Abstract— Government hospitals provide medicines and treatment to patients based on proper diagnosis. These hospitals maintain both historical and current patient data in secure cloud-based systems, which helps doctors access accurate medical records anytime and ensures better continuity of care. In the proposed system, users can register with their personal details, and this information is stored in the admin-controlled database for secure access and management. The system also includes a feature that helps users locate nearby government hospitals using a predictive algorithm, which considers factors like location, urgency, and medical requirements. It provides detailed information about hospitals such as available doctors, specialists, departments, emergency services, and medicine availability. This improves transparency and helps patients make informed decisions quickly. The system further enhances patient experience by providing real-time updates on doctor availability, waiting time, and medicine stock. From an administrative perspective, it improves financial and operational efficiency by ensuring proper utilization of resources, reducing fraud and misuse, and optimizing supply chain and human resource management. Overall, the system aims to make government healthcare services more efficient, accessible, and patient-friendly through better data management and intelligent decision-making.
Karre Surya Prakash, Kaja Masthan, P. Rani· International Scientific Jou...· 0 citations
ABSTRACT - Rumours and misinformation propagate rapidly across online social networks, posing significant challenges to maintaining the integrity of information dissemination. In recent years, machine learning (ML) techniques have emerged as promising tools for automating the detection and mitigation of rumours. This review paper provides a comprehensive examination of the advancements in rumour detection using ML approaches. The paper begins by outlining the landscape of rumour dissemination in online social networks, highlighting the characteristics and challenges associated with rumour detection. Subsequently, it systematically categorizes and analyzes various ML methods employed for rumour detection, including supervised, unsupervised, and semi-supervised learning approaches. Furthermore, the review delves into the diverse features and representations utilized in ML models for rumour detection, such as textual content, user engagement patterns, network structures, and temporal dynamics. It discusses the strengths and limitations of different feature sets and their impact on the effectiveness of rumour detection systems. Moreover, the paper explores the intricacies of dataset construction and evaluation methodologies for training and testing rumour detection models. It examines commonly used benchmark datasets and evaluation metrics, emphasizing the importance of robust evaluation frameworks for assessing the performance of ML-based rumour detection systems accurately. Additionally, the review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models. It also discusses potential directions for future research aimed at advancing the state-of-the-art in rumour detection and mitigation.
C.Sandeep Reddy, Dr.Kiran B.M, P. Rani· International Scientific Jou...· 0 citations
The goal is to employ artificial intelligence to develop a medical chatbot that, when presented with symptoms, can diagnose a patient's condition before the patient ever sees a doctor.
Manisha Y, P. Rani, D. Masthan et al.· International Scientific Jou...· 0 citations