The emergence of Wireless Sensor Network enabled the pervasive monitoring local environments, however, to better suit large scale requirements for Information Technology industry applications, devices must be able operate within internet infrastructure. IoT (Internet Things) has emerged fill this gap and deliver sensed data widely, therefore increasing connectivity devices. However, implementation integration devices, storage development applications are still considered challenging. This...

Cryptocurrencies have had a huge presence on social media since their creation. In current days, the constant increase of mass data produced by this environment has attracted several researchers to try identify patterns with potential allow identification volatility in crypto market before it happens. This approach involves concept wisdom crowds, popular theory economy field that days may perfect tools prove itself true. scenario creates an opportunity unite two new technologies, media, and...

Recent works on Multi-Label Classification (MLC) present multiple strategies to explore label correlations in a way improve classifiers performances. However, these focus only the traditional local and global approaches, i.e., transforming original problem into set of binary problems, or dealing globally with all classes simultaneously. Very few have investigated use order partition space different ways. While partitions several are used (one per label), one classifier deal labels. On...

Classification is a common task in Machine Learning and Data Mining. Some classification problems need to take into account hierarchical taxonomy establishing an order between involved classes are called hierarchical classification problems. The protein function prediction can be considered classification problem because their functions may arranged of classes. This paper presents an algorithm for using centroid-based approach with two versions named HCCS and HCCSic...

Classification is a common task in Machine Learning and Data Mining. Some classification problems are called hierarchical because they need to take into account taxonomy which establishes an order between involved classes. The protein's function prediction considered problem their functions arranged of This paper presents algorithm for using the jumping emerging patterns approach. Jumping have been used flat this work we explore its adoption scenario. proposed was evaluated eight real...

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all at once (global and combine classifiers (ensemble approach). Regardless approach used, exploring label correlations important improve classifier predictions. Ensemble Classifier Chains (ECC) well-known multi-label method considers achieve good...

Multi-label classification (MLC) problems, where instances are associated with multiple labels, commonly employed in everyday applications. There several approaches to solving MLC problems and the ensemble of classifier chains (ECC) is one such method used as basis this article. ECC uses a binary for each label creates chain these classifiers specific sequence. However, has issues related order number labels. Many studies try find best or reduce labels improve results. This article aims...

Abstract Multi-Label Classification is the task of simultaneously predicting a set labels for an instance. Typically, two approaches are used: global, which trains single classifier to deal with all classes at once, and local, divides problem into many binary problems. In both approaches, learning label correlations still open issue. this paper, we propose method cluster space find partitions disjoint correlated called hybrid partition, can be considered in-between local combining benefits...

Este trabalho apresenta uma metodologia para pesagem de aves corte através análise imagens. Nosso método consiste na obtenção imagens a altura fixa do solo, com o processamento ocorrendo em três etapas. Primeiro, utiliza-se rede neural classificação, capaz determinar contorno das da aplicação elipses, resultado binárias. Segundo, aplica-se um extração características geométricas partir binárias geradas. Por último, inferência peso geométricas. Como resultado, nossa técnica permite in seu...

Multi-Label Classification is the task of simultaneously predicting a set labels for an instance in Machine Learning. Typically, two approaches are used: global, which trains single classifier to deal with all classes at once (what we refer as global partition); and local, divides problem into many binary problems local partition), solves them separately then combines results. Learning label correlations that occur multi-label data can improve prediction performances both approaches. We...

Abstract Multi-label classification (MLC) is a very explored field in recent years. The most common approaches that deal with MLC problems are classified into two groups: (i) problem transformation which aims to adapt the multi-label data, making use of traditional binary or multiclass algorithms feasible, and (ii) algorithm adaptation focuses on modifying used classification, enabling them make predictions. Several have been proposed aiming explore relationships among labels, some through...

Agentes podem cooperar na exploração e mapeamento de ambientes desconhecidos. Os mapas produzidos ser ilimitados, forma que, ao andar em uma mesma direção, o agente eventualmente retornará ponto origem. O tamanho do ambiente, que é a distância volta completa até retornar origem, informação relevante Sem ela, um mesmo pode mapeado múltiplas vezes como se todos os mapeamentos fossem pontos distintos. No entanto, alguns sistemas não fornecem essa sequer referencial global posicionamento. Este...