2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)最新文献_第3页

Bangladeshi dialect recognition using Mel Frequency Cepstral Coefficient, Delta, Delta-delta and Gaussian Mixture Model 基于Mel频率倒谱系数、Delta、Delta- Delta和高斯混合模型的孟加拉方言识别

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449852

Pronaya Prosun Das, S. M. Allayear, Ruhul Amin, Zahida Rahman

{"title":"Bangladeshi dialect recognition using Mel Frequency Cepstral Coefficient, Delta, Delta-delta and Gaussian Mixture Model","authors":"Pronaya Prosun Das, S. M. Allayear, Ruhul Amin, Zahida Rahman","doi":"10.1109/ICACI.2016.7449852","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449852","url":null,"abstract":"Automatic recognition systems are generally applied successfully in speech processing to categorize observed utterances by the speaker identity, dialect and linguistic communication. A lot of research has been performed to detect speeches, dialects and languages of different region throughout the world. But the work on dialects of Bangladesh is infrequent to our research. These dialects, in turn, differ quite a bit from each other. In this paper, we present a method to detect Bangladeshi different dialects which utilizes Mel Frequency Cepstral Coefficient (MFCC), its Delta and Delta-delta as main features and Gaussian Mixture Models (GMM) to classify characteristics of a specific dialect. Particularly we extract the MFCCs, Deltas and Delta-deltas from the speech signal. Then they are merged together to form a feature vector for a specific dialect. GMM is trained using the iterative Expectation Maximization (EM) algorithm where feature vectors are served as input. This scheme is tested on 5 databases of 30 speech samples each. Speech samples contain dialects of Borishal, Noakhali, Sylhet, Chittagong and Chapai Nawabganj regions of Bangladesh. Experiments show that GMM adaptation gives comparable good performance.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134027535","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: 25

Neural network controller based on PID using an extended Kalman filter algorithm for multi-variable non-linear control system 基于PID的神经网络控制器采用扩展卡尔曼滤波算法对多变量非线性控制系统进行控制

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449843

A. Sento, Y. Kitjaidure

{"title":"Neural network controller based on PID using an extended Kalman filter algorithm for multi-variable non-linear control system","authors":"A. Sento, Y. Kitjaidure","doi":"10.1109/ICACI.2016.7449843","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449843","url":null,"abstract":"The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. However, many researchers have been proposed the neural network controller based on PID (NNPID) to apply for both of the single and multi-variable control system but the NNPID controller that uses the conventional gradient descent-learning algorithm has many disadvantages such as a low speed of the convergent stability, difficult to set initial values, especially, restriction of the degree of system complexity. Therefore, this paper presents an improvement of recurrent neural network controller based on PID, including a controller structure improvement and a modified extended Kalman filter (EKF) learning algorithm for weight update rule, called ENNPID controller. We apply the proposed controller to the dynamic system including inverted pendulum, and DC motor system by the MATLAB simulation. From our experimental results, it shows that the performance of the proposed controller is higher than the other PID-like controllers in terms of fast convergence and fault tolerance that are highly required.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014923","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: 10

Output feedback control of cluster synchronization for Lur'e networks with packet dropouts 带丢包的Lur'e网络集群同步输出反馈控制

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449832

Ze Tang, Ju H. Park, Ho-Youl Jung

引用次数: 0

Correlating Twitter with the stock market through non-Gaussian SVAR 通过非高斯SVAR将Twitter与股票市场关联起来

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449835

Shaohua Tan, Xinhai Liu, Shuai Zhao, Yunhai Tong

{"title":"Correlating Twitter with the stock market through non-Gaussian SVAR","authors":"Shaohua Tan, Xinhai Liu, Shuai Zhao, Yunhai Tong","doi":"10.1109/ICACI.2016.7449835","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449835","url":null,"abstract":"In this paper, we aim at studying the correlation between Twitter and the stock market. Specifically, we first apply non-Gaussian SVAR (structural vector autoregression) to identify possible relationships among the Twitter and stock market factors. Compared with conventional models such as Granger causality method which assume that the error items are Gaussian and only consider time-lag effect, non-Gaussian SVAR is under the assumption that the error items are non-Gaussian, better fitting the data in the stock market, and takes both instantaneous and time-lagged effects into account. We also visualize some distinctive relationships in parallel coordinates which is a well-developed multivariate visualization technique but seldom used in financial studies to the best of knowledge. Then, with the purpose of examining whether the Twitter-stock market relationship returned by non-Gaussian SVAR can help predict the stock market indicators, we build a series of regression models to predict DJI (Dow Jones Industrial Average Index) return in a sliding time window. Our experiments demonstrate that all the Twitter factors correlate with DJI return, and only the negative sentiment in tweets (posts on Twitter) is associated with DJI return volatility. Moreover, the lagged Twitter factors are more effective than the lagged stock market indicators in terms of predicting DJI return in the period of our data set.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"PC-29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126680724","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: 11

