Daily Archives: November 21, 2025

Authors: Kranthi Kumar Routhu

Abstract: The digital transformation of Human Resource Management (HRM) has entered a new phase with the convergence of Artificial Intelligence (AI), advanced analytics, and cloud-based Human Capital Management (HCM) systems. This evolution reflects a global shift from administrative HR operations to data-driven workforce intelligence. Among the leading solutions, Oracle Cloud HCM stands out for its integration of AI-powered analytics, predictive modeling, and configurable dashboards that deliver actionable insights to managers across all levels of the organization. By embedding analytics within HR workflows, Oracle’s HCM platform enables enterprises to automate decision processes, identify workforce trends, and enhance compliance through real-time monitoring and intelligent recommendations. AI-driven dashboards transform traditional HR reporting into a dynamic, interactive decision-support environment, where key performance indicators (KPIs) are continuously analyzed to reveal emerging risks, opportunities, and performance gaps. These systems not only consolidate complex datasets from payroll, recruitment, and performance management modules but also apply machine learning algorithms to predict employee attrition, engagement levels, and talent acquisition efficiency. This paper explores the evolution, architecture, and strategic importance of AI-augmented dashboards in Oracle Cloud HCM, emphasizing their role in enhancing managerial decision-making. It develops a conceptual framework for AI-enabled decision support, detailing how predictive analytics and visualization work together to improve accuracy, transparency, and responsiveness in HR operations. Furthermore, the study discusses practical implementation challenges including data quality, explainability, and user adoption and evaluates the tangible benefits of integrating AI-driven dashboards within enterprise HCM systems. The findings highlight how Oracle Cloud HCM serves as a model for intelligent HR transformation, aligning technology, analytics, and human expertise to support sustainable organizational growth.

DOI: http://doi.org/10.5281/zenodo.17670797

Published by: vikaspatanker

Authors: Kranthi Kumar Routhu

Abstract: The emergence of intelligent chatbots has fundamentally reshaped how employees interact with enterprise systems, especially within the domain of human capital management (HCM). Unlike traditional portals and static forms, conversational interfaces offer a more natural, intuitive, and immediate way for employees to access services and information. Organizations increasingly leverage these digital assistants to simplify complex HR transactions, provide continuous 24/7 self-service capabilities, and enhance overall employee engagement. Oracle Digital Assistant (ODA), when seamlessly integrated with Oracle HCM Cloud, enables a secure, scalable, and intelligent foundation for modern service delivery. Through advanced natural language understanding, prebuilt HCM skills, and REST API–based integrations, organizations can automate high-volume, routine tasks such as leave requests, payslip retrieval, and benefits inquiries by reducing response time while improving accuracy. This article outlines the underlying architecture, deployment strategies, and measurable business impacts of conversational AI, demonstrating how chatbot-driven service models modernize and elevate the employee experience.

DOI: http://doi.org/10.5281/zenodo.17670667

Published by: vikaspatanker

Authors: Kranthi Kumar Routhu

Abstract: The ability to build scalable, reusable integration frameworks has emerged as a cornerstone of modern HR digital transformation strategies. As enterprises move away from fragmented, point-to-point integrations toward standardized architectures, Oracle HCM Cloud plays a pivotal role by offering a rich set of integration capabilities including REST and SOAP APIs, HCM Extracts, Atom Feeds, and prebuilt adapters through Oracle Integration Cloud. These components enable organizations to unify data exchange across systems, orchestrate complex workflows, and embed automation at the integration layer rather than relying on manual interventions. By focusing on reusability, modularity, and event-driven architecture, enterprises can reduce redundant development efforts, improve data consistency, ensure compliance, and accelerate project delivery cycles ultimately creating a more resilient, cost-efficient, and scalable HR technology ecosystem.

DOI: http://doi.org/10.5281/zenodo.17670619

Published by: vikaspatanker

Authors: Sameer Agarwal, Vaibhav Kalukar

Abstract: Human–Robot Interaction (HRI) is an interdisciplinary field that examines how humans communicate, collaborate, and coexist with robotic systems. With major advancements in artificial intelligence, sensor technologies, and automation, robots are increasingly becoming interactive partners in healthcare, manufacturing, education, and personal assistance. This research investigates current trends in HRI, emphasizing the shift from traditional command-based systems to socially aware, adaptive, and collaborative robots. A qualitative review methodology is used to analyze studies published between 2015 and 2024, focusing on social robots, industrial cobots, healthcare and assistive robotics, and service-oriented systems. Findings reveal rapid growth in socially interactive robots, widespread adoption of collaborative robots in industry, and enhanced communication through AI-driven speech, gesture, and emotion recognition. Despite challenges such as limited emotional intelligence, ethical concerns, and high costs, the increasing integration of robots into human environments highlights significant potential for future development. The study concludes that HRI will play a crucial role in shaping intelligent, human-centric robotic systems, requiring continued research in transparency, trust, and ethical design.

