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Research Article | Volume: 1, Issue: 1 Published Date: September 20, 2024 Designing Auditable Architectures for Generative AI Systems in Enterprise EnvironmentsAuthor(s): 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 A Governance Oriented Study of Fine-Tuning Domain Specific Large Language Models with Transaction and Operations DataAuthor(s): 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 Secure Prompt Engineering for Banking and Payment Applications Design Principles, Threat Models, and Governance Controls for Generative AI in Regulated Financial SystemsAuthor(s): 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 Event-Driven Microservices for Omni-Channel Banking: U.S. Case Study-Driven Architectural Patterns and Operational OutcomesAuthor(s): 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 LLM-Enabled Transformation Framework for Migrating SOA Services to Cloud-Native Spring Boot MicroservicesAuthor(s): 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. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 26, 2025 Enhancing Customer Experience through ML-Based Chatbots in E-Commerce PlatformsAuthor(s): Pradyumna Kumar* Abstract: The extensive proliferation of e-commerce systems has seen a tremendous surge in consumer reviews that have given valuable information on customer satisfaction levels as well as product performance. This paper covers the increasing demand of smart and receptive chatbots in the e-commerce realm to ameliorate customer experience by properly interpreting user queries and moods. The issue is that it is difficult to interpret subtle customer reviews and create text answers in a given context with traditional machine learning methods. The main objective is to design and test chatbots based on machine learning that can provide stable intent recognition and sentiment analysis during the e-commerce conversation. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Smarter Care: Architecting Intelligence into the Healthcare Delivery EcosystemAuthor(s): Verena Lengston* Abstract: The healthcare delivery system is a complex, high-stakes, and often inefficient organism, plagued by fragmentation, information overload, and operational inertia. "Smarter Care" posits the systematic and ethical infusion of artificial intelligence not merely into clinical tools, but into the very fabric of healthcare operations, workflows, and coordination mechanisms. This paper moves beyond the vision of AI for clinical diagnosis to articulate a comprehensive framework for how intelligence can be architected to make the entire system more proactive, adaptive, efficient, and humane. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 The Patient Algorithm: From Passive Subject to Computational Entity in Personalized MedicineAuthor(s): Verena Lengston* Abstract: The advent of high-dimensional personal data from genomics and continuous biometrics to social determinants and behavioral logs has given rise to a new conceptual model: the Patient Algorithm. This construct refers to the dynamic, computational representation of an individual, a data-driven "digital twin" or personal health model that is continuously updated, simulated, and queried to predict health trajectories, optimize interventions, and personalize care. This paper interrogates the paradigm shift this represents: the patient is no longer merely a subject of care but an active, evolving algorithm that can be run forward in time ("prognostic mode") or subjected to in-silico clinical trials ("intervention mode"). We trace the technological genesis of this concept from early risk scores to contemporary multi-modal AI integration. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Code to Cure: The Translation of Algorithms into Clinical TherapeuticsAuthor(s): Verena Lengston* Abstract: The convergence of computational science and biomedicine has birthed a new therapeutic paradigm: the translation of abstract algorithms into tangible clinical cures. This paper explores the journey from "code to cure" the process by which mathematical models, machine learning architectures, and software systems are engineered to diagnose, treat, and prevent disease at a previously unimaginable scale and precision. We trace the therapeutic pipeline from foundational computational models of biology to deployed clinical AI systems. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Medicine's New Lens: Refocusing Healthcare through Artificial IntelligenceAuthor(s): Verena Lengston* Abstract: This paper examines artificial intelligence not merely as a new tool in medicine's toolkit, but as a fundamental paradigm shift a new lens through which we perceive, diagnose, treat, and understand human health and disease. Unlike incremental technological advances, AI represents a cognitive partner capable of identifying patterns across data modalities at a scale a nd precision previously unimaginable. Through this new lens, medicine transitions from reactive to predictive, from populationbased to profoundly personalized, and from episodic to continuous. