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  • Open Access

    ARTICLE

    Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls

    Anshu Parashar*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.017478

    Abstract Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input… More >

  • Open Access

    ARTICLE

    Multi-Head Attention Graph Network for Few Shot Learning

    Baiyan Zhang1, Hefei Ling1,*, Ping Li1, Qian Wang1, Yuxuan Shi1, Lei Wu1, Runsheng Wang1, Jialie Shen2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1505-1517, 2021, DOI:10.32604/cmc.2021.016851

    Abstract The majority of existing graph-network-based few-shot models focus on a node-similarity update mode. The lack of adequate information intensifies the risk of overtraining. In this paper, we propose a novel Multi-head Attention Graph Network to excavate discriminative relation and fulfill effective information propagation. For edge update, the node-level attention is used to evaluate the similarities between the two nodes and the distribution-level attention extracts more in-deep global relation. The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature. For node update, we embrace the label-level attention to soften the noise of irrelevant nodes and optimize… More >

  • Open Access

    ARTICLE

    General Steganalysis Method of Compressed Speech Under Different Standards

    Peng Liu1, Songbin Li1,*, Qiandong Yan1, Jingang Wang1, Cheng Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1565-1574, 2021, DOI:10.32604/cmc.2021.016635

    Abstract Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. Most of the current steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for a specific coding standard or category of steganography methods, and thus lack generalization capability. In this paper, a general steganalysis method for detecting steganographies in low-bit-rate compressed speech under different standards is proposed. First, the code-element matrices corresponding to different coding standards are concatenated to obtain a synthetic code-element matrix, which will be mapped into an intermediate feature representation by utilizing the pre-trained dictionaries. Then, bidirectional long short-term memory… More >

  • Open Access

    ARTICLE

    Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

    Muhammad Usman Ghani Khan1,2, Zubaira Naz1, Javeria Khan1, Tanzila Saba3, Ibrahim Abunadi3, Amjad Rehman3, Usman Tariq4,*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2363-2376, 2021, DOI:10.32604/cmc.2021.014550

    Abstract The National Health Interview Survey (NHIS) shows that there are 13.2% of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder (ADHD), globally. The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy. These traditional methods, namely therapy, need a large number of visits to hospitals and include medication. Neurogames could be used for the effective treatment of ADHD. It could be a helpful tool in improving children and ADHD patients’ cognitive skills by using Brain–Computer Interfaces (BCI). BCI enables the user to interact with the computer through brain activity using… More >

  • Open Access

    ARTICLE

    Leverage External Knowledge and Self-attention for Chinese Semantic Dependency Graph Parsing

    Dianqing Liu1,2, Lanqiu Zhang1,2, Yanqiu Shao1,2,*, Junzhao Sun3

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 447-458, 2021, DOI:10.32604/iasc.2021.016320

    Abstract Chinese semantic dependency graph (CSDG) parsing aims to analyze the semantic relationship between words in a sentence. Since it is a deep semantic analysis task, the parser needs a lot of prior knowledge about the real world to distinguish different semantic roles and determine the range of the head nodes of each word. Existing CSDG parsers usually use part-of-speech (POS) and lexical features, which can only provide linguistic knowledge, but not semantic knowledge about the word. To solve this problem, we propose an entity recognition method based on distant supervision and entity classification to recognize entities in sentences, and then… More >

  • Open Access

    ARTICLE

    Chinese Relation Extraction on Forestry Knowledge Graph Construction

    Qi Yue, Xiang Li, Dan Li*

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 423-442, 2021, DOI:10.32604/csse.2021.014448

    Abstract Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance.… More >

  • Open Access

    ARTICLE

    YOLOv3 Attention Face Detector with High Accuracy and Efficiency

    Qiyuan Liu, Shuhua Lu*, Lingqiang Lan

    Computer Systems Science and Engineering, Vol.37, No.2, pp. 283-295, 2021, DOI:10.32604/csse.2021.014086

    Abstract In recent years, face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision. However, the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied. In this paper, using Darknet-53 as backbone, we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency. The attention mechanism is introduced to enhance much higher discrimination of the deep features, and the trick of data augmentation is used in the training… More >

  • Open Access

    ARTICLE

    Mind-Body Exercises (Yoga/Tai Chi) for Attention-Deficit/Hyperactivity Disorder: A Quantitative Evidence of Experimental Studies

    Erfei Zuo1, Yanjie Zhang2, Qian Yu2, Tianyou Guo2, Can Jiao2, Ye Yu3, Patrick Müller4, Xinli Chi2, Md Mahhub Hossain5, Albert S. Yeung6, Notger G. Müller4, Liye Zou2,*

    International Journal of Mental Health Promotion, Vol.22, No.4, pp. 221-231, 2020, DOI:10.32604/IJMHP.2020.014552

    Abstract Background: Attention-deficit/hyperactivity disorder (ADHD) is a common pediatric psychiatric disorder. Although mindful exercises (Yoga and Tai Chi) have been increasingly accepted as alternative medicine for ADHD, no meta-analytic review has been conducted on this topic. Objective: We systematically and critically evaluated the existing literature regarding the effects of the two most widely practiced mindful exercises on ADHD symptoms and social problems in children and adolescents with ADHD. Methods: Searching literature databases included PubMed, Web of Science, Scope, China National Knowledge Infrastructure and Wanfang. Only randomized controlled trials (RCT) and nonrandomized controlled studies (NRS) that investigated the beneficial effects of Yoga… More >

  • Open Access

    ARTICLE

    Adversarial Active Learning for Named Entity Recognition in Cybersecurity

    Tao Li1, Yongjin Hu1,*, Ankang Ju1, Zhuoran Hu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 407-420, 2021, DOI:10.32604/cmc.2020.012023

    Abstract Owing to the continuous barrage of cyber threats, there is a massive amount of cyber threat intelligence. However, a great deal of cyber threat intelligence come from textual sources. For analysis of cyber threat intelligence, many security analysts rely on cumbersome and time-consuming manual efforts. Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence. As the foundation for constructing cybersecurity knowledge graph, named entity recognition (NER) is required for identifying critical threat-related elements from textual cyber threat intelligence. Recently, deep neural network-based models have attained very good results in NER. However, the performance of these… More >

  • Open Access

    ARTICLE

    Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment

    Yuanyuan Liu1,2, Shuo Zhang1, Haiye Yu3, Yueyong Wang4,*, Yuehan Feng1, Jiahui Sun1, Xiaokang Zhou1

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 247-262, 2021, DOI:10.32604/cmc.2020.012328

    Abstract Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual structure, and the whole network… More >

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