ABSTRACT
Python-based web application developed to identify and analyse malicious Android APK files. It integrates static analysis, using Andro guard to extract permissions and metadata, with dynamic analysis, employing Mobs to monitor runtime behaviour in a controlled sandbox environment. This dual-layered approach enables the detection of both overt and hidden threats. At its core, the system leverages a Random Forest classifier trained on labelled datasets of malware and benign applications, providing accurate threat classification along with confidence scoring. The application features a Flask-based web interface that allows secure APK uploads, real-time scanning, and the generation of detailed PDF reports. A history of previous scans is maintained to support long-term tracking and analysis. The technical architecture consists of three main components: a user interface built with HTML, CSS, and Flask templates; a Python backend responsible for feature extraction, safe testing, and machine learning predictions; and an SQLite database for storing results. By automating the identification of dangerous applications, the system addresses critical mobile security challenges. It is designed with scalability in mind, making it easy to expand for future needs such as cloud integration. Built with Python’s security tools and thoroughly tested, the project demonstrates a practical solution to combat evolving Android malware. It ensures usability for both programmers and security experts, combining technical rigor with accessibility. Ultimately, the Android Malware Detection System provides a comprehensive, reliable, and user-friendly platform to safeguard mobile ecosystems against emerging threats.
G. Vamsi, Vedula Sai Vinay· International Scientific Jou...· 1 citation
The ClinicalML uses advanced machine learning algorithms to analyze patient data and identify health conditions accurately using important parameters like age, BMI, blood pressure, and glucose levels to assist in early disease prediction and personalized treatment support.
G. Vamsi, K. Devendra· International Scientific Jou...· 0 citations
Hypertension, commonly known as high blood pressure, is a major risk factor for cardiovascular diseases and premature mortality worldwide. Early detection and prevention are critical in reducing its health impact. This study explores the application of machine learning (ML) techniques to predict the likelihood of hypertension in individuals using clinical and demographic data. A variety of supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, were evaluated for their predictive performance [1]. The dataset was preprocessed through feature selection, normalization, and handling of missing values to improve model accuracy.[2] Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the models [4]. The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare [5]. This approach underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
Key words: Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
The resulting prototype confirms that a cloud-hosted multimodal LLM, when combined with a minimal and well-structured web stack, can serve as a practical foundation for next-generation digital assistants suitable for customer support, education, and personal productivity applications.
G. Vamsi, Vinay Kumar Male· International Scientific Jou...· 0 citations