Abstract: The field of online pharmacies is expanding rapidly and has become a crucial component in delivering pharmaceutical services across Saudi Arabia. This study aims to explore the factors influencing the adoption of online pharmacies by the Saudi population, utilizing an enhanced version of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This study employed a cross-sectional design, utilizing an online questionnaire that was pre-validated and shared via social media channels between August 2024 and October 2024. The study focused on Arabic-speaking individuals aged >18 years. The gathered data were examined through structural equation modeling (SEM) using AMOS software (v.22.0). The study found that the UTAUT model demonstrates strong applicability in explaining the adoption of online pharmacies, accounting for 62.4% (R² = 0.624) of the variance in behavioral intention. Among the key factors, effort expectancy (p < 0.001) emerges as the most influential predictor of behavioral intention, followed by facilitating conditions (p < 0.001) and health literacy (p < 0.001). However, the analysis reveals that perceived risk (p > 0.05) and personal innovativeness (p > 0.05) do not have a statistically significant impact on behavioral intention. The study results would help formulate more effective strategies for establishing online pharmacy operations in developing nations like Saudi Arabia. This study contributes to enhancing the traditional UTAUT model through the integration of additional factors such as health literacy, personal innovativeness, perceived risk, and perceived trust. These variables introduce new dimensions to the existing literature on online pharmacies.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Social commerce is an effective instrument for enterprises aiming to expand their customer base and enhance revenues. By mastering the implementation of social media platforms (SMPs) and surmounting the accompanying hurdles, brands may achieve significant achievements in social commerce. The article investigates the impact of social commerce information sharing (SCIN) on purchase intention and perceived risk in Turkey. This examines the influence of SCIN on customer relationship quality (CRQ) dimensions, including brand trust, commitment, and satisfaction. The study precisely intends to investigate the mediating of CRQ dimensions in the relationship between SCIN and purchase intention. The study also examines the mediator role of perceived risk in the relationship between SCIN and purchase intention. Also, the study examines how online reviews moderate the relationship between SCIN and customer outcomes such as purchase intention and perceived risk. The current study employs a sample of 314 participants from Turkey to explore the relationship between SCIN, brand trust, commitment, satisfaction, purchase intention, and perceived risk. The proposed conceptual model is tested using the Structural Equation Modeling-AMOS statistical approach. The results show that SCIN strongly predicts perceived risk, purchase intention, and CRQ dimensions, such as brand trust, commitment, and satisfaction. Furthermore, the study reveals that perceived risk does not directly mediate the relationship between SCIN and purchase intention. Instead, it confirms that purchase intention is a significant consequence of CRQ dimensions and perceived risk. The results also indicate that online reviews do not moderate the relationship between SCIN and customer outcomes, such as perceived risk and purchase intention. In summary, this study underscores the pivotal role of SCIN in influencing the decision-making process of Turkish customers, particularly in the context of making purchases. The findings carry significant practical implications for marketers of SMPs aiming to influence Turkish consumers, providing valuable insights to enhance their strategy in the Turkish market.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In the rapidly evolving landscape of higher education, Social media marketing (SMM) has evolved a critical factor in shaping brand perception (B.P.) and the decision-making process of prospective students in India. This paper intends to explore the intricate dynamics among Social media (S.M.), influencer marketing, and the selection of higher academic institutes (HEIs), focusing on understanding how these factors shape students' perceptions. We conducted this research on 560 students, who represented the research's population. SEM-PLS was applied to analyze the data and acquire procedures. Surveys on the Internet were used. Employing exogenous/endogenous elements to create sequences, the SEM approach examines causal associations among elements. It provides solutions to research into causation in dimensional and structural frameworks. According to the research's findings, selecting HEI is positively impacted by SMM initiatives. The aspects of SMM activities (electronic word-of-mouth (eWOM), personalization, interaction, and trendiness) affect the HEI selection. In addition, personalization and two have an impact on perceptions of the brand. Understanding and utilizing the efficacy of S.M. and influencer marketing will be crucial for HEIs looking to draw in and hold on to potential students as the higher education environment keeps evolving in the age of digitization.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In a context where ICTs are indispensable elements for the development of business activities, it is necessary to know their influence on the internal improvement of processes. This research seeks to establish the influence of ICTs on the competitive advantage of micro-enterprises in Huancayo 2024. Research developed under a basic type study, has a quantitative approach with a correlational and cross-sectional level, the sample was made up of 59 entrepreneurs in the bakery sector in the province of Huancayo. Data analysis and processing was carried out using structural equations based on PLS. The study obtained the following results: a Sperman 's Rho correlation coefficient of 0.821 with a significance level of ,000 which shows a high and positive degree of influence between ICTs and competitive advantage as well as its different dimensions Level of use, alignment of use and training; Likewise, the general hypothesis is accepted, which establishes that there is a significant relationship between Information and Communication Technology ( ICTs ) and the competitive advantage in SMEs in the pastry sector - Huancayo 2024.