Prenatal screening is the process of analyzing various clinical variables to estimate the risk of genetic disorders like Down Syndrome (DS), which is distinguished by intellectual disability, distinct facial features, and developmental delays. The accuracy of these risk assessments is heavily reliant on the suitability of the risk algorithm for the target population. This study proposes an enhanced machine learning (ML) approach for predicting Down Syndrome (DS) risk using first-trimester screening (FTS) data. The dataset includes clinical information from 959 women with singleton pregnancies at the Çukurova University Gynecology and Obstetrics Unit between 2020 and 2024. To address limitations in existing studies, GPT-4 was utilized to generate synthetic minority-class samples, and advanced feature engineering techniques were incorporated to enhance model robustness and interpretability. A predictive ML model was created, and Hyperparameter Tuning (HT) was applied to optimize it for performance. Eight classifiers were tested, and CatBoost performed the best, achieving 97.39% accuracy and a 2.62% false-positive rate, outperforming the second-best classifier (XGBoost) across all primary evaluation metrics. These improvements highlight the novelty of the framework, particularly its integration of GPT-4–based augmentation and engineered biochemical interaction features. The results demonstrate the model’s potential for reliable DS risk prediction, offering a more efficient and less invasive alternative to traditional diagnostic procedures. By enhancing early risk detection, the method could reduce unnecessary referrals for invasive tests like amniocentesis, thereby minimizing patient anxiety and potential complications. Overall, the study contributes to the development of intelligent, data-driven solutions for prenatal care.

Abstract Memory-based collaborative filtering schemes are among the most effective recommendation technologies in terms of prediction quality, despite commonly facing issues related to accuracy, scalability, and privacy. A prominent approach suggests an intuitively reasonable modification to the similarity function, which has been proven to provide more accurate recommendations than those generated by state-of-the-art memory-based collaborative filtering methods. However, this scheme exacerbates the scalability problem due to additional computational costs and fails to protect individual privacy. In this study, we recommend using a preprocessing method to eliminate relatively dissimilar items from the prediction estimation process, thereby enhancing the scalability of the proposed approach. We explore how to provide recommendations based on the previously proposed similarity function while preserving privacy and propose privacy-preserving schemes to accomplish this task. Additionally, we apply our preprocessing approach to our proposed privacy-preserving schemes to improve both scalability and accuracy. After analyzing our schemes with respect to privacy and additional costs, we conduct experiments with real data to examine the impact of our schemes on scalability and accuracy. The empirical outcomes indicate that our preprocessing scheme significantly alleviates scalability issues in both conventional and privacy-preserving environments and enhances accuracy within privacy-preserving frameworks.

Abstract Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi‐criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi‐criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi‐criteria rating‐based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power‐item attack model, and introduce its four distinct variants adapted for multi‐criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model‐based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles.

Abstract Recommender systems are subject to well‐known popularity bias issues, that is, they expose frequently rated items more in recommendation lists than less‐rated ones. Such a problem could also have varying effects on users with different gender, age, or rating behavior, which significantly diminishes the users' overall satisfaction with recommendations. In this paper, we approach the problem from the view of user personalities for the first time and discover how users are inclined toward popular items based on their personality traits. More importantly, we analyze the potential unfairness concerns for users with different personalities, which the popularity bias of the recommenders might cause. To this end, we split users into groups of high, moderate, and low clusters in terms of each personality trait in the big‐five factor model and investigate how the popularity bias impacts such groups differently by considering several criteria. The experiments conducted with 10 well‐known algorithms of different kinds have concluded that less‐extroverted people and users avoiding new experiences are exposed to more unfair recommendations regarding popularity, despite being the most significant contributors to the system. However, discrepancies in other qualities of the recommendations for these user characteristics, such as accuracy, diversity, and novelty, vary depending on the utilized algorithm.