发布求助
文献互助 智能选刊 最新文献

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献

筛选
英文 中文
Learning on Fisher-Bingham Model Based on Normalizing Constant 基于归一化常数的Fisher-Bingham模型学习
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-23 DOI: 10.1109/ISCMI.2016.38
Muhammad Ali, M. Antolovich
{"title":"Learning on Fisher-Bingham Model Based on Normalizing Constant","authors":"Muhammad Ali, M. Antolovich","doi":"10.1109/ISCMI.2016.38","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.38","url":null,"abstract":"Our focus in this work is on the practical applicability of matrix variate Fisher-Bingham model for statistical inferences via Maximum Likelihood Estimation (MLE) technique using simple Bayesian classifier. The practicability of such parametric models on high dimensional data (e.g., via manifold valued data) remained a big hurdle since long i.e., mainly due to the difficult normalising constant naturally appear with them. We applied the method of Saddle Point Approximation (SPA) for calculating the corresponding normalising constant and then tested the validity and performance of the proposed algorithm on two datasets against the state of the art existing techniques and observed that the proposed technique is more suitable for recognition on Grassmann manifolds via a simple Bayesian classifier.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127326561","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: 0
Aggregated Activity Recognition Using Smart Devices 使用智能设备的聚合活动识别
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.52
Khaled Eskaf, W. Aly, Alyaa Aly
{"title":"Aggregated Activity Recognition Using Smart Devices","authors":"Khaled Eskaf, W. Aly, Alyaa Aly","doi":"10.1109/ISCMI.2016.52","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.52","url":null,"abstract":"Activity recognition has become of great importance in many fields especially in fitness monitoring, health and elder care by offering the opportunity for large amount of applications which recognize human's daily life activities. Human activity recognition (HAR) was not only limited on health care field or monitoring sports, but it also started to emerge in the religious branch and monitor people behavior while performing their religious activity like praying. The prevalence of smart phones in our society with their ever growing sensing power has opened the door for more sophisticated data mining applications which takes the raw sensor data as input and classify the motion activity performed. The main sensor used in performing activity recognition is the accelerometer. This paper presents a framework for activity recognition using smart phone sensors to recognize simple daily activities and then aggregate these simple activities (walking, standing, sitting,…) to recognize a more complex one which is prayer. Features extracted from raw sensor data are used to train and test supervised machine learning algorithms.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125682317","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
Rhinoceros Search Algorithm 犀牛搜索算法
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.16
Zhonghuan Tian, S. Fong, Rui Tang, S. Deb, R. Wong
{"title":"Rhinoceros Search Algorithm","authors":"Zhonghuan Tian, S. Fong, Rui Tang, S. Deb, R. Wong","doi":"10.1109/ISCMI.2016.16","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.16","url":null,"abstract":"In this paper, a novel metaheuristic search algorithm inspired by rhinoceros' natural behaviour is proposed, namely Rhinoceros Search Algorithm (RSA). Similar to our earlier version called Elephant Search Algorithm, RSA simplifies certain habitual characteristics and stream line the search operations, thereby reducing the number of operational parameters required to configure the model. Via computer simulation, it is shown that RSA is able to outperform certain classical metaheuristic algorithms. Different dimensions of optimization problems are tested, and good results are observed by RSA.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129613566","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
Feature Selection and Hyperparameter Optimization of SVM for Human Activity Recognition 支持向量机在人体活动识别中的特征选择与超参数优化
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.30
Zubin A. Sunkad, Soujanya
{"title":"Feature Selection and Hyperparameter Optimization of SVM for Human Activity Recognition","authors":"Zubin A. Sunkad, Soujanya","doi":"10.1109/ISCMI.2016.30","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.30","url":null,"abstract":"Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built. Data was collected from the database provided by the University of Southern California (USC) for human activity recognition. Six features were computed to obtain the feature set. Different feature subsets were then evaluated based on the precision and recall scores. Using grid search algorithm, the best subset of hyperparameters (SVM kernel, regularization parameter(C) and γ) for the SVM classifier which gives the highest precision and recall score was selected from the parameter space. The best set of features and the SVM hyperparameters for obtaining best results in activity recognition are proposed in this work.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129243982","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
Fireworks Algorithm with New Feasibility-Rules in Solving UAV Path Planning 基于新可行性规则的烟花算法求解无人机路径规划
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.33
Adis Alihodžić
{"title":"Fireworks Algorithm with New Feasibility-Rules in Solving UAV Path Planning","authors":"Adis Alihodžić","doi":"10.1109/ISCMI.2016.33","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.33","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) path planning is a high dimensional NP-hard problem. It is related to optimizing the flight route subject to various constraints inside the battlefield environments. Since the number of control points is high as well as the number of radars, the traditional methods could not produce acceptable results when tackling this problem. In this paper, we have converted the UAV path planning problem to the constrained one based on new feasibility-rules and then we have implemented the Fireworks algorithm (FWA) and applied it later in solving this issue. For experimental purposes, we used the parameters of the battlefield environments from the literature to verify the proposed FWA. The simulation results show that the proposed FWA in all cases outperforms PSO, DE, BA, and CS.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115525138","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
Time Optimal Control and Switching Curve Analysis for Caputo Fractional Systems Caputo分数阶系统的时间最优控制及切换曲线分析
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.49
Zeinab Zolfaghari, M. Baradarannia, F. Hashemzadeh, S. Ghaemi
