Prediction of Trypanosoma evansi infection in dromedaries using artificial neural network (ANN)

Benfodil, Karima; Benbouras, Mohammed Amin; Ansel, Samir; Mohamed-Cherif, Abdellah; Aït-Oudhia, Khatima

doi:10.1016/j.vetpar.2022.109716

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As Redes Neurais Artificiais (RNAs) são ferramentas de inteligência computacional utilizadas amplamente nas áreas da psicologia, robótica, biologia, ciência da computac ¸ão, engenharia e em diversas outras áreas [7]. Por possuírem grande capacidade de previsão de processos complexos e não lineares aliados à facilidade e flexibilidade de implementac ¸ão, nos últimos houve crescente uso de RNAs para fornecer modelos matemáticos de previsão e classificac ¸ão de processos biológicos [8]- [10].…”

Section: Introduc ¸ãOunclassified

Rede neural artificial para predição de brucelose bovina a partir de dados desbalanceados

Queiroz Alves,

Ferreira,

Fortunato

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

The expressiveness of Brazilian livestock is unquestionable. According to data from the United States Department of Agriculture (USDA), in 2021 Brazil was the worlds largest exporter of beef. Bovine brucellosis is one of the most worrying diseases for the sector. In Brazil, bovine brucellosis causes annual losses of around 448 million dollars. Several factors threaten the establishment of actions of the current animal defense programs in Brazil, the main ones being: lack of distinct guidelines for the diagnosis of brucellosis cases, infected animals remain asymptomatic when infected, extensive Brazilian territory and large number of herds. The use of Artificial Neural Networks (ANNs) can be very useful in health and epidemiological surveillance services, helping to screen properties with different risks for the disease. The objective of this work is the development of ANN with class balancing techniques and selection of variables via genetic algorithm, for the classification and segregation of bovine herds, regarding seroprevalence for brucellosis. Five ANNs were designed combining different approaches of class balancing technique and variable selection, in order to compare which approach would perform better results. The results showed that ANN combined with the variable selection technique, via Genetic Algorithms, and class balancing, is a promising approach.

show abstract

Section: Introduc ¸ãOunclassified

Rede neural artificial para predição de brucelose bovina a partir de dados desbalanceados

Queiroz Alves,

Ferreira,

Fortunato

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

show abstract

Evaluation of Applied Artificial Neuronal Networks with a Timely Cervical Cancer Diagnosis in an Emerging Economy

Mota-López,

Barojas-Payán,

Hernández-Cisneros

et al. 2024

Management and Industrial Engineering

View full text Add to dashboard Cite

Scoping Review of Machine Learning and Deep Learning Algorithm Applications in Veterinary Clinics: Situation Analysis and Suggestions for Further Studies

Min

2023

J Vet Clin

View full text Add to dashboard Cite

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

show abstract

Prediction of Trypanosoma evansi infection in dromedaries using artificial neural network (ANN)

Cited by 3 publications

References 30 publications

Rede neural artificial para predição de brucelose bovina a partir de dados desbalanceados

Rede neural artificial para predição de brucelose bovina a partir de dados desbalanceados

Evaluation of Applied Artificial Neuronal Networks with a Timely Cervical Cancer Diagnosis in an Emerging Economy

Scoping Review of Machine Learning and Deep Learning Algorithm Applications in Veterinary Clinics: Situation Analysis and Suggestions for Further Studies

Contact Info

Product

Resources

About