A review of artificial intelligence and its subfields within teaching and learning
Artificial Intelligence (AI) and its subfields are increasingly adopted in education, yet persistent conceptual ambiguity surrounds terms such as AI, Machine Learning (ML), Deep Learning (DL), and Cognitive Computing (CC). Despite the pervasive interchangeable use of terms such as Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing, this practice critically obfuscates their crucial differences, […]
A proposed deep learning model for ransomware detection in developing countries: Egypt as a case study
Ransomware attacks have emerged as one of the most significant cybersecurity threats facing organizations today, with attacks noted highest in 2023 and continuing to grow (5). This research presents a deep learning framework leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to detect and prevent ransomware attacks in developing countries, especially in […]
AI-Driven nutrition: A review of deep learning applications, challenges, and future directions
Artificial Intelligence (AI), particularly Deep Learning (DL), is rapidly transforming nutrition science by overcoming limitations of traditional self-reported dietary methods, which are prone to bias and inaccuracy. This review presents a comprehensive, integrative analysis of DL applications across five key domains: dietary assessment, food recognition and tracking, personalized nutrition, disease diagnosis and monitoring, and predictive […]
A predictive model for climate change using advanced machine learning algorithms in Egypt
This study evaluates advanced machine learning (ML) models for forecasting daily average temperatures in Egypt, using a dataset from one of the world’s most climate databases, the GHCN-D of the NCEI under NOAA (United States). The dataset spans nine years (January 1, 2015 – December 31, 2023) and consists of 73,562 daily records from 23 […]
A novel integrated model for investigation into employee compliance with information security policies
A key component of a company’s integrity, both financially and in terms of reputation, is information security. When given the right direction, employees can play a significant role in strengthening information security, despite the fact that they are frequently seen as the weakest link in the chain. Businesses are doing this by implementing information security […]
A review of artificial intelligence based control techniques for power electronics
The integration of Artificial Intelligence (AI) into power electronics marks a major advancement in control techniques, providing increased efficiency, adaptability, and reliability across various industrial and commercial applications. This research paper aims to present and review the fields of AI in control systems, focusing on key AI-based techniques such as neural networks, fuzzy logic, genetic […]
Automatic term extraction for Arabic text: Approaches, techniques, and challenges
Automatic Term Extraction (ATE) is an essential task in Natural Language Processing (NLP) that aims to identify domain-specific terms from large corpora. In the context of Arabic, ATE plays an essential role in applications such as ontology construction, dictionary development, information retrieval, and text mining. However, the rich morphological structure, and orthographic ambiguities of Arabic […]
The feature-value paradox: Unsupervised discovery of strategic archetypes in the smartphone market using machine learning
This study employs unsupervised machine learning, a core branch of Artificial Intelligence (AI), and feature importance analysis to identify strategic archetypes in the smartphone market based solely on technical specifications. Moving be- yond traditional price prediction models, we analyze a comprehensive dataset to discover latent product strategies. Using K-Means clustering, we identify five distinct strategic […]
Automated COVID-19 detection from chest X-rays using HOG features
The COVID-19 pandemic has necessitated the development of rapid and accurate diagnostic tools to assist healthcare professionals in disease detection and management [1]. This study presents a different machine learning framework for COVID-19 classification using chest X-ray images, employing Histogram of Oriented Gradients (HOG) feature extraction combined with Principal Component Analysis (PCA) for dimensionality reduction […]
Development and evaluation of an effective machine learning model for well log prediction: A case study of sonic log prediction of zircon field Niger-Delta Nigeria
The accurate prediction of sonic log data is critical for subsurface characterization and reservoir management in hydrocarbon exploration. Conventional methods of predicting missing well logs which often relied on interpolation techniques or empirical correlations are limited in their ability to capture the complex, nonlinear relationships that exist in subsurface formations. In this study we present […]