scholarly journals Determining Factors Affecting Cooperative Membership of the Beekeepers Using Decision Tree Algorithms

2022 ◽  
pp. 25-32
Author(s):  
Tayfun ÇUKUR ◽  
Figen ÇUKUR
Keyword(s):  
Decision Tree ◽  
Factors Affecting ◽  
Determining Factors ◽  
Tree Algorithms

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020 ◽  
pp. 1-26
Author(s):  
Remzi Fiskin ◽  
Erkan Cakir ◽  
Coşkan Sevgili
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Decision Tree ◽  
Influential Factor ◽  
Factors Affecting ◽  
Port Authorities ◽  
Prevention Programmes ◽  
Tree Algorithms ◽  
Using Data

As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
John T. Hancock ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Decision Tree ◽  
Fraud Detection ◽  
Medicare Fraud ◽  
Boosted Decision Tree ◽  
Tree Algorithms

scholarly journals Factors Contributing to Heat Resistance of Clostridium perfringens Endospores

2008 ◽  
Vol 74 (11) ◽  
pp. 3328-3335 ◽  
Author(s):  
Benjamin Orsburn ◽  
Stephen B. Melville ◽  
David L. Popham
Keyword(s):  
Cell Wall ◽  
Heat Resistance ◽  
Clostridium Perfringens ◽  
Food Poisoning ◽  
High Heat ◽  
Relative Importance ◽  
High Heat Resistance ◽  
Factors Affecting ◽  
Cooking Process ◽  
Determining Factors

ABSTRACT