2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)最新文献

Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm 基于支持向量机和混合引力搜索算法的乳房x线影像肿瘤检测

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482133

Fatemeh Shirazi, E. Rashedi

引用次数: 21

Hierarchical gradient based control optimized by shuffled frog leaping algorithm for large-scale systems 基于层次梯度的大规模系统混沌蛙跳优化控制

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482115

Mehrnoosh Zaeifi, M. M. Farsangi, E. Bijami, F. Karami

引用次数: 0

A neuro-fuzzy fan speed controller for dynamic management of processor fan power consumption 一种用于处理器风扇功耗动态管理的神经模糊风扇转速控制器

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482121

J. M. N. Abad, Ali Gholipour Soleimani

{"title":"A neuro-fuzzy fan speed controller for dynamic management of processor fan power consumption","authors":"J. M. N. Abad, Ali Gholipour Soleimani","doi":"10.1109/CSIEC.2016.7482121","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482121","url":null,"abstract":"The progress in silicon process technology have prepared the processors with more amount of cores. The more cores lead to increase the amount of required power. Therefore, the heat will be increased for the shortage of die size. The high temperature can cause degradation in performance, reliability, transistor aging, transition speed, and an increase in leakage current. One of the primitive thermal management technique is to use cooling equipment which can decrease temperature with no performance reduction. Although the high speed of fan reduces the temperature, it also brings the more power consumption. The increase in fan speed causes an increase in power consumption, large noise levels in the system, and a decrease of fan lifetime that may impact reliability. The Neuro-Fuzzy (NF) fan speed controller (NFSC) that we offered decreases fan power consumption while preventing the temperature violation from an expected temperature. NFSC estimates the minimum required fan speed that holds the temperature close to the desired temperature with the aim of saving power. The primarily practical results demonstrate that our suggested model in compared with the traditional fan controller is significantly able to reduce the average of fan power consumption approximately 30% with increasing of average temperature by 9% (4.7°C) compared to the traditional fan controller.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127015966","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

The inference of predictor set in gene regulatory networks using gravitational search algorithm 基于引力搜索算法的基因调控网络预测集推断

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482128

M. Jafari, V. S. Naeini, B. Ghavami

{"title":"The inference of predictor set in gene regulatory networks using gravitational search algorithm","authors":"M. Jafari, V. S. Naeini, B. Ghavami","doi":"10.1109/CSIEC.2016.7482128","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482128","url":null,"abstract":"The inference of Gene Regulatory Network (GRN) using the gene expression data is a growing field in bioinformatics and biological systems. As a matter of fact, the inference of GRN is crucial in order to predict the biological processes. In addition, it would be beneficial to determine the behavior of the processes in order to avoid the occurrence of some unplanned processes (disease). Inferring truly GRN requires the accurate inference of the predictor set. The process of predictor set inference consists of realizing the dependency of target genes and their potential predictors. Generally, the main limitations of an accurate inference of predictor set are the large number of genes, the low number of samples and the presence of noise in the gene expression data. This paper presents an accurate framework using Gravitational Search Algorithm (GSA) to infer predictor subset of each target gene in a GRN. In this work, one heuristic algorithm is utilized for each target gene independently. In each population, a mass presents the predictor subset related to the target gene. To generate the initial population per each target gene, instead of choosing predictors randomly, they are chosen using the Pearson correlation coefficient. The Mean Conditional Entropy (MCE) is used to guide GSA (as fitness function). Experimental results on biological data and comparative analysis including a recently method based on Genetic Algorithm (GA) for the same purpose, reveal that the proposed framework achieves superior accuracy.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"605 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193427","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: 2

A novel steganography method based on matrix pattern and LSB algorithms in RGB images 基于矩阵模式和LSB算法的RGB图像隐写新方法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482107

