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Applying Structural Equation Modelling for Examining the Impact of Quality Dimensions in Improving the Adoption of Digital-Learning Platforms Alkhdour, Tayseer; Almaiah, Mohammed Amin; Shishakly, Rima; AlAli, Rommel
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.518

Abstract

Although a number of studies have proved the significance of quality characteristics in improving Digital-learning platforms success, there is few research about the impact of quality dimensions in increasing system adoption and usage. As a result, our research investigated the impact of quality indicators such as Quality of Service, quality of learning content and information, and quality of system on Digital-learning platforms usability. Quality of Service, quality of learning content and information, and quality of system characteristics were determined to be the essential components impacting Digital-learning platforms adoption among learners. The study revealed that system quality was the most critical factor influencing the perceived ease of use and usefulness of Digital-learning platforms. Information quality also had a significant impact on both perceived ease of use and usefulness. Additionally, service quality affected these usability factors as well. The findings indicate that system quality significantly influenced usability factors, specifically perceived ease of use and perceived usefulness (H1: β = 0.321; H2: β = 0.366). Additionally, service quality is found to have a significant effect on both usability factors, ease of use and usefulness (H5: β = 0.371; H6: β = 0.366). Furthermore, the results are essential in determining the importance of those quality components that can be utilized by developers in institutions of higher education to enhance their Digital-learning platforms experiences.
The Impact of Industrial Security Risk Management on Decision-Making in SMEs: A Confirmatory Factor Analysis Approach Almaiah, Mohammad; Mekimah, Sabri; zighed, Rahma; Alkhdour, Tayseer; AlAli, Rommel; Shehab, Rami
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.543

Abstract

This study focuses on the importance of industrial risk management for small and medium-sized enterprises (SMEs) in Algeria, particularly given the administrative, economic, and financial challenges they face, as well as their limited experience in this field. Risk management serves as a strategic tool that aids institutions in achieving safety and sustainability by identifying potential risks that may lead to industrial disasters, such as chemical incidents and technical malfunctions, then analyzing, assessing, and responding to these risks in ways that minimize their impact on the safety of individuals, property, and the environment. The study aims to analyze the impact of risk management on SMEs' ability to make accurate and timely decisions during critical moments while fostering a culture of safety and proactive risk handling. To achieve these objectives, a survey was conducted on a sample of 390 Algerian industrial SMEs. The study employed the Confirmatory Factor Analysis methodology (CB-SEM) to analyze data from these SMEs, which helped in identifying core risk management processes such as risk description, analysis, and conclusion, and evaluating their effectiveness in supporting decision-making. The findings indicate that the impact of the risk description process on decision-making is positive but weak at 14.7%, while the impact of the risk analysis process on decision-making is also positive and weak at 18.9%. However, the effect of the risk conclusion process on decision-making was positive and moderate, at 64.8%. The results further reveal that SMEs that adopt a comprehensive and sustainable approach to risk management have a greater ability to manage disasters and ensure operational safety. The study highlights the importance of regularly reviewing safety protocols, providing training and simulations for employees, improving risk response strategies, and enhancing organizational performance. However, it was observed that some SMEs lack reliance on modern systems for risk avoidance. The study recommends the importance of allocating an independent budget to address potential risks, activating proactive systems for risk prediction, and employing internal and external experts for risk analysis. The study recommends that SMEs focus on developing mechanisms for describing and analyzing risks and collaborating with specialized entities to implement modern systems that support safety and sustainability. Additionally, it advises organizations to raise employees' awareness and provide training on risk handling to enhance the effectiveness of risk management and ensure business continuity.
Assimilate Grid Search and ANOVA Algorithms into KNN to Enhance Network Intrusion Detection Systems Alsharaiah, Mohammad A.; Almaiah, Mohammed Amin; Shehab, Rami; Alkhdour, Tayseer; AlAli, Rommel; Alsmadi, Fares
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.604

Abstract

The recent progress of operational network intrusion detection systems (NIDS) has become increasingly essential. Herein, a fruitful attempt to introduce an innovative NIDS methodology that integrates the grid search optimization algorithm and ANOVA techniques with the K nearest neighbor (KNN) algorithm to analyze both spatial and temporal characteristics of data for network traffic. We employ the UNSW-NB15 benchmark dataset, which presents various patterns and a notable imbalance between the training and testing data, with 257674 samples. Therefore, the Synthetic Minority Oversampling Technique has been used since this method is effective in handling imbalanced datasets. Further, to handle the overfitting issue the K folds cross-validation method has been applied. The feature sets within the dataset are meticulously selected using ANOVA mechanisms. Subsequently, the KNN classifier is fine-tuned through hyperparameter tuning using the grid search algorithm. This tuning process includes adjusting the number of K neighbors and evaluating various distance metrics such as 'euclidean', 'manhattan', and 'minkowski'. Herein, all attack types in the dataset were labeled as either 1 for abnormal instances or 0 for normal instances. Our model excels in binary classification by harnessing the strengths of these integrated techniques. By conducting extensive experiments and benchmarking against cutting-edge machine learning and deep learning models, the effectiveness and advantages of our proposed approach are thoroughly demonstrated. Achieving an impressive performance of 99.1%. Also, several performance metrics have been applied to assess the proposed model's efficiency.