Academic institutions operate in an extremely demanding and competitive environment. Some difficulties confronting most schools are delivering high-quality education to the students, developing systems for evaluating student performance, analyzing performance, and recognizing the future demands...

Artificial neural networks (ANNs) are commonly used to solve nonlinear problems. ANNs are composed of parallel interconnected layers that include processing elements called neurons. These neurons use mathematical functions to process the data, mimicking the human brain. In this study, we use...

Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and solve common real-world problems. Machine learning and deep learning are Artificial intelligence technologies that use algorithms to predict outcomes more accurately without relying on human intervention....

Technology is pivotal in the rapid growth of services and intensifying the quality of life. Recent technology, like the Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS) is used as a lifeline to prevent attacks by...

In this work investigate the use of stochastic hybrid models, statistical model checking and machine learning to analyze, predict and control the rapid spreading of Covid-19. During the pandemic numerous studies using stochastic models have been produced. Most of these studies are used to...

The number of people using the Internet of Things (IoT) devices has exploded in recent years. The instantaneous development in deploying constrained devices in numerous areas makes them vulnerable to assaults due to limited resources. Advanced cryptography cannot be constructed in these modest...

Product perishability is an important problem in Pharmaceutical Supply Chains (PSC). Demand perishability and unpredictability add enormous complexity to successfully implementing efficient Pharmaceutical Supply Chain Network Design (PSCND). This study develops a mathematical model for the...

Electroencephalogram (EEG) of Alzheimer's disease (AD) patients show a slowing effect and less synchronization. EEG signal's transient and abrupt nature is captured from various mother wavelets. However, better performance can be obtained by balancing time-frequency localization in wavelet...

Evaluating and selecting suitable suppliers involves several criteria, making it a complex and difficult problem for decision-makers. Several approaches have been developed to help achieve the best possible results for selecting the most suitable suppliers. Traditionally, multi-criteria...

Training a Convolutional Neural Network (CNN) from scratch is time-consuming and expensive. In this study, we propose implementing the DenseNet architecture for classification of AD in three classes. Our approach leverages transfer learning architectures as the base model and showcases superior...