Feature‐weighted AdaBoost classifier for punctuation prediction in Tamil and Hindi NLP systems

2021 ◽  
Author(s):  
Mrinalini K ◽  
Vijayalakshmi P ◽  
Nagarajan T
Keyword(s):  
Adaboost Classifier

A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy

2021 ◽  
pp. 000313482110635
Author(s):  
Li-Yue Sun ◽  
Qing Ouyang ◽  
Wen-Jian Cen ◽  
Fang Wang ◽  
Wen-Ting Tang ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Validation Cohort ◽  
Tnm Staging ◽  
Resonance Imaging ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Dynamic Variations ◽  
Included Patient ◽  
Artificial Intelligence Algorithm ◽  
Adaboost Classifier

Background There is no satisfactory indicator for monitoring recurrence after resection of hepatocellular carcinoma (HCC). This retrospective study aimed to design and validate an HCC monitor recurrence (HMR) model for patients without metastasis after hepatectomy. Methods A training cohort was recruited from 1179 patients with HCC without metastasis after hepatectomy between February 2012 and December 2015. An HMR model was developed using an AdaBoost classifier algorithm. The factors included patient age, TNM staging, tumor size, and pre/postoperative dynamic variations of alpha-fetoprotein (AFP). The diagnostic efficacy of the model was evaluated based on the area under the receiver operating characteristic curves (AUCs). The model was validated using a cohort of 695 patients. Results In preoperative patients with positive or negative AFP, the AUC of the validation cohort in the HMR model was .8877, which indicated better diagnostic efficacy than that of serum AFP (AUC, .7348). The HMR model predicted recurrence earlier than computed tomography/magnetic resonance imaging did by 191.58 ± 165 days. In addition, the HMR model can predict the prognosis of patients with HCC after resection. Conclusions The HMR model established in this study is more accurate than serum AFP for monitoring recurrence after hepatectomy for HCC and can be used for real-time monitoring of the postoperative status in patients with HCC without metastasis.

Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier

2018 ◽  
Vol 71 ◽  
pp. 346-358
Author(s):  
Alaa Tharwat ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
Mohamed Elhoseny
Keyword(s):  
Toxicity Test ◽  
Test Model ◽  
Model Based ◽  
Adaboost Classifier

scholarly journals Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Hufei Yu ◽  
Shucai Huang ◽  
Xiaojie Zhang ◽  
Qiuping Huang ◽  
Jun Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Resting State Fmri ◽  
Regional Homogeneity ◽  
Learning Approaches ◽  
Control Subjects ◽  
Methamphetamine Dependence ◽  
Adaboost Classifier ◽  
The Right ◽  
Leave One Out

Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.

scholarly journals A Data Segmentation-Based Ensemble Classification Method for Power System Transient Stability Status Prediction with Imbalanced Data

2019 ◽  
Vol 9 (20) ◽  
pp. 4216 ◽  
Author(s):  
Zhen Chen ◽  
Xiaoyan Han ◽  
Chengwei Fan ◽  
Zirun He ◽  
Xueneng Su ◽  
...  
Keyword(s):  
Transient Stability ◽  
Imbalanced Data ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Segmentation Strategy ◽  
Data Segmentation ◽  
Unstable Set ◽  
Data Problem ◽  
Adaboost Classifier ◽  
Training Subset

In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for TSSP is proposed in this paper. Firstly, the effects of the imbalanced data problem on the decision boundary and classification performance of TSSP are investigated in detail. Then, a three-step DSEC method is presented. In the first step, the data segmentation strategy is utilized for dividing the stable samples into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable ones, then each stable subset is combined with the unstable set into a training subset. For the second step, an AdaBoost classifier is built based on each training subset. In the final step, decision values from each AdaBoost classifier are aggregated for determining the transient stability status. The experiments are conducted on the Northeast Power Coordinating Council 140-bus system and the simulation results indicate that the proposed approach can significantly improve the classification performance of TSSP with imbalanced data.

scholarly journals Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0188939 ◽  
Author(s):  
Nogol Memari ◽  
Abd Rahman Ramli ◽  
M. Iqbal Bin Saripan ◽  
Syamsiah Mashohor ◽  
Mehrdad Moghbel
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Matched Filtering ◽  
Fundus Images ◽  
Retinal Vessel Segmentation ◽  
Color Fundus Images ◽  
Adaboost Classifier
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