Inteligencia Artif.最新文献_第6页

A robust multi-agent Negotiation for advanced Image Segmentation: Design and Implementation 一种用于高级图像分割的鲁棒多智能体协商:设计与实现

Inteligencia Artif. Pub Date : 2019-12-12 DOI: 10.4114/intartif.vol22iss64pp102-122

Hanane Allioui, M. Sadgal, A. E. Fazziki

{"title":"A robust multi-agent Negotiation for advanced Image Segmentation: Design and Implementation","authors":"Hanane Allioui, M. Sadgal, A. E. Fazziki","doi":"10.4114/intartif.vol22iss64pp102-122","DOIUrl":"https://doi.org/10.4114/intartif.vol22iss64pp102-122","url":null,"abstract":"It is generally accepted that segmentation is a critical problem that influences subsequent tasks during image processing. Often, the proposed approaches provide effectiveness for a limited type of images with a significant lack of a global solution. The difficulty of segmentation lies in the complexity of providing a global solution with acceptable accuracy within a reasonable time. To overcome this problem, some solutions combined several methods. This paper presents a method for segmenting 2D/3D images by merging regions and solving problems encountered during the process using a multi-agent system (MAS). We are using the strengths of MAS by opting for a compromise that satisfies segmentation by agents’ acts. Regions with high similarity are merged immediately, while the others with low similarity are ignored. The remaining ones, with ambiguous similarity, are solved in a coalition by negotiation. In our system, the agents make decisions according to the utility functions adopting the Pareto optimal in Game theory. Unlike hierarchical merging methods, MAS performs a hypothetical merger planning then negotiates the agreements' subsets to merge all regions at once.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123083668","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: 8

Uma Nova Meta-heurística Adaptativa Baseada em Vetor de Avaliações para Otimização de Portfólios de Investimentos 一种新的基于评估向量的自适应元启发式投资组合优化方法

Inteligencia Artif. Pub Date : 2019-12-09 DOI: 10.4114/intartif.vol22iss64pp85-101

O. A. C. Cortes, Leticia De Fátima Corrêa Costa, J. Costa

引用次数: 1

An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers 一种高效的概率估计决策树后处理方法挖掘具有多类别客户的企业最优盈利知识

Inteligencia Artif. Pub Date : 2019-11-14 DOI: 10.4114/intartif.vol22iss64pp63-84

J. Muneiah, C. SubbaRao

{"title":"An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers","authors":"J. Muneiah, C. SubbaRao","doi":"10.4114/intartif.vol22iss64pp63-84","DOIUrl":"https://doi.org/10.4114/intartif.vol22iss64pp63-84","url":null,"abstract":"Enterprises often classify their customers based on the degree of profitability in decreasing order like C1, C2, ..., Cn. Generally, customers representing class Cn are zero profitable since they migrate to the competitor. They are called as attritors (or churners) and are the prime reason for the huge losses of the enterprises. Nevertheless, customers of other intermediary classes are reluctant and offer an insignificant amount of profits in different degrees and lead to uncertainty. Various data mining models like decision trees, etc., which are built using the customers’ profiles, are limited to classifying the customers as attritors or non-attritors only and not providing profitable actionable knowledge. In this paper, we present an efficient algorithm for the automatic extraction of profit-maximizing knowledge for business applications with multi-class customers by postprocessing the probability estimation decision tree (PET). When the PET predicts a customer as belonging to any of the lesser profitable classes, then, our algorithm suggests the cost-sensitive actions to change her/him to a maximum possible higher profitable status. In the proposed novel approach, the PET is represented in the compressed form as a Bit patterns matrix and the postprocessing task is performed on the bit patterns by applying the bitwise AND operations. The computational performance of the proposed method is strong due to the employment of effective data structures. Substantial experiments conducted on UCI datasets, real Mobile phone service data and other benchmark datasets demonstrate that the proposed method remarkably outperforms the state-of-the-art methods.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125775556","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

On the Enhancement of Classification Algorithms Using Biased Samples 基于有偏样本的分类算法的改进

Inteligencia Artif. Pub Date : 2019-10-24 DOI: 10.4114/intartif.vol22iss64pp36-46

S. Al-Mamory

引用次数: 0

Feature Learning with Multi-objective Evolutionary Computation in the generation of Acoustic Features 声学特征生成中的多目标进化计算特征学习

