Cardiovascular disease (CVD) is a leading cause of death worldwide, and early detection and prevention are essential to reduce its burden. Although traditional risk factors such as hypertension and lipid levels are standard predictors, they tend to identify the risk of disease at a later date There is an urgent need to develop more accurate, leading-edge biomarkers indicating molecular changes that occur before the onset of clinical symptoms. Epigenetic changes, in particular DNA methylation, provide a promising approach to identify individuals at risk for CVD. Despite advances in understanding the genetic underpinnings of CVD, little is known about how dynamic epigenetic modifications influenced by environmental and lifestyle factors can serve as early predictive biomarkers the risk of the disease. This study aims to address this gap by investigating whether DNA methylation patterns can predict future CVD risk before clinical manifestations. The primary objective of this study was to systematically investigate the association between DNA methylation patterns and CVD risk. Specifically, the study seeks to identify regions where DNA methylation changes are associated with cardiovascular outcomes and investigate the predictive potential of these epigenetic changes for future disease. 1,000 participants aged 30-75 years were followed for 5 to 10 years. Periodic blood samples were collected for DNA methylation analysis by bisulfite sequencing and microarrays. Major CVD risk factors such as adiposity, hypertension and smoking status were also controlled. Comprehensive statistical models were used to assess the association between DNA methylation changes and CVD incidence. Participants who developed CVD showed significant differences in DNA methylation in regions associated with lipid metabolism (e.g., APOA5) and inflammation (e.g., TNF-α) Over time, those who developed heart disease and vascular events revealed progressive hyper methylation of key genes, with 85% sensitivity 78 to predict future CVD risk % and specificity skin. These methylation patterns were discovered years before the appearance of conventional clinical pathology. This study shows that DNA methylation patterns are determinants of acute cardiovascular risk, providing a novel approach for early detection and prevention The findings suggest the inclusion of epigenetic biomarkers in routine risk assessment about may improve the identification of individuals at high risk for CVD It is important to inform individuals at high risk of CVD of the diagnosis and to explore possible interventions that can alter change a occurs in this epigenetic and reduced CVD risk.

Recommender systems are essential engines to deliver product recommendations for e‐commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF‐IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF‐IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e‐commerce recommender systems with sparse data issues.

Growing global awareness that attention to health care is the basis for maintaining citizens' quality of life. Health institutions seek to increase interest in electronic care services and enhance patient results by integrating artificial intelligence techniques. Artificial intelligence tools are indispensable to diagnosis, treatment, and patient care. Integrating artificial intelligence techniques into the development of the electronic healthcare environment works to enhance public health and disease prevention and provide free services to all citizens. Designing electronic platforms raises health awareness in society, provides health programs and initiatives, and reaches homes, gardens, schools, and universities through applications based on artificial intelligence. The primary purpose of this article is to challenge the extent to which artificial intelligence is related to medicine and its contribution to the positive and negative effects of revolutionizing healthcare services.

This research explores the impact of engagement metrics on predicting customer conversion rates within digital marketing, employing three advanced predictive modeling techniques: Logistic Regression, Decision Trees, and Random Forests. Using a comprehensive dataset of 8,000 customer interactions, the study evaluates critical engagement metrics such as PagesPerVisit, TimeOnSite, and EmailClicks to determine their influence on conversion outcomes. The results indicate that PagesPerVisit and TimeOnSite are the most significant predictors of customer conversion, with the Random Forest model outperforming others, achieving an accuracy of 87.1% and an ROC-AUC score of 0.6979. The Logistic Regression model demonstrated the highest recall for the conversion class at 99.8%, but its performance in predicting non-conversions was less robust, highlighting the challenges of imbalanced datasets. Decision Trees, while offering valuable interpretability, showed a lower accuracy of 79.6% and struggled with precision in identifying non-conversions. These findings suggest that enhancing on-site customer engagement and refining email marketing strategies are pivotal for improving conversion rates. The study contributes to the field of digital marketing analytics by providing empirical evidence on the relative importance of various engagement metrics and offering practical insights for optimizing digital marketing strategies. Additionally, it highlights the benefits of using ensemble methods like Random Forests to achieve more balanced and accurate predictions in customer conversion scenarios.