Hybrid Filter Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data

In this study, we present a novel approach to improve cancer classification using high-dimensional microarray data. The proposed method combines a hybrid filter and a genetic algorithm-based feature selection process, incorporating Chi-square and Recursive Feature Elimination (RFE) techniques to identify critical gene expressions for cancer classification. Experiments using diverse datasets have yielded significant results. In the Lung Cancer Dataset, Logistic Regression Analysis (LR) and Support Vector Machine (SVM) achieved remarkable accuracy rates of 97.56%, with a precision and recall of 98.0%, resulting in an F1-score of 97.0%. This highlights the effectiveness of the feature selection method in enhancing classification accuracy. In the Ovarian Cancer Dataset, Gradient Boosting emerged as the top-performing classifier, achieving an accuracy of 92.85% along with precision, recall, and F1-score values of 94.0%, 93.0%, and 92.0%, respectively. These results demonstrate the versatility of the proposed feature-selection approach. This demonstrates the adaptability of the proposed feature selection technique in improving classifier performance. In summary, the hybrid filter and genetic algorithm-based feature selection method, incorporating Chi-square and RFE, proved to be a valuable tool for enhancing cancer classification in high-dimensional microarray data. The consistently high accuracy, precision, recall, and F1-score across diverse cancer datasets underscore the effectiveness and versatility of the proposed approach, holding promise for the development of more accurate cancer classification models in the future

The s-Commerce usage and acceptance modelling in Malaysia

The evolution of technology acceptance theories and models have started since the beginning of the 20th century and it is still evolving. This evolution is happened in different theoretical perspectives, such as: cognitive, affective, motivational, and behavioral intentions and reactions for individuals. Nowadays, understanding the reason of accepting or rejecting any new technology by users has become one of the most important areas in the IT field. The social media applications are benefited and enhanced the E-Commerce, Electronic Marketing (E-Marketing), and Electronic Shopping (E-Shopping) usage behaviors to get any information of any offered commodity in the easiest, fastest, and most familiar way, that will increase the retail profit as well. Social Commerce (S-Commerce) has become one of the most important fields and one of the fastest growing areas of the high technology sector development, especially in the trading and commercial environments. In this scope, it is presenting here the theories and models which were developed to study the acceptance by users and their adoption for new technology. this study adheres to the methodology of quantitative research, which offers a numerical measurement and analysis of the factors that determine adoption for samples 30 as a pilot study in Malaysia as a limit of this research specifically among 2 Malaysian Universities, that will lead to distribute the updated survey around 484 samples later. That results a high ratio of questionnaire validity and the effectiveness of the research hypothesis also found that the new model identifies the factors affecting S-Commerce usage behavior and continued usage intention, find the relationship between education and S-Commerce usage behavior and found such relationship between age and S- Commerce usage behavio