Python
PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping
Plant Disease Detection and Classification by Deep Learning: A Review
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
Recent Advances in Deep-Learning Based SAR Image Target Detection and Recognition
Recognition of Fish in Aqua Cage by Machine Learning with Image Enhancement
Uncertain Facial Expression Recognition via Multi-Task Assisted Correction
Whale and Dolphin Classification
The proposed method involves a multi-step process to classify whale and dolphin species from images. First, the dataset is collected and pre-processed to ensure high-quality input data. The VGG16 model is used to extract features from the images, capturing complex patterns and details. These features are then used to train a Support Vector Machine (SVM) model, which excels in binary and multi-class classification tasks.