Mr. Robert Murphy

Analyzing the impact of AI on financial markets, exploring predictive models, risk assessment, and automated trading strategies. The integration of Artificial Intelligence (AI) in finance is reshaping the landscape of financial markets, with predictive analytics and algorithmic trading at the forefront of this transformation. [....] » Read More

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Mr. Suresh Deepak Gurubasannavar

This presentation explores a transformative shift in the software development landscape, moving beyond the traditional Software Development Lifecycle (SDLC) to a more dynamic and autonomous Agentic Intelligent Development Lifecycle (IDLC). We introduce Agentic AI as a new class of autonomous systems capable of complex decision-making and task execution, and highlight the critical role of the Model Context Protocol (MCP) as the foundational open standard that enables these agents to seamlessly integrate with and orchestrate external tools and data sources. [....] » Read More

Mr. Varun Venkatesh Dandasi

The retail industry is undergoing a rapid transformation driven by the integration of Artificial Intelligence (AI) into Business Intelligence (BI) systems. This paper explores how AI-powered analytics are revolutionizing retail operations by enabling real-time decision-making, personalized customer experiences, and demand forecasting with high precision. By leveraging machine learning algorithms, natural language processing, and computer vision, retailers can extract actionable insights from vast and diverse data sources—ranging from point-of-sale systems to social media and IoT devices. [....] » Read More

Mr. Praveen Kumar Kanumarlapudi,

The evolution of AI agents—from narrow, task-specific programs to autonomous, adaptive systems—has underscored the need for advanced memory architectures that support long-term learning, real-time adaptation, and contextual reasoning. This paper examines the design, implementation, and evaluation of memory systems for AI agents, emphasizing the interplay between episodic, semantic, and procedural memory models. We explore hybrid storage-retrieval frameworks combining symbolic and neural representations, enabling agents to recall past experiences while generalizing to novel situations. [....] » Read More

Divya Soundarapandian,

In today's digital economy, dotcom-scale systems must operate with unprecedented levels of reliability, scalability, and resilience. As user expectations grow and architectures become increasingly complex, traditional reliability engineering approaches face significant limitations. This paper explores the integration of Artificial Intelligence (AI) techniques into the reliability engineering lifecycle of large-scale internet systems. It presents a framework where AI models, such as machine learning-based failure prediction, anomaly detection, and self-healing automation, proactively manage system reliability. Real-world case studies from high-traffic platforms are analyzed to demonstrate how AI-driven strategies improve uptime, reduce mean time to recovery (MTTR), and enable predictive maintenance. [....] » Read More

Mr. Nagabhushan Rao Singampalli,

Artificial Intelligence (AI) is redefining operational models across industries, enabling automation, predictive insights, and personalized experiences at unprecedented scales. This paper explores how AI technologies—ranging from machine learning and natural language processing to computer vision and generative models—are driving transformation in manufacturing, healthcare, finance, retail, and logistics. We analyze case studies highlighting productivity gains, cost reductions, and improved decision-making, alongside challenges such as data quality, ethical considerations, and integration complexity. [....] » Read More

Dr. Suryakiran Navath, Ph.D.,

Exploring the application of computer vision in bioinformatics, particularly for analyzing medical images and identifying patterns related to diseases. Advances in computer vision are revolutionizing the field of bioinformatics, offering powerful tools for analyzing medical images and identifying disease-related patterns. By applying sophisticated image processing and machine learning techniques, computer vision algorithms can automatically detect, segment, and classify various biological structures and abnormalities within medical images, such as MRI scans, X-rays, and histopathology slides. [....] » Read More

Mr. Karthik Perikala

In the age of rapid digital transformation, static, linear user journeys no longer meet the dynamic expectations of modern users. This paper explores the evolution from traditional, predefined workflows—termed Static Journeys—to adaptive, real-time, and context-aware Intelligent Experiences. Powered by AI, machine learning, and behavioral analytics, intelligent systems now enable personalized interactions, proactive recommendations, and responsive interfaces that evolve based on user behavior and contextual data. We examine key technological enablers of this shift, including conversational interfaces, adaptive UX, real-time data processing, and predictive modeling. Through case studies in sectors such as e-commerce, healthcare, and smart mobility, the paper highlights measurable improvements in engagement, satisfaction, and operational efficiency. [....] » Read More

Mr. Sudhakar Peram

The exponential growth of digital communication has led to unprecedented volumes of network traffic, posing both challenges and opportunities for real-time monitoring, anomaly detection, and security threat mitigation. Traditional network analysis methods struggle to process and interpret such vast and complex datasets efficiently. This presentation explores how Artificial Intelligence (AI) techniques—including machine learning, deep learning, and graph-based analytics—can revolutionize the analysis of network traffic data. By leveraging AI’s pattern recognition and predictive capabilities, organizations can enhance intrusion detection systems, identify performance bottlenecks, and forecast network congestion with higher accuracy.. [....] » Read More