{"title":"Time Optimal Control and Switching Curve Analysis for Caputo Fractional Systems","authors":"Zeinab Zolfaghari, M. Baradarannia, F. Hashemzadeh, S. Ghaemi","doi":"10.1109/ISCMI.2016.49","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.49","url":null,"abstract":"In this paper we present a method for fractional time optimal control problems in terms of Caputo fractional derivatives. The studies up to now are about systems in terms of Riemann-Liouville fractional derivatives. But in this paper we investigated Caputo fractional systems. Firstly, by utilizing matrix approach method to discrete fractional derivatives, fractional Caputo derivative is solved. Then, the original problem is solved by traditional optimal problem solvers. Finally, time optimal problem is studied for a double fractional integrator and a method for obtaining switching curve has presented.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122086768","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
Elastic Multi-stage Decision Rules for Infrequent Class 非频繁类的弹性多阶段决策规则
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.20
Soma Datta, S. Mengel
{"title":"Elastic Multi-stage Decision Rules for Infrequent Class","authors":"Soma Datta, S. Mengel","doi":"10.1109/ISCMI.2016.20","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.20","url":null,"abstract":"Typically, decision trees are used to represent knowledge by rule generation. To have a better understanding of the rules, it is sometimes necessary to minimize the number of nodes by minimizing the depth of the tree. This study optimizes the depth of the tree by minimizing the number of nodes. Rules that are generated using either decision trees or class association mining are from the major class of the dataset. To enable rules to be created for the infrequent class, this study uses an elastic method, Elastic Multi-Stage Decision Methodology (EMSDM), to create rules for the infrequent group. EMSDM is elastic in that it expands and contracts to accommodate the characteristics of the dataset. In addition, the data analysis occurs in stages: clustering, minimizing the depth of the decision tree, and association mining, to increase the ability of EMSDM to find infrequent class rules. EMSDM shows promise to find infrequent class rules with increased accuracy.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521626","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
Headless Chicken Guaranteed Convergence Particle Swarm Optimization Algorithms for Improved Diversity in a Dynamically Changing Environment 无头鸡保证收敛粒子群优化算法在动态变化环境下提高多样性
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.45
J. Grobler, A. Engelbrecht
{"title":"Headless Chicken Guaranteed Convergence Particle Swarm Optimization Algorithms for Improved Diversity in a Dynamically Changing Environment","authors":"J. Grobler, A. Engelbrecht","doi":"10.1109/ISCMI.2016.45","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.45","url":null,"abstract":"This paper investigates various strategies for incorporating the headless chicken macromutation operator and the guaranteed convergence particle swarm optimization velocity update into a dynamic particle swarm optimization algorithm. Three different dynamic headless chicken guaranteed convergence particle swarm optimization algorithms are proposed and evaluated on a diverse set of single-objective dynamic benchmark problems. Competitive performance is demonstrated by a Von Neumann headless chicken guaranteed convergence particle swarm optimization algorithm.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125727251","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: 0
Evidence Accumulation from Some Clustering Algorithms to Improve Gene Expression Data Classification 基于聚类算法改进基因表达数据分类的证据积累
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.54
Ranjita Das, S. Saha
{"title":"Evidence Accumulation from Some Clustering Algorithms to Improve Gene Expression Data Classification","authors":"Ranjita Das, S. Saha","doi":"10.1109/ISCMI.2016.54","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.54","url":null,"abstract":"The idea of ensemble based clustering is to combine the data partitions produced by multiple clustering algorithms. Here we have considered several recently developed clustering algorithms like point symmetry distance based genetic clustering technique (GAPS), symmetry based differential evolution and particle swarm optimization based clustering algorithms, popular K-means and fuzzy C-means clustering algorithms as the basic approaches for the generation of multiple clustering solutions. Here those basic algorithms perform the decomposition of initial N X d-dimensional data into k compact clusters. The objective of the use of ensemble clustering to get a single combined solution from the set of different individual partitionings is to increase the accuracy of final partitioning. Here the evidence on pattern association is accumulated by a Link based ensemble method called CTS. This produces a mapping of the partitioning into a N X N matrix that represents new similarity measure between patterns. The final data partition is obtained by applying the single-linkage clustering algorithm using this new similarity matrix. For experimental purpose some publicly available gene expression datasets have been used. Moreover to validate the clustering solutions obtained from the link based cluster ensemble method as well as from the individual base clustering algorithms, some internal cluster validity indices, DB-index and DUNN-index have been used.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115951893","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: 0
An Image Inpainting Method Using Information of Damage Region 一种基于损伤区域信息的图像修复方法
2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.11
Guoyue Chen, Xingguo Zhang, Kazutaka Nakui, Kazuki Saruta, Yuki Terata, Min Zhu
{"title":"An Image Inpainting Method Using Information of Damage Region","authors":"Guoyue Chen, Xingguo Zhang, Kazutaka Nakui, Kazuki Saruta, Yuki Terata, Min Zhu","doi":"10.1109/ISCMI.2016.11","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.11","url":null,"abstract":"With the development of digital images processing, image inpainting is become one of the most impressive and useful technique. Based on partial derivative equations or texture synthesis, many image inpainting techniques have been proposed. Amano et al. proposed a method that based on eigenspace analyzes, which is called Back Projection for Lost Pixels (BPLP). It obtains the eigenspace from a set of learning samples from original image, then estimating the missing region by inverse projection and a linear combination of the eigenspace. But it remains have some obvious discomfort surrounding the damage region after restoration treatment. In this paper, we have proposed an adaptation of BPLP algorithms, which are performing well on images with natural defects.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127984641","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信