User experience (UX) is the key to increased productivity by enhancing the usability and interactivity of the product. Machine learning (ML) solutions have raised user and academic awareness of technical innovation. As a result, ML is becoming increasingly popular to improve the quality of UX. Several investigations have highlighted a potential lack of studies on the overall challenges and recommendations for UX using ML. Therefore, more attention should be paid to ML’s existence and potential applications across various applications to get the most out of ML techniques to improve the UX design process. To this objective, a systematic review of the literature was performed as to determine the challenges faced by UX designers when incorporating ML in their design process. Recommendations that help UX designers incorporate ML into UX design will be highlighted. Furthermore, the PRISMA approach is used (a process that has been established in the literature), to restrict the chance of bias at the selection stage. Relevant articles in the following four databases were searched: IEEE Xplore, Scopus, Web of Science, and ACM. The findings revealed that the number of publications on issues linked to UX with ML had advanced exponentially. This review highlights the challenges, recommendations, tools, algorithms, techniques and datasets used in different studies. In addition, suggestions are given for future investigations.

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.

Phishing attacks have evolved into sophisticated threats, making effective cybersecurity detection strategies essential. While many studies focus on either URL or HTML features, limited work has explored the comparative impact of engineered feature sets across different machine learning models. This study aims to bridge that empirical gap by evaluating the effectiveness of URL-based, HTML-based, and derived features, individually and in combination, on phishing URL detection. The proposed approach utilizes the PhishOFE dataset of 101,063 phishing and legitimate URLs. Features are organized into four sets: (1) URL only, (2) HTML only, (3) URL + HTML, and (4) URL + HTML + derived features. Ten machine learning models are employed, including Random Forest, k-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, and advanced ensemble methods such as LightGBM, XGBoost, and CatBoost. Performance is assessed using accuracy, precision, recall, and F1-score, while permutation importance is used to evaluate feature significance. Experimental results demonstrate that ensemble models outperform traditional classifiers, with CatBoost achieving the highest accuracy of 99.45% using the complete feature set. Moreover, URL features like URLLength and NoOfSubDomain consistently rank high in importance, while derived features such as SuspiciousCharRatio and URLComplexityScore notably enhance detection performance in specific models.

To improve Web site, we need to evaluate current usage of it. Web usage mining and statistical analysis are two ways to evaluate usage of Web site. The combination of Web usage mining and statistical analysis gives more accurate information about Web usage. Through Web usage mining methods, graph mining covers complex Web browsing behaviors such as parallel browsing. Through statistical analysis methods, analyzing page browsing time gives valuable information about Web site and its users. This paper presents a Web usage mining method which combines Web usage mining and statistical analysis considering client side data. In other words, it combines graph based Web usage mining and browsing time analysis with taking client side data into account. It helps us to reconstruct user session exactly as it has been and based on these data, we find Web usage patterns with more accuracy.

Phishing website detection has been widely explored, yet most existing surveys focus on detection algorithms or general cybersecurity methods, offering only a brief treatment of feature engineering. This systematic literature review addresses this gap by analyzing recent studies on feature engineering for phishing URL detection using machine learning. The review categorizes feature types, such as URL/lexical, HTML/content, domain, behavioral, and emerging derived and hybrid features, and examines the application of feature selection and dimensionality reduction techniques. The findings reveal a persistent reliance on URL/lexical features for their simplicity and broad applicability, despite growing interest in integrating content-based and composite approaches for improved robustness. Key challenges include adapting to evolving phishing tactics, handling dataset limitations, mitigating high dimensionality, and balancing computational efficiency with detection accuracy. The review also identifies opportunities that have been underexplored in prior work, such as leveraging explainable AI methods (e.g., SHAP, LIME) to validate and optimize feature sets, expanding cross-lingual and adversarial-resilient features, and developing lightweight solutions for real-time or resource-constrained environments. This study offers a targeted roadmap for advancing phishing detection research by consolidating and reviewing the role of feature engineering. It highlights its pivotal role in creating more accurate, adaptive, and efficient detection systems.