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: June 11, 2025 Authors: Uday Dhembare and Siddharth Chaudhary Abstract: This paper presents an innovative framework for addressing complex supply chain optimization problems through a staged implementation approach, progressing from simple rule-based logic to sophisticated mathematical models. The framework emphasizes business acceptance and practical implementation while maintaining continuous improvement capabilities. Our extensive research across multiple industries demonstrates how organizations can evolve from basic Excel-based decision models to advanced mixed-integer linear programming solutions while maintaining high business acceptance rates. The study shows that this progressive approach achieves a 92% business acceptance rate while improving operational efficiency by 35% across various supply chain scenarios. |
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Research | Volume: 1, Issue: 1 Published Date: May 31, 2025 The Rise of Robotics in Healthcare: Automation and Precision Medicine Authors: Soren Falkner Abstract: The healthcare landscape is undergoing a significant transformation with the increasing integration of robotics. This paper explores the burgeoning role of robotics in healthcare, highlighting its impact on both automation of routine tasks and the advancement of precision medicine. From robotic surgery and rehabilitation to automated dispensing systems and diagnostic support, we examine how these technologies are enhancing efficiency, accuracy, and patient outcomes. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 31, 2025 The AI Doctor: Artificial Intelligence in Medical Diagnosis and Treatment Authors: Soren Falkner Abstract: Artificial intelligence (AI) is rapidly transforming healthcare, particularly in the realms of medical diagnosis and treatment. This abstract explores the burgeoning role of AI algorithms and machine learning techniques in augmenting the capabilities of medical professionals. AI-powered systems are being developed and implemented for a wide range of applications, including the analysis of medical images for early disease detection, the prediction of patient outcomes and risk stratification, the personalization of treatment plans based on individual patient data, and the acceleration of drug discovery and development. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 28, 2025 Ethical Challenges of using Artificial Intelligence in Medicaid Services Authors: Anand Laxman Mhatre Abstract: AI is one of the technologies that is quickly being incorporated into Medicaid services. Although the assimilation of AI in Medicaid services promises a plethora of benefits, these benefits will only be achievable if CMS addresses ethical issues related to the use of the technology. This paper discusses AI ethical issues in Medicaid and proposes ways these concerns can be resolved. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 26, 2025 AI-Driven Risk Stratification Models for Medicaid: Algorithms, Bias, and Validation Challenges Authors: Anand Laxman Mhatre Abstract: AI risk stratification models can play a significant role in creating patient risk scores, allowing Medicaid managers to project care delivery costs and allocate resources more efficiently. However, these models can be biased and opaque, hampering their effectiveness in risk stratification. This document explores algorithms that can be deployed in Medicaid risk stratification and their vulnerabilities. |