Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

Smart city environments, when applied to healthcare, improve the quality of people\u27s lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians\u27 daily work. Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services. The data produced by smart devices are accurate and reliable for predictive and decision-making contexts. This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring. To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases. The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms. The methodology used to carry out the study followed three steps. First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies. The results demonstrated the feasibility of the proposal. The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values

Data analysis in social networks for agribusiness: a systematic review.

The ability of companies to react to changes imposed by the market can be aided by to information acquisition and knowledge generation. Big data technologies, crowdsourcing, and Online Social Networks (OSN) are used for knowledge generation. These technologies have assumed a significant position in agribusiness in recent decades. This work investigates how social network analysis can promote agribusiness to provide a basis for future applications and evaluations. We adopted a hybrid systematic mapping to conduct the investigation. Two hundred twenty-three works that propose solutions for agribusiness were found and categorized. Results showed the most used OSN is Twitter and revealed an increase in the number of studies in this area. The information obtained indicates how social media monitoring can complement traditional decision-making methods in managing and regulating agricultural systems. However, more studies in agribusiness using data analysis tools on social networks are required, considering the importance of social networks on marketing strategies. Based on our results, we discuss some challenges and research directions

Feed efficiency service: an architecture for thecomparison of data from multiple studies related todairy cattle feed efficiency indices.

The increased demand for food worldwide, the reduced land availability for livestock production, the increasing cost of animal feed and the need for mitigating livestock-related greenhouse gas emissions have driven the search for animal feeding systems that proves more efficient. To tackle this problem, we propose the use of computational support to help researchers compare data on feed efficiency, therefore improving economic and environmental gains. As a solution, we present an integrative architecture capable of combining heterogeneous data from multiple experiments related to dairy cattle feed efficiency indices. The proposed architecture, called FeedEfficiencyService, classifies animals according to feed efficiency indices and allows visualizations through ontologies and inference engines. The results obtained from a case study with researchers from the Brazilian Agricultural Research Corporation ? Dairy Cattle (EMBRAPA) demonstrate that this architecture is a supporting tool in their daily work routine. The researchers highlighted the importance of the proposed architecture as it allows analyzing animal data, comparing experiments, having reliable data analyses, and standardizing and organizing data from experiments. The novelty of our approach is the use of ontologies and inference engines to enable the discovery of new knowledge and new relationships between data from feed efficiencyrelated experiments. We store such data, relationships, and analyses of results in an integrated repository. This solution ensures unified access to the processing history and data from diverse experiments, including those conducted at external research centers.First online

@grogest_Ambiental: a web-based decision support system for agribusiness.

In agribusiness, the treatment and sustainable management of waste in intensive production systems must consider productivity and economic gains in the short term and the sustainability of agricultural production. The acquisition of technical information, and the knowledge about the characteristics of the rural property, are fundamental for stakeholders, especially considering the adequacy of sustainable environmental practices. It is necessary to provide a web-based system to support stakeholders in discovering and understanding their productive context. This paper proposes a solution to support stakeholders? decision-making to improve sustainable practices in agribusiness. The proposed solution is applied in the agricultural domain, focusing on sustainability, by implementing a web system supported by an ontology to organize and provide strategic information through a mobile app. Technical and regulatory environmental practices documents are recommended for a relevant context, considering a feasibility study with experts in the field. As a result, the approach provided decision-making support offering technical information efficiently and agile for stakeholders.WebMedia 2021

Nutrin Price: uma plataforma colaborativa para seleção de produtos alimentícios.

Parte da população apresenta deficiência nutricional devido às más condições de alimentação. O custo dos produtos alimentícios e o desconhecimento dos nutrientes são fatores relevantes para a má nutrição. Selecionar produtos ricos nutricionalmente e com baixo custo são desafios. Este trabalho propõe uma plataforma para captação, organização e visualização de dados e preços dos produtos consumidos. A solução busca, por meio da colaboração de usuários e supermercados, prover informações referentes aos produtos, de modo a apoiar a seleção desses produtos. Como resultado, buscamos mitigar o problema da má nutrição na sociedade. Um estudo de viabilidade foi conduzido a partir dos dados capturados.Evento online

CAERS: A Conversational Agent for Intervention in MOOCs' Learning Processes

Massive Open Online Courses (MOOCs) make up a teaching modality that aims to reach a large number of students using Virtual Learning Environments. In these courses, the intervention of tutors and teachers is essential to support students in the teaching-learning process, answer questions about their content, and provide engagement for students. However, as these courses have a vast and diverse audience, tutors and teachers find it difficult to monitor them closely and efficiently with prompt interventions. This work proposes an architecture to favor the construction of knowledge for students, tutors, and teachers through autonomous interference and recommendations of educational resources. The architecture is based on a conversational agent and an educational recommendation system. For the training of predictive models and extraction of semantic information, ontology and logical rules were used, together with inference algorithms and machine learning techniques, which act on a dataset with messages exchanged between course forum participants in the humanities, medicine, and education fields. The messages are classified according to the type (question, answer, and opinion) and parameters about feeling, confusion, and urgency. The architecture can infer the moment in which a student needs help and, through a Conversational Recommendation System, provides the student with the opportunity to revise his or her knowledge on the subject. To help in this task, the architecture can provide educational resources via an autonomous agent, contributing to reducing the degree of confusion and urgency identified in the posts. Initial results indicate that integrating technologies and resources, complementing each other, can support the students and help them succeed in their educational training

An architecture for food product recommendation focusing on nutrients and price.

Part of the world?s population is nutrient deficient, a phenomenon known as hidden hunger. Poor eating conditions cause this deficiency, leading to illnesses and recovery difficulties. Malnourished patients are more easily affected by Covid-19 and have a difficult recovery after the illness. An effective food choice has the price and nutritional value of food products as the most relevant factors, with the price being the most relevant, considering the context of countries such as Brazil. Thus, having identified a scenario in which the access and food price mainly cause malnutrition. This work proposes an architecture, called Nutri?n Price, to recommend high nutritional foods with low costs. The architecture encompasses a network of ontologies, inference algorithms, information retrieval and collaborative filtering techniques to recommend the best foods according to nutrient choice, price, and user contextual information. A prototype of a mobile application was developed to evaluate the feasibility of the proposed architecture

CAERS: A Conversational Agent for Intervention in MOOCs’ Learning Processes

Massive Open Online Courses (MOOCs) make up a teaching modality that aims to reach a large number of students using Virtual Learning Environments. In these courses, the intervention of tutors and teachers is essential to support students in the teaching-learning process, answer questions about their content, and provide engagement for students. However, as these courses have a vast and diverse audience, tutors and teachers find it difficult to monitor them closely and efficiently with prompt interventions. This work proposes an architecture to favor the construction of knowledge for students, tutors, and teachers through autonomous interference and recommendations of educational resources. The architecture is based on a conversational agent and an educational recommendation system. For the training of predictive models and extraction of semantic information, ontology and logical rules were used, together with inference algorithms and machine learning techniques, which act on a dataset with messages exchanged between course forum participants in the humanities, medicine, and education fields. The messages are classified according to the type (question, answer, and opinion) and parameters about feeling, confusion, and urgency. The architecture can infer the moment in which a student needs help and, through a Conversational Recommendation System, provides the student with the opportunity to revise his or her knowledge on the subject. To help in this task, the architecture can provide educational resources via an autonomous agent, contributing to reducing the degree of confusion and urgency identified in the posts. Initial results indicate that integrating technologies and resources, complementing each other, can support the students and help them succeed in their educational training