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Reading between the Pixels: The Transformative Impact of Artificial Intelligence in Radiology and PathologyAuthor(s): Verena Lengston* Abstract: This paper examines the profound and rapidly evolving integration of Artificial Intelligence (AI), particularly deep learning, into the fields of radiology and pathology. It explores how AI algorithms are moving from research tools to clinical partners , capable of detecting, segmenting, and characterizing abnormalities in medical images with superhuman speed and, in some cases, accuracy. In radiology, we analyze applications in chest X-rays, mammography, CT, and MRI for tasks ranging from triage and detection to prognostication. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 The Empathy Gap: Can AI Ever Understand the Human Condition of Illness?Author(s): Verena Lengston* Abstract: As Artificial Intelligence transforms diagnostic and therapeutic aspects of medicine, a fundamental question persists: Can AI ever bridge what this paper terms "the empathy gap" the chasm between technical competence and genuine understanding of the human experience of illness? This paper explores the philosophical, psychological, and clinical dimensions of this question through three analytical lenses: phenomenological (what it means to experience illness), relational (how healing occurs through human connection), and technological (what current and future AI can simulate versus authentically experience). |
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Research Article | Volume: 1, Issue: 1 Published Date: December 08, 2025 Beyond Human Limits: How AI is Expanding the Boundaries of Medical PossibilityAuthor(s): Verena Lengston* Abstract: For millennia, medicine has been constrained by inherent human limitations biological senses that perceive only narrow spectra, cognitive capacities that process limited information, and clinical experiences bounded by individual lifetimes. This paper argues that Artificial Intelligence represents a fundamental transcendence of these biological constraints, expanding medicine's boundaries across four dimensions: perceptual (seeing the invisible), cognitive (thinking beyond intuition), temporal (learning across generations), and operational (acting with superhuman precision). |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 The End of Guesswork? AI and the Quest for Objective MedicineAuthor(s): Verena Lengston* Abstract: Medicine has historically been an art of probability a practice built on pattern recognition, clinical intuition, and statistical inference that clinicians colloquially term "educated guesswork". This paper examines whether Artificial Intelligence (AI) heralds the end of this uncertainty and the dawn of truly objective medicine. We trace medicine's epistemological journey from anecdotal observation to evidence-based practice, arguing that AI represents the next evolutionary leap toward quantification and prediction. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Healing Code: Can Artificial Intelligence Solve Healthcare's Biggest Challenges?Author(s): Verena Lengston* Abstract: Healthcare globally faces unprecedented challenges: Rising costs, aging populations, physician shortages, health inequities, and chronic disease epidemics. This paper examines whether Artificial Intelligence (AI) represents the transformative "healing code" capable of solving these systemic problems. Through a comprehensive analysis of AI applications in prevention, diagnosis, treatment, and system optimization, we argue that AI possesses remarkable potential to address healthcare's most persistent challenges. However, its effectiveness is constrained by fundamental limitations in technology implementation, ethical considerations, and human factors. |
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sub@gmail.com | Volume: 1, Issue: 2 Published Date: December 08, 2025 Will Your Next Doctor Be an Algorithm? The Promise and Peril of Medical AIAuthor(s): Verena Lengston* Abstract: The rapid integration of Artificial Intelligence (AI) into clinical practice heralds a paradigm shift in healthcare, moving f rom a traditionally reactive model to one that is predictive, personalized, and precision-based. This paper examines the dual-edged nature of this transformation. We first explore the profound "promise" of medical AI, detailing its revolutionary applications in diagnostic imaging, drug discovery, personalized treatment plans, and administrative efficiency. We then confront the inherent "peril", analyzing critical challenges including algorithmic bias, the "black box" problem of explainability, data privacy concerns, regulatory hurdles, and the potential erosion of the patient-clinician relationship. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 The Language of Life: How Large Language Models are decoding the Narrative in Clinical Notes and Patient CommunicationAuthor(s): Verena Lengston* Abstract: The patient's story recorded in clinical notes, transcribed from encounters, and exchanged in digital messages constitutes the richest, yet most underutilized, data source in medicine. Large Language Models (LLMs) represent a paradigm shift in our ability to decode this unstructured narrative, moving beyond keyword extraction to a nuanced understanding of context, sentiment, and subtext. This paper explores the transformative dual application of LLMs in healthcare: as tools for clinical documentation intelligence and as engines for enhanced patient-clinician communication. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Who's Liable? Responsibility, Regulation, and the New World of AI-Assisted MedicineAuthor(s): Verena Lengston* Abstract: The rapid deployment of Artificial Intelligence (AI) in clinical settings has precipitated a legal and regulatory crisis, revealing a profound mismatch between adaptive, opaque, and continuously evolving algorithms and static, product -centric liability frameworks. This paper examines the shifting landscape of responsibility when medical decisions are co-produced by clinicians and "black box" AI systems. We argue that the traditional binary of product liability (targeting developers) and professional malpractice (targeting clinicians) is insufficient for the tripartite, dynamic relationship between AI vendor, healthcare institution, and treating professional. |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Over diagnosed by Algorithm? The Perils and Promise of AI's Superhuman SensitivityAuthor(s): Verena Lengston* Abstract: Artificial Intelligence (AI) systems are achieving diagnostic sensitivity that meets or exceeds human experts in domains from radiology to pathology. While this capability promises to reduce missed diagnoses and save lives, it simultaneously amplifies a long-standing challenge in medicine: over diagnosis. This paper argues that AI's superhuman sensitivity does not merely scale existing clinical workflows but fundamentally transforms the epistemic landscape of diagnosis, creating a new paradigm of "algorithmic over diagnosis". |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 The Empathy Algorithm: Can AI Learn the Art of Human Compassion?Author(s): Verena Lengston* Abstract: The integration of Artificial Intelligence (AI) into clinical care has predominantly focused on diagnostic accuracy, predictive analytics, and operational efficiency. However, a growing frontier involves deploying AI systems to perform tasks central to the humanistic core of medicine: Recognizing emotion, demonstrating empathy, and providing psychosocial support. This paper interrogates the possibility, ethics, and clinical implications of an "empathy algorithm". We distinguish between affective empathy (emotional contagion), cognitive empathy (theory of mind), and compassionate action (motivated response). |
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Research Article | Volume: 1, Issue: 2 Published Date: December 08, 2025 Ghosts in the Machine: Confronting Bias and Equity in Medical AIAuthor(s): Verena Lengston* Abstract: The rapid integration of Artificial Intelligence (AI) into clinical decision support, diagnostics, and resource allocation promises a revolution in healthcare efficacy and efficiency. However, this technological advancement risks perpetuating and amplifying existing healthcare disparities if the inherent biases within its development and deployment are not critical ly addressed. This paper argues that AI systems are not neutral arbiters of care but often reflect the "ghosts" of historical inequities, biased datasets, and homogeneous design teams encoded within their algorithms. |
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Research Article | Volume: 1, Issue: 1 Published Date: October 24, 2024 Beyond Automation: AI-Powered Employee Engagement Journeys in Oracle HCM CloudAuthor(s): Kranthi Kumar Routhu* Abstract: Artificial intelligence is rapidly reshaping how organizations engage their workforces. In Oracle HCM Cloud, embedded generative AI and the Journeys module allow HR leaders to craft responsive, personalized employee engagement journeys across the lifecycle. This article synthesizes both academic and practitioner literature (up to mid‑2024) to propose a conceptual framework linking AI features, trust, and journey outcomes. We highlight key success factors, risks, and design principles, illustrating with four figures drawn from prior work. The article concludes with implications for HR strategy and future research directions. |
