Neural Network World最新文献_第9页

Traffic data analysis using deep Elman and gated recurrent auto-encoder 使用深度Elman和门控循环自编码器的交通数据分析

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/NNW.2020.30.023

S. Mehralian, M. Teshnehlab, B. Nasersharif

{"title":"Traffic data analysis using deep Elman and gated recurrent auto-encoder","authors":"S. Mehralian, M. Teshnehlab, B. Nasersharif","doi":"10.14311/NNW.2020.30.023","DOIUrl":"https://doi.org/10.14311/NNW.2020.30.023","url":null,"abstract":"Traffic flow prediction is one of the most interesting machine learning applications in real-world problems that can help anyone move around. In this study, we proposed a feature extraction structure for multivariate time series using Elman recurrent auto-encoder. We added loopback from the encoder layer of the normal auto-encoder to regard sequence information between successive data. The feedback layer implemented using Elman neural network and GRU cells, then the model is trained by different optimization algorithms. The models are also trained using the Emotional Learning method in which we involve the derivative of the error in the cost function to avoid local minimums and keep the last state of the network. We used the proposed method for classification and prediction problems on traffic data from the California Department of Transportation Performance Measurement System (PeMS). The results show that our structure can successfully extract a compact representation of traffic data useful for reconstructing of original data, classification, and prediction. The results also show that adding the recurrent layer to the feature extractor (auto-encoder) leads to better results in the classification phase in comparison with standard methods that do not use the recurrence during feature extraction.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A discrete butterfly-inspired optimization algorithm for solving Permutation Flow-Shop scheduling Problems 求解置换流水车间调度问题的离散蝴蝶启发优化算法

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/nnw.2020.30.015

X. Qi, Yuan Zhonghu, Xiaowei Han, Shixin Liu

引用次数: 1

THE ECG SIGNAL CLASSIFICATION BASED ON ENSEMBLE LEARNING OF PSO-ELM ALGORITHM 基于pso-elm算法集成学习的心电信号分类

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/nnw.2020.30.018

Wei Li, Bin Li, Hong Guo, Yixian Fang, Fengjuan Qiao, Shuwang Zhou

{"title":"THE ECG SIGNAL CLASSIFICATION BASED ON ENSEMBLE LEARNING OF PSO-ELM ALGORITHM","authors":"Wei Li, Bin Li, Hong Guo, Yixian Fang, Fengjuan Qiao, Shuwang Zhou","doi":"10.14311/nnw.2020.30.018","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.018","url":null,"abstract":"ECG anomaly detection plays an important role in clinical medicine. So far, a number of ECG recognition technologies have emerged in this field, but most often suffer from slow training and instability. Considering that the Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithm have the advantages of fast learning speed and strong generalization ability, this paper integrates multiple independent PSO-ELM model and proposes a novel ensemble learning framework termed as E-PSO-ELM to realize ECG signals recognition. More specifically, the individual PSO-ELM adopts the input weight and hidden layer deviation of ELM as the particles in the PSO algorithm, and takes the root mean square error of ELM training sample as the adaptive value of the particles, so as to enhance the stability of the network and realize high ECG recognition rate. The simulation results on MIT-BIH Arrhythmia Database show that E-PSO-ELM has a high classification accuracy rate of 98.23 %. In addition, compared with other algorithms, the stability of E-PSO-ELM is more prominent, which can reduce the probability of operating errors. Therefore, E-PSO-ELM has a high practical value.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"30 1","pages":"265-279"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Unsupervised facial expression detection using genetic algorithm 基于遗传算法的无监督面部表情检测

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/nnw.2020.30.005

Rahool Dembani, Wang Zheng, Meijun Sun, Nooruddin

引用次数: 2

PROBABILISTIC ANALYSIS OF THE CONVERGENCE OF THE DIFFERENTIAL EVOLUTION ALGORITHM 差分进化算法收敛性的概率分析

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/nnw.2020.30.017

R. Knobloch, J. Mlynek

引用次数: 1

Underwater acoustic signal analysis: preprocessing and classification by deep learning 水声信号分析:深度学习预处理与分类

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/nnw.2020.30.007

Hao Wu, Qingzeng Song, Guanghao Jin

引用次数: 5

Working memory, impulsivity and emotional regulation correlates with frontal asymmetry of healthy young subjects during auditory session 听觉过程中工作记忆、冲动和情绪调节与大脑额叶不对称相关

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2020-01-01 DOI: 10.14311/NNW.2020.30.024

