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Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decomposed into a finite set of narrowband signals using a Fourier-transformation-based bandpass filter. Rhythmic components of EEG are represented by each of the narrowband signals that characterize the brain activities related to MI tasks. The subband signals are arranged to extend the dimension of the EEG trial in the spatial domain. The spatial features are extracted from the set of extended trials using a common spatial pattern (CSP). An optimum number of features are employed to classify the motor imagery tasks using an artificial neural network. An integrated approach with full-band and narrowband signals is implemented to derive discriminative features for MI classification. In addition, the subject-dependent parameter optimization scheme enhances the performance of the proposed method. The performance evaluation of the proposed method is obtained using two publicly available benchmark datasets (Dataset I and Dataset II). The experimental results in terms of classification accuracy (93.88% with Dataset I and 91.55% with Dataset II) show that it performs better than the recently developed algorithms. The enhanced MI classification accuracy is very much applicable in BCI implementation. [ABSTRACT FROM AUTHOR]

Hydraulic rock drills are widely used in drilling, mining, construction, and engineering applications. They typically operate in harsh environments with high humidity, large temperature differences, and vibration. Under the influence of environmental noise and operational patterns, the distributions of data collected by sensors for different operators and equipment differ significantly, which leads to difficulty in fault classification for hydraulic rock drills. Therefore, an intelligent and robust fault classification method is highly desired. In this paper, we propose a fault classification technique for hydraulic rock drills based on deep learning. First, considering the strong robustness of x−vectors to the features extracted from the time series, we employ an end−to−end fault classification model based on x−vectors to realize the joint optimization of feature extraction and classification. Second, the overlapping data clipping method is applied during the training process, which further improves the robustness of our model. Finally, the focal loss is used to focus on difficult samples, which improves their classification accuracy. The proposed method obtains an accuracy of 99.92%, demonstrating its potential for hydraulic rock drill fault classification. [ABSTRACT FROM AUTHOR]

Tongue color classification serves as important assistance for traditional Chinese medicine (TCM) doctors to make a precise diagnosis. This paper proposes a novel two-step framework based on deep learning to improve the performance of tongue color classification. First, a semantic-based CNN called SegTongue is applied to segment the tongues from the background. Based on DeepLabv3+, multiple atrous spatial pyramid pooling (ASPP) modules are added, and the number of iterations of fusions of low-level and high-level information is increased. After segmentation, various classical feature extraction networks are trained using softmax and center loss. The experiment results are evaluated using different measures, including overall accuracy, Kappa coefficient, individual sensitivity, etc. The results demonstrate that the proposed framework with SVM achieves up to 97.60% accuracy in the tongue image datasets. [ABSTRACT FROM AUTHOR]

With increasing wafer sizes and diversifying die patterns, automated optical inspection (AOI) is progressively replacing traditional visual inspection (VI) for wafer defect detection. Yet, the defect classification efficacy of current AOI systems in our case company is not optimal. This limitation is due to the algorithms' reliance on expertly designed features, reducing adaptability across various product models. Additionally, the limited time available for operators to annotate defect samples restricts learning potential. Our study introduces a novel hybrid self-training algorithm, leveraging semi-supervised learning that integrates pseudo-labeling, noisy student, curriculum labeling, and the Taguchi method. This approach enables classifiers to autonomously integrate information from unlabeled data, bypassing the need for feature extraction, even with scarcely labeled data. Our experiments on a small-scale set show that with 25% and 50% labeled data, the method achieves over 92% accuracy. Remarkably, with only 10% labeled data, our hybrid method surpasses the supervised DenseNet classifier by over 20%, achieving more than 82% accuracy. On a large-scale set, the hybrid method consistently outperforms other approaches, achieving up to 88.75%, 86.31%, and 83.61% accuracy with 50%, 25%, and 10% labeled data. Further experiments confirm our method's consistent superiority, highlighting its potential for high classification accuracy in limited-data scenarios. [ABSTRACT FROM AUTHOR]

Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria. [ABSTRACT FROM AUTHOR]

The classification of biological signals is important in detecting abnormal conditions in observed biological subjects. The classifiers are trained on feature vectors, which often constitute the parameters of the observed time series data models. Since the feature extraction is usually the most time-consuming step in training a classifier, in this paper, signal folding and the associated folding operator are introduced to reduce the variability in near-cyclostationary biological signals so that these signals can be represented by models that have a lower order. This leads to a substantial reduction in computational complexity, so the classifier can be learned an order of magnitude faster and still maintain its decision accuracy. The performance of different classifiers involving signal folding as a pre-processing step is studied for sleep apnea detection in one-lead ECG signals assuming ARIMA modeling of the time series data. It is shown that the R-peak-based folding of ECG segments has superior performance to other more general, similarity based signal folding methods. The folding order can be optimized for the best classification accuracy. However, signal folding requires precise scaling and alignment of the created signal fragments. [ABSTRACT FROM AUTHOR]

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions. [ABSTRACT FROM AUTHOR]

Masses are the early indicators of breast cancer, and distinguishing between benign and malignant masses is a challenging problem. Many machine learning- and deep learning-based methods have been proposed to distinguish benign masses from malignant ones on mammograms. However, their performance is not satisfactory. Though deep learning has been shown to be effective in a variety of applications, it is challenging to apply it for mass classification since it requires a large dataset for training and the number of available annotated mammograms is limited. A common approach to overcome this issue is to employ a pre-trained model and fine-tune it on mammograms. Though this works well, it still involves fine-tuning a huge number of learnable parameters with a small number of annotated mammograms. To tackle the small set problem in the training or fine-tuning of CNN models, we introduce a new method, which uses a pre-trained CNN without any modifications as an end-to-end model for mass classification, without fine-tuning the learnable parameters. The training phase only identifies the neurons in the classification layer, which yield higher activation for each class, and later on uses the activation of these neurons to classify an unknown mass ROI. We evaluated the proposed approach using different CNN models on the public domain benchmark datasets, such as DDSM and INbreast. The results show that it outperforms the state-of-the-art deep learning-based methods. [ABSTRACT FROM AUTHOR]