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Research Article | Volume: 1, Issue: 1 Published Date: October 22, 2020 Intelligent Remote Workforce Management: AI, Integration, and Security Strategies Using Oracle HCM CloudAuthor(s): Kranthi Kumar Routhu* Abstract: The rapid global shift to remote work has dramatically heightened the need for workforce management systems that are not only scalable and secure but also intelligent and adaptive. Traditional HR systems, built around centralized operations, often struggle to support the flexibility and immediacy required by a distributed workforce. Oracle HCM Cloud addresses this gap by integrating AI-driven decision support, mobile accessibility, and cloud-native interoperability to deliver a unified and resilient platform. With Oracle Integration Cloud (OIC) and digital assistant frameworks, organizations can automate routine transactions, enable real-time communication, and provide personalized employee experiences that mirror in-office efficiency. These intelligent integrations help HR leaders maintain operational consistency, enforce compliance, and drive engagement across geographies. |
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Research Article | Volume: 1, Issue: 1 Published Date: March 20, 2020 Strategic Compensation Equity and Rewards Optimization: A Multi-cloud Analytics Blueprint with Oracle Analytics CloudAuthor(s): Kranthi Kumar Routhu* Abstract: Achieving compensation equity and optimizing reward structures has become a core strategic imperative for modern enterprises navigating increasingly complex labor markets. Heightened regulatory oversight, rising demands for pay transparency, and workforce expectations around fairness and inclusion have pushed organizations to rethink how they design and manage compensation programs. Oracle Analytics Cloud (OAC) provides an advanced analytical foundation to meet this challenge, enabling HR leaders to align compensation strategies with performance, equity goals, and business outcomes. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 20, 2019 Hybrid Machine Learning Architecture for Absence Forecasting within Oracle Cloud HCMAuthor(s): Kranthi Kumar Routhu* Abstract: Employee absenteeism remains a persistent and multifaceted challenge for modern enterprises, directly influencing productivity, operational costs, and workforce morale. Traditional absence management systems, while effective at documenting attendance records, are predominantly reactive focusing on post-event analysis rather than proactive forecasting. This limitation often leads to delayed interventions, increased administrative burden, and reduced organizational agility. This paper introduces a machine-learning-driven absence forecasting framework within Oracle Cloud Human Capital Management (HCM) that transforms conventional absence tracking into an anticipatory, data-informed decision process. |
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Research Article | Volume: 1, Issue: 1 Published Date: February 20, 2019 AI-Enhanced Payroll Optimization: Improving Accuracy and Compliance in Oracle HCMAuthor(s): Kranthi Kumar Routhu* Abstract: Payroll management remains one of the most compliance-sensitive and error-prone functions in enterprise human-capital systems, requiring meticulous coordination between financial, legal, and HR processes to ensure that employee compensation aligns with statutory regulations and organizational policies. Despite significant advances in automation technologies and the adoption of integrated payroll platforms, many organizations continue to encounter persistent challenges such as payroll discrepancies, delayed or inaccurate tax filings, and inconsistent reporting that often stem from manual data entry, legacy integrations, and fragmented approval workflows. |
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Review Article | Volume: 1, Issue: 2 Published Date: September 23, 2025 The Role of Artificial Intelligence in the Early Detection of Alzheimer's DiseaseAuthor(s): Mohamed Atef Mohamed Moselhy Elasalouty* Abstract: Alzheimer's disease (AD) is one of the most prevalent causes of dementia worldwide, with incidence rising alongside aging populations. Early detection is critical, as it enables timely intervention, slows progression, and improves quality of life. In recent years, artificial intelligence (AI) has demonstrated remarkable potential in identifying early signs of AD through neuroimaging, speech and language analysis, and predictive modeling. This review summarizes the current applications of AI in these domains, highlights the challenges limiting its integration into clinical practice, and discusses future perspectives for AI driven diagnostics. |
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Research Article | Volume: 1, Issue: 2 Published Date: September 22, 2025 Sustainable Health Data Systems Using Machine Learning and Human Computer Interaction for Public Health Decision-MakingAuthor(s): Idowu Olugbenga Adewumi* and Sarah Adanini Abstract: Health emergencies like COVID-19 highlighted the critical requirement for sustainable health data systems that integrate predictive analytics with human-focused decision support. This research creates a unified framework that combines machine learning (ML) forecasting with collaborative human-computer interaction (HCI) dashboards to enhance public health decision-making. We created a large synthetic dataset consisting of 165,000 patient health records, 42,000 mobility trajectories, and 18,500 indicators of social determinants sourced from WHO, CDC, and three national health organizations. |
