2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)最新文献_第7页

Autoencoder based Features Extraction for Automatic Classification of Earthquakes and Explosions 基于自编码器的地震与爆炸自动分类特征提取

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466464

O. Saad, Koji Inoue, A. Shalaby, Lotfy Sarny, M. Sayed

{"title":"Autoencoder based Features Extraction for Automatic Classification of Earthquakes and Explosions","authors":"O. Saad, Koji Inoue, A. Shalaby, Lotfy Sarny, M. Sayed","doi":"10.1109/ICIS.2018.8466464","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466464","url":null,"abstract":"Monitoring illegal explosions is mandatory for the safety of human life, environment, and protect the important buildings such as High-dam in Egypt. This kind of monitoring can be accomplished by detecting and identifying the explosions. If an illegal explosion happens such as quarry blast, an alarm should be reported to the government to take immediate action. However, the main problem is that many measured signals from received explosions are similar to earthquakes in their shape and both cannot differentiate from each other. Also, incorrect classification possibly will distort the real seismicity nature of the region. This problem motivates us to search for unique discriminating features to distinguish between earthquakes and explosions with precise accuracy. Therefore, in this paper, we propose to extract the discriminative features based on Autoencoder from the first few seconds after the P-wave arrival time of the event. The discriminative features are found to be in the first 60 samples after the arrival time of P-wave. Thus the first stage of the proposed algorithm is extracting the discriminative features via the Autoencoder. Then, softmax classifies the event based on these extracted features. The proposed algorithm achieves a classification accuracy of 98.55% when applied to 900 earthquakes and quarry blasts waveforms recorded by Egyptian National Seismic Network (ENSN).","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133571781","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

Using CNN-based Representation Learning Method for Malicious Traffic Identification 基于cnn的表示学习方法用于恶意流量识别

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466404

Yao Wang, Jing An, Wei Huang

引用次数: 22

Level-wise Segment Parser for Semantic Role Labelling for Myanmar Text 缅甸语文本语义角色标注的分层分段解析器

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466416

Zin Mar Kyu, Naw Lay Wah

引用次数: 0

An Improved Prorank Algorithm for Detection of Overlapping Protein Complexes using Protein Interaction and Gene Expression Datasets in Yeast 利用酵母蛋白相互作用和基因表达数据集检测重叠蛋白复合物的改进Prorank算法

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466500

P. Manikandan, D. Ramyachitra

{"title":"An Improved Prorank Algorithm for Detection of Overlapping Protein Complexes using Protein Interaction and Gene Expression Datasets in Yeast","authors":"P. Manikandan, D. Ramyachitra","doi":"10.1109/ICIS.2018.8466500","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466500","url":null,"abstract":"Detection of protein complex plays a significant role in holding out various biological purposes and it can be applied to learning the pathways from the network of Protein-Protein Interactions (PPI). The increasing amount of computational techniques for predicting the protein complex has been motivated by high-throughput experimental techniques that create a huge sum of protein interaction datasets. From the high-throughput technologies, PPI data are gathered and the datasets contain noisy. The clustering algorithms on the interaction datasets are usually not enough to attain trustworthy prediction results. The possible way is to enhance the accuracy of protein complex detection is achieved by exploiting the domain-domain interactions (DDI) and Protein-Protein Interactions (PPI). In this research, an improved prorank algorithm has been developed to detect the overlapping protein complexes. The rough set fuzzy algorithm is integrated with the prorank algorithm behind the ranking step to find the overlapping protein complexes. The proposed and the existing algorithms such as Improved Prorank, Prorank, CAMWI and PEWCC are examined on different datasets namely Protein-Protein Interaction (PPI) and Gene Expression such as GSE3076, GSE16799, GSE19213, PPI-D1, PPI-D2, Gavin and MIPS. The experimental outcomes illustrate that the proposed Improved prorank algorithm predicts more complex networks with superior results contrast to other techniques.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"72 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879940","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

Facial Expression Recognition Using Adaptive Neuro-fuzzy Inference Systems 基于自适应神经模糊推理系统的面部表情识别

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466438

I. Perikos, M. Paraskevas, I. Hatzilygeroudis

{"title":"Facial Expression Recognition Using Adaptive Neuro-fuzzy Inference Systems","authors":"I. Perikos, M. Paraskevas, I. Hatzilygeroudis","doi":"10.1109/ICIS.2018.8466438","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466438","url":null,"abstract":"Facial expressions form a universal language of emotions, which can instantly express a wide range of emotional states and feelings. The analysis of facial expressions and the accurate recognition of their emotional content are highly desired and assistive in a wide spectrum of domains. In this paper, we present a work on the analysis of facial expressions and the recognition of emotions using an approach that is based on adaptive neuro fuzzy inference systems. Initially, given a new image, human faces are detected using the Viola-Jones algorithm. Then, facial expressions are analyzed and facial deformations of specific regions such as eyes, eyebrows and mouth are located and then characteristics such as locations, length, width and shape are extracted. The feature vectors represent the deformations of the facial expression and based on them the emotional content of the facial expressions is recognized using an approach that is based on adaptive neuro fuzzy inference systems. An evaluation study was conducted on the JAFFE database and the results collected were very encouraging indicating that the approach is efficient and accurate in analyzing facial expressions and recognizing their emotional content.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133982360","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: 7

Play games using Reinforcement Learning and Artificial Neural Networks with Experience Replay 玩游戏使用强化学习和人工神经网络与经验回放

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466428

Meng Xu, Haobin Shi, Yao Wang

引用次数: 7

Verifying CPS for Self-Adaptability 验证CPS的自适应性

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466527

Ying Zhou, Xufang Gong, Jiakai Li, Bixin Li

{"title":"Verifying CPS for Self-Adaptability","authors":"Ying Zhou, Xufang Gong, Jiakai Li, Bixin Li","doi":"10.1109/ICIS.2018.8466527","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466527","url":null,"abstract":"CPS (Cyber-Physical Systems) are physical and engineered systems featuring a tight combination of computation and physical processes by communication networks. CPS are mainly applied in some critical domains, so it is very essential to ensure the correctness of CPS. As a very important method for verifying system correctness, formal verification has been successfully applied in the verification of CPS; however, the high theoretical level of formal modeling techniques in formal verification and the lack of generality of formal models make it difficult to integrate formal verification with enterprise standard system development process. In this paper, we model CPS by HybridUML, an extension of UML which is now the de facto standard modeling language in system development practice, and then transform HybridUML model into the input language of theorem prover KeYmaera-QHP(Quantified Hybrid Program), and finally verify the QHP code with KeYmaera. When transforming a HybridUML model, we define the metamodels of HybridUML and QHP, and then find the association between them based on semantic consisteency, and finally define transformation rules using ATL (ATLAS Transformation Language) and template based code generation method to achieve transformation.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125847405","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

Research on Feature Dimensionality Reduction in Content Based Public Cultural Video Retrieval 基于内容的公共文化视频检索特征降维研究

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466379

Yumeng Liu, Aina Sui

引用次数: 6

Dynamic Face and Upper-Body Emotion Recognition for Service Robots 服务机器人动态面部和上半身情感识别

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466505

T. Vithanawasam, B. Madhusanka

引用次数: 6

Matrix Operation and Its Application in Computer Engineering 矩阵运算及其在计算机工程中的应用

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466378

Fang Dong, Y. Lv

引用次数: 0