Jing Peng^1,2,4, Chaoyang Xia², Yuanwei Xu³, Xiaojie Li², Xi Wu², Xiao Han^1,4, Xinlai Chen⁵, Yucheng Chen³, Zhe Cui^1,4,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749

Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that obtains high- and low-level features… More >

Yatian Shen¹, Yubo Mai², Xiajiong Shen², Wenke Ding^{2, *}, Mengjiao Guo³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 529-541, 2020, DOI:10.32604/cmc.2020.011139

Abstract Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer in the training process of… More >

Chao Luo¹, Canghong Shi¹, Xiaojie Li^{1, *}, Xin Wang⁴, Yucheng Chen³, Dongrui Gao¹, Youbing Yin⁴, Qi Song⁴, Xi Wu¹, Jiliu Zhou¹

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 995-1012, 2020, DOI:10.32604/cmc.2020.07968

Abstract Myocardial segmentation and classification play a major role in the diagnosis of cardiovascular disease. Dilated Cardiomyopathy (DCM) is a kind of common chronic and life-threatening cardiopathy. Early diagnostics significantly increases the chances of correct treatment and survival. However, accurate and rapid diagnosis of DCM is still challenge due to high variability of cardiac structure, low contrast cardiac magnetic resonance (CMR) images, and intrinsic noise in synthetic CMR images caused by motion artifact and cardiac dynamics. Moreover, visual assessment and empirical evaluation are widely used in routine clinical diagnosis, but they are subject to high inter-observer variability and are both subjective… More >

Dongjie Zhu¹, Yuhua Wang¹, Chuiju You^2,*, Jinming Qiu^2,3, Ning Cao², Chenjing Gong⁴, Guohua Yang⁵, Helen Min Zhou⁶

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1105-1115, 2019, DOI:10.32604/cmc.2019.06041

Abstract With the rapid development of the mobile Internet, users generate massive data in different forms in social network every day, and different characteristics of users are reflected by these social media data. How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services, marketing, and recommendation systems. In this paper, we propose Multi-source & Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user. Firstly, we design their own feature extraction models… More >

Ibrahim M. Abdelsalam¹, Mostafa Elshobary^1,3,*, Mohamed M. Eladawy¹, Mohammed Nagah²

Phyton-International Journal of Experimental Botany, Vol.88, No.2, pp. 69-90, 2019, DOI:10.32604/phyton.2019.06831

Abstract Heavy metal contamination of land and freshwater resources is a serious concern worldwide. It adversely affects the health of animals, plants and humans. Therefore, remediation of toxic heavy metals must be highly considered. Unlike other techniques, phytoremediation is a holistic technology and can be used in large scale for soil remediation as it is costless, novel, environmentally-safe and solar-driven technology. Utilization of non-edible plants in phytoremediation is an ingenious technique as they are used to generate new bioenergy resources along with the remediation of contaminated soils. Some nonfood bioenergy crops such as Salix species, Miscanthus species, Populus species, Eucalyptus species,… More >

Chunlong Hu^1,*, Jianjun Chen¹, Xin Zuo¹, Haitao Zou¹, Xing Deng¹, Yucheng Shu²

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.3, pp. 547-559, 2019, DOI:10.31614/cmes.2019.04032

Abstract Micro-expression recognition has attracted growing research interests in the field of compute vision. However, micro-expression usually lasts a few seconds, thus it is difficult to detect. This paper presents a new framework to recognize micro-expression using pyramid histogram of Centralized Gabor Binary Pattern from Three Orthogonal Panels (CGBP-TOP) which is an extension of Local Gabor Binary Pattern from Three Orthogonal Panels feature. CGBP-TOP performs spatial and temporal analysis to capture the local facial characteristics of micro-expression image sequences. In order to keep more local information of the face, CGBP-TOP is extracted based on pyramid sub-regions of the micro-expression video frame.… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1223-1235, 2019, DOI:10.32604/cmc.2019.05636

Abstract Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single… More >

Rui Wang¹, Miaomiao Shen^1,*, Yanping Li¹, Samuel Gomes²

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408

Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for… More >