2017 Artificial Intelligence and Signal Processing Conference (AISP)最新文献

An improvement to gravitational fixed radius nearest neighbor for imbalanced problem 不平衡问题引力固定半径最近邻的改进

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324109

Mahin Shabani-kordshooli, Bahareh Nikpour, H. Nezamabadi-pour

{"title":"An improvement to gravitational fixed radius nearest neighbor for imbalanced problem","authors":"Mahin Shabani-kordshooli, Bahareh Nikpour, H. Nezamabadi-pour","doi":"10.1109/AISP.2017.8324109","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324109","url":null,"abstract":"Mining of imbalanced data is one of the basic challenges in the field of machine learning and data mining. In the recent years, a lot of approaches have been proposed to handle imbalanced learning problem. A group of these methods are algorithmic level methods, which are adapted to the nature of imbalanced datasets. Gravitational fixed radius nearest neighbor algorithm (GFRNN) is an algorithmic level method, proposed in order to improve k nearest neighbor classifier when dealing with imbalanced datasets. This algorithm finds the fixed radius nearest neighbors as candidate set. Then, by computing the sum of gravitational forces on a query instance from candidate set, predict its label. Simplicity and no need for manually parameter setting during the run of algorithm are the main advantages of this method. In this paper, gravitational search algorithm (GSA) is used with the aim of finding the mass of training instances to improve the performance of GFRNN. Also we utilize the all training instances to make a decision about query instance. Experimental result on fifteen datasets show the superiority of it compared with four other algorithms.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"52 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121010682","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: 5

A novel method to detect android malware using Locality Sensitive Hashing algorithms 一种利用位置敏感哈希算法检测android恶意软件的新方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324089

Ebrahim Sayahi, A. Hamzeh

引用次数: 0

Locally anomaly detection in crowded scenes using Locality constrained Linear Coding 基于局域约束线性编码的拥挤场景局部异常检测

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324082

Hajar Yousefi, Z. Azimifar, A. Nazemi

引用次数: 5

Recognizing Persian handwritten words using deep convolutional networks 使用深度卷积网络识别波斯语手写文字

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324114

Rasool Sabzi, Zahra Fotoohinya, Abdullah Khalili, S. Golzari, Zeinab Salkhorde, Sajjad Behravesh, Shahin Akbarpour

{"title":"Recognizing Persian handwritten words using deep convolutional networks","authors":"Rasool Sabzi, Zahra Fotoohinya, Abdullah Khalili, S. Golzari, Zeinab Salkhorde, Sajjad Behravesh, Shahin Akbarpour","doi":"10.1109/AISP.2017.8324114","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324114","url":null,"abstract":"Handwritten word recognition is an active research area due to numerous commercial applications in offline and online recognition systems. The diversity and complexity of Persian handwritten words makes them more difficult to recognize. In current methods, discriminative features are manually extracted from images by humans so their performance depends on human creativity. This process is called shallow learning. In this study, deep Convolutional Neural Networks (CNNs), a widely used type of deep learning, is employed to automatically extract the discriminative features. Deep learning is able to discover complex structure (discriminative feature here) in large datasets. First in the proposed method, a preprocessing algorithm converts the images to equal size while maintaining handwritten words structure. Then, the images are given to two different architectures of CNNs, AlexNet and GoogLeNet with and without batch normalization. Finally, the proposed method is evaluated on “IRANSHAHR” dataset which includes 15383 images of 503 different city names of Iran. Experimental results show that GoogLeNet with preprocessed data and batch normalization achieves higher accuracy (99.13%) and outperforms the current methods.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"41 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":"129185044","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: 6

Galaxy gravity optimization(GGO) an algorithm for optimization, inspired by comets life cycle 星系引力优化(GGO)是一种受彗星生命周期启发的优化算法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515125

