Artificial Intelligence, Machine Learning, Data Science, and Robotics Journal is a peer-reviewed publication that explores the latest innovations and research in the fields of AI, machine learning, data science, and robotics. The journal covers a broad spectrum of topics, including algorithms, automation, intelligent systems, predictive analytics, and the integration of robotics in various industries. Its goal is to highlight cutting-edge advancements and interdisciplinary applications that push the boundaries of technology, improve efficiency, and solve complex real-world problems. By bringing together experts from diverse fields, the journal fosters knowledge-sharing and drives progress in the rapidly evolving landscape of artificial intelligence and automation.
Artificial Intelligence; AI Ethics; AI Models; AI Applications; AI Bias; AI Governance; AI in Healthcare; AI in Finance; AI-Powered Automation; Machine Learning; Supervised Learning; Unsupervised Learning; Reinforcement Learning; Neural Networks; Autoencoders; Data Science; Data Analytics; Data Engineering; Data Processing; Data Mining; Data Visualization; Business Intelligence; Cloud Computing; Hadoop; Apache Spark; SQL; Text Mining; Sentiment Analysis; Chatbots; Speech Synthesis; Named Entity Recognition; Language Modelling; Tokenization; Text-To-Speech (TTS); Speech-To-Text (STT); Robotics; Autonomous Robots; Humanoid Robots; Industrial Robots; Collaborative Robots (Cobots); Swarm Robotics; Robot Perception; Robot Motion Planning; Simultaneous Localization And Mapping (SLAM); Soft Robotics; Transformer Models; Natural Language Processing (NLP); Speech Recognition; Image Classification
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Research Article | Volume: 1, Issue: 1 Published Date: September 20, 2024 Designing Auditable Architectures for Generative AI Systems in Enterprise Environments Authors: Ramani Teegala Abstract: By late 2024, generative artificial intelligence systems were increasingly embedded within enterprise workflows that demanded accountability, traceability, and regulatory defensibility. Large language models were no longer confined to experimental use cases, but were deployed to support decision assistance, content generation, operational analysis, and customer interaction across regulated and high risk domains. This shift exposed a structural gap between the probabilistic nature of generative AI systems and the audit expectations traditionally applied to enterprise software. |
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Research Article | Volume: 1, Issue: 1 Published Date: March 19, 2024 Authors: Ramani Teegala Abstract: By early 2024, large language models were increasingly explored beyond general purpose language tasks and into enterprise environments that manage transaction and operations data. Organizations across finance, retail, logistics, healthcare administration, and platform operations evaluated fine tuning as a mechanism to adapt generic models to domain specific terminology, workflows, and decision contexts. Unlike prompt-based adaptation, fine tuning directly modifies model behavior through exposure to curated datasets, making it a materially different control surface with distinct architectural, operational, and governance implications. |
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Research Article | Volume: 1, Issue: 1 Published Date: December 20, 2023 Authors: Ramani Teegala Abstract: By December 2023, generative artificial intelligence systems based on large language models were increasingly evaluated for use in banking and payment applications, including customer support automation, internal operations assistance, fraud analysis support, compliance interpretation, and developer productivity tools. While these systems demonstrated strong natural language reasoning capabilities, their deployment within regulated financial environments introduced a new class of security and governance challenges centered on prompt construction, context control, and interaction boundaries. |
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Research Article | Volume: 1, Issue: 1 Published Date: December 20, 2022 Authors: Ramani Teegala Abstract: By December 2022, omni-channel banking had become a core strategic requirement for U.S. financial institutions as customers increasingly expected seamless, real-time experiences across mobile, web, branch, call center, and third party digital channels. Traditional channel specific architectures and tightly coupled integration models proved inadequate for meeting these expectations, particularly under conditions of high transaction volume, regulatory scrutiny, and continuous product evolution. As banks expanded digital offerings such as real time payments, instant account servicing, and personalized engagement, architectural bottlenecks emerged around synchronous service dependencies, shared databases, and brittle orchestration layers. |
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Research Article | Volume: 1, Issue: 1 Published Date: January 20, 2021 Authors: Ramani Teegala Abstract: LLM (Large Language Model) assisted migration of legacy SOA services to Spring Boot microservices addresses the growing need for scalable, resilient and cloud compatible architectures as organizations face rising maintenance costs and limited extensibility in traditional service riented systems. This study examines the modernization problem where enterprises struggle to convert tightly coupled SOA components into independently deployable microservices without disrupting operational continuity. |