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Research Article | Volume: 1, Issue: 2 Published Date: September 20, 2025 Integrating Sustainability into Business Strategies: Emerging Trends in Sustainable Development for Competitive AdvantageAuthor(s): Dr. Ashwini Sonawane* Abstract: Sustainable development has emerged as a critical driver of business success in the modern corporate landscape. Organizations worldwide are shifting towards sustainable practices, integrating environmental, social, and governance (ESG) principles into their core strategies to achieve long-term growth. This paper explores emerging trends in sustainable development and their impact on management practices, emphasizing how businesses can align profitability with environmental and social responsibility. |
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Short Communication | Volume: 1, Issue: 2 Published Date: September 06, 2025 Training an ML Model Just Like How a Baby LearnsAuthor(s): Jacintah Kimeu* Abstract: Training data + machine learning = A machine learning model There are three parts to a machine learning model. i. The training data. ii. The machine algorithm programmed to have machines learn from the data. iii. The model itself. But how do the machines learn from the data? |
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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-OncologyAuthor(s): 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 Driving Digital Transformation through Intelligent ERP: A Perspective on SAP S/4HANA, AI, and Supply Chain InnovationAuthor(s): 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 SystemsAuthor(s): 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 The Generative AI Sales Paradox (GASP)-Enhancing Sales Scalability at the Cost of Human-Led Relationship Building in B2B MarketsAuthor(s): 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 ConsiderationsAuthor(s): 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. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 13, 2025 Immersive Learning for Future Dentists: VR, AR, and AI IntegrationAuthor(s): Fenella Chadwick Abstract: The landscape of dental education is rapidly evolving, driven by advancements in Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI). This paper explores the transformative potential of integrating these immersive technologies to enhance the learning experience for future dentists. By moving beyond traditional didactic methods, VR offers realistic and risk-free simulations of complex procedures, allowing for repeated practice and skill refinement. AR overlays digital information onto the real-world clinical environment, providing just-in-time guidance and enhancing diagnostic capabilities. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 12, 2025 Robots in the Recliner: The Transformation of Dental Practices by 2060Author(s): Fenella Chadwick Abstract: This paper envisions the profound transformation of dental practices by the year 2060, driven by the pervasive integration of advanced robotic systems. We explore the anticipated capabilities of these ?robots in the recliner?, ranging from autonomous diagnostic support and precise treatment execution to patient comfort enhancement and administrative task automation. The discussion encompasses the potential impact on clinical workflows, the evolving roles of dental professionals, and the implications for patient access and the overall dental experience. Furthermore, the abstract touches upon the key technological advancements, ethical considerations, and economic factors that will shape the widespread adoption of robotics in the future of dental care. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 11, 2025 Smarter Smiles: The Convergence of Artificial Intelligence and Robotics in 2050 Dental CareAuthor(s): Fenella Chadwick Abstract: This paper explores the transformative potential of converging Artificial Intelligence (AI) and robotics in shaping the landscape of dental care by the year 2050. We delve into the anticipated advancements in AI-powered diagnostic tools, personalized treatment planning, and the integration of sophisticated robotic systems for precise surgical interventions and routine procedures. The confluence of these technologies promises to enhance efficiency, accuracy, and patient experience, potentially leading to earlier disease detection, minimally invasive treatments, and improved long-term oral health outcomes. This paper also considers the ethical, economic, and educational implications of this technological shift, highlighting the need for proactive adaptation within the dental profession to fully leverage the benefits of this evolving paradigm. |
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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