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Produção científica recente
Cost-sensitive random forest with adaptive threshold optimization for imbalanced financial fraud detection
Background: Financial transaction fraud detection is a challenging binary classification problem characterized by severe class imbalance, rare fraudulent cases, and asymmetric misclassification costs. In real-world banking systems, failing to detect fraudulent transactions can lead to substantial financial losses, whereas false alarms mainly increase operational review costs. Therefore, fraud detection models should not be optimized only for overall accuracy, as accuracy-oriented classifiers may favor the majority non-fraud class and fail to identify minority fraud cases effectively. To address this issue, this study proposes a cost-sensitive ML framework for financial transaction fraud detection under class imbalance and asymmetric financial risk.Methods: In this study, a cost-sensitive RF framework with adaptive probability threshold optimization is proposed for binary fraud classification. Misclassification costs are incorporated during model training through class-weighted learning, allowing the classifier to assign greater importance to minority fraud cases. In addition, instead of using the default probability threshold of 0.5, the decision threshold is optimized using validation data to minimize the expected misclassification cost and improve minority-class detection. The proposed framework was evaluated on two publicly available financial fraud datasets with different transaction characteristics: the Credit Card Fraud Detection dataset and the Financial Fraud Prediction dataset. Its performance was compared with conventional ML baselines using fraud-oriented evaluation metrics, including precision, recall, F1-score, false-negative rate, false-positive rate, and expected misclassification cost.Results: The proposed framework achieved strong and consistent performance across both datasets. On the Credit Card Fraud Detection dataset, it achieved a precision of 94.4%, recall of 85.7%, and F1-score of 89.8%. On the Financial Fraud Prediction dataset, it achieved a precision of 93.1%, a recall of 83.8%, and an F1-score of 88.2%. Compared with conventional fixed-threshold ML models, the adaptive threshold strategy reduced false-negative predictions while maintaining high precision. These results indicate that the proposed cost-sensitive framework provides a more balanced decision strategy for fraud detection under highly imbalanced transaction distributions.Conclusion: The findings demonstrate that combining class-weighted learning with adaptive threshold optimization can improve fraud detection performance under asymmetric misclassification costs. The proposed framework offers a practical and interpretable decision-support approach for banking fraud detection systems, particularly in settings where reducing undetected fraudulent transactions is more important than maximizing overall accuracy alone.
Dietary microRNAs from dairy products and their potential relevance to breast cancer: A systematic review
Background The potential role of dairy products in breast cancer development remains controversial, with studies reporting both protective and detrimental effects. MicroRNAs, small non-coding RNA molecules involved in post-transcriptional gene regulation, are increasingly recognized as important mediators in cancer biology, including breast cancer. Milk and other dairy products contain extracellular vescicle-associated microRNAs, some of which are conserved, bioactive, and capable of surviving digestion. Additionally, dairy intake may modulate the expression of endogenous microRNAs in humans. Scope and approach This review explores the landscape of dairy-related microRNAs—both exogenous microRNAs naturally present in dairy products and endogenous microRNAs influenced by dairy consumption—and examines their potential overlap with those implicated in breast cancer. A structured PubMed search identified studies addressing: (1) the most relevant microRNAs detected in dairy products, (2) human-based studies investigating microRNA modulation following dairy intake, and (3) reported associations with breast cancer across clinical and molecular contexts. Key findings and conclusions We identified a group of dairy-associated microRNAs—particularly, miR-148a-3p-, miR-21-5p, miR-30a-5p-5p, miR-125b-5p, miR-26a-5p, miR-155-5p, and miR-146a-5p—that have been linked to key processes involved in breast cancer development, including cell proliferation, apoptosis, epithelial-to-mesenchymal transition, and chemoresistance. Some of these microRNAs are also differentially expressed in mastitic milk or modulated by farming systems and stress-related conditions, highlighting the need for interdisciplinary approaches bridging food systems and human health. While causal inference is beyond the scope of this review, the widespread and sustained consumption of dairy products supports the need for further research to clarify how dairy intake may influence microRNA-mediated regulatory mechanisms and breast cancer risk in humans. The available evidence also suggests that dairy subtype, origin, and processing may be relevant when considering potential dietary implications.
