Python
A Holistic Framework for Crime Prevention, Response, and Analysis With Emphasis on Women Safety Using Technology and Societal Participation
A Novel Integrated Approach for Stock Prediction Based on Modal Decomposition Technology and Machine Learning
Achieving Secure, Verifiable, and Efficient Boolean Keyword Searchable Encryption for Cloud Data Warehouse
An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud
Designing Secure Data Storage and Retrieval Scheme in Cloud-Assisted Internet-of-Drones Environment
Enhancing Smishing Detection A Deep Learning Approach for Improved Accuracy and Reduced False Positives
The aim of this work is to explore and develop advanced methods for enhancing the detection and prevention of smishing attacks. This involves utilizing cutting-edge technologies such as machine learning, artificial intelligence, and behavioral analysis to identify and block fraudulent SMS messages, protecting users from financial and personal data theft. The goal is to create more effective, real-time detection systems to mitigate the growing threat of smishing attack
Ephemeral Secret Leakage-Free ID-Role-Based Access Control Authentication and Key Exchange Protocol for Securing Electric Vehicle Data
Predicting Market Performance Using Machine and Deep Learning Techniques
The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors