Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974499
Haoxin Wang, Jingdong Zhong, Defu Zhang, Xinyao Zou
This paper develops a new combination model incorporating three excellent classification algorithms to solve the bank customer credit rating problem. Computational results on well-known credit records from German, Australian, Chinese and Japan banks show that, compared with other state-of-the-art classification algorithms, the proposed algorithm has higher efficiency and better evaluation results in most experimental cases.
{"title":"A new classification algorithm for the bank customer credit rating","authors":"Haoxin Wang, Jingdong Zhong, Defu Zhang, Xinyao Zou","doi":"10.1109/ICACI.2017.7974499","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974499","url":null,"abstract":"This paper develops a new combination model incorporating three excellent classification algorithms to solve the bank customer credit rating problem. Computational results on well-known credit records from German, Australian, Chinese and Japan banks show that, compared with other state-of-the-art classification algorithms, the proposed algorithm has higher efficiency and better evaluation results in most experimental cases.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122582829","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}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974507
Tianzheng Wang, Qingang An, Jie Li, Yujia Zhang, Junyu Han, Shuai Wang, Shiying Sun, Xiaoguang Zhao
Nowadays, unattended monitoring system has been widely used in substation for its efficiency and efficacy, and it sometimes may cause safety problems for utility workers. In order to ensure workers' safety, in this paper, we focus on the problem of illegal human ladder climbing action recognition in substation using vision-based algorithm. Specifically, we first detect “forbidden” and “allowing” types of signboards on the ladder to localize the ladder and then define the unsafe area. We use HSV-based algorithm and do hough circle detection to recognize “forbidden” signboard. To recognize “allowing” signboard, we propose HOG-based feature extraction algorithm with SVM classifier, and then use color analysis for further detection. After that, we detect and localize human action applying Visual Background Extractor(ViBE) algorithm. Finally, we can recognize illegal human ladder climbing action based on the relative position between human and signboards. The experiments demonstrate the relative high accuracy of our proposed algorithm.
{"title":"Vision-based illegal human ladder climbing action recognition in substation","authors":"Tianzheng Wang, Qingang An, Jie Li, Yujia Zhang, Junyu Han, Shuai Wang, Shiying Sun, Xiaoguang Zhao","doi":"10.1109/ICACI.2017.7974507","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974507","url":null,"abstract":"Nowadays, unattended monitoring system has been widely used in substation for its efficiency and efficacy, and it sometimes may cause safety problems for utility workers. In order to ensure workers' safety, in this paper, we focus on the problem of illegal human ladder climbing action recognition in substation using vision-based algorithm. Specifically, we first detect “forbidden” and “allowing” types of signboards on the ladder to localize the ladder and then define the unsafe area. We use HSV-based algorithm and do hough circle detection to recognize “forbidden” signboard. To recognize “allowing” signboard, we propose HOG-based feature extraction algorithm with SVM classifier, and then use color analysis for further detection. After that, we detect and localize human action applying Visual Background Extractor(ViBE) algorithm. Finally, we can recognize illegal human ladder climbing action based on the relative position between human and signboards. The experiments demonstrate the relative high accuracy of our proposed algorithm.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127499555","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}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974483
Shyi-Ming Chen, Zhi-Cheng Huang
A novel multiattribute decision making (MADM) method is proposed in this paper. It uses interval-valued intuitionistic fuzzy values (IVIFVs) and particle swarm optimization (PSO) techniques where the evaluating attribute values of alternatives provided by the decision maker and the weights of attributes are represented by IVIFVs. The PSO techniques are used for obtaining optimal weights of attributes. The proposed MADM method is very useful for dealing with MADM problems.
{"title":"A novel multiattribute decision making method based on interval-valued intuitionistic fuzzy values and particle swarm optimization techniques","authors":"Shyi-Ming Chen, Zhi-Cheng Huang","doi":"10.1109/ICACI.2017.7974483","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974483","url":null,"abstract":"A novel multiattribute decision making (MADM) method is proposed in this paper. It uses interval-valued intuitionistic fuzzy values (IVIFVs) and particle swarm optimization (PSO) techniques where the evaluating attribute values of alternatives provided by the decision maker and the weights of attributes are represented by IVIFVs. The PSO techniques are used for obtaining optimal weights of attributes. The proposed MADM method is very useful for dealing with MADM problems.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121373574","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}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974479
Yinlan Chen
For any complex square matrix A, this paper characterizes the invertibility and group inverse of the combinations P = a1 PR(A) + a2 PR(A∗) +a3 PR(A) PR(A∗) +a4 PR(A∗) PR(A) by M-C-S decomposition of A. Necessary and sufficient conditions of the invertibility and its inverse are presented completely. Also, we characterize the group inverse and give an expression for P# when P is group invertible.
