging advanced algorithms and data-driven insights, ML enables unprecedented levels of accuracy, efficiency, and personalization. The study also explores emerging trends, challenges in implementation, and ethical considerations associated with ML adoption. Highlighting its potential to revolutionize industries, this work underscores the pivotal role of machine learning in shaping the future of technology and society.

International Scientific Journal for Research, 2018

The adoption of IoT technologies in urban health monitoring has revolutionized public health mana... more The adoption of IoT technologies in urban health monitoring has revolutionized public health management by enabling real-time data collection, analysis, and decision-making. However, these advancements bring significant challenges in preserving patient privacy and safeguarding sensitive information. This paper provides a comparative analysis of privacy-preserving techniques employed in IoT-driven urban health monitoring systems. Techniques such as data anonymization, encryption, secure multi-party computation, and blockchain-based solutions are evaluated based on effectiveness, scalability, computational efficiency, and usability. The study highlights strengths and limitations across diverse urban health applications, identifying best practices and areas for further improvement. Recommendations are proposed to guide the development and implementation of secure, privacy-centric IoT frameworks for sustainable urban healthcare ecosystems.

International Journal of Sustainable Development in Computing Science, 2018

International Scientific Journal for Research, 2018

IoT technologies have transformed urban health systems by enabling extensive data collection and ... more IoT technologies have transformed urban health systems by enabling extensive data collection and analytics to improve public health outcomes. However, focusing solely on technological capabilities often overlooks the critical importance of human-centric design in creating solutions that are accessible, equitable, and responsive to community needs. This paper explores the principles and practices of human-centric design in IoT-enabled urban health solutions. By prioritizing usability, inclusivity, and ethical considerations, such designs bridge the gap between technology and end-users, fostering trust and adoption. The paper examines successful case studies and identifies challenges, such as balancing technological complexity with user accessibility and addressing socio-economic disparities. Recommendations are provided to ensure IoT-driven urban health systems are not only innovative but also empathetic to the diverse populations they serve.

International Scientific Journal for Research, 2023

Data ethics in AI plays a crucial role in ensuring responsible and trustworthy applications of ma... more Data ethics in AI plays a crucial role in ensuring responsible and trustworthy applications of machine learning (ML) and data governance. With the rise of AI technologies, ethical concerns such as bias, fairness, privacy, transparency, and accountability are becoming more significant. This paper explores the ethical challenges in machine learning and data governance, focusing on how they impact the development and deployment of AI systems. It examines the importance of establishing robust frameworks for responsible data science, emphasizing the need for comprehensive policies to manage ethical risks, data protection, and the socio-technical implications of AI. Additionally, it discusses potential solutions and best practices to address these challenges while fostering innovation and societal benefits through AI.

International Journal of Sustainable Development in Computing Science, 2019

Machine learning (ML) for predictive analytics is revolutionizing data-driven decision-making acr... more Machine learning (ML) for predictive analytics is revolutionizing data-driven decision-making across industries by leveraging vast datasets and advanced algorithms to uncover hidden patterns and forecast future trends. This paper explores key machine learning techniques, including supervised learning, unsupervised learning, and deep learning, and their applications in predictive analytics. By integrating these techniques into business processes, organizations can make more informed decisions, enhance operational efficiency, and gain a competitive edge. The paper also addresses challenges such as data quality, model interpretability, and scalability, offering insights into how industries like healthcare, finance, and retail are utilizing ML to predict customer behavior, optimize supply chains, and improve outcomes.

International Journal of Creative Research In Computer Technology and Design (jrctd.in), 2020

In recent years, the proliferation of big data has transformed various sectors, necessitating the... more In recent years, the proliferation of big data has transformed various sectors, necessitating the development of advanced techniques for efficient data processing and analysis. This paper explores the intersection of big data and machine learning, highlighting strategies to handle vast datasets effectively. We discuss various machine learning algorithms tailored for big data environments, emphasizing scalability, performance optimization, and resource management. Additionally, we investigate data preprocessing methods, feature selection, and model evaluation metrics to enhance the accuracy of machine learning models. The findings underscore the importance of integrating big data technologies with machine learning approaches to unlock valuable insights and drive decision-making processes in data-intensive applications.

International Machine Learning Journal and Computer Engineering, 2022

In the rapidly evolving digital landscape, businesses increasingly leverage Artificial Intelligen... more In the rapidly evolving digital landscape, businesses increasingly leverage Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics to enhance customer experience and personalization. This synergy enables organizations to gather and analyze vast amounts of customer data, leading to insights that drive tailored marketing strategies and personalized service delivery. By employing AI and ML algorithms, companies can predict customer preferences, automate responses, and optimize engagement across various touchpoints. This paper explores the integration of these technologies and their impact on customer satisfaction, loyalty, and overall business performance. It also discusses best practices for implementing AI and ML solutions to foster a customer-centric culture, ultimately positioning organizations to thrive in a competitive marketplace.