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This research explores the impact of social capital on the adoption of digital applications within Micro, Small, and Medium Enterprises (MSMEs). In the current competitive environment, embracing digitalization is essential for improving the competitiveness of MSMEs; however, challenges such as weak social networks can impede technology uptake. Utilizing a quantitative approach, the study distributed questionnaires to 160 MSME participants, with data analysed through path analysis using SmartPLS. The findings indicate that social capital—which includes relationships, networks, and trust among business stakeholders—plays a critical role in facilitating access to and adoption of digital applications. Results suggest that MSMEs with strong social networks are more adept at integrating digital technologies, fostering innovation, and improving overall business performance. The study recommends initiatives to enhance collaboration and strengthen social networks among MSMEs to support their digitalization efforts. Ultimately, this research deepens the understanding of social capital's influence on MSME digitalization and offers practical strategies for stakeholders to increase the utilization of digital applications in this sector.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancemenThe purpose of this study is to: (1) evaluate the technological skills of the final-year undergraduate students, (2) how such abilities influence their likelihood of getting employed and (3) student opinions on whether a computer lab-based specialization in software training can boost employability. This study is a survey-based methodology. The sample size encompasses 140 final year students in the College of Business Administration at Prince Sattam bin Abdulaziz University during the academic year 2023-2024. Descriptive statistical analyses indicate that most students believe they have sufficient technical skills but not enough for securing better jobs. Moreover, it is clear among them that expertise in specialized software packages enhances career prospects significantly. The research results also show a huge gap between technological competencies learners have now and what employers demand currently. In response, this study suggests that PSAU should establish software laboratories in their colleges for specialized training on software as required by the job market and workplace. The Vice Rectorate for Academic and Educational Affairs launches a program called “Graduate and Professional Skills Development Program (PSAU-GPSDP)”, which emphasizes student employability as it develops, implements, and evaluates mechanisms to enhance students’ chances of getting jobs upon graduation. The results of this study are in line with SDG No. 8 (Decent Work and Economic Growth), SDG No. 4 (Quality Education), and Saudi Vision 2030. Therefore, this study has practical implications for decision-makers at the Ministry of Education and university levels, university professors, researchers on how employability skills of students could be enhanced in the higher education institutions.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The growth in Information and Communication Technology has brought dynamic changes to financial payment systems through smart devices, and mobile payment systems are significant among different smart payment systems. Further, the Technology Acceptance Model's components affect the smart payment user's behavior. Moreover, students are a substantial part of society and are more inclined to use mobile payments. Therefore, the present research examines the influence of technology adoption factors on the perception of students using mobile payments. The study adopted TAM components as influencing factors, such as Perceived usefulness, perceived ease of use, Perceived cost, and Perceived trust. The data was collected from 100 respondents consisting of male and female students. The study employed simple regression analysis to report the results. The results show that the perception of students towards the use of m-payment is strong, with a mean of 1.52. The result is similar to the explanatory variables, ranging from 1.77 to 1.97. The study found that the technology adoption factors, such as Perceived usefulness, perceived ease of use, and Perceived cost, positively influenced the students' perception of using mobile payments with p-values ranging from 0.001 to 0.049. The results of Perceived trust were positive but insignificant. Therefore, the present research observed a significant influence of technology adoption factors on the perception of students using mobile payment.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The purpose of the study was to examine the effectiveness of the Mobile Food Ordering Application (MFOA) in a collectivist country like Pakistan. Data was gathered using an online survey-based approach from 354 MFOA users and was analyzed using the structural equation modeling technique through Smart PLS 3.0. The results show that consumers’ online reviews strongly influence customer satisfaction and continued intention. Similarly, price value and online tracking of food services are strongly associated with customer satisfaction. Consumer habits and facilitation conditions are significantly associated with consumer continued intention. Habit is also found to partially mediate consumer satisfaction and continued intention. The study did not find any support for performance expectancy, effort expectancy, social influence, price value, hedonic motivation, or online review with continued intention. Similarly, performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation were not associated with consumer satisfaction. The present work is the first of its kind that has empirically examined the effectiveness of MFOAs in Pakistan. It lays down useful practical implications for practitioners, policymakers, and academia.