Inteligencia Artif. Pub Date : 2019-07-01 DOI: 10.4114/intartif.vol22iss64pp14-35

J. A. Menezes, G. Cabral, Bruno Gomes, Paulo Pereira

{"title":"Feature Learning with Multi-objective Evolutionary Computation in the generation of Acoustic Features","authors":"J. A. Menezes, G. Cabral, Bruno Gomes, Paulo Pereira","doi":"10.4114/intartif.vol22iss64pp14-35","DOIUrl":"https://doi.org/10.4114/intartif.vol22iss64pp14-35","url":null,"abstract":"To choice audio features has been a very interesting theme for audio classification experts. They have seen that this process is probably the most important effort to solve the classification problem. In this sense, there are techniques of Feature Learning for generate new features more suitable for classification model than conventional features. However, these techniques generally do not depend on knowledge domain and they can apply in various types of raw data. However, less agnostic approaches learn a type of knowledge restricted to the area studded. The audio data requires a specific knowledge type. There are many techniques that seek to improve the performance of the new generation of acoustic features, among which stands the technique that use evolutionary algorithms to explore analytical space of function. However, the efforts made leave opportunities for improvement. The purpose of this work is to propose and evaluate a multi-objective alternative to the exploitation of analytical audio features. In addition, experiments were arranged to be validated the method, with the help a computational prototype that implemented the proposed solution. After it was found the effectiveness of the model and ensuring that there is still opportunity for improvement in the chosen segment.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121327787","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

Building Dynamic Lexicons for Sentiment Analysis 为情感分析构建动态词汇

Inteligencia Artif. Pub Date : 2019-05-17 DOI: 10.4114/intartif.vol22iss64pp1-13

Nicolás Mechulam, Damián Salvia, Aiala Rosá, Mathías Etcheverry

引用次数: 2

Genetic Algorithms for Satellite Launcher Attitude Controller Design 卫星发射装置姿态控制器设计的遗传算法

Inteligencia Artif. Pub Date : 2019-05-15 DOI: 10.4114/intartif.vol22iss63pp150-161

P. R. Silva, I. Abreu, Paulo Almeida Forte, Henrique Mariano Costa do Amaral

引用次数: 4

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation 基于混合自适应计算智能的多传感器数据融合在无人机实时自主导航中的应用

Inteligencia Artif. Pub Date : 2019-05-09 DOI: 10.4114/intartif.vol22iss63pp162-195

Ângelo de Carvalho Paulino, L. Guimarães, E. H. Shiguemori

引用次数: 11

Article Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot 使用卷积神经网络和FastDTW控制移动机器人运动的Kinect传感器用户活动手势识别

Inteligencia Artif. Pub Date : 2019-04-04 DOI: 10.4114/intartif.vol22iss63pp121-134

Miguel Pfitscher, D. Welfer, E. Nascimento, M. A. Cuadros, D. Gamarra

引用次数: 13

A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval 一种灵活的监督项加权技术及其在变量抽取和信息检索中的应用

Inteligencia Artif. Pub Date : 2019-02-27 DOI: 10.4114/INTARTIF.VOL22ISS63PP61-80

Mariano Maisonnave, Fernando Delbianco, F. Tohmé, Ana Gabriela Maguitman

{"title":"A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval","authors":"Mariano Maisonnave, Fernando Delbianco, F. Tohmé, Ana Gabriela Maguitman","doi":"10.4114/INTARTIF.VOL22ISS63PP61-80","DOIUrl":"https://doi.org/10.4114/INTARTIF.VOL22ISS63PP61-80","url":null,"abstract":"Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables. This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents labeled as relevant or irrelevant to the domain under analysis. Based on the labeled document collection, we propose a supervised technique that weights terms based on their descriptive and discriminating power. Finally, the descriptive and discriminating values are combined into a general measure that, through the use of an adjustable parameter, allows to independently favor different aspects of retrieval such as maximizing precision or recall, or achieving a balance between both of them. The proposed technique is applied to the economic domain and is empirically evaluated through a human-subject experiment involving experts and non-experts in Economy. It is also evaluated as a term-weighting technique for query-term selection showing promising results. We finally illustrate the applicability of the proposed technique to address diverse problems such as building prediction models, supporting knowledge modeling, and achieving total recall.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117295552","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