Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.18
Errison dos Santos Alves, J. B. O. S. Filho, Rafael Mello Galliez, A. Kritski
Tuberculosis is an infectious disease widely present in developing countries, which is largely motivated by the difficulty of a rapid and efficient diagnosis. In order to reduce the number of patients suspected of having TB unnecessarily isolated in hospitals, thus optimize the use of health resources, we propose a systematic procedure for developing a decision support system based on specialized MLP network committee. The system based on 3 MLP models, which response to input data clusters inferred by the k-means technique, exhibits a better classification performance than a single network in terms of the number of false positives, achieving a sensitivity of 83.3% and specificity of 94.3%.
{"title":"Specialized MLP Classifiers to Support the Isolation of Patients Suspected of Pulmonary Tuberculosis","authors":"Errison dos Santos Alves, J. B. O. S. Filho, Rafael Mello Galliez, A. Kritski","doi":"10.1109/BRICS-CCI-CBIC.2013.18","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.18","url":null,"abstract":"Tuberculosis is an infectious disease widely present in developing countries, which is largely motivated by the difficulty of a rapid and efficient diagnosis. In order to reduce the number of patients suspected of having TB unnecessarily isolated in hospitals, thus optimize the use of health resources, we propose a systematic procedure for developing a decision support system based on specialized MLP network committee. The system based on 3 MLP models, which response to input data clusters inferred by the k-means technique, exhibits a better classification performance than a single network in terms of the number of false positives, achieving a sensitivity of 83.3% and specificity of 94.3%.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130619627","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.119
César Augusto Borges de Andrade, C. Gomes de Mello, J. C. Duarte
The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying.
{"title":"Malware Automatic Analysis","authors":"César Augusto Borges de Andrade, C. Gomes de Mello, J. C. Duarte","doi":"10.1109/BRICS-CCI-CBIC.2013.119","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.119","url":null,"abstract":"The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"11 16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123692128","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.28
A. Engelbrecht
It has been shown recently that unconstrained particles that follow the position and velocity update rules of a standard global best particle swarm optimization algorithm leave the boundaries of the search space within the first few iterations of the search process. Provided that a better solution does not exist outside of the search boundaries, these roaming particles are eventually pulled back within the search boundaries. This article illustrates the consequence of roaming particles should better solutions exist outside of the search boundaries, namely that particles are pulled outside of the search boundaries and that such infeasible solutions are found. The article also evaluates the hypothesis that it is the roaming behavior of unconstrained particles that improves the ability of particle swarm algorithms to locate feasible solutions outside of the particle initialization space.
{"title":"Roaming Behavior of Unconstrained Particles","authors":"A. Engelbrecht","doi":"10.1109/BRICS-CCI-CBIC.2013.28","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.28","url":null,"abstract":"It has been shown recently that unconstrained particles that follow the position and velocity update rules of a standard global best particle swarm optimization algorithm leave the boundaries of the search space within the first few iterations of the search process. Provided that a better solution does not exist outside of the search boundaries, these roaming particles are eventually pulled back within the search boundaries. This article illustrates the consequence of roaming particles should better solutions exist outside of the search boundaries, namely that particles are pulled outside of the search boundaries and that such infeasible solutions are found. The article also evaluates the hypothesis that it is the roaming behavior of unconstrained particles that improves the ability of particle swarm algorithms to locate feasible solutions outside of the particle initialization space.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127543025","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.16
J. Ranhel
Neural assembly computing (NAC) is a framework for investigating computational operations realized by spiking cell assemblies and for designing spiking neural machines. NAC concerns the way assemblies interact and how it results in information processing with causal and hierarchical relations. In addition, NAC investigates how assemblies represent states of the world, how they control data flux carried by spike streaming, how they create parallel processes by branching and dismantling other assemblies, how they reverberate and create memory loops, among other issues. As cell coalitions interact they realize logical functions. Memory loops and logical functions are the elements engineers use to create finite state machines (FSM). An overview of NAC is provided, a methodology for implementing FSM in NAC is presented in this paper, a finite state automaton is designed, and a simulation and respective results are shown. Supplemental materials are available for download. Discussions about how FSM on NAC and how NAC itself can contribute for designing new types of spiking neural machines are presented.
