In the field of image processing and computer vision (CV), machine learning (ML) architectures are widely used. Image compression problems can be solved using convolutional neural networks (CNNs). As a result bandwidth memory constraints, images is necessity. There three types information found in images: useful, redundant, irrelevant. this survey, we will discuss how ML used to compress lossy ...

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given ill-posed nature problem and its popularity a broad range real-world scenarios, number large-scale benchmark datasets have been established, on which considerable methods developed demonstrated with significant progress recent years -- predominantly by deep learning (DL)-based method...

Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the distance metric for the input data patterns. Distance Metric learning is to learn a distance metric for the input space of data from a given collection of pair of similar/dissimilar points that preserves the distance relation among the training data. In recent years, many studies have demonstrated, both empi...

Several industries, including agriculture, water resource management, disaster preparedness, and urban planning, depend heavily on rainfall forecasting. Traditional approaches for predicting rely numerical simulations statistical models, which frequently have accuracy computing efficiency issues. Growing interest has been shown in using machine learning (ML) algorithms to enhance prediction as ...

The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security a long run challenge in the IoT systems which many attack vectors, security flaws and vulnerabilities. Securing billions is must task to realize full potential Recently, researchers have proposed solutions IoT. Machine learning as one emerging reinforcement (RL) ...