AI conference in Asia, the SuperAI conference - May 2026

Abstract: In multi level botnet attack in prevailing cyber attacks in the IoT environment starts and ends detection activities. In existing detection of botnet attacks compromising the IoT devices initially performs ddos attacks. According to the various performances of existing machine learning botnet detection model is limited to the trained data which are already specified. The consequences towards the datasets according to the diversified attack patterns that performs perfectly will be questionable. In our proposed methodology the generalized scanning of datasets in DDoS attacks generates 33 varieties of detection patterns. Integration of detecting samples of DDoS at tacks with publicly available datasets within the limit of more attacks. Proposed prevention and detection of double layered machine learning techniques helps in training the dataset models. Prior to the attacking stage the IoT botnet attacks identifies from the trained double layered attack identification and detection models. In the next layer efficiency of datasets detection approach with more accuracy and precise training models will be provided.

Keywords: Botnet Attack, Cyber Attacks, Datasets, Double Layered Machine Learning Techniques, Training Datasets.

Abstract: This paper presents MediCare Pro, a comprehensive full-stack Hospital Management System (HMS) developed using FastAPI, SQLAlchemy, and modern web technologies. The system addresses the growing need for digitised, integrated healthcare administration by providing a unified platform covering patient registration and management, doctor scheduling, appointment booking, billing with UPI payment integration, medical records, inventory control, staff management, and real-time analytics. A distinguishing feature of MediCare Pro is its incorporation of biometric face recognition login using face-api.js, eliminating the need for manual credential entry. The backend exposes 30+ RESTful API endpoints, while the frontend is a Single-Page Application (SPA) delivering a responsive, role-aware dashboard. Evaluation demonstrates that the system achieves complete CRUD coverage across eight data entities, real- time KPI monitoring through 14 computed statistics, and seamless UPI-based payment flows. The paper details the system architecture, data model, module design, security considerations, and future enhancement roadmap.

Keywords: Hospital Management System, FastAPI, SQLAlchemy, Face Recognition, UPI Payment, RESTful API, Single-Page Application, Healthcare Digitisation, SQLite, Python