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: 2 Published Date: July 25, 2025 Neural Scalpel: Generative AI and Surgical Robotics for Next-Generation Neuro-Oncology Authors: Santosh Kumar* Abstract: The intersection of Generative Artificial Intelligence (Gen AI) and autonomous surgical robotics is poised to redefine the landscape of neuro-oncology. This paper introduces the "Neural Scalpel" framework?an intelligent surgical paradigm where transformer-based Gen AI models guide robotic agents to perform patient-specific brain tumor resections with sub-millimeter precision. From AI-driven tumor mapping and federated learning pipelines to intraoperative neural guidance systems, we explore the technologies enabling this new frontier. Ethical, technical, and educational implications are discussed, highlighting the need for transparent, explainable, and accessible AI-robotic integration in brain cancer care. |
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Research Article | Volume: 1, Issue: 2 Published Date: July 24, 2025 Authors: Pavan Kumar Devarashetty* Abstract: Digital transformation has become imperative for businesses aiming to remain competitive in a rapidly evolving technological landscape. Intelligent enterprise resource planning (ERP) systems, particularly SAP S/4HANA integrated with artificial intelligence (AI), have emerged as pivotal tools enabling transformative efficiencies and innovation within supply chain operations. This paper explores SAP S/4HANA's capabilities as an intelligent ERP, emphasizing its AI-driven functionalities such as predictive analytics, robotic process automation, and real-time decision-making. Through detailed analysis and real-world case studies, the research highlights the significant impact of integrating intelligent ERP on supply chain management, demonstrating measurable improvements in efficiency, responsiveness, and strategic decision-making. |
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Research Article | Volume: 1, Issue: 2 Published Date: July 23, 2025 Federated Reinforcement Learning for Edge AI Decision-Making in 6G-Enabled V2X Systems Authors: Ronak Indrasinh Kosamia Abstract: The evolution toward sixth‑generation (6G) networks introduces transformative capabilities in intelligent transportation, particularly through ultra‑reliable, low‑latency vehicle‑to‑everything (V2X) communication. As autonomous and connected vehicles generate vast amounts of data at the edge, conventional centralized learning approaches are increasingly constrained by privacy, bandwidth, and latency limitations. In this paper, we present a federated reinforcement learning (FRL) framework that enables distributed edge agents-such as vehicles and roadside units-to collaboratively learn real‑time decision policies for navigation, collision avoidance, and traffic optimization, without sharing raw data. |
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Research Article | Volume: 1, Issue: 2 Published Date: July 23, 2025 Authors: Dr. Ryosuke NAKAJIMA Abstract: This study examines a growing concern in B2B sales, which it refers to as the Generative AI Sales Paradox (GASP). Generative AI tools help sales teams move faster and reach more clients. Still, they can unintentionally weaken the kind of personal relationships that matter most in high-value, trust-based transactions. This research focuses on industries such as manufacturing, professional services, and enterprise technology, where long-term client trust is essential. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 23, 2025 Integrating AI in Mobile Applications: Security and Privacy Considerations Authors: Naga Satya Praveen Kumar Yadati Abstract: The rapid evolution of Artificial Intelligence (AI) technologies has revolutionized mobile applications, introducing advanced personalization, real-time assistance, and predictive capabilities. However, integrating AI into mobile platforms raises critical concerns regarding user data privacy, model security, ethical compliance, and regulatory adherence. This paper examines the architectural considerations, threat models, and mitigation strategies necessary for secure and privacy-preserving AI integration in mobile applications. |