In this paper, a hybrid Otsu and improved Particle Swarm Optimization (PSO) algorithm is presented to deal with multilevel color image thresholding problem, named APSOW. In APSOW, the historical information represented by the local best solutions found so far by PSO population are permuted among the current population, using a randomized greedy process. APSOW also implements a weedout operator to prune the worst individuals from the population. The proposed APSOW is compared to other hybrid EAs and Otsu approaches from literature (include standard PSO model) through twelve benchmark color image problems, showing its potential and robustness.

A Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation

Recipe sharing websites have become very popular in the past years, allowing individuals to use such systems in an attempt to find a desired recipe. But sometimes finding recipes which best fit the user’s wishes, while still satisfying his food restrictions, may become a very time consuming and difficult task. In this work, we propose a recipe multi-label classification approach as part of a recipe recommendation system for people with food restrictions, in an attempt to automatically identify whether an input recipe or list of ingredients fits into one or more food restrictions, satisfying both user’s expectations and needs. The experimental evaluation includes two approaches for feature selection, as a manner to reduce the computational costs for the proposed system.

A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification

The huge amount of textual data made available every day on the internet encouraged research in several areas that automatically process and analyze these texts. One of the most popular areas is Sentiment Analysis (SA), but although SA has been a topic widely discussed in recent years, it still faces a shortage of available resources in the Brazilian Portuguese language. This work presents the steps for a complete process of sentiment analysis and classification, from the development of a dataset in Brazilian Portuguese (in the books domain) to the sentiment classification, using some of the main classifiers in the literature and different feature extraction methods.

A Sentiment Classification Approach for Books Reviews in Brazilian Portuguese Using Different Feature Extraction Methods

In this work, a tool for inferring the degree of difficulty of cooking recipes will be proposed. The inference will be made by the textual classification of the recipe preparation methods. The tool will be a fundamental piece to the development of a context-aware cooking recipe recommendation system. Some of the main classifiers in Text Classification literature will be adopted, in addition to different feature extraction methods. An experimental evaluation is performed, in order to select the best approaches to compose the system.

Automatic Cooking Recipe Difficulty Level Inference using Natural Language Processing Techniques

The correct identification of plants species is an important task in several areas. The identification is even more important when the consumption and use of those plants are adopted as a way of preventing and treating diseases, where incorrect classification can bring serious consequences. In this work, an automatic plant species identification system is proposed, using machine learning and computer vision techniques. For the experimental evaluation, a real database of medicinal plants, composed of color, shape and texture features from the leaves, is proposed. Some of the main classifiers in the Machine Learning literature were adopted and evaluated.

Medicinal Plant Recognition Using Color, Texture and Shape Features

ENIAC: Technical Session 1

technical paper

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Larissa Britto

5

5

SHORT BIO

Presentations

A Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation

A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification

A Sentiment Classification Approach for Books Reviews in Brazilian Portuguese Using Different Feature Extraction Methods

Automatic Cooking Recipe Difficulty Level Inference using Natural Language Processing Techniques

Medicinal Plant Recognition Using Color, Texture and Shape Features

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES