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International journal of hybrid intelligent systems最新文献

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Using machine learning to perform early diagnosis of Autism Spectrum Disorder based on simple upper limb movements 基于简单的上肢运动,使用机器学习进行自闭症谱系障碍的早期诊断
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190278
Mohammad O. Wedyan, Adel Al-Jumaily, A. Crippa
Autism like other diseases requires early cure in order to magnify the remedy’s results. The impact of Autism Spectrum Disorder (ASD) is embodied in the following things: the inability for kids to interact with other people, the difficulty in socialize with others, speaking after a long time comparing with other kids, lack of eye contact with other. Such activities are utilized for the resolution regarding diagnosing ASD. For instance, kids shift their upper limb before other activities and such moving considers as an indicator to decide whether such children suffer from autism. The current paper checks diagnosing autism that simply depends on altering upper-limb for kids between two to four years old that depends on carrying out certain mechanisms and machine learning. Such study utilized a Linear Discriminant Analysis (LDA) method to elicit features and, the Support Vector Machines (SVM) in order to categorize thirty kids i.e. categorizing around fifteen autistic kids out of fifteen normal children by analyzing kinematic information that is collected from implementing simple task. However, such study achieved an optimal precision categorization of 100% as well as 93% of intermediate precision. Such findings provide more clues for simple upper-limb movement that can be utilized in order to precisely categorize the kids who suffer from low-functioning autism.
自闭症和其他疾病一样,需要早期治疗,以扩大治疗的效果。自闭症谱系障碍(Autism Spectrum Disorder, ASD)的影响主要表现在以下几个方面:儿童无法与他人互动,与他人交往困难,说话时间较其他孩子长,缺乏与他人的眼神交流。这些活动被用于诊断自闭症谱系障碍。例如,孩子们在其他活动之前移动上肢,这种移动被认为是决定这些孩子是否患有自闭症的一个指标。目前的论文检查了仅仅依靠改变2到4岁儿童上肢的自闭症诊断,这取决于执行某些机制和机器学习。该研究使用线性判别分析(LDA)方法提取特征,并使用支持向量机(SVM)对30个孩子进行分类,即通过分析从执行简单任务中收集的运动学信息,从15个正常儿童中对大约15个自闭症儿童进行分类。然而,该研究实现了100%的最优精度分类和93%的中间精度分类。这些发现为简单的上肢运动提供了更多线索,可以用来精确地对患有低功能自闭症的孩子进行分类。
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引用次数: 6
Solving feature selection problem by hybrid binary genetic enhanced particle swarm optimization algorithm 利用混合二元遗传增强粒子群优化算法求解特征选择问题
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190271
M. Tawhid, K. B. Dsouza
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引用次数: 2
Compression of community graph using graph mining techniques 利用图挖掘技术压缩社区图
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190260
B. Rao, Sarojananda Mishra
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引用次数: 0
Solving class imbalance problem using bagging, boosting techniques, with and without using noise filtering method 利用套袋、增压技术,带和不带噪声滤波方法解决班级失衡问题
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190261
G. Rekha, A. Tyagi, V. Reddy
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引用次数: 12
Assessing optimization algorithms based on ant colony using adapted networks science metrics 利用自适应网络科学指标评估基于蚁群的优化算法
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190265
S. F. Ribeiro, C. J. A. B. Filho
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引用次数: 0
Lung cancer recognition and prediction according to random forest ensemble and RUSBoost algorithm using LIDC data 基于随机森林集成和RUSBoost算法的肺癌识别与预测
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190263
Subrato Bharati, Prajoy Podder, Pinto Kumar Paul
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引用次数: 19
Flow graphs as data structures for inducing classifiers 流图作为归纳分类器的数据结构
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190262
M. C. Nicoletti, Emilio Carlos Rodrigues
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引用次数: 0
Actuator and system component fault tolerant control using interval type-2 Takagi-Sugeno fuzzy controller for hybrid nonlinear process 区间型2型Takagi-Sugeno模糊控制器用于混合非线性过程的执行器和系统部件容错控制
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190267
H. Patel, V. Shah
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引用次数: 26
Analysis indices of human relationships based on fuzzy model 基于模糊模型的人际关系指标分析
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190270
Toshihiro Yoshizumi, Tomoo Sumida, Yasunori Shiono, M. Namekawa, K. Tsuchida
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引用次数: 2
Implementation of bio-inspired optimization algorithms in state feedback control of power system 仿生优化算法在电力系统状态反馈控制中的实现
Pub Date : 2019-01-01 DOI: 10.3233/HIS-190269
Anju G. Pillai, E. R. Samuel, A. Unnikrishnan
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引用次数: 0
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International journal of hybrid intelligent systems
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