Plant Leaf Recognition Based on Naive Bayesian Classification and Linear Discriminant Analysis Model

2019 ◽  
Author(s):  
Ding Min Yue ◽  
Feng Qin
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Bayesian Classification ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Plant Leaf ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Linear Discriminant Analysis Model

Statistical models to predict discharge overflow

2018 ◽  
Vol 78 (5) ◽  
pp. 1208-1218 ◽  
Author(s):  
Bartosz Szeląg ◽  
Łukasz Bąk ◽  
Roman Suligowski ◽  
Jarosław Górski
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Statistical Models ◽  
Probabilistic Models ◽  
Drainage System ◽  
Annual Number ◽  
Hydrodynamic Modelling ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Linear Discriminant Analysis Model

Abstract In the paper, a comparison of prediction results concerning the annual number of discharges of stormwater from the drainage system due to stormwater overflows is depicted. The prediction has been computed by means of storm water management model (SWMM) and probabilistic models. Regarding the probabilistic modelling some simple statistical models such as logit, probit, Gompertz and linear discriminant analysis model have been applied, and as for the hydrodynamic modelling a generator of synthetic rainfall based on the Monte Carlo method has been used. The analyses conducted has shown that logit, probit and Gompertz models give outputs that are comparable with the results of hydrodynamic modelling and are concordant with observations. Whereas the annual number of stormwater discharge predicted by the linear discriminant analysis model is significantly lower than the number obtained by hydrodynamic modelling. The calculations made have confirmed the possibility of using statistical models as an alternative for developing labour-consuming and complex hydrodynamic models. The statistical models can be used successfully to predict the stormwater overflows operation provided that the measurements of rainfall in the catchment and of filling the overflow are available.

scholarly journals Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

2016 ◽  
Vol 16 (4) ◽  
pp. 86-98 ◽  
Author(s):  
Ralph Olusola Aluko ◽  
Olumide Afolarin Adenuga ◽  
Patricia Omega Kukoyi ◽  
Aliu Adebayo Soyingbe ◽  
Joseph Oyewale Oyedeji
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Academic Success ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Undergraduate Students ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Analysis Model ◽  
Linear Discriminant

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

scholarly journals 5-Hydroxymethylcytosine Profiles Are Prognostic of Outcome in Neuroblastoma and Reveal Transcriptional Networks That Correlate With Tumor Phenotype

2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mark A. Applebaum ◽  
Erin K. Barr ◽  
Jason Karpus ◽  
Ji Nie ◽  
Zhou Zhang ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
High Risk ◽  
Linear Discriminant Analysis ◽  
Prognostic Markers ◽  
Epigenetic Modification ◽  
Transcriptional Networks ◽  
Oncology Group ◽  
Analysis Model ◽  
Discovery Cohort ◽  
Linear Discriminant

PURPOSE Whole-genome profiles of the epigenetic modification 5-hydroxymethylcytosine (5-hmC) are robust diagnostic biomarkers in adult patients with cancer. We investigated if 5-hmC profiles would serve as novel prognostic markers in neuroblastoma, a clinically heterogeneous pediatric cancer. Because this DNA modification facilitates active gene expression, we hypothesized that 5-hmC profiles would identify transcriptomic networks driving the clinical behavior of neuroblastoma. PATIENTS AND METHODS Nano-hmC-Seal sequencing was performed on DNA from Discovery (n = 51), Validation (n = 38), and Children’s Oncology Group (n = 20) cohorts of neuroblastoma tumors. RNA was isolated from 48 tumors for RNA sequencing. Genes with differential 5-hmC or expression between clusters were identified using DESeq2. A 5-hmC model predicting outcome in high-risk patients was established using linear discriminant analysis. RESULTS Comparison of low- versus high-risk tumors in the Discovery cohort revealed 577 genes with differential 5-hmC. Hierarchical clustering of tumors from the Discovery and Validation cohorts using these genes identified two main clusters highly associated with established prognostic markers, clinical risk group, and outcome. Genes with increased 5-hmC and expression in the favorable cluster were enriched for pathways of neuronal differentiation and KRAS activation, whereas genes involved in inflammation and the PRC2 complex were identified in the unfavorable cluster. The linear discriminant analysis model trained on high-risk Discovery cohort tumors was prognostic of outcome when applied to high-risk tumors from the Validation and Children’s Oncology Group cohorts (hazard ratio, 3.8). CONCLUSION 5-hmC profiles may be optimal DNA-based biomarkers in neuroblastoma. Analysis of transcriptional networks regulated by these epigenomic modifications may lead to a deeper understanding of drivers of neuroblastoma phenotype.

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