Hepatic encephalopathy (HE) is a serious disease in cirrhotic subjects with unclear pathogenesis and no standardized diagnostic method. As HE affects brain function, it can affect handwriting, demanding coordination, and cognitive task. This study explores the potential correlation between HE, liver cirrhosis, and handwriting. The handwriting data were captured using a graphic pen and tablet, with which the subjects performed eight writing tasks and two drawing tasks. A total of 1965 basic and advanced features from both surface and in-air movements were obtained from these data. Several experiments were performed to analyze the impact of different feature groups on the prediction results. In this study, the weighted$k$-nearest neighbors feature selection (WkNN-FS) was used. The final XGBoost model was trained using the selected set of 300 features, and achieved an accuracy of 81.96% (95% CI: 79.32-91.62) and an AUROC of 87.31% (95% CI: 78.96-95.66).

This work investigates the impact of automatic data augmentation on the generalization performance of CNN models on speech-based Parkinson's disease classification. We propose a method that sequentially applies 12 voice-specific augmentations, selecting the most effective one based on performance. We use a pre-trained CNN as a feature extractor. To assess inter-dataset generalization, we conduct experiments where each dataset is used for training while the others are used for external validation. The results demonstrate that augmentation helps reduce the generalization gap, with specific augmentation strategies enhancing the accuracy by as much as 25 % compared to the baseline setup. Despite the increase in computational complexity due to the proposed method, this study reinforces the importance of augmentation in domain adaptation for speech-based PD classification.

Skewed data distribution poses many challenges in various domains, including the financial sector. Information about a company's potential bankruptcy is crucial for financial institutions and decision-making managers. As bankruptcy prediction has been a key concern for practitioners and research workers for decades, advancements in machine learning offer numerous methods to address imbalanced data scenarios with promising results. Among these, ensembles and neural networks showed remarkable results regarding prediction effectiveness. In this study, we propose a novel deep ensemble boosting approach to overcome imbalanced scenarios called Boosting TabNet. Achieved results showed promising results, specifically in the case of realworld datasets characterized by significant class imbalance ratios. Boosting TabNet outperformed or achieved results comparable to other approaches in most utilized datasets. The highest achieved score in terms of geometric mean (GM) score was over$\mathbf{9 6 \%}$.

Feature selection (FS) is an important step in machine learning since it has been shown to improve prediction accuracy while suppressing the curse of dimensionality of high-dimensional data. Neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose a new neural-network-based FS approach that introduces two constraints, the satisfaction of which leads to a sparse FS layer. We performed extensive experiments on synthetic and real-world data to evaluate the performance of our proposed FS method. In the experiments, we focus on high-dimensional, low-sample-size data since they represent the main challenge for FS. The results confirm that the proposed FS method based on a sparse neural-network layer with normalizing constraints (SNeL-FS) is able to select the important features and yields superior performance compared to other conventional FS methods.

Existing decision support system frameworks for diagnosing Parkinson’s disease (PD) through handwriting, speech, or gait characteristics share very similar pipelines. Although in some cases, patient data can be captured by commercially available devices, specialized devices or even custom-made prototypes are often required for such tasks. Captured data are used for extracting features that are carefully designed on the basis of domain and problem knowledge. These features are then fed to classifiers that provide a final decision. In this article, we present an approach in which end-to-end processing by a convolutional neural network (CNN) is utilized to diagnose PD from handwriting images, without the use of additional signals. This eliminates any need for specialized devices or feature engineering. To improve the performance of the proposed pretrained CNN, we propose the idea of multiple fine tuning to bridge the gap between semantically different source and target datasets and facilitate more efficient transfer learning. The proposed architecture, which is based on multiple fine tuning and an ensemble of multiple-fine-tuned CNNs, achieves 94.7% accuracy in the classification of PD from offline handwriting.

Feature selection is considered as one of the important steps in processing of high-dimensional data. Identification of significant genes in micro-array sequences, and dimensionality reduction in multimedia data are the example areas where the feature selection is beneficial. In this paper, we present set of methods based on distance and attribute weighted k-nearest neighbors algorithm. The new methods are obtained by deployment of additional distance measures and loss functions. Moreover, we present some extensions of original weighted k-nearest neighbors feature selection method such as multi-class classification, regularization, and computationally effective TensorFlow implementation.

We present a framework for combining different denoising front-ends for robust speech enhancement for recognition in noisy conditions. This is contrasted against results of optimally fusing diverse parameter settings for a single denoising algorithm. All frontends in the latter case exploit the same denoising algorithm, which combines harmonic decomposition, with noise estimation and spectral subtraction. The set of associated parameters involved in these steps are dependent on the noise conditions. Rather than explicitly tuning them, we suggest a strategy that tries to account for the trade-off between average word error rate and diversity to find an optimal subset of these parameter settings. We present the results on Aurora4 database and also compare against traditional speech enhancement methods e.g. Wiener filtering and spectral subtraction.

Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected with PD. Moreover, conjunction of in-air trajectories with conventional on-surface handwriting allows us to build predictive model with PD classification accuracy over 80%. In total, we compute over 600 handwriting features. Then, we select smaller subset of these features using two feature selection algorithms: Mann-Whitney U-test filter and relief algorithm, and map these feature subsets to binary classification response using support vector machines.