Mr. Raghavendra Sunku

As AI systems evolve from passive tools to proactive, autonomous agents, the design patterns underpinning their architecture have become crucial for scalability, reliability, and interpretability. This presentation introduces the concept of Agentic AI Design Patterns—reusable, domain-agnostic templates that guide the development of AI agents capable of perception, reasoning, planning, and action in dynamic environments. We will explore foundational patterns such as Goal-Oriented Planning, Tool Augmentation, Memory-Driven Context Management, Multi-Agent Collaboration, and Feedback-Loop Optimization. Each pattern will be illustrated with practical examples, highlighting implementation approaches, benefits, and trade-offs. The walkthrough will demonstrate how these patterns can be combined to build robust agentic systems for applications ranging from autonomous customer support to adaptive decision-making in enterprise workflows. [....] » Read More

Mr. Kiran Kumar Mandula Samuel

Artificial Intelligence (AI) tools are transforming the landscape of academic and industrial research, offering unprecedented capabilities in data analysis, visualization, and hypothesis generation. These tools enable researchers to process vast datasets efficiently, extract meaningful insights, and identify patterns that were previously undetectable. Natural Language Processing (NLP) technologies simplify literature review processes by summarizing research papers and identifying relevant studies, while machine learning algorithms streamline experimental design and predictive modeling. By automating repetitive tasks, AI allows researchers to focus on higher-level conceptual work, fostering innovation across various disciplines.
AI-driven platforms, such as automated coding assistants and AI-powered statistical analysis tools, are now integral to modern research workflows. These platforms enhance collaboration by providing real-time data sharing and insights, bridging gaps between interdisciplinary teams. Additionally, AI tools democratize access to advanced computational capabilities, empowering researchers without extensive technical expertise. As AI continues to evolve, it promises to redefine research methodologies, making exploration faster, more accurate, and more accessible than ever before. This abstract explores the potential of AI tools to advance scientific discovery and transform the research process. [....] » Read More

Mr. Rajender Radharam

The rapid digitization of healthcare data—spanning electronic health records, medical imaging, wearable devices, and genomic datasets—presents a unique opportunity for predictive modeling to improve patient outcomes, optimize clinical workflows, and reduce costs. Leveraging Google Cloud AI’s scalable infrastructure and advanced machine learning services, this presentation demonstrates how healthcare organizations can design, train, and deploy predictive models with greater speed and precision. We will explore end-to-end workflows, including data ingestion through BigQuery, preprocessing with Dataflow, model development using Vertex AI, and integration into clinical decision support systems. Real-world use cases will illustrate applications such as early disease detection, readmission risk prediction, and personalized treatment recommendations. The session will also address key considerations in healthcare AI, including compliance with HIPAA, model interpretability, and bias mitigation. [....] » Read More

Mr. Venkata Pavan Kumar Aka

SAP S/4HANA is not only a next-generation ERP platform but also a strategic enabler for Machine Learning (ML) and Artificial Intelligence (AI) integration across enterprise operations. This presentation examines how S/4HANA’s in-memory computing, real-time analytics, and embedded intelligence empower organizations to automate decision-making, enhance predictive capabilities, and drive process optimization. We will explore real-world applications of ML and AI within S/4HANA, including demand forecasting, fraud detection, supply chain optimization, and customer experience personalization. The session will also highlight integration with SAP AI Core, AI Business Services, and third-party ML frameworks, demonstrating how organizations can leverage the platform’s data models and APIs to scale AI solutions. Attendees will leave with a clear understanding of how S/4HANA serves as the digital backbone for AI-driven transformation, enabling agile, data-informed business strategies. [....] » Read More

Mr. Sridhar Reddy Kakulavaram

Seasonal hiring in retail demands speed, accuracy, and adaptability, especially during the holiday surge. This session explores how Abercrombie & Fitch modernized its holiday hiring process by integrating AI-driven candidate screening, workforce analytics, and demand forecasting. Leveraging historical sales data, traffic patterns, and skills-based matching, the company implemented a predictive staffing model that reduced hiring time, improved candidate quality, and aligned workforce capacity with peak store demand. Attendees will learn how data insights and intelligent automation transformed recruitment from a reactive process into a proactive strategy, ensuring operational excellence and an enhanced customer experience during the busiest retail season. [....] » Read More

Mr. Tejo Rama Venkata Durga PavanKumar Gowthu

The rapid growth of unstructured visual data has created an urgent need for efficient, accurate, and scalable image-to-text conversion solutions. This presentation showcases how AI-powered image-to-text automation leverages advanced computer vision, deep learning, and natural language processing to extract, interpret, and structure information from images in real time. We will explore use cases across industries—including document digitization, e-commerce cataloging, accessibility solutions, and compliance reporting—highlighting the benefits of reduced manual effort, improved accuracy, and faster processing. The session will also address integration strategies with enterprise workflows, quality control measures, and future advancements in multimodal AI. Attendees will gain practical insights into deploying robust image-to-text systems that transform visual content into actionable intelligence. [....] » Read More