With the rapid development of artificial intelligence (AI), natural language processing (NLP), and mobile health technologies, remote healthcare services have significantly improved. Intelligent healthcare chatbots have emerged as an important tool for providing scalable, affordable, and continuous patient care outside clinical environments. These chatbots use conversational interfaces to deliver medical information, perform symptom checks, schedule appointments, remind patients about medications, provide mental health support, and offer personalized health education. The increasing workload in healthcare systems, shortage of healthcare professionals, and rising chronic diseases have accelerated the adoption of chatbot-based remote care solutions. This paper presents a comprehensive study of intelligent healthcare chatbots for supporting remote patients, including their architecture, features, integration with health information systems, and clinical applications. Modern chatbots employ machine learning models, deep learning-based NLP techniques, and medical knowledge bases to enable context-aware, adaptive, and personalized patient interactions. Healthcare chatbots also improve access to medical services, particularly in underserved and rural areas where healthcare facilities may be limited. The paper further discusses ethical, legal, and privacy concerns such as patient data protection, regulatory compliance, and potential algorithmic bias. Chatbot performance is evaluated using metrics like response accuracy, user satisfaction, task completion rate, and clinical relevance, highlighting their advantages over traditional telehealth methods. The proposed framework integrates multimodal data sources, real-time patient feedback, and active learning techniques to enhance clinical decision-making and patient engagement. Overall, intelligent healthcare chatbots can reduce response time, improve treatment adherence, and enhance patient experience. The study concludes that healthcare chatbots have strong potential to transform remote healthcare delivery while emphasizing the need for further research to improve clinical reliability and regulatory compliance.
Anita Verma· International Journal of Mod...· 0 citations
Robotic manipulators are essential in modern fields such as industrial automation, precision manufacturing, medical robotics, and aerospace. While traditional control methods like PID, computed torque, and adaptive control perform well in structured environments, they struggle with nonlinearities, uncertainties, payload variations, and disturbances. To address these limitations, hybrid control strategies combining conventional and intelligent techniques—such as fuzzy logic, neural networks, and sliding mode control—have emerged as effective solutions. This work focuses on hybrid control approaches that enhance accuracy, robustness, and adaptability. It begins with dynamic modeling using the Euler-Lagrange formulation, highlighting the nonlinear and complex nature of robotic systems. The proposed method integrates computed torque control for trajectory tracking, fuzzy logic for handling uncertainties, and neural networks for adaptive tuning and parameter estimation. Literature indicates that hybrid methods like fuzzy-PID and neural-based adaptive control significantly improve tracking performance, reduce steady-state error, and enhance robustness, though challenges like computational complexity remain. Simulation and experimental results demonstrate that the proposed hybrid controller outperforms traditional methods in terms of tracking accuracy, settling time, and disturbance rejection. In conclusion, hybrid control strategies offer a powerful framework for high-precision robotic manipulation by effectively addressing nonlinearities and uncertainties. Future work will focus on real-time implementation, optimization of hybrid designs, and integration with advanced sensing technologies.
Anita Verma· International Journal of Int...· 0 citations
It is concluded that MARL is a promising solution for future intelligent collaborative robotics and highlights future research directions including federated reinforcement learning, explainable AI, edge-based robotic intelligence, and adaptive swarm robotics for Industry 4.0 applications.
Suresh Babu Reddy, Anita Verma· International Journal of Int...· 0 citations
