Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

This work explores reinforcement learning (RL) for on-board planning and scheduling of an
agile Earth-observing satellite (AEOS). In this formulation of the AEOS scheduling problem …

This study proposes a conformable artificial neural network model to improve the void
fraction prediction in helical heat exchangers. The obtained model had only one neuron in …

Automatic modulation identification (AMI) is a technique to detect the modulation type and
order of a received signal, which has the potential to enhance cognitive radio capabilities for …

In this paper, a relatively simple and ultra-sensitive Photonic crystal fiber (PCF) based
surface plasmon resonance (SPR) sensor is proposed for detecting different analyte …

This study focuses on real-time hand gesture recognition in the Turkish sign language
detection system. YOLOv4-CSP based on convolutional neural network (CNN), a state-of …

Hyperspectral imaging has a strong capability respecting distinguishing surface objects due
to the ability of collect hundreds of bands along the electromagnetic spectrum. Hyperspectral …

Deep Neural Network Topologies are used by subfield of machine learning called" deep
learning" that are similar but different to handle a variety of challenges in domains including …

Industry 4.0 has embraced process models in recent years, and the use of model-based
digital twins has become even more critical in process systems engineering, monitoring, and …

Digital signal processing relies on the Nyquist-Shannon sampling theorem that applies to
and requires a continuous signal with limited bandwidth. However, many systems or …