Dengue models based on machine learning techniques: A systematic literature review
Background Dengue modeling is a research topic that has increased in recent years. Early
prediction and decision-making are key factors to control dengue. This Systematic Literature …
prediction and decision-making are key factors to control dengue. This Systematic Literature …
[HTML][HTML] Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research
AM Hemeida, SA Hassan, AAA Mohamed… - Ain Shams Engineering …, 2020 - Elsevier
Recently, an explosive growth in the potential use of natural metaphors in modelling and
solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a …
solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a …
Software defect prediction for healthcare big data: an empirical evaluation of machine learning techniques
Software defect prediction (SDP) in the initial period of the software development life cycle
(SDLC) remains a critical and important assignment. SDP is essentially studied during few …
(SDLC) remains a critical and important assignment. SDP is essentially studied during few …
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review
E Sylvestre, C Joachim, E Cecilia-Joseph… - PLoS neglected …, 2022 - journals.plos.org
Background Traditionally, dengue surveillance is based on case reporting to a central health
agency. However, the delay between a case and its notification can limit the system …
agency. However, the delay between a case and its notification can limit the system …
Evaluation of recurrent neural network and its variants for intrusion detection system (IDS)
R Vinayakumar, KP Soman… - International Journal of …, 2017 - igi-global.com
This article describes how sequential data modeling is a relevant task in Cybersecurity.
Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent …
Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent …
The classification of medical and botanical data through majority voting using artificial neural network
Data classification has many approaches in data mining and machine learning. The artificial
neural network (ANN) is applied to classify the data that might belong to various domains …
neural network (ANN) is applied to classify the data that might belong to various domains …
Severe dengue prognosis using human genome data and machine learning
Dengue has become one of the most important worldwide arthropod-borne diseases.
Dengue phenotypes are based on laboratorial and clinical exams, which are known to be …
Dengue phenotypes are based on laboratorial and clinical exams, which are known to be …
A reliable method for colorectal cancer prediction based on feature selection and support vector machine
Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths
per year worldwide. Thus, it is very important to find the related factors and detect the cancer …
per year worldwide. Thus, it is very important to find the related factors and detect the cancer …
Performance assessment of classification algorithms on early detection of liver syndrome
In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally
normal everywhere throughout the world. It has been found that liver disease is exposed …
normal everywhere throughout the world. It has been found that liver disease is exposed …
Fragmented plant leaf recognition: Bag-of-features, fuzzy-color and edge-texture histogram descriptors with multi-layer perceptron
Plants species recognition is one of the most important research topics in the biological
sciences. Although leaves are convenient markers for identification, a major drawback is that …
sciences. Although leaves are convenient markers for identification, a major drawback is that …