An Indexed Approach for Expectation-Confirmation Theory: A Trust-based model

The present study utilised the Expectation-Confirmation Theory (ECT) as a theoretical framework to examine the temporal development of customer trust, satisfaction, and repurchase intent. In subsequent phases of the ECT, the significance of expectations in influencing customers’ attitudes towards confirmed trust and satisfaction was emphasised. The Trust-based Expectation-Confirmation model was therefore proposed to study trust at the appropriate level of abstraction to capture and analyse the relationships between Expected Trust, Perceived Trust, and the Confirmation of Expected Trust. The evaluation of the proposed ECT Trust-based model was conducted through a web-based survey with 559 participants, aiming to examine the direct and indirect approaches of measuring the Confirmation phase. Both approaches were found to be problematic in terms of the gap between the Perceived and Expected construct measured, which cannot be adjusted by the middle point on the Likert scale when using the direct approach either. This research article proposes the Indexed Approach as a new relevant assessment approach to transform data gathered from participants, which were measured throughout the Expectation and Perceived Performance stages, into a common format that could be used to determine each participant’s Confirmation. In order to validate the Indexed Approach, PLS path modelling evaluation and comparison for each approach were conducted; the results indicated that the Indexed Approach was the superior alternative to the direct and indirect approaches for transformation confirmation data to be used in the ECT model

Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering

Text clustering is suitable for dividing many text documents into distinct groups. The size of the documents has an impact on the performance of text clustering, reducing its effectiveness. Text documents often include sparse and uninformative characteristics, which can negatively impact the efficiency of the text clustering technique and increase the computational time required. Feature selection is a crucial strategy in unsupervised learning that involves choosing a subset of informative text features to enhance the efficiency of text clustering and decrease computing time. This work presents a novel approach based on an improved Prairie dog algorithm to solve the feature selection problem. K-means clustering is employed to assess the efficacy of the acquired subgroups of features. The proposed algorithm is being compared to other algorithms published in the literature. The feature selection strategy ultimately promotes the clustering algorithm to get precise clusters

Ant Lion Optimizer: A Comprehensive Survey of Its Variants and Applications

This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO’s applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.No Full Tex

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Abstract Due to the advantages offered by AI in containment the COVID-19 pandemic, the number of AI techniques has increased greatly. Although these techniques provide an acceptable start to COVID-19 pandemic control, they differ in terms of purpose, AI synthesis methods, datasets, validation approach. This increase and diversity in the numbers of proposed AI techniques can confuse decision makers and lead them to the dilemma of what is the appropriate technique under the specific conditions. Yet, studies that assess, analyze, and summarize the unresolved problems and shortcomings of current AI techniques for COVID-19 are limited. In the existing review studies, only individual parts of AI techniques, rarely the full solution, are reviewed and examined. Thus, this study aims to present a comprehensive systematic review on the application of AI techniques in containment the COVID-19 pandemic. The applied search strategy led to include 73 papers related to the Application of AI techniques for COVID-19 published from December 2019 to May 2020. Ten applications of AI for containment COVID-19 were identified. In addition, the analysis results of the systematic review revealed five deficiencies so that future research should take them into consideration.</jats:p

Fortifying network security: machine learning-powered intrusion detection systems and classifier performance analysis

Intrusion detection systems (IDS) protect networks from threats; they actively monitor network activity to identify and prevent malicious actions. This study investigates the application of machine learning methods to strengthen IDS, explicitly emphasizing the comprehensive CICIDS 2017 dataset. The dataset was refined by implementing stringent preprocessing methods such as feature normalization, class imbalance management, feature reduction, and feature selection to ensure its quality and lay the foundation for developing robust models. The performance evaluation of three classifiers-support vector machine (SVM), extreme gradient boosting (XGBoost), and naive Bayes was highly impressive. Vital accuracy, precision, recall, and F1-score values of 0.984389, 0.984479, 0.984375, and 0.984304, respectively, were achieved by SVM. Notably, XGBoost demonstrated exceptional performance across all metrics, attaining flawless scores of 1.0. naive Bayes demonstrated noteworthy accuracy, precision, recall, and F1-score performance, which were recorded as 0.877392, 0.907171, 0.877007, and 0.876986, respectively. The results of this study emphasize the critical importance of preparation methods in improving the effectiveness of IDS via machine learning. This further demonstrates the potential of particular classifiers to detect and prevent network intrusions efficiently, thereby substantially contributing to cybersecurity measures

Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering

Text clustering is suitable for dividing many text documents into distinct groups. The size of the documents has an impact on the performance of text clustering, reducing its effectiveness. Text documents often include sparse and uninformative characteristics, which can negatively impact the efficiency of the text clustering technique and increase the computational time required. Feature selection is a crucial strategy in unsupervised learning that involves choosing a subset of informative text features to enhance the efficiency of text clustering and decrease computing time. This work presents a novel approach based on an improved Prairie dog algorithm to solve the feature selection problem. K-means clustering is employed to assess the efficacy of the acquired subgroups of features. The proposed algorithm is being compared to other algorithms published in the literature. The feature selection strategy ultimately promotes the clustering algorithm to get precise clusters.</jats:p