Explore the vast range of titles available.

Sex differences in brain activity evoked by sexual stimuli remain elusive despite robust evidence for stronger enjoyment of and interest toward sexual stimuli in men than in women. To test whether visual sexual stimuli evoke different brain activity patterns in men and women, we measured hemodynamic brain activity induced by visual sexual stimuli in two experiments with 91 subjects (46 males). In one experiment, the subjects viewed sexual and nonsexual film clips, and dynamic annotations for nudity in the clips were used to predict hemodynamic activity. In the second experiment, the subjects viewed sexual and nonsexual pictures in an event‐related design. Men showed stronger activation than women in the visual and prefrontal cortices and dorsal attention network in both experiments. Furthermore, using multivariate pattern classification we could accurately predict the sex of the subject on the basis of the brain activity elicited by the sexual stimuli. The classification generalized across the experiments indicating that the sex differences were task‐independent. Eye tracking data obtained from an independent sample of subjects (N = 110) showed that men looked longer than women at the chest area of the nude female actors in the film clips. These results indicate that visual sexual stimuli evoke discernible brain activity patterns in men and women which may reflect stronger attentional engagement with sexual stimuli in men. To test whether visual sexual stimuli evoke different brain activity patterns in men and women, we measured hemodynamic brain activity induced by visual sexual stimuli in two experiments with 91 subjects (46 males). Males showed stronger activation than females in the visual and prefrontal cortices and dorsal attention network for both erotic movies and still pictures. Furthermore, using multivariate pattern classification we could accurately predict the sex of the subject on the basis of the brain activity elicited by the sexual stimuli.

Defect clusters on the wafer map can provide important clue to identify the process failures so that it is important to accurately classify the defect patterns into corresponding pattern types. In this research, we present an image-based wafer map defect pattern classification method. The presented method consists of two main steps: without any specific preprocessing, high-level features are extracted from convolutional neural network and then the extracted features are fed to combination of error-correcting output codes and support vector machines for wafer map defect pattern classification. To the best of our knowledge, no prior work has applied the presented method for wafer map defect pattern classification. Experimental results tested on 20,000 wafer maps show the superiority of presented method and the overall classification accuracy is up to 98.43%.

Decreasing the number of matching comparisons between presented fingerprints and their respective templates in automated fingerprint identification systems (AFIS) is salient, especially when dealing with large databases. Fingerprint classification abets the achievement of this goal by stratifying fingerprints into their respective pattern profiles. However, the significant inter-class variation among patterns and minor intra-class variations among fingerprint patterns belonging to a similar class remains a obstacle. Unlike the verification process of fingerprints that requires 1:1 matching of templates, the identification process of fingerprint patterns requires 1:N matching to attest the presence of fingerprint in the database, which leads to a higher number of comparisons. Motivated by this problem, we employed the use of deep transfer learning and data augmentation to develop a fingerprint pattren clasifier to clasify six fingerprint patterns. Three separate models were birth from the utilization of the VGG16, VGG19, and DenseNet121 pre-trained models following some preliminary experiment. Results from the implementation of the proposed deep transfer learning with some data augmentation schemes on the selected VGG16, VGG19, and DenseNet121 pre-trained models manifested classification accuracy of 98.2%, 97%, and 97.8%, respectively, as compared to the 93.9%, 93.7% and 92% rendered by the same models devoid of data augmentation. Hence, experimental results proved that data augmentation improves the efficacy of fingerprint pattern classifier models.

