In today’s volatile market environment, supply chain management (SCM) must address complex challenges such as fluctuating demand, fraud, and delivery delays. This study applies machine learning techniques—Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks (RNNs)—to optimize demand forecasting, inventory policies, and risk mitigation within a unified framework. XGBoost achieves high forecasting accuracy (MAE = 0.1571, MAPE = 0.48%), while RNNs excel at fraud detection and late delivery prediction (F1-score ≈ 98%). To evaluate models beyond accuracy, we introduce two novel metrics: Cost–Accuracy Efficiency (CAE) and CAE-ESG, which combine predictive performance with cost-efficiency and ESG alignment. These holistic measures support sustainable model selection aligned with the ISO 14001, GRI, and SASB benchmarks; they also demonstrate that, despite lower accuracy, Random Forest achieves the highest CAE-ESG score due to its low complexity and strong ESG profile. We also apply SHAP analysis to improve model interpretability and demonstrate business impact through enhanced Customer Lifetime Value (CLV) and reduced churn. This research offers a practical, interpretable, and sustainability-aware ML framework for supply chains, enabling more resilient, cost-effective, and responsible decision-making.

Stroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. In this study, we propose a Convolutional Neural Network (CNN)-based model for detecting strokes from brain Computed Tomography (CT) images. The model is trained on a dataset consisting of 2501 images, including both normal and stroke cases, and employs a series of preprocessing steps, including resizing, normalization, data augmentation, and splitting into training, validation, and test sets. The CNN architecture comprises three convolutional blocks followed by dense layers optimized through hyperparameter tuning to maximize performance. Our model achieved a validation accuracy of 97.2%, with precision and recall values of 96%, demonstrating high efficacy in stroke classification. Additionally, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIME), occlusion sensitivity, and saliency maps were used to visualize the model’s decision-making process, enhancing transparency and trust for clinical use. The results suggest that deep learning models, particularly CNNs, can provide valuable support for medical professionals in detecting strokes, offering both high performance and interpretability. The model demonstrates moderate generalizability, achieving 89.73% accuracy on an external, patient-independent dataset of 9900 CT images, underscoring the need for further optimization in diverse clinical settings.

In today’s data-driven business landscape, effective customer segmentation is crucial for enhancing engagement, loyalty, and profitability. Traditional clustering methods often struggle with datasets containing both numerical and categorical variables, leading to suboptimal segmentation. This study addresses this limitation by introducing a novel application of Factor Analysis of Mixed Data (FAMD) for dimensionality reduction, integrated with K-means and Agglomerative Clustering for robust customer segmentation. While FAMD is not new in data analytics, its potential in customer segmentation has been underexplored. This research bridges that gap by demonstrating how FAMD can harmonize mixed data types, preserving structural relationships that conventional methods overlook. The proposed methodology was tested on a Kaggle-sourced retail dataset comprising 3900 customers, with preprocessing steps including correlation ratio filtering (η ≥ 0.03), standardization, and encoding. FAMD reduced the feature space to three principal components, capturing 81.46% of the variance, which facilitated clearer segmentation. Comparative clustering analysis showed that Agglomerative Clustering (Silhouette Score: 0.52) outperformed K-means (0.51) at k = 4, revealing distinct customer segments such as seasonal shoppers and high spenders. Practical implications include the development of targeted marketing strategies, validated through heatmap visualizations and cluster profiling. This study not only underscores the suitability of FAMD for customer segmentation but also sets the stage for more nuanced marketing analytics driven by mixed-data methodologies.

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability.

In many developing regions, recruitment still relies heavily on traditional methods that often ignore the importance of aligning a candidate’s personality with the job role. This mismatch can lead to poor performance, dissatisfaction, and high turnover. To address this, the study presents a personality-aware recommendation system that combines the Myers–Briggs Type Indicator (MBTI) with machine learning to support smarter hiring decisions. The system is tailored for the South Asian job market and includes two main components: a web-based MBTI assessment for applicants and a dashboard for HR professionals powered by a XGBoost classifier. This model was trained on a dataset correlating applicant profiles and the flagged preferences of MBTI with the job. Experience and the number of skills, education level, and encoded MBTI types were the key features, and the SMOTE method was employed to balance the dataset. The model attained an accuracy of 74.30%, having balanced precision and recall measures. It was also discriminative, the ROC AUC was 0.84, and the precision–recall AUC was 0.85. One example of utilizing the Software Developer position in real life demonstrated the success of the system to filter and rank candidates at the same time according to both technical and personality-specific criteria. Overall, this study emphasizes the worth of combining insights from psychological profiling with machine learning in order to develop a more holistically, fair, and efficient hiring process.