2017 Artificial Intelligence and Signal Processing Conference (AISP)最新文献_第5页

Melanoma skin cancer detection using color and new texture features 黑色素瘤皮肤癌检测使用颜色和新的纹理特征

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324108

Farzam Kharaji Nezhadian, S. Rashidi

引用次数: 32

Topology tracking of static and dynamic networks based on structural equation models 基于结构方程模型的静态和动态网络拓扑跟踪

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324119

S. Akhavan, H. Soltanian-Zadeh

{"title":"Topology tracking of static and dynamic networks based on structural equation models","authors":"S. Akhavan, H. Soltanian-Zadeh","doi":"10.1109/AISP.2017.8324119","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324119","url":null,"abstract":"Most of the complex networks have hidden topologies, therefore, their structures must first be modeled in order to conduct meaningful network analytics. Structural equation models (SEMs) are from prominent network models and they often express the relationship between exogenous inputs of the network and outputs linearly. In this paper, based on SEMs, we propose a method to track the topology of static and dynamic networks over time. The static networks have fixed topologies while the topology of the dynamic networks changes over time. The proposed tracking algorithm will improve the topology estimation in static networks, and trace the changes of topology in dynamic networks. The important advantage of the proposed method is about exogenous inputs. Ordinary SEMs assume full knowledge of the exogenous inputs, which may not always be a correct hypothesis. We assume that the exogenous inputs are piecewise stationary and in each piece, the correlation matrix of the exogenous inputs is known, which is a more practical assumption than given exogenous inputs. Numerical tests demonstrate the effectiveness of the proposed algorithm in tracking the topology of static and dynamic networks.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123398819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Classification of normal and dysphagia in patients with GERD using swallowing sound analysis 吞咽声分析对胃食管反流患者正常与吞咽困难的分类

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324095

Babak Basiri, M. Vali, S. Agah

引用次数: 2

An experimental study on a learning-based approach for the push recovery of NAO humanoid robot 基于学习的NAO类人机器人推恢复方法的实验研究

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515159

Milad Ghorbani, Fatemeh Kakavandi, M. T. Masouleh

{"title":"An experimental study on a learning-based approach for the push recovery of NAO humanoid robot","authors":"Milad Ghorbani, Fatemeh Kakavandi, M. T. Masouleh","doi":"10.1109/AISP.2017.8515159","DOIUrl":"https://doi.org/10.1109/AISP.2017.8515159","url":null,"abstract":"Push recovery and keeping balance in humanoid robots are two important issues which ensure that robot can perform the imitation procedure and can be readily integrated in any environment. This paper represents an approach for push detection in NAO-H25 which is based on robot's FSRs (force sensitive resistor) and learning algorithms. Two challenges are involved in this paper, namely, the low quality of NAO FSR and different levels of force which robot should detect. In this study, NAO's sensors data are gathered by Choregraphe. In the stand position (with no push) and by applying the maximum forces, FSR's output specified as base vectors in such a way that the differences between various vectors and base vectors indicate the type of the push (back, front ...). By getting different data set, the learning algorithms can be used in order to detect the type of the push based on the previous data. However, in this paper different approaches which require less computation than classical learning algorithms is used. Some specific data should be updated every time so that the robot can detect a new push better. After detection, robot should be in equilibrium, which is performed by the controlling part. Two actuators which are located in the robot's ankles are used to apply the required control signals. The experimental results indicate the correct detection in less computation volume.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125175504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Age-based human face image retrieval using zernike moments 基于年龄的zernike矩人脸图像检索

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515123

Mohsen Eshghan Malek, Z. Azimifar, R. Boostani

引用次数: 0

Generalized phase synchrony using fuzzified Johansen test 基于模糊化约翰森检验的广义相位同步

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324100

Zahra Ghanbari, M. Moradi

引用次数: 0

Genetic algorithm and fuzzy C-means for feature selection: Based on a dual fitness function 基于对偶适应度函数的遗传算法和模糊c均值特征选择

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515120

Elmira Amiri Souri, Azadeh Mohebi, Abbas Ahmadi

引用次数: 1

Arabic named entity recognition using boosting method 阿拉伯语命名实体识别的增强方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324098

Mohamad Bagher Sajadi, Behrooz Minaei

{"title":"Arabic named entity recognition using boosting method","authors":"Mohamad Bagher Sajadi, Behrooz Minaei","doi":"10.1109/AISP.2017.8324098","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324098","url":null,"abstract":"In Natural Language Processing (NLP) studies, developing resources and tools makes a contribution to extension and Effectiveness of researches in each language. In recent years, Arabic Named Entity Recognition (ANER) has been considered by NLP researchers. While most of these researches are based on Modern Standard Arabic (MSA), in this paper, we focus on Classical Arabic (CA) literature. We propose a corpus called NoorCorp with 200k labeled words for research purposes which is annotated by expert human resources manually. We also collected about 18k proper names from old Hadith books as gazetteer which is called NoorGazet. Using ensemble learning, we develop a new approach for extraction of named entities (NEs) including person, location and organization. Adaboost.M2 algorithm, as implementation of multiclass Boosting method, is applied to train the prediction model. Results show that performance of the method is better than decision tree as the base classifier. We have used tokenizing, part of speech (POS) tagging, and base phrase chunking (BPC) to overcome linguistic obstacles in Arabic. An overall F-measure value of 96.04 is obtained. In addition, we have studied the effect of preprocessing and external resources on the system results. Finally, the proposed approach is applied on ANERCorp as MSA corpus and we have compared the results with NoorCorp.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features 基于局部独立特征的非参数混合高斯朴素贝叶斯分类器

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324083

Ali Haghpanah Jahromi, M. Taheri

引用次数: 96

Abnormal event detection in indoor video using feature coding 基于特征编码的室内视频异常事件检测

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324127

Mona Izadi, Z. Azimifar, Gholam-Hossein Jowkar

引用次数: 2