Discovering long maximal frequent pattern 发现长最大频率模式

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449817

Shu-Jing Lin, Yi-Chung Chen, Don-Lin Yang, Jungpin Wu

{"title":"Discovering long maximal frequent pattern","authors":"Shu-Jing Lin, Yi-Chung Chen, Don-Lin Yang, Jungpin Wu","doi":"10.1109/ICACI.2016.7449817","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449817","url":null,"abstract":"Association rule mining, the most commonly used method for data mining, has numerous applications. Although many approaches that can find association rules have been developed, most utilize maximum frequent itemsets that are short. Existing methods fail to perform well in applications involving large amounts of data and incur longer itemsets. Apriori-like algorithms have this problem because they generate many candidate itemsets and spend considerable time scanning databases; that is, their processing method is bottom-up and layered. This paper solves this problem via a novel hybrid Multilevel-Search algorithm. The algorithm concurrently uses the bidirectional Pincer-Search and parameter prediction mechanism along with the bottom-up search of the Parameterised method to reduce the number of candidate itemsets and consequently, the number of database scans. Experimental results demonstrate that the proposed algorithm performs well, especially when the length of the maximum frequent itemsets are longer than or equal to eight. The concurrent approach of our multilevel algorithm results in faster execution time and improved efficiency.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127808320","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: 4

Comparison of decision tree and stepwise regression methods in classification of FDG-PET brain data using SSM/PCA features 决策树与逐步回归方法在FDG-PET脑数据SSM/PCA分类中的比较

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449841

D. Mudali, J. Roerdink, L. K. Teune, K. Leenders, R. Renken

{"title":"Comparison of decision tree and stepwise regression methods in classification of FDG-PET brain data using SSM/PCA features","authors":"D. Mudali, J. Roerdink, L. K. Teune, K. Leenders, R. Renken","doi":"10.1109/ICACI.2016.7449841","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449841","url":null,"abstract":"Objective: To compare the stepwise regression (SR) method and the decision tree (DT) method for classification of parkinsonian syndromes. Method: We applied the scaled subprofile model/principal component analysis (SSM/PCA) method to FDG-PET brain image data to obtain covariance patterns and the corresponding subject scores. The subject scores formed the input to the C4.5 decision tree algorithm to classify the subject brain images. For the SR method, scatter plots and receiver operating characteristic (ROC) curves indicate the subject classifications. We then compare the decision tree classifier results with those of the SR method. Results: We found out that the SR method performs slightly better than the DT method. We attribute this to the fact that the SR method uses a linear combination of the best features to form one robust feature, unlike the DT method. However, when the same robust feature is used as the input for the DT classifier, the performance is as high as that of the SR method. Conclusion: Even though the SR method performs better than the DT method, including the SR procedure in the DT classification yields a better performance. Additionally, the decision tree approach is more suitable for human interpretation and exploration than the SR method.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114559051","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: 4

Intelligent power allocation for collaborative spectrum sharing with non-orthogonal cooperative relaying 基于非正交协同中继的协同频谱共享智能功率分配

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449839

Jihyun Shin, Dongwoo Kim

引用次数: 0

A two-time-scale neurodynamic approach to robust pole assignment 鲁棒极点配置的双时间尺度神经动力学方法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449804

Xinyi Le, Jun Wang

引用次数: 2

An imbalanced data classification algorithm of improved autoencoder neural network 一种改进自编码器神经网络的不平衡数据分类算法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449810

Chenggang Zhang, Wei Gao, Jiazhi Song, Jinqing Jiang

引用次数: 33

RFID indoor localization based on relational aggregation 基于关系聚合的RFID室内定位

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449800

Jiali Zheng, Tuanfa Qin, Jieming Wu, Li Wan

引用次数: 6