2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)最新文献_第3页

Counterattack detection in broadcast soccer videos using camera motion estimation 基于摄像机运动估计的广播足球视频反击检测

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123487

M. Sigari, H. Soltanian-Zadeh, Vahid Kiani, Amid-Reza Pourreza

引用次数: 9

Fast image segmentation based on adaptive histogram thresholding 基于自适应直方图阈值的快速图像分割

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123514

A. Mirkazemi, S. E. Alavi, G. Akbarizadeh

引用次数: 4

JSObfusDetector: A binary PSO-based one-class classifier ensemble to detect obfuscated JavaScript code JSObfusDetector:一个基于二进制pso的单类分类器集成，用于检测混淆的JavaScript代码

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123508

Mehran Jodavi, M. Abadi, Elham Parhizkar

{"title":"JSObfusDetector: A binary PSO-based one-class classifier ensemble to detect obfuscated JavaScript code","authors":"Mehran Jodavi, M. Abadi, Elham Parhizkar","doi":"10.1109/AISP.2015.7123508","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123508","url":null,"abstract":"JavaScript code obfuscation has become a major technique used by malware writers to evade static analysis techniques. Over the past years, a number of dynamic analysis techniques have been proposed to detect obfuscated malicious JavaScript code at runtime. However, because of their runtime overheads, these techniques are slow and thus not widely used in practice. On the other hand, since a large quantity of benign JavaScript code is obfuscated to protect intellectual property, it is not effective to use the intrinsic features of obfuscated JavaScript code for static analysis purposes. Therefore, we are forced to distinguish between obfuscated and non-obfuscated JavaScript code so that we can devise an efficient and effective analysis technique to detect malicious JavaScript code. In this paper, we address this issue by presenting JSObfusDetector, a novel one-class classifier ensemble to detect obfuscated JavaScript code. To construct the classifier ensemble, we apply a binary particle swarm optimization (PSO) algorithm, called ParticlePruner, on an initial ensemble of one-class SVM classifiers to find a sub-ensemble whose members are both accurate and have diversity in their outputs. We evaluate JSObfusDetector using a dataset of obfuscated and non-obfuscated JavaScript code. The experimental results show that JSObfusDetector can achieve about 97% precision, 91 % recall, and 94% F-measure.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128579778","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: 15

Super-resolution via a patch-based sparse algorithm 通过基于补丁的稀疏算法实现超分辨率

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123496

Maryam Dashti, S. S. Ghidary, Tahmineh Hosseinian, Mohammadreza Pourfard, K. Faez

引用次数: 1

High performance implementation of APSO algorithm using GPU platform 基于GPU平台的高性能APSO算法实现

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123524

Seyyedeh Hamideh Sojoudi Ziyabari, A. Shahbahrami

引用次数: 3

Clustering of multivariate time series data using particle swarm optimization 基于粒子群算法的多元时间序列数据聚类

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123516

A. Ahmadi, Atefeh Mozafarinia, Azadeh Mohebi

{"title":"Clustering of multivariate time series data using particle swarm optimization","authors":"A. Ahmadi, Atefeh Mozafarinia, Azadeh Mohebi","doi":"10.1109/AISP.2015.7123516","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123516","url":null,"abstract":"Particle swarm optimization (PSO) is a practical and effective optimization approach that has been applied recently for data clustering in many applications. While various non-evolutionary optimization and clustering algorithms have been applied for clustering multivariate time series in some applications such as customer segmentation, they usually provide poor results due to their dependency on the initial values and their poor performance in manipulating multiple objectives. In this paper, a particle swarm optimization algorithm is proposed for clustering multivariate time series data. Since the time series data sometimes do not have the same length and they usually have missing data, the regular Euclidean distance and dynamic time warping can not be applied for such data to measure the similarity. Therefore, a hybrid similarity measure based on principal component analysis and Mahalanobis distance is applied in order to handle such limitations. The comparison between the results of the proposed method with the similar ones in the literature shows the superiority of the proposed method.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130783054","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

Automatic soccer field line recognition by minimum information 自动足球场线识别的最小信息

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123505

Mehran Fotouhi, Afshin Bozorgpour, S. Kasaei

引用次数: 2

Document clustering using gravitational ensemble clustering 利用引力系综聚类的文献聚类

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123481

A. Sadeghian, H. Nezamabadi-pour

{"title":"Document clustering using gravitational ensemble clustering","authors":"A. Sadeghian, H. Nezamabadi-pour","doi":"10.1109/AISP.2015.7123481","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123481","url":null,"abstract":"Text Mining is a field that is considered as an extension of data mining. In the context of text mining, document clustering is used to set apart likewise documents of a collection into the identical category, called cluster, and divergent documents to distinctive groups. Since every dataset has its own characteristics, finding an appropriate clustering algorithm that can manage all kinds of clusters, is a big challenge. Clustering algorithms has theirs unique approaches for computing the number of clusters, imposing a structure on the data, and attesting the out coming clusters. The idea of combining different clustering is an effort to overwhelm the faults of single algorithms and further enhance their executions. On the other hand, inspired by the gravitational law, different clustering algorithms have been introduced that each one attempted to cluster complex datasets. Gravitational Ensemble Clustering (GEC) is an ensemble method that employs both the concepts of gravitational clustering and ensemble clustering to reach a better clustering result. This paper represents an application of GEC to the problem of document clustering. The proposed method uses a modification of the original GEC algorithm. This modification tries to produce a more varied clustering ensemble using new parameter setting. The GEC algorithm is assessed using document datasets. Promising results of the presented method were obtained in comparison with competing algorithms.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127357155","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

Online signature verification based on feature representation 基于特征表示的在线签名验证

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123528

Mohsen Fayyaz, M. H. Saffar, M. Sabokrou, M. Hoseini, M. Fathy

引用次数: 30

Prediction of ventricular tachycardia using morphological features of ECG signal 利用心电信号形态学特征预测室性心动过速

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) Pub Date : 2015-03-03 DOI: 10.1109/AISP.2015.7123515

Atiye Riasi, M. Mohebbi

{"title":"Prediction of ventricular tachycardia using morphological features of ECG signal","authors":"Atiye Riasi, M. Mohebbi","doi":"10.1109/AISP.2015.7123515","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123515","url":null,"abstract":"Ventricular tachyarrhythmia particularly ventricular tachycardia (VT) and ventricular fibrillation (VF) are the main causes of sudden cardiac death in the world. A reliable predictor of an imminent episode of ventricular tachycardia that could be incorporated in an implantable defibrillator capable of preventive therapy would have important clinical utilities. As variability of T wave, ST segment and QT interval are indicators of cardiac instability, these changes can lead us to develop accurate predictor for VT. In this study, we present an algorithm that predicts VT using morphological features of electrical signal of ventricles activity obtained from Electrocardiogram (ECG). Changes in T wave, ST segment, QT interval and numbers of premature ventricular complexes(PVCs) are considered as effective indicators of VT. Classification of selected features by a Support Vector Machine (SVM) can identify hidden patterns in ECG signals before VT occurrence. Evaluation of this algorithm on 40 recods of VT patient and 40 control records shows that the proposed algorithm can reach sensitivity of 88% and specificity of 100% in VT prediction.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121259965","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