Autonomic computing investigates how systems can achieve (user) specified “control”
outcomes on their own, without the intervention of a human operator. Autonomic computing …

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI
refers to the practice of doing AI computations near the users at the network's edge, instead …

Resource management in computing is a very challenging problem that involves making
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …

There has been rapid development in the number of Internet of Things (IoT) connected
nodes and devices in our daily life in recent times. With this increase in the number of …

The heterogeneous and high-dimensional nature of omics data presents various challenges
in gaining insights while analysis. In the era of big data, omics data is available as genome …

Medical systems incorporate modern computational intelligence in healthcare. Machine
learning techniques are applied to predict the onset and reoccurrence of the disease …

Segmentation of biomedical images is the method of semiautomatic and automatic detection
of boundaries within 2D and 3D images. The major challenge of medical image …

The advancements in the use of Internet of Things (IoT) devices is increasing continuously
and generating huge amounts of data in a fast manner. Cloud computing is an important …

This study uses machine learning, including Support Vector Machines, Decision Trees, K-
Nearest Neighbors, to examine Bangladesh's tourism industry to forecast traveller …

The emergence of different computing methods such as cloud-, fog-, and edge-based
Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems …