scholarly journals Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms

2015 ◽  
Vol V4 (07) ◽  
Author(s):  
P.Swathi Baby ◽  
T. Panduranga Vital ◽  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Kidney Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Machine Learning Framework to Analyze Against Spear Phishing

2019 ◽  
Vol 8 (12) ◽  
pp. 3605-3611
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Learning Algorithms ◽  
Ensemble Methods ◽  
Analysis Data ◽  
Graphical Analysis ◽  
Machine Learning Algorithms ◽  
Advanced Persistent Threat ◽  
Spam Filter ◽  
Algorithm Comparison

The objective of this paper is to design and implement machine learning based ensemble algorithm on dataset to fit into the models that can be understood and executed by machines. In this paper we discussed different algorithms and machine learning concepts that can be implemented on the datasets, we taken email spam filter dataset for experiment and analysis, as the Advanced persistent threat the latest threat is intruded using the emails and major intrusion is done through spam emails. Machine learning uses different datamining techniques and mechanisms and accepts the input-data and gives the output as the statistical analysis. We implemented different email classification algorithms on the datasets based on spam and ham emails where spear phishing methods are identified and implemented different classification and regression methods to get the accurate results. In this paper for the better results in spite of existing algorithms we introduced the ensemble methods such as boosting, bagging, stacking and voting for much accuracy and higher level of classification and combining different algorithm. This paper will measure different machine learning algorithms performance on spam email filtering on the huge datasets. The framework provides implementation of learning algorithms that you can apply to larger datasets. An obvious approach to making decisions more reliable is to combine the output of different models. We even compared the existing algorithms and proposed algorithm; comparison tables are drawn along with the statistical analysis, data and graphical analysis is given.

Statistical analysis and machine learning algorithms for optical biopsy

2018 ◽  
Author(s):  
Binlin Wu ◽  
Cheng-Hui Liu ◽  
Susie Boydston-White ◽  
Hugh Beckman ◽  
Vidyasagar Sriramoju ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Optical Biopsy

scholarly journals Is It Time for Machine Learning Algorithms to Predict the Risk of Kidney Failure in Patients with Chronic Kidney Disease?

2021 ◽  
Vol 10 (5) ◽  
pp. 1121
Author(s):  
Charat Thongprayoon ◽  
Wisit Kaewput ◽  
Avishek Choudhury ◽  
Panupong Hansrivijit ◽  
Michael A. Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Failure ◽  
Kidney Diseases ◽  
Learning Algorithms ◽  
Clinical Problem ◽  
Machine Learning Algorithms ◽  
Common Clinical Problem

Chronic kidney disease (CKD) is a common clinical problem affecting more than 800 million people with different kidney diseases [...]

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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