We conducted several experiments using a classical machine learning approach (random forests), as well as different convolutional neural networks (CNNs)鈥擱esNet50, ResNet101, VGG16, and Inception-v3鈥攐n different datasets and classes to evaluate the potential of these algorithms for tree health...
Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data ...
Innovation and research in speech enhancement have been opened up by the power of deep learning techniques with implications across a wide range of real time applications. By reviewing the important datasets, feature extraction methods, deep learning models, training algorithms and evaluation metrics ...
Deep learning has been applied to image recognition, speech recognition, video synthesis, and drug discoveries. In addition, it has been applied to complex creations, like self-driving cars, which use deep learning algorithms to identify obstacles and perfectly navigate around them. You must feed l...
Deep learning algorithms have shown exceptional effectiveness in a wide range of supervised and unsupervised learning tasks in a variety of fields, including image processing, computer vision, natural language processing, and speech or voice processing. In this paper, a comprehensive analysis is conducte...
A set of continuous state-action space-based deep reinforcement learning algorithms are used for the path following of a ship in calm water and waves. The mathematical model of a KVLCC2 tanker represents the ship dynamics. The mathematical model includes the hull force, rudder force, propulsion...
Although the general performance of SMOTE-based machine learning algorithms is excellent, finding the appropriate SMOTE-balancing technique to get the best results from ML algorithms is tricky. There is no single SMOTE-balancing technique can achieve the best results for all ML algorithms. The current...
Understanding Deep Learning by Simon J.D. Prince Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Large Language Models A Visual Guide to Quantization: Demystifying the Compression of Large Language Models by Maarten Grootendorst Foundations of Larg...
Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found
Machine Learning Models: We have tried boosted algorithms (XGBoost, LightGBM, Catboost) and fully connected neural network (FCNN) in this project. There are some technical differences in the application of different algorithms that needs to noticed: ...