S. Maitra, M. Chatterjee, A. Sasidharan, S. Sinha, K. Mukhopadhyay

{"title":"Working memory, impulsivity and emotional regulation correlates with frontal asymmetry of healthy young subjects during auditory session","authors":"S. Maitra, M. Chatterjee, A. Sasidharan, S. Sinha, K. Mukhopadhyay","doi":"10.14311/NNW.2020.30.024","DOIUrl":"https://doi.org/10.14311/NNW.2020.30.024","url":null,"abstract":"Background : Specific frequency oscillations provide idea about functioning of underlying brain regions. Brain oscillations and event based assessment of cognitive functions like working memory (WM), impulsivity (Imp) and emotional regulation (ER) were reported to influence each other in different ethnic groups. But how these traits are regulated in healthy Indian adults was not explored widely. Aims: We analyzed link between scalp electrical activity and different neuropsychological traits in higher education aspirants. Method: All the traits were self-assessed using standard questionnaires. QEEG was performed during an audio-sensory session. Tracings collected through BESS software were analyzed using SPSS. Results: Less impulsive individuals exhibited higher frontal theta and beta activity. Higher frontal theta activity was associated with higher ER, whereas higher theta and alpha activity showed association with WM deficit. Individuals with higher Imp and happiness exhibited higher frontal hemispheric asymmetry for theta and alpha, while those with lower asymmetry for alpha and beta activity showed higher ER. Beta asymmetry was positively related with happiness. Conclusions: We infer that variability in behaviour of healthy adults is influenced by differential frontal brain impulses and could be considered for providing individualized assistance to emotionally vulnerable individuals.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"93 1","pages":"365-378"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67124376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AN OPTIMAL DATA AGGREGATION SCHEME FOR WIRELESS SENSOR NETWORK USING QOS PARAMETERS WITH EFFICIENT FAILURE DETECTION AND LOSS RECOVERY TECHNIQUE 一种基于qos参数的无线传感器网络数据聚合方案，并结合有效的故障检测和损失恢复技术

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.019

A. R. Basha, C. Yaashuwanth

{"title":"AN OPTIMAL DATA AGGREGATION SCHEME FOR WIRELESS SENSOR NETWORK USING QOS PARAMETERS WITH EFFICIENT FAILURE DETECTION AND LOSS RECOVERY TECHNIQUE","authors":"A. R. Basha, C. Yaashuwanth","doi":"10.14311/nnw.2019.29.019","DOIUrl":"https://doi.org/10.14311/nnw.2019.29.019","url":null,"abstract":"WSN: Wireless Sensor Networks play a significant part in its modern era but its limited power supply acts as a blocking stone in it growth. In order to save energy in WSN the concept of aggregator node is introduced, where the aggregator node would act as a mid-point between the source and destination node during the data transmission. The data aggregation process creates major problems like excess energy expenditure, and delay. In the process of eliminating or reducing the delay and energy expenditure, the researchers have been handled in different ways. Applications like environment monitoring, target tracking, military surveillance and health care require reliable and accurate information. Many researchers have proposed data aggregation techniques to enhance the latency, average energy consumption and average network lifetime. However, these techniques are not sufficient to address situations like node failure and loss recovery. This paper proposes to build a solid wireless sensor system which concentrate on efficient optimal data aggregation along with additional QoS metrics such as failure detection and loss recovery. The first contribution of this paper is to propose an Improved Wolf Optimization (IWO) algorithm for clustering. The clustering process includes an efficient cluster formation like, Cluster Head (CH), and Sub Head (SH) selection. The second contribution of this paper is inclusion of failure detection and loss recovery. The former is developed based on Multi-criteria Moths-Flame Decisionmaking (MMFD) model and the latter is achieved through SH. SH node will act as the backup node for cluster head when failure instances are detection. CH recovers the lost data through SH, which minimize the additional delay of backup node selection process and save much more energy. The results are simulated using network simulator 2 tool and it is compared with existing techniques. The Network Simulator – 2 results disclose that the findings are better than the available existing methodologies.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

REGP: A NEW POOLING ALGORITHM FOR DEEP CONVOLUTIONAL NEURAL NETWORKS Regp:一种新的深度卷积神经网络池化算法

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/NNW.2019.29.004

Ozal Yildirim, U. Baloglu

引用次数: 5

DEEP HOG: A HYBRID MODEL TO CLASSIFY BANGLA ISOLATED ALPHA-NUMERICAL SYMBOLS 深猪:分类孟加拉语孤立字母数字符号的混合模型

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/NNW.2019.29.009

S. Sharif, M. Mahboob

引用次数: 4