A5017287168- Metaheuristic Optimization Algorithms Research
- Advanced Algorithms and Applications
- Evolutionary Algorithms and Applications
- IoT and Edge/Fog Computing
- Cloud Computing and Resource Management
- Advanced Multi-Objective Optimization Algorithms
- Blockchain Technology Applications and Security
- Data Stream Mining Techniques
- Advanced Control Systems Design
- AI and HR Technologies
- Engineering and Technology Innovations
- FinTech, Crowdfunding, Digital Finance
- Frequency Control in Power Systems
- Gene expression and cancer classification
- Online Learning and Analytics
- Internet of Things and AI
- Face and Expression Recognition
- Machine Learning in Bioinformatics
- Machine Learning and ELM
- Artificial Intelligence in Healthcare
- Artificial Immune Systems Applications
- Energy Load and Power Forecasting
- Library Science and Information Literacy
- Online and Blended Learning
- Organizational and Employee Performance
Kafrelsheikh University
2020-2024
Minia University
2023
Abstract Feature Selection (FS) is an important preprocessing step that involved in machine learning and data mining tasks for preparing (especially high-dimensional data) by eliminating irrelevant redundant features, thus reducing the potential curse of dimensionality a given large dataset. Consequently, FS arguably combinatorial NP-hard problem which computational time increases exponentially with increase complexity. To tackle such type, meta-heuristic techniques have been opted...
All the educational organizations mainly aim at elevating academic performance of students for improving overall quality education. In this direction, Educational Data Mining (EDM) is a rapidly trending research area that utilizes essence (DM) concepts to help institutions figure out useful information on Student Satisfaction Level (SSL) with Online Learning process (OL) during COVID-19 lock-down. Different practices have been tried EDM predict students’ behaviors reach best settings....
In recent years, ongoing advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes data, taxing capabilities cloud computing infrastructure. Allocating limited resources to numerous incoming requests is one difficulties computing, which typically referred as a Task-Scheduling-in-Cloud-Computing (TSCC) problem. order ameliorate performance particle swarm optimizer (PSO) and broaden its application TSCC, this paper introduces an Opposition-based Simulated Annealing...
Abstract In today’s data-driven digital culture, there is a critical demand for optimized solutions that essentially reduce operating expenses while attempting to increase productivity. The amount of memory and processing time can be used process enormous volumes data are subject number limitations. This would undoubtedly more problem if dataset contained redundant uninteresting information. For instance, many datasets contain non-informative features primarily deceive given classification...