DOI: https://doi.org/10.5281/zenodo.17670137

Published by: vikaspatanker

Authors: Kranthi Kumar Routhu

Abstract: The digitization of human capital management has fundamentally reshaped how organizations design, deliver, and optimize employee benefits by replacing rigid, manual workflows with intelligent, data-driven ecosystems. Historically, benefits administration depended on paper-based processes, fragmented eligibility checks, and periodic reconciliations that increased operational risk and slowed employee service delivery. By mid-2021, forward-looking enterprises began embedding AI-driven decision support, standardized data exchange protocols, and event-based automation into Oracle HCM Cloud, creating adaptive benefits ecosystems that respond dynamically to workforce needs. These transformations enable not only faster and more accurate enrollment but also personalized plan recommendations, automated compliance validation, and seamless integration with carriers and payroll systems through Oracle Integration Cloud (OIC). This paper outlines a practical automation blueprint that leverages recommendation engines, predictive triggers, and secure real-time integrations to deliver scalable, compliant, and employee-centric benefits experiences at enterprise scale.

DOI: https://doi.org/10.5281/zenodo.17669918

Published by: vikaspatanker

Authors: Kranthi Kumar Routhu

Abstract: The rapid evolution of information technology over the past two decades has fundamentally transformed the way organizations manage their human capital. Human Resource (HR) systems, once confined to on-premise infrastructures, have steadily progressed toward cloud-based Human Capital Management (HCM) ecosystems that integrate data, analytics, and user experience into a unified digital platform. This transformation reflects not merely a technological shift, but a strategic reorientation of HR’s role from administrative recordkeeping to value-driven talent management. Traditional on-premise HR systems such as Oracle E-Business Suite and PeopleSoft provided strong control, customization, and data security but required heavy maintenance, complex upgrades, and significant capital expenditure. In contrast, cloud-based HCM platforms introduced a service-oriented model that offers agility, scalability, and continuous innovation through subscription-based delivery. Oracle’s HCM Cloud represents a culmination of this digital evolution, combining the reliability and maturity of legacy systems with the adaptability of cloud-native architecture. The purpose of this study is to examine how this migration reshaped enterprise HR strategy and infrastructure. It explores the transformation journey from on-premise deployment to Oracle Cloud HCM, focusing on the organizational, technical, and regulatory challenges encountered during migration. Furthermore, it evaluates the strategic opportunities created through the adoption of Oracle’s cloud-driven HR ecosystem, particularly in enhancing workforce analytics, compliance automation, and employee engagement. Through this lens, the study demonstrates how Oracle’s Cloud HCM framework serves as both a technological and organizational enabler of modern HR excellence.

DOI: https://doi.org/10.5281/zenodo.17669776

Published by: vikaspatanker

Authors: Mr. Karthiban R, Tharun S, Naveen B, Yuvaraj S, Pragathiswaran G

Abstract: Urban site visitors congestion has come to be one of the most critical challenges confronted via cutting-edge towns. Emergency automobiles, wi-fi ambulances, enjoy frequent delays due to traffic wireless indicators and roadblocks, which may be deadly in lifestyles-threatening conditions. This paper proposes a smart visitors control device that mixes automatic wide variety Plate recognition (ANPR) and ambulance signal detection to make sure actual-time prioritization of ambulances. The proposed system makes use of digicam-primarily based vehicle identity, an IoT-enabled site visitors manage network, and adaptive sign timing to create a “inexperienced corridor” for emergency automobiles. Experimental simulations imply that the device can reduce ambulance waiting time at intersections via extra than 60%, improving the wi-fi of emergency reaction services. the combination of ANPR era permits automatic detection of ambulances without counting on guide systems or wireless tags, making it scalable and price-effective for clever city deployment.

Published by: vikaspatanker

Authors: Dhyanesh M, Dharshini S, Deepak P, Aisha Amna A, Mr. S. Dhinakaran

Abstract: EduHelm is an intelligent AI-driven career and learning platform designed to personalize and optimize student growth through dynamic learning paths, adaptive projects, and real-time skill tracking. The system leverages machine learning and large language models to recommend personalized roadmaps, micro-projects, and certification-based goals aligned with each user’s academic profile and aspirations. EduHelm integrates AI mentorship, progress analytics, and gamified challenges to foster continuous improvement and engagement. It also enables students to upload learning journals and research patterns, automatically analyzing them for feedback and improvement suggestions. Developed using a React frontend, Flask backend, and PostgreSQL database, with AI modules powered by OpenAI and Hugging Face, EduHelm delivers a seamless and intelligent experience that empowers learners to take control of their career growth in the digital age.

Published by: vikaspatanker