Optimization of Feature Selection Using Greylag Goose Optimization Algorithm for Monkeypox
Monkeypox is an illness like smallpox that began to spread through several countries at a relatively rapid pace. The rash is among monkeypox’s most outstanding clinical features; however, a similar rash is evident in measles and chickenpox patients as well. AI and computer vision are well on their way to becoming must-have medical tools. For […]
NiOA: A Novel Metaheuristic Algorithm Modeled on the Stealth and Precision of Japanese Ninjas
This paper presents a new metaheuristic optimization algorithm called the Ninja Optimization Algorithm (NiOA) owing to its characteristics such as stealth, precision, and adaptability of the ninjas of Japan. NiOA is proposed to avoid high exploration and exploitation costs within such complex search spaces and to avoid the problem of getting trapped in local optima. […]
iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection
In this paper, we propose the iHow Optimization Algorithm (iHowOA), a novel metaheuristic algorithm inspired by human-like cognitive processes such as learning, knowledge acquisition, and experience-based decision-making. The iHowOA aims to enhance the exploration-exploitation balance inherent to solving complex optimization problems by mimicking how humans gather data, learn, and improve over time. We tested the […]
Anticipating Malicious Server Attacks: Evaluating the Effectiveness of Various Machine Learning Models
The global shift to online payments means that companies face growing cyber dangers, especially to servers. The target of this analysis is on malicious server hacks to be forecasted based on anonymized incident data of several features that are logging parameters and an outcome variable of hack occurrence. Based on the problems context, several machine […]
Predicting Student Adaptability in Online Education: A Comparative Study of Machine Learning Models and Copula-Based Analysis
The rapid shift to online education has underscored the need to understand and predict students’ adaptability levels to ensure effective learning outcomes. This study aims to classify students’ adaptability in online education using a range of machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Neural Network […]
Urban Noise Pollution: A Dataset for Spatiotemporal and Environmental Analysis
To establish noise pollution levels in El Mansoura, Egypt during 2023, the current study utilizes all around acoustic data acquired from May to December. Concerning noise control in urban areas, the study focuses on change over time, environmental conditions, as well as legal requirements. The analysis of the 1465 measurements provided the following results: Mean […]
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 […]