{"title":"Neural Assemblies and Finite State Automata","authors":"J. Ranhel","doi":"10.1109/BRICS-CCI-CBIC.2013.16","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.16","url":null,"abstract":"Neural assembly computing (NAC) is a framework for investigating computational operations realized by spiking cell assemblies and for designing spiking neural machines. NAC concerns the way assemblies interact and how it results in information processing with causal and hierarchical relations. In addition, NAC investigates how assemblies represent states of the world, how they control data flux carried by spike streaming, how they create parallel processes by branching and dismantling other assemblies, how they reverberate and create memory loops, among other issues. As cell coalitions interact they realize logical functions. Memory loops and logical functions are the elements engineers use to create finite state machines (FSM). An overview of NAC is provided, a methodology for implementing FSM in NAC is presented in this paper, a finite state automaton is designed, and a simulation and respective results are shown. Supplemental materials are available for download. Discussions about how FSM on NAC and how NAC itself can contribute for designing new types of spiking neural machines are presented.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133131310","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 : 2013-09-01DOI: 10.1109/BRICS-CCI-CBIC.2013.64
Alisson Marques da Silva, Walmir Matos Caminhas, Andre Paim Lemos, F. Gomide
This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature.
{"title":"Evolving Neo-fuzzy Neural Network with Adaptive Feature Selection","authors":"Alisson Marques da Silva, Walmir Matos Caminhas, Andre Paim Lemos, F. Gomide","doi":"10.1109/BRICS-CCI-CBIC.2013.64","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.64","url":null,"abstract":"This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132327002","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 : 2013-09-01DOI: 10.1109/BRICS-CCI-CBIC.2013.53
Jorge Guerra Pires
On the current manuscript, we dissert on the application of Artificial Neural Network (ANN) as alternative and complementary approach for the observer-based method in transcription network, which is a recent proposed tool for modeling transcription networks. In view of that, it is claimed that neural networks as models for applied mathematics may solve the problem of gene expression estimation addressed by the technique with some advantages inherent in the technique compared to the observer-based method, the target is enforcement, not denying. Thus, systems biology is the gene based science under attention and computational intelligence is the intelligence-based tool under investigation.
{"title":"Neural Networks in Transcription Networks: An Alternative and Complementary Approach for the Observer-Based Method","authors":"Jorge Guerra Pires","doi":"10.1109/BRICS-CCI-CBIC.2013.53","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.53","url":null,"abstract":"On the current manuscript, we dissert on the application of Artificial Neural Network (ANN) as alternative and complementary approach for the observer-based method in transcription network, which is a recent proposed tool for modeling transcription networks. In view of that, it is claimed that neural networks as models for applied mathematics may solve the problem of gene expression estimation addressed by the technique with some advantages inherent in the technique compared to the observer-based method, the target is enforcement, not denying. Thus, systems biology is the gene based science under attention and computational intelligence is the intelligence-based tool under investigation.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121687285","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 : 2013-08-10DOI: 10.1109/BRICS-CCI-CBIC.2013.80
I. Boulkabeit, Linda Mthembu, T. Marwala, Fernando Buarque de Lima-Neto
A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.
将一种受自然启发的优化算法鱼群搜索(Fish School Search, FSS)应用于有限元模型更新问题。该方法在GARTEUR SM-AG19飞机结构上进行了试验。将该算法与遗传算法(GA)和粒子群算法(PSO)进行了比较。观察到，平均而言，FSS和PSO算法比遗传算法给出更准确的结果。提出了对金融监督制度的一个小修改。这种改进提高了FSS在有约束搜索空间的有限元更新问题上的性能。
{"title":"Finite Element Model Updating Using Fish School Search Optimization Method","authors":"I. Boulkabeit, Linda Mthembu, T. Marwala, Fernando Buarque de Lima-Neto","doi":"10.1109/BRICS-CCI-CBIC.2013.80","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.80","url":null,"abstract":"A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128066930","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 : 2013-08-10DOI: 10.1109/BRICS-CCI-CBIC.2013.107
Satyakama Paul, A. Janecek, Fernando Buarque de Lima-Neto, T. Marwala
In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems - AIS) to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in the methodology's ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of AIS algorithms.
{"title":"Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study","authors":"Satyakama Paul, A. Janecek, Fernando Buarque de Lima-Neto, T. Marwala","doi":"10.1109/BRICS-CCI-CBIC.2013.107","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.107","url":null,"abstract":"In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems - AIS) to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in the methodology's ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of AIS algorithms.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128298681","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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