Seyed Muhammad Hossein Mousavi, S. Mirinezhad, M. Dezfoulian

{"title":"Galaxy gravity optimization(GGO) an algorithm for optimization, inspired by comets life cycle","authors":"Seyed Muhammad Hossein Mousavi, S. Mirinezhad, M. Dezfoulian","doi":"10.1109/AISP.2017.8515125","DOIUrl":"https://doi.org/10.1109/AISP.2017.8515125","url":null,"abstract":"The aim of this paper is to propose an optimization algorithm which is inspired by the comet's life. Like other evolutionary algorithms, this proposed algorithm commences with an initial population. The individuals of the population are comets which are composed of two parts: a nucleus and small celestial bodies. These comets after exit of Kuiper belt due to the gravitational disorder which has been triggered by solar system planets, and entering to the solar system, start the main competition for more survival in the solar system. Along this competition the weakened comets collapse and convert to rubbles along the solar orbit which comets where orbiting and other comets depending on their gravitational power relatively absorb these rubbles (small celestial bodies). The comet which has been able to lose least of its mass and gain the most, along its orbits and based on gravitational mutation (having better orbits); has been able to spend more time in solar system so it converges with a higher fitness function in a global maximum. The results of the proposed algorithm which have been experimented on some benchmark functions, represent that this algorithm is capable of dealing with a variety of optimization problems.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"297 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":"124253483","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: 5

Quick sift(QSIFT), an approach to reduce SIFT computational cost 快速筛选(QSIFT)是一种降低sift计算成本的方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515128

Zahra Fazel, M. Famouri, A. Nazemi, Z. Azimifar

引用次数: 1

N-scan δ-generalized labeled multi-bernoulli-based approach for multi-target tracking 基于n扫描δ-广义标记多伯努利的多目标跟踪方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324118

M. H. Sepanj, Z. Azimifar

引用次数: 0

Evolving vascular morphogenesis controller to demonstrate locomotion 进化血管形态发生控制器以演示运动

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515124

Payam Zahadat, T. Schmickl

引用次数: 2

A PSO fuzzy-expert system: As an assistant for specifying the acceptance by NOET measures, at PH.D level 一种粒子群模糊专家系统:作为用NOET方法确定博士级验收的辅助

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.13140/RG.2.2.27541.22247

Seyed Muhammad Hossein Mousavi, S. Mirinezhad, Mehrdad Shafaei Mosleh, M. Dezfoulian

{"title":"A PSO fuzzy-expert system: As an assistant for specifying the acceptance by NOET measures, at PH.D level","authors":"Seyed Muhammad Hossein Mousavi, S. Mirinezhad, Mehrdad Shafaei Mosleh, M. Dezfoulian","doi":"10.13140/RG.2.2.27541.22247","DOIUrl":"https://doi.org/10.13140/RG.2.2.27541.22247","url":null,"abstract":"The intelligent decision making systems are useful tools for the assistance of human expert, and or as a perfect alternative for expert in a variety of auto-decision making fields. The use of such systems in education, agriculture, industry, fishery, animal husbandry etc., can decrease manpower errors or need of it; In the other hand, it can increase the quality and the pace of service giving. The interview at the PH.D level or even Master's degree, due to the high sensitivity in scoring to the candidates, is of high importance. Therefore, creating a system for storing these scores, and inferring the results can be beneficial when there is a large number of candidates. In this paper, the expert system has an educational use, and classifies the probability of acceptance or unacceptance of PH.D candidates in the exam and interview, based on the (National Organization of Educational Testing) NOET measures, also estimates scientific level of candidates. The proposed fuzzy-expert system takes advantage of the particle swarm optimization (PSO) evolutionary algorithm to specifying the score of each variable, and eventually the final condition of the candidate. The acquired results of evaluating the fuzzy-expert system proves its functionality. This system is also able to function well in scoring similar educational cases to specify acceptance.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"286 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":"133138164","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

Breast cancer detection without removal pectoral muscle by extraction turn counts feature 不切除胸肌的乳腺癌提取转数特征检测

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/aisp.2017.8324112

Farzam Kharaji Nezhadian, S. Rashidi

引用次数: 5