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    A Hybrid Model for Psoriasis Subtype Classification: Integrating Multi Transfer Learning and Hard Voting Ensemble Models
    (Mdpi, 2025) Avci, Ismail Anil; Zirekgur, Merve; Karakaya, Baris; Demir, Betul
    Background: Psoriasis is a chronic, immune-mediated skin disease characterized by lifelong persistence and fluctuating symptoms. The clinical similarities among its subtypes and the diversity of symptoms present challenges in diagnosis. Early diagnosis plays a vital role in preventing the spread of lesions and improving patients' quality of life. Methods: This study proposes a hybrid model combining multiple transfer learning and ensemble learning methods to classify psoriasis subtypes accurately and efficiently. The dataset includes 930 images labeled by expert dermatologists from the Dermatology Clinic of F & imath;rat University Hospital, representing four distinct subtypes: generalized, guttate, plaque, and pustular. Class imbalance was addressed by applying synthetic data augmentation techniques, particularly for the rare subtype. To reduce the influence of nonlesion environmental factors, the images underwent systematic cropping and preprocessing steps, such as Gaussian blur, thresholding, morphological operations, and contour detection. DenseNet-121, EfficientNet-B0, and ResNet-50 transfer learning models were utilized to extract feature vectors, which were then combined to form a unified feature set representing the strengths of each model. The feature set was divided into 80% training and 20% testing subsets and evaluated using a hard voting classifier consisting of logistic regression, random forest, support vector classifier, k-nearest neighbors, and gradient boosting algorithms. Results: The proposed hybrid approach achieved 93.14% accuracy, 96.75% precision, and an F1 score of 91.44%, demonstrating superior performance compared to individual transfer learning models. Conclusions: This method offers significant potential to enhance the classification of psoriasis subtypes in clinical and real-world settings.
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    Novel multi-cube extreme learning machine with sparse attention and ridge regression technique for robust machine learning
    (Elsevier, 2026) Zirekgur, Merve; Karakaya, Baris; Sengur, Abdulkadir; Acharya, U. Rajendra
    This study introduces a learning framework designed to enhance the representational capacity and stability of single-layer feed-forward networks (SLFN) when modelling nonlinear and high-dimensional data. To this end, the proposed multi-cube unit with sparse attention and ridge regularisation (MCU-SAR) method integrates three complementary components: (i) a multi-cube unit (MCU) architecture that explicitly encodes higher-order feature interactions, (ii) a sparse attention mechanism that suppresses low-informative multiplicative terms, and (iii) a ridge-regularised extreme learning machine (ELM) output layer to improve generalisation. The proposed model is evaluated on 25 publicly available datasets, including 17 classification and 8 regression tasks, and benchmarked against 15 baseline methods comprising gradient-based optimisation techniques, support vector machines (SVM), and various ELM-based approaches. Performance comparisons are conducted using the Friedman test. MCU-SAR demonstrates consistently strong performance, ranking first on the majority of the 25 benchmark datasets and achieving competitive accuracy in classification as well as low error levels in regression tasks, with all results supported by statistically significant p-values. These results demonstrate that the proposed framework provides a scalable, generalisable, and computationally efficient solution for both classification and regression problems, offering robust performance on engineering-oriented real-world datasets.