Future Institute of Technology

This study investigates three quantum machine learning (QML) approaches: variational quantum circuits (VQCs), quantum kernel methods (QKMs), and quantum neural networks (QNNs). The evaluation was performed on the Sonar Mines vs. rocks dataset using a common experimental protocol. This dataset represents a challenging high-dimensional binary classification problem. Among the models, the QNN achieved perfect classification performance, whereas the VQC and QKM obtained accuracies of 85% and 80%, respectively. These findings suggest that models with greater circuit depths can capture more complex class boundaries. Nevertheless, the observed perfect accuracy may indicate overfitting and limited generalization when the training data are scarce. This study further explores representational capacity, training dynamics, and scalability, and outlines practical challenges for applying quantum learning models in safety-critical sonar detection scenarios.

This paper examines the epistemological crisis facing contemporary humanities education within increasingly technocratic and reductionist academic systems. It argues that the marginalization of the humanities is not merely institutional but philosophical, rooted in the dominance of quantification, instrumental rationality, and market-driven evaluation. In response, the essay proposes a renewed framework grounded in cultural praxis, applied psychology, interdisciplinarity, and Sri Aurobindo’s integral philosophy of education. Without rejecting scientific rigor, the paper advances an integrative model of knowledge that recognizes the distinct yet complementary contributions of empirical inquiry and interpretive understanding. By demonstrating how the humanities cultivate narrative identity, ethical imagination, emotional intelligence, and civic consciousness, the study positions humanities education as essential to holistic human development in a reductionist age.

Polycystic Ovary Syndrome (PCOS) is a heterogeneous endocrine disorder with diverse clinical manifestations, making patient stratification challenging. This study investigated the use of Principal Component Analysis (PCA) combined with Fuzzy C-Means (FCM) clustering to analyze a dataset of 541 PCOS patient records containing 41 clinical features. PCA was applied to reduce dimensionality, resulting in 29 principal components that preserved 90.9% of the dataset variance. FCM clustering identified two meaningful patient subgroups, with an average maximum membership of 0.823 and 22.4% of patients exhibiting partial membership, highlighting the boundary cases and heterogeneity. A comparative analysis with the traditional K-means algorithm, which was not elaborated in detail owing to its well-known methodology, demonstrated that FCM outperformed K-means in cluster quality metrics, including the silhouette score, Calinski-Harabasz index, and Davies-Bouldin index, while additionally providing membership confidence and boundary detection. The findings indicate that combining PCA with fuzzy clustering effectively reveals hidden structures in complex medical datasets and offers a robust framework for PCOS patient stratification, with potential applications in clinical decision support and personalized management.

Polycystic ovary syndrome (PCOS) is a highly heterogeneous endocrine disorder that exhibits substantial variations in clinical features, metabolic profiles, and reproductive outcomes, complicating accurate diagnosis and treatment. Standard diagnostic criteria often inadequately reflect this heterogeneity, resulting in the misclassification of PCOS phenotypes. To address this limitation, we applied an unsupervised clustering framework to clinical, biochemical, metabolic, and lifestyle variables to identify latent PCOS subgroups without imposing prior assumptions. The analysis revealed four distinct clusters: (i) a metabolically severe PCOS phenotype characterized by obesity, insulin resistance, hyperandrogenism, and ovulatory dysfunction; (ii) a lean PCOS phenotype marked by dominant neuroendocrine disturbances in the absence of metabolic abnormalities; (iii) a healthy or remitted PCOS phenotype displaying normal metabolic and reproductive function with preserved ovulation; and (iv) a single-subject outlier cluster reflecting data irregularities. These results indicate that PCOS comprises biologically distinct subtypes with divergent underlying mechanisms and clinical implications. The proposed clustering approach underscores the value of phenotype-oriented diagnosis, supports individualized management strategies targeting metabolic or hormonal pathways, and demonstrates the role of unsupervised learning in enhancing the data quality in clinical studies.