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Research Article | Volume: 1, Issue: 1 Published Date: June 11, 2025 A Progressive Framework for Supply Chain Optimization: From Rule-Based Logic to Advanced Mathematical ModelsAuthor(s): 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 MedicineAuthor(s): 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 TreatmentAuthor(s): 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 ServicesAuthor(s): 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 ChallengesAuthor(s): 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. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 23, 2025 Securing AI Chatbots for Medicaid Services: Architecture, Security, and Best PracticesAuthor(s): Anand Laxman Mhatre Abstract: Although chatbots are state-of-the-art technologies that enhance user engagement and reduce operational costs, recent evidence suggests that cybercriminals are exploiting them as conduits for accessing company networks and protected data. This document discusses mechanisms and strategies that can be employed to protect Medicaid chatbots. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 22, 2025 Modernizing Medicaid IT: Cybersecurity Compliance, Cybersecurity Requirements, and Zero Trust ArchitectureAuthor(s): Anand Laxman Mhatre Abstract: The Medicaid program serves close to 90 million people and receives funding of about 3.5 percent of the GDP. To handle this large number of users and efficiently manage and administer allocated funds, Medicaid systems must be secure and compliant with healthcare data privacy regulations. This document discusses cybersecurity regulations that apply to Medicaid IT systems, and explores requirements and frameworks necessary for ensuring safety and compliance of these systems. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 21, 2025 Future Trends: The Next Frontier of AI Innovations in Medicaid Health Care DeliveryAuthor(s): Anand Laxman Mhatre Abstract: Although artificial intelligence is widely used in the healthcare sector, its proliferation in Medicaid services has been limited to less complex functions despite having many use cases for advanced roles. This limited use is attributed to regulatory uncertainties, funding constraints for tech initiatives, and fragmented data in Medicaid services. The good news is that the federal government and state governments have begun addressing these barriers. This paper discusses the current state of AI in Medicaid and future innovations when the aforementioned issues are addressed. |
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Review Article | Volume: 1, Issue: 1 Published Date: May 23, 2025 Cross-Layer Coordination of Multi-Agent Systems in Internet of Streams Architectures: Enhancing Efficiency and Interoperability in IoT and Big Data FrameworksAuthor(s): Raghu K Para Abstract: The emergence of the Internet of Streams (IoS, a paradigm emphasizing the real-time generation, processing, and management of continuous, high-velocity data streams, has introduced significant challenges in scalability, interoperability, and resource optimization. These challenges are particularly pronounced in Internet of Things (IoT) and big data frameworks, where data flows span multiple layers, from physical sensing devices to application-level decision-making. Multi-Agent Systems (MAS) have emerged as a powerful framework for managing such dynamic environments, enabling distributed, autonomous, and adaptive coordination. However, the integration of MAS into IoS architectures introduces significant challenges, particularly in achieving cross-layer coordination across data, network, and application layers. |
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Research Article | Volume: 1, Issue: 1 Published Date: April 19, 2025 AI Workflow Optimization in Clinical and Regulatory EnvironmentsAuthor(s): Ramesh Pingili Abstract: In clinical and pharmacy benefit environments, automation is often hindered by regulatory constraints, policy volatility, and the need for human judgment. This article introduces a layered architecture for regulatory-grade automation, integrating AI-driven recommendations with mandatory oversight checkpoints. Drawing on a case from a U.S. health system's drug replenishment and utilization review workflow-where delays in clinical approvals resulted in 12-18% lag in patient access-the system redesign introduces structured roles for AI assistance, human intervention and traceable audit logging. Results show a 36% improvement in approval accuracy and a 22% reduction in cycle time, without sacrificing compliance. |
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Editorial Article | Volume: 1, Issue: 1 Published Date: April 18, 2025 Enhancing Metacognitive Competencies through Human-Centered AI: The Role of Custom-Trained Intelligent Agents in Workforce UpskillingAuthor(s): James Hutson Abstract: This editorial examines the integration of human-computer intelligent interaction (HCII), specifically through human-centered artificial intelligence (AI) and custom-trained intelligent agents, to foster metacognitive competencies critical for workforce upskilling. With 59% of the workforce projected to require substantial upskilling by 2030, developing personalized AI models tailored to individual cognitive and learning profiles presents an innovative pathway. |
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Research Article | Volume: 1, Issue: 1 Published Date: March 31, 2025 Client-AI Synergy: A Framework for Collaborative Inference between Edge and Cloud in Real-Time ApplicationsAuthor(s): Mariappan Ayyarrappan* Abstract: The integration of AI capabilities into edge devices has opened new frontiers for real-time applications across industries. However, the trade-offs between client-side performance and cloud-based intelligence require a hybrid approach. This paper introduces "Client-AI Synergy," a novel framework for collaborative inference that dynamically distributes machine learning tasks between client and cloud environments based on latency, computational load, and data sensitivity. The proposed system enables real-time adaptation using reinforcement learning techniques to optimize inference routing. Performance evaluations in simulated environments show significant improvements in responsiveness and resource efficienc |
Editor in Chief
Dr. James Hutson
Lead XR Disruptor and Department Head of Art History
AI, and Visual Culture
Lindenwood University, USA