International Journal of Management Education for Sustainable Development, 2021

In today's data-driven world, organizations are increasingly relying on real-time data analytics ... more In today's data-driven world, organizations are increasingly relying on real-time data analytics to enhance decision-making processes. This paper explores the integration of artificial intelligence (AI) with stream processing technologies to facilitate dynamic decision support. By leveraging advanced algorithms and machine learning techniques, organizations can analyze data streams in real time, gaining actionable insights and improving operational efficiency. We discuss the challenges and opportunities associated with implementing AI-driven stream processing systems, as well as case studies that demonstrate their effectiveness in various industries. The findings indicate that such systems not only improve response times but also foster a proactive approach to decision-making in dynamic environments.

International Journal of Research in Engineering and Applied Sciences(IJREAS), 2021

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed ... more The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, including healthcare, finance, and transportation. However, these developments raise critical ethical concerns that require careful consideration. This review explores the early research and perspectives on the ethical implications of AI, focusing on issues such as bias and discrimination, transparency and accountability, privacy, and the impact on employment. By analyzing a range of scholarly articles, industry reports, and case studies, the review identifies key ethical challenges and frameworks proposed for addressing these issues. The findings highlight the necessity of developing ethical guidelines and regulatory frameworks to ensure that AI technologies are designed and deployed responsibly. Ultimately, this review aims to provide insights that contribute to the ongoing discourse on ethical AI, fostering a better understanding of the potential risks and benefits associated with its integration into society.

International Journal of Research in Engineering and Applied Sciences(IJREAS), 2021

Reinforcement Learning (RL) has emerged as a pivotal area in artificial intelligence, revolutioni... more Reinforcement Learning (RL) has emerged as a pivotal area in artificial intelligence, revolutionizing the way agents learn optimal behaviors through interaction with their environment. This paper explores the evolution of RL techniques, tracing the journey from traditional Q-learning to the advent of Deep Q-Networks (DQN). Initially, Q-learning provided a foundational framework for value-based learning, enabling agents to make decisions by estimating action-value functions. However, the limitations of Q-learning in handling high-dimensional state spaces necessitated the integration of deep learning techniques. The introduction of DQNs marked a significant breakthrough, leveraging deep neural networks to approximate Q-values, which allowed for the successful application of RL in complex environments such as video games and robotic control. This review discusses the advancements in algorithmic strategies, architectural designs, and the resulting impact on various applications. Furthermore, we highlight current trends and future directions in reinforcement learning research, emphasizing the ongoing quest to create more efficient and robust learning algorithms.

INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY, 2021

Artificial Intelligence (AI) has increasingly become a transformative force in the financial mark... more Artificial Intelligence (AI) has increasingly become a transformative force in the financial markets, offering innovative solutions to improve decision-making, risk management, and trading strategies. This review explores the major AI algorithms and their applications in financial markets, including machine learning, deep learning, and natural language processing techniques. The study highlights how AI is used in algorithmic trading, fraud detection, credit scoring, and market sentiment analysis. Furthermore, the review examines the challenges of integrating AI into finance, such as regulatory concerns, data privacy, and the potential for model bias. Overall, AI offers significant potential to enhance efficiency, accuracy, and profitability in financial market operations. However, ethical considerations and the need for robust governance frameworks are also crucial to ensure responsible deployment of AI technologies in the financial sector.

Novateur Publications, International Journal of Innovations in Engineering Research and Technology., 2020

Explainable Artificial Intelligence (XAI) is a rapidly evolving field aimed at making AI systems ... more Explainable Artificial Intelligence (XAI) is a rapidly evolving field aimed at making AI systems more interpretable and transparent to human users. As AI technologies become increasingly integrated into critical sectors such as healthcare, finance, and autonomous systems, the need for explanations behind AI decisions has grown significantly. This survey provides a comprehensive review of XAI techniques, categorizing them into post-hoc and intrinsic methods, and examines their application in various domains. Additionally, the paper explores the major challenges in achieving explainability, including balancing accuracy with interpretability, scalability, and the trade-off between transparency and complexity. The survey concludes with a discussion on the future directions of XAI, emphasizing the importance of interdisciplinary approaches to developing robust and interpretable AI systems.

International Journal of Innovations in Engineering Research and Technology [IJIERT], 2024

Smart contact lenses equipped with embedded artificial intelligence (AI) represent a groundbreaki... more Smart contact lenses equipped with embedded artificial intelligence (AI) represent a groundbreaking advancement in continuous health monitoring and disease detection. This paper explores the development and application of smart contact lenses that integrate real-time data collection with sophisticated AI algorithms to monitor various health parameters. By incorporating sensors and AI processing units directly into the lenses, these devices can continuously measure and analyze biometric data such as glucose levels, intraocular pressure, and ocular health indicators. The embedded AI system processes this data to provide real-time feedback, identify potential health issues, and predict disease onset with high accuracy. The study evaluates the efficacy of these smart contact lenses in detecting early signs of chronic conditions like diabetes and glaucoma, and their ability to enhance patient engagement through personalized health insights. Additionally, the paper discusses the technological challenges, including data accuracy, device comfort, and privacy concerns, and proposes solutions to address these issues. The integration of smart contact lenses with AI technology offers a novel approach to proactive health management, paving the way for more accessible and effective continuous monitoring and early disease detection.