AI, Volume 4, Issue 3

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource...

The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and n...

Deep learning (DL) algorithms can improve healthcare applications. DL has improved medical imaging diagnosis, therapy, and illness management. The use of deep learning algorithms on sensitive medical images presents privacy and data security problems...

Water and nitrogen (N) are major factors in plant growth and agricultural production. However, these are often confounded and produce overlapping symptoms of plant stress. The objective of this study is to verify whether the different levels of N tre...

Chatbots are programs with the ability to understand and respond to natural language in a way that is both informative and engaging. This study explored the current trends of using transformers and transfer learning techniques on Arabic chatbots. The...

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine le...

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in...

Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL), they do not usually design evaluations with high scaling factors. Moreover, the datasets are generally bench...

Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a...

Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer’s disease detection and brain tumor identification. While CNNs optimize the...

Artificial intelligence (AI) plays a critical role in the operation of robot vacuum cleaners, enabling them to intelligently navigate to clean and avoid indoor obstacles. Due to limited computational resources, manufacturers must balance performance...

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems...

Perhaps one of the best-known machine learning models is the artificial neural network, where a number of parameters must be adjusted to learn a wide range of practical problems from areas such as physics, chemistry, medicine, etc. Such problems can...

Context. This commentary is based on an innovative approach to the development of predictive analytics. It is centered on the development of predictive models for varying stages of chronic disease through integrating all types of datasets, adds vario...