Search

To accurately segment subcortical structures and therefore profit for numerous neuroimaging applications, we proposed a multi-atlas subcortical segmentation method by orchestrating a 3D fully convolutional network and a generalized mixture function. Template atlases were first aligned to the target image. Then, target image patches and several most similar atlas patches were extracted from the transformed template atlases by employing a proposed similar atlas selection network and fed into the proposed multi-atlas driven 3D fully convolutional neural network. To sufficiently extract the subcortical features and improve the segmentation performance, a restricted region thought as a bounding box was utilized to roughly locate the subcortical structures. Additionally, a generalized mixture function was introduced to reduce the impact of the size and stride in 3D patch extraction. Two datasets consisting of 16 and 18 T1-weighted magnetic resonance images images (MRIs) were included to evaluate the proposed method, respectively. The results showed significantly higher segmentation accuracy than several state-of-the-art subcortical segmentation approaches for most subcortical structures. Furthermore, the proposed method achieved notable higher mean Dice similarity coefficients being, respectively, 0.915 and 0.869. The proposed method automatically and accurately segments subcortical structures in MRIs, which may assist the artificial diagnosis of brain disorders. [ABSTRACT FROM AUTHOR]

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement. [ABSTRACT FROM AUTHOR]

Precise and robust wind power prediction can effectively alleviate the problem caused by the randomness and volatility of wind power. Ensemble learning can successfully improve forecasting precision and robustness, and quantify the uncertainty of the prediction. This paper presents a new ensemble probabilistic forecasting framework, based on modified randomized maximum a posteriori (MAP) sampling technique, echo state network (ESN) and generalized mixture (GM) function to bring superior forecasting results. The proposed model first trains a set of independent ESN models for probabilistic forecasting using the modified randomized MAP sampling technique, and then dynamically weighs and ensembles the base model forecasting through the GM function. The proposed model and other benchmark models have been implemented on four wind power datasets from different places to illustrate the advantage of the proposed method. The compared result indicates that the suggested model outperforms some state-of-the-art models and can successfully achieve dynamic ensemble probabilistic prediction.

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Precise and robust wind power prediction can effectively alleviate the problem caused by the randomness and volatility of wind power. Ensemble learning can successfully improve forecasting precision and robustness, and quantify the uncertainty of the prediction. This paper presents a new ensemble probabilistic forecasting framework, based on modified randomized maximum a posteriori (MAP) sampling technique, echo state network (ESN) and generalized mixture (GM) function to bring superior forecasting results. The proposed model first trains a set of independent ESN models for probabilistic forecasting using the modified randomized MAP sampling technique, and then dynamically weighs and ensembles the base model forecasting through the GM function. The proposed model and other benchmark models have been implemented on four wind power datasets from different places to illustrate the advantage of the proposed method. The compared result indicates that the suggested model outperforms some state-of-the-art models and can successfully achieve dynamic ensemble probabilistic prediction., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)