{"title":"On invertibility and group inverse of combinations of two orthogonal projectors about a complex square matrix","authors":"Yinlan Chen","doi":"10.1109/ICACI.2017.7974479","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974479","url":null,"abstract":"For any complex square matrix A, this paper characterizes the invertibility and group inverse of the combinations P = a<inf>1</inf> P<inf>R(A)</inf> + a<inf>2</inf> P<inf>R(A∗)</inf> +a<inf>3</inf> P<inf>R(A)</inf> P<inf>R(A∗)</inf> +a<inf>4</inf> P<inf>R(A∗)</inf> P<inf>R(A)</inf> by M-C-S decomposition of A. Necessary and sufficient conditions of the invertibility and its inverse are presented completely. Also, we characterize the group inverse and give an expression for P<sup>#</sup> when P is group invertible.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124378320","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}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974496
Fang Han, X. Liao, Huiwei Wang, Bo Yang, Yushu Zhang
Most of existing optical color image encryption schemes have born security risks due to the adoption of linear transform, and data redundancy for the generation of complex image. To settle these problems effectively, a self-adaptive scheme for double color-image encryption is proposed in this paper. In this scheme, each RGB color component of two secret color images is first compressed and encrypted by 2D compressive sensing (CS) in which measurement matrices are generated by compound chaotic systems. Then, the two measured images are regarded as the real part and imaginary part, constituting a complex image to reduce data redundancy caused by following optical encryption. In the end, the complex image is reencrypted by self-adaptive random phase encoding and discrete fractional random transform (DFrRT) to obtain the final encrypted data. In the process of DFrRT and random phase encoding, the correlations between R, G, B components are adequately utilized. The production of key streams not only depends on the initial value but also on plain-text, and the three color components affect each other to enhance the ability against the known plaintext attack. The projection neural network algorithm is adopted to obtain the decryption images. Simulation results also verify the validity and security of the proposed method.
{"title":"A self-adaptive scheme for double color-image encryption","authors":"Fang Han, X. Liao, Huiwei Wang, Bo Yang, Yushu Zhang","doi":"10.1109/ICACI.2017.7974496","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974496","url":null,"abstract":"Most of existing optical color image encryption schemes have born security risks due to the adoption of linear transform, and data redundancy for the generation of complex image. To settle these problems effectively, a self-adaptive scheme for double color-image encryption is proposed in this paper. In this scheme, each RGB color component of two secret color images is first compressed and encrypted by 2D compressive sensing (CS) in which measurement matrices are generated by compound chaotic systems. Then, the two measured images are regarded as the real part and imaginary part, constituting a complex image to reduce data redundancy caused by following optical encryption. In the end, the complex image is reencrypted by self-adaptive random phase encoding and discrete fractional random transform (DFrRT) to obtain the final encrypted data. In the process of DFrRT and random phase encoding, the correlations between R, G, B components are adequately utilized. The production of key streams not only depends on the initial value but also on plain-text, and the three color components affect each other to enhance the ability against the known plaintext attack. The projection neural network algorithm is adopted to obtain the decryption images. Simulation results also verify the validity and security of the proposed method.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126718081","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}
A college student's life can be primarily categorized into domains such as education, health, social and other activities which may include daily chores and traveling time. Time management is crucial for every student. A self-realization of one's daily time expenditure in various domains is therefore essential to maximize one's effective output. This paper presents how a mobile application using Fuzzy Logic and Global Positioning System (GPS) analyzes a student's lifestyle and provides recommendations and suggestions based on the results.
{"title":"A fuzzy logic system to analyze a student's lifestyle","authors":"Sourish Ghosh, Aaditya Sanjay Boob, N. Nikhil, Nayan Raju Vysyaraju, Ankit Kumar","doi":"10.1109/ICACI.2017.7974514","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974514","url":null,"abstract":"A college student's life can be primarily categorized into domains such as education, health, social and other activities which may include daily chores and traveling time. Time management is crucial for every student. A self-realization of one's daily time expenditure in various domains is therefore essential to maximize one's effective output. This paper presents how a mobile application using Fuzzy Logic and Global Positioning System (GPS) analyzes a student's lifestyle and provides recommendations and suggestions based on the results.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"150 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125887391","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}
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