This study developed a gait pattern classification system based on ground contact forces measured by six force sensors embedded inside the shoe sole. The data transmission is facilitated via the Bluetooth module integrated into an STM32 microcontroller. The extreme gradient boosting (XGBoost) algorithm is used to identify the gait patterns, and the basic idea of XGBoost is to use second-order derivatives to make the loss function more precise, incorporate regularization to prevent tree overfitting, and enable block storage for parallel computation. By optimizing the XGBoost algorithm with four algorithms, the exploration capabilities of these algorithms are effectively incorporated into the fusion model. Experimental results indicate that the XGBoost algrithm optimized by Harris' hawks optimization (HHO) outperforms the other optimization algorithms. Specifically, the HHO-XGBoost achieved high values of 97.41%, 97.03%, and 97.22% severally in the metrics of precision, recall, and F1 score. This research illustrates the HHO-XGBoost method's superiority in gait phase recognition.

Defect pattern analysis of wafer bin maps (WBMs) is an important means of identifying process problems. Recently, automated analysis methods using machine learning or deep learning have been studied as alternatives to manual classification by engineers. In this paper, we propose a method to improve the feature extraction performance of defect patterns by transforming the polar coordinate system instead of the existing WBM image input. To reduce the variability of the location representation, defect patterns in the Cartesian coordinate system, where the location of the distributed defect die is not constant, were converted to a polar coordinate system. The CNN classifier, which uses polar coordinate transformed input, achieved a classification accuracy of 91.3%, which is 4.8% better than the existing WBM image-based CNN classifier. Additionally, a tree-structured classifier model that sequentially connects binary classifiers achieved a classification accuracy of 94%. The method proposed in this paper is also applicable to the defect pattern classification of WBMs consisting of different die sizes than the training data. Finally, the paper proposes an automated pattern classification method that uses individual classifiers to learn defect types and then applies ensemble techniques for multiple defect pattern classification. This method is expected to reduce labor, time, and cost and enable objective labeling instead of relying on subjective judgments of engineers.

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.

Human measures many things consciously or subconsciously by touching and sensing without any measurable challenges. However, measuring intangible features like human behavior is a challenging task. Human behavior analysis is an important computer vision technique with a lot of attention which includes human–computer interaction and assisted living. It is essential not only for a wide range of applications but also to understand the abnormal behavior of humans. This survey paper reviews various methods used for the analysis of human behavior by dermatoglyphics fingerprint pattern classifications. This survey studies 50 research papers based on fingerprint pattern classification and presents techniques related overviews such as deep learning (DL)-based methods, convolutional neural network (CNN)-based DL methods, machine learning (ML)-based methods, Support vector machine (SVM)-based ML methods, and optimization methods. The overview of this survey comprised of classification of research methods, year of publication, evaluation metrics, employed datasets, and toolsets for human behavior analysis. The analysis demonstrates that accuracy is the most commonly used evaluation parameter in fingerprint classification which is used by 33 research papers. Finally, the research gap of analyzed methods is explained, which encourages researchers to develop new effective methods for human behavior analysis using fingerprint pattern classification.

Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products.

Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.

Background Functional near infrared spectroscopy (fNIRS) finds extended applications in a variety of neuroscience fields. We investigated the potential of fNIRS to monitor voluntary engagement of users during neurorehabilitation, especially during combinatory exercise (CE) that simultaneously uses both, passive and active exercises. Although the CE approach can enhance neurorehabilitation outcome, compared to the conventional passive or active exercise strategies, the active engagement of patients in active motor movements during CE is not known. Methods We determined hemodynamic responses induced by passive exercise and CE to evaluate the active involvement of users during CEs using fNIRS. In this preliminary study, hemodynamic responses of eight healthy subjects during three different tasks (passive exercise alone, passive exercise with motor imagery, and passive exercise with active motor execution) were recorded. On obtaining statistically significant differences, we classified the hemodynamic responses induced by passive exercise and CEs to determine the identification accuracy of the voluntary engagement of users using fNIRS. Results Stronger and broader activation around the sensorimotor cortex was observed during CEs, compared to that during passive exercise. Moreover, pattern classification results revealed more than 80% accuracy. Conclusions Our preliminary study demonstrated that fNIRS can be potentially used to assess the engagement of users of the combinatory neurorehabilitation strategy.