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 High-Performance DNA Multiple Pattern Matching Algorithm Based on Index Binding and ASCII Hashing
Beyond health, the exponential trend in the volume of genomic data, combined with broad access to personal genome sequencing, has generated a pressing demand for fast and scalable algorithms to explore patterns in DNA sequences. Rapid and specific identification of DNA sub-sequences is of critical importance in various applications, such as personalized medicine, evolutionary biology, […]
Evaluating AI Performance in Academic Settings: A Comparative Study of ChatGPT-4 and Gemini
This study conducts a systematic comparison of ChatGPT-4 and Gemini in addressing academic queries across four disciplines: Python programming, financial accounting, business administration, and medical sciences. Through a mixed-methods analysis of 40 standardized questions (balanced between numerical and narrative formats), we evaluate the models’ accuracy, reasoning capabilities, and limitations. Results reveal ChatGPT-4’s superior performance with […]
Predicting employee retention using artificial intelligence and survival analysis approaches
Background: Employee retention is a critical concern for organizations seeking to maintain a stable and productive workforce. Understanding the factors driving turnover is essential for designing effective HR interventions. Methods: This study applies advanced survival analysis techniques, including the Kaplan–Meier estimator, Cox proportional hazards model, and Random Survival Forests (RSF), to predict employee retention and […]
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 […]