Dietary (Poly)phenol Intake, Inflammatory Biomarkers, and Mild Cognitive Impairment in Italian Adults
Background: Dietary (poly)phenols have been proposed to protect against age-related cognitive decline through antioxidant, anti-inflammatory, and neuroprotective mechanisms. However, evidence specifically addressing mild cognitive impairment (MCI) remains limited. This study investigated the association between habitual dietary (poly)phenol intake, cognitive status, and circulating inflammatory biomarkers in older adults. Methods: In this cross-sectional study, Italian adults aged ≥65 years attending a memory clinic underwent standardized neuropsychological assessment for MCI diagnosis. Habitual dietary intake was assessed using a validated food frequency questionnaire, and dietary (poly)phenol intake was estimated using the Phenol-Explorer database. Plasma transforming growth factor-β1 (TGF-β1) and tumor necrosis factor-α (TNF-α) concentrations were measured by ELISA. Logistic regression analyses were performed to evaluate associations between quartiles of (poly)phenol intake and MCI after adjustment for energy intake, age, sex, education, and smoking status. Results: Participants without MCI reported significantly higher intakes of total (poly)phenols and several subclasses, including flavonoids, anthocyanins, flavanones, flavones, hydroxycinnamic acids, and lignans. Higher intakes of flavanones, flavones, lignans, hesperetin, naringenin, lariciresinol, matairesinol, and pinoresinol were positively associated with cognitive performance. Individuals in the highest quartile of total (poly)phenol intake had substantially lower odds of MCI (adjusted OR = 0.08; 95% CI: 0.01–0.62), with similar inverse associations observed for flavonoids (OR = 0.16; 95% CI: 0.02–0.92), lignans (OR = 0.18; 95% CI: 0.04–0.89), flavones (OR = 0.18; 95% CI: 0.04–0.87), naringenin (OR = 0.19; 95% CI: 0.04–0.92), lariciresinol (OR = 0.18; 95% CI: 0.04–0.89), matairesinol (OR = 0.19; 95% CI: 0.04–0.93), and pinoresinol (OR = 0.16; 95% CI: 0.03–0.82), when compared to the lowest quartile. Total (poly)phenol intake was not associated with circulating inflammatory biomarkers, although biochanin A and caffeic acid were inversely associated with TNF-α concentrations. Conclusions: Higher habitual intake of selected dietary (poly)phenols, particularly flavonoids and lignans, was associated with a lower likelihood of MCI and better cognitive performance in older adults. However, the absence of consistent associations with inflammatory biomarkers does not support inference regarding an anti-inflammatory mechanism. Given the observational design, small sample size, and multiple statistical comparisons, these findings should be considered exploratory and hypothesis-generating. Prospective studies and randomized controlled trials are needed to confirm these associations and clarify the underlying biological pathways.
Framework to Detect Adulterated Honey Using Hyperspectral Imaging and Machine Learning
Honey is a natural product renowned for its nutritional and medicinal properties. However, the increasing market demand in Pakistan has led to frequent instances of adulterated and fraudulent honey. Conventional detection methods are widely used but are often ineffective in accurately identifying adulteration. This study proposes a novel approach to detecting adulteration in honey, specifically the mixing of sugar, using machine learning (ML) techniques in conjunction with hyperspectral imaging (HSI). By leveraging HSI, we capture distinct spectral features of both pure and adulterated honey. These spectral features are processed and analyzed using ML algorithms trained on a dataset comprising pure and mixed honey samples. Experiments involve binary, as well as, multiclass (5 classes) honey samples with different levels of adulteration. In addition, data balancing is also carried out using synthetic minority oversampling technique (SMOTE). The results prove the proposed approach to be efficient in detecting adulteration across various honey varieties, with linear regression achieved 99.9% while support vector machine and multilayer perceptron obtaining a 98.7% accuracy for binary class. For the multiple classes of honey, the multilayer perceptron shows a 96.67% accuracy outperforming the remaining ML and deep learning models. Among the deep learning models, the convolutional neural network (CNN) achieved the best performance with an accuracy of 94.67% after applying the SMOTE × 3 (three times of over samples of original samples of each class) data balancing strategy, whereas recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models showed comparatively lower performance.
Algorithmic human resource management and employee well-being: currentevidence from a systematic literature review
This study examines the relationship between artificial intelligence (AI), algorithmic human resource management, and employee well-being through a systematic review of the scientific literature published between 2020 and 2026. The review addresses the growing need to understand how algorithm-supported decision-making influences employee experience, psychological well-being, organizational trust, and perceptions of fairness in contemporary workplaces. The study followed the PRISMA 2020 guidelines and included peer-reviewed articles retrieved from Scopus, Web of Science, ScienceDirect, and SpringerLink. Following the identification, screening, and eligibility stages, 82 studies were selected for qualitative thematic analysis and descriptive synthesis. The findings indicate that artificial intelligence enhances recruitment and selection, performance evaluation, and human resource analytics while simultaneously introducing challenges related to digital surveillance, algorithmic opacity, technological anxiety, and reduced employee autonomy. Overall, the evidence suggests that algorithmic human resource management can promote employee well-being when implemented within transparent governance frameworks characterized by human oversight, algorithmic fairness, ethical data governance, and active employee participation. The review contributes by synthesizing current evidence, identifying major research trends and knowledge gaps, and highlighting future research priorities, particularly for Latin American organizational contexts, where empirical evidence remains limited.