Juan B. Gutierrez

Bio: Juan B. Gutierrez is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: Population & Malaria. The author has an hindex of 16, co-authored 57 publications receiving 1405 citations. Previous affiliations of Juan B. Gutierrez include University of Georgia & Florida State University.

A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

Abstract: The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.

Text Summarization Techniques: A Brief Survey

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

A comprehensive investigation and comparison of Machine Learning Techniques in the domain of heart disease

Abstract: This paper aims to investigate and compare the accuracy of different data mining classification schemes, employing Ensemble Machine Learning Techniques, for the prediction of heart disease. The Cleveland data set for heart diseases, containing 303 instances, has been used as the main database for the training and testing of the developed system. 10-Fold Cross-Validation has been applied in order to increase the amount of data, which would otherwise have been limited. Different classifiers, namely Decision Tree (DT), Naive Bayes (NB), Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN), Single Conjunctive Rule Learner (SCRL), Radial Basis Function (RBF) and Support Vector Machine (SVM), have been employed. Moreover, the ensemble prediction of classifiers, bagging, boosting and stacking, has been applied to the dataset. The results of the experiments indicate that the SVM method using the boosting technique outperforms the other aforementioned methods.

Text Summarization Techniques: A Brief Survey

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

Current knowledge on non-native freshwater fish introductions

Abstract: This review provides a contemporary account of knowledge on aspects of introductions of non-native fish species and includes issues associated with introduction pathways, ecological and economic impacts, risk assessments, management options and impact of climate change. It offers guidance to reconcile the increasing demands of certain stakeholders to diversify their activities using non-native fishes with the long-term sustainability of native aquatic biodiversity. The rate at which non-native freshwater fishes have been introduced worldwide has doubled in the space of 30 years, with the principal motives being aquaculture (39%) and improvement of wild stocks (17%). Economic activity is the principal driver of human-mediated non-native fish introductions, including the globalization of fish culture, whereby the production of the African cichlid tilapia is seven times higher in Asia than in most areas of Africa, and Chile is responsible for c. 30% of the world's farmed salmon, all based on introduced species. Consequently, these economic benefits need balancing against the detrimental environmental, social and economic effects of introduced non-native fishes. There are several major ecological effects associated with non-native fish introductions, including predation, habitat degradation, increased competition for resources, hybridization and disease transmission. Consideration of these aspects in isolation, however, is rarely sufficient to adequately characterize the overall ecological effect of an introduced species. Regarding the management of introduced non-native fish, pre-introduction screening tools, such as the fish invasiveness scoring kit (FISK), can be used to ensure that species are not introduced, which may develop invasive populations. Following the introduction of non-native fish that do develop invasive populations, management responses are typified by either a remediation or a mitigation response, although these are often difficult and expensive to implement, and may have limited effectiveness.

Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: Systematic review and meta-analysis

Abstract: Background: Knowing the prevalence of true asymptomatic coronavirus disease 2019 (COVID-19) cases is critical for designing mitigation measures against the pandemic. We aimed to synthesize all avai...