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The rapid digitalization in recent years has opened up many technological possibilities, gradually transforming various sectors and society as a whole. This digital shift has enabled advancements in a number of fields, leading to improved resource efficiency, systems and processes. The Internet of Things (IoT) refers to a system of interconnected devices that share information that exchange information with one another via the internet. IoT devices are now everywhere, found in applications ranging from unmanned aerial vehicles to smart home environments, from the Industrial Internet of Things to the Internet of Medical Things. The core concept of IoT revolves around establishing a seamless and intelligent communication ecosystem, facilitating interactions between devices over the internet. This is anticipated to create new opportunities for enhancing services in various societal sectors, such as transportation, farming and smart cities. However, IoT-based networks face limitations and challenges that hinder the realization of their full potential. This paper outlines these challenges and proposes solutions, emphasizing the importance of collaboration and innovation. The paper also anticipates future trends in IoT, particularly the integration of 5G connectivity, cloud computing and AI, and identifies areas for future research to address current challenges and explore new applications.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Eco-friendly business practices range from energy efficiency to sustainable management; therefore, the implementation of these practices not only has the potential to mitigate environmental impacts, but also to improve the long-term competitiveness and sustainability of companies. In this study we analyze eco-friendly business practices and their impact on environmental sustainability in companies in a region of the Peruvian Amazon. A quantitative, non-experimental approach was considered, with an explanatory design; for which, a sample size of 200 companies was considered, which were randomly selected, to which two instruments were applied, the first with 7 indicators and the second with 3, valid, with an alpha of 0.720 and 0.670 respectively, assessing structural equation modeling. The results show that eco-friendly business practices are oriented towards sustainability (C.R. = 19.280), as well as preventive and eco-efficient practices (C.R. = 5.023, C.R.= 14.185); these findings can serve as a basis for the creation or improvement of public policies that promote eco-friendly business practices, encouraging companies to adopt measures that favor environmental sustainability. Therefore, eco-friendly business practices have a positive impact on environmental sustainability in companies in a region of the Peruvian Amazon; in addition, preventive practices and eco-efficient practices have a positive impact on environmental sustainability in companies in a region of the Peruvian Amazon.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The primary objective of this study was to examine the antecedents of digital transformation (DT) within multinational corporations (MNCs) in Malaysia, from the perspectives of corporate managers. Amidst limited research on DT within the MNC context, this paper examines the key drivers of DT in Malaysian MNCs. A quantitative method using non-probability sampling was used to gather the required data via the distribution of questionnaires among the MNCs’ managers. To evaluate the underlying theoretical model based on the collected data, we chose SmartPLS as the preferred method. Findings revealed that business value, digital leadership, inter-functional coordination and decision-making quality were significant drivers of DT, while DT exerted a positive influence on business performance. However, collaborative innovation did not have a significant relationship with digital transformation adoption in MNCs. The findings offer novel insights for both academics and international corporate managers, enhancing their understanding of the drivers behind DT adoption from the perspective of managers within MNCs in Malaysia.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This paper presents RIFD-DLT, an advanced near-lossless image compression algorithm that combines Delta Encoding with the original rounding the intensity followed by division (RIFD) method. The RIFD method first minimizes the image intensities, which makes the next compression stages more efficient. Subsequently, Delta Encoding subtracts neighboring rows in each of the image's three-color matrices, using the proximity of pixel values in adjacent rows to further reduce the image intensity. Extensive investigations show that RIFD-DLT outperforms the state-of-the-art algorithms and benchmarks with respect to compression ratio and processing time. More specifically, RIFD-DLT compresses data from 11520 KBs to 2131 KBs with an 81.93% reduction and a 43.7% improvement over original RIFD-Huffman when compressing Kodak Image set. When comparing the RIFD-DLT with LICA algorithm, the total file size is reduced by 71.2%, representing a 10.73% improvement for RIFD-DLT. Also, RIFD-DLT shows notable speed gains over RIFD-Huffman, requiring only 34.62 seconds to compress and decompress all images from three datasets (Waterloo, Kodak and EPFL), as compared to 50.99 seconds for RIFD-Huffman. As for the image quality, the proposed algorithm achieved an average PSNR values of 58.51 dB, 51.3 dB, and 52.22 dB for the EPFL, Kodak, and Waterloo image sets, respectively, demonstrate the excellent image quality that persists after decompression, with a minimal distortion that is imperceptible to the human visual system and identically to the RIFD-Huffman PSNR. These findings show that, while preserving excellent image quality, RIFD-DLT provides an incredibly efficient and fast method of image compression.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This research aims to examine how data privacy concerns influence DOI in Digital Marketing and investigates how artificial intelligence trust mechanism integration modulates that effect. Primary data was collected by accessing the structured questionnaire targeting ZAIN Telecom employees as the chosen study site. The PLS-SEM technique was used in this research to analyze data privacy, digital marketing effectiveness, and AI-driven trust constructs. According to this study, digital marketing processes significantly focus on data privacy and, ultimately, AI-driven trust. Further, powerful AI trust systems will be required to reinforce data privacy and systematically configure and drive digital marketing enterprises. Thus, such systems could allow firms a sustainable competitive advantage in this new Battle for data security age. The findings discovered that data privacy concerns significantly impede AI-driven trust, diminishing digital marketing effectiveness. Therefore, they contribute to the literature by offering empirical support for AI-powered trust mediating between factors within a model and offering practical implications and extensions of the theoretical models. This literature helps industry practitioners and policymakers build trust in AI interventions to alleviate data privacy risks and improve support for digital marketing strategies.