Amirfarhad Nilizadeh, A. Nilchi

{"title":"A novel steganography method based on matrix pattern and LSB algorithms in RGB images","authors":"Amirfarhad Nilizadeh, A. Nilchi","doi":"10.1109/CSIEC.2016.7482107","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482107","url":null,"abstract":"In this paper, a new steganography algorithm that combines two different steganography methods, namely Matrix Pattern (MP) and Least Significant Bit (LSB), is presented for RGB images. These two methods use the spatial domain of images for hiding secret messages; however, they differ from each other, fundamentally. The MP method is an algorithm which, firstly, divides the \"Cover-Image\" into non-overlapping B×B blocks. Then, it hides the data in the 4th through 7th bit layers of the blue layer of the \"Cover-Image\", by generating unique tixt2 matrix patterns for each character in each block. The LSB method is an algorithm that hides data in the least significant bit of the \"Cover-Image\" pixels, which has the least visible effect on the transparency of the \"Stego-Image\". In the proposed algorithm, the first three bit layers, and the 4th to 7th bit layers of the blue layer of the RGB \"Cover-Image\" is used for hiding the \"Message\", with LSB and MP methods, respectively. This algorithm has two entrances for the \"Message\"; one of them can be only text, \"Text Message\", which is hidden with the MP method. The other one, \"Binary Message\", can be any digital media, and is hidden with the LSB method. Our simulation and evaluation results show that this new method has a better capacity than the LSB and MP methods, by more than 1.265 and 4.77 times, correspondingly. Our results also indicate that the final \"Stego-Image\" has a high quality PSNR.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134117312","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: 18

Multi relational-upgraded methods: Classification and analysis in supervised learning 多关系升级方法:监督学习中的分类与分析

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482131

R. Zall, M. Keyvanpour

{"title":"Multi relational-upgraded methods: Classification and analysis in supervised learning","authors":"R. Zall, M. Keyvanpour","doi":"10.1109/CSIEC.2016.7482131","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482131","url":null,"abstract":"Today, Relational databases are used to store structured and complex data. They consist of multiple relations that are linked together conceptually via entity-relationship links. While the need to analyze these complex and structured data now have been increased, but many traditional learning techniques mine a single table as input and could not meet this requirement. So Multi-relational data mining methods are proposed which they search for the useful patterns involved in multiple relations in a relational database. Classification is one of the most popular tasks in data mining, so numerous studies have been done on solving multi-relational classification problems. Due to variety and plenty of proposed methods in this field, it seems useful to understand better the classification of these methods and the differences between them. In this work, we provide an analysis focuses on Relational-upgraded methods that upgrade traditional supervised classification algorithms such as KNN, Naive Bayesian, decision tree, genetic algorithms into the multi relational classification. We classify these methods according to traditional methods that are upgraded, then review and analyze the existing multi relational classification techniques in each group. Therefore, this study presents a formal classification and analysis that provides a useful roadmap for new researchers in the area of multi relational classification.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"104 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281506","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: 2

Analysis of swarm intelligence and evolutionary computation techniques in IIR digital filters design 群体智能与进化计算技术在IIR数字滤波器设计中的应用

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482117

A. Mohammadi, S. Zahiri

{"title":"Analysis of swarm intelligence and evolutionary computation techniques in IIR digital filters design","authors":"A. Mohammadi, S. Zahiri","doi":"10.1109/CSIEC.2016.7482117","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482117","url":null,"abstract":"Digital filters provide excellent advantages, compared to analog filters, such as better stability and precision. According to the duration/length of the impulse response, digital filters are categorized as Finite-Impulse-Response (FIR) and Infinite-Impulse-Response (IIR) filters. Because the error surface of IIR filters is mostly multimodal, powerful global optimization techniques are preferred for avoid local minima in the filter design process. Artificial Intelligence (AI)-based approaches, Swarm Intelligence (SI) and Evolutionary Computation (EC) techniques are candidate methods to address this problem and to produce desirable solutions. SI is used to model the collective behavior of social swarms in nature, such as ant colonies, honey bees, and bird flocks. The EC is based on the principle of evolution (survival of the fittest). In this paper, a novel index for IIR filter design is introduced (called \"Indicator of Success\") and SI and EC algorithms are tested and evaluated for several numbers of novel and conventional heuristic algorithms. The reduced-order identification of two benchmarked IIR plants are carried out. We analyzed the performance of the proposed algorithms in IIR digital filters design in terms of the reliability, Mean-Square-Error (MSE) and IoS. The results demonstrate the proper and reliable performance of the SI algorithms compared to that achieved by EC algorithms.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782755","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

A new version of Gravitational Search Algorithm with negative mass 一个新版本的负质量引力搜索算法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482123

Fatemeh Khajooei, E. Rashedi

引用次数: 13

Applying Tabu Search to the Freeze-Tag Problem 禁忌搜索在冻结标签问题中的应用

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482136

Hamidreza Keshavarz

引用次数: 0

A GSA-based method in human identification using finger vein patterns 基于gsa的手指静脉识别方法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482109

F. Saadat, Mehdi Nasri

引用次数: 7