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The accurate and efficient classification of leukemia images is crucial for early diagnosis and effective treatment planning. Traditional methods often face challenges in handling the complexity and variability of medical images. To address these challenges, we propose a novel approach that leverages the Gray Level Co-occurrence Matrix (GLCM) and statistical feature-based segmentation techniques. In this paper, we present a comprehensive framework for the automated classification of leukemia images using advanced image processing techniques. The methodology involves six key stages: input of leukemia images, preprocessing to enhance image quality, segmentation to isolate relevant features, feature extraction using texture analysis, classification using multiple distance metrics Euclidean, Manhattan, Canberra, and Chebyshev, and performance evaluation. Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. For instance, using the Chebyshev distance, we achieved an average accuracy of 82.69%, sensitivity of 85.95%, and specificity of 82.77%. The Canberra distance provided optimal performance with 65 features, yielding an accuracy of 85.18%, sensitivity of 86.39%, and specificity of 86.31%. These findings underscore the efficacy of our approach in distinguishing between healthy and leukemic cells, thereby contributing to early diagnosis and effective treatment planning for leukemia.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The Kingdom of Saudi Arabia has witnessed a significant surge in online shopping in recent years, fueled by factors like growing internet penetration, smartphone adoption, and government initiatives supporting e-commerce growth. This rise in online activity has led to a corresponding increase in the utilization of online courier services, playing a crucial role in ensuring timely and efficient delivery of goods In this context, understanding public perception of online courier services becomes crucial for businesses to improve their offerings, address customer concerns, and maintain a competitive edge. Social media platforms have emerged as a valuable source of customer feedback and user-generated content, offering insights into customer experiences and opinions. This paper presents a sentiment analysis on online couriers in Saudi Arabia using natural language processing techniques combined with Decision Tree and Support Vector Machine (SVM) classifiers of machine learning. A dataset on customers’ sentiments was created by a crawling process from X social media. Both classifiers perform well, with Decision Tree classifier performs slightly better on accuracy, i.e. 95.01% compared to 93.60% of the Support Vector Machine. Other metrics support the robustness of the classification.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Augmented reality (AR) has garnered considerable interest for its potential to motivate engagement and advance customer-brand interactions. This study explored the impact of reality congruence (RC) of AR-menus on brand information sharing (BIS) in fast-food restaurants, particularly among Generation Z (Gen Z), as well as the mediating effects of usefulness of AR menu (UAR) and brand positivity (BP) on this relationship. Media Richness Theory (MRT) was employed as the theoretical umbrella for developing the study model. To validate the research model, we employed structural equation modeling (PLS-SEM) with a sample of 209 respondents. The results demonstrate that reality congruence of AR menus is a relevant predictor of Gen Z members’ behavior in sharing information about the brand. Furthermore, this relationship was mediated by the UAR and BP. The findings also demonstrated that UAR and BP had a sequential mediating effect on the relationship between the RC of AR-menu and BIS. This revolutionary study revealed that RC of AR-menu in restaurants fosters positive behaviors in fast food settings. By highlighting AR's potential to create engaging dining experiences for Gen Z members, this study offers valuable insights for service businesses, marketing managers, and the hospitality industry. Addressing this gap in existing research emphasizes the importance of adopting innovative technologies to enhance Gen Z's customer experience and engagement in the restaurant industry.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aimed to determine the impact of digital transformation (DT) and digital accounting (DA) on the financial reports quality (FRQ) in companies listed in the Saudi capital market. Data were collected using a questionnaire distributed via email. The research sample consisted of 116 individuals, including accountants and executive directors in companies listed. The study adopted the smart PLS method to test hypotheses. The study found that there is an incomplete positive relationship between digital transformation and the quality of financial reports through the positive impact on the characteristics of relevance and understandability, as well as the existence of a complete positive relationship between digital accounting and the FRQ through the impact on all characteristics of accounting information.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. Currently, existing systems use frequency-based methods for feature selection (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)) not to capture the subtleties of emotions expressed in student feedback and do not provide insights into the specific concerns of students via topics or themes. In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. The SSF framework classifies student sentiments and subsequently groups feedback into themes using semantic networks based on word co-occurrence. Our innovative feature selection approach combines TF-IDF with sentiment-based features derived from SentiWordNet and intensifiers, creating a robust feature vector that enhances the dataset’s richness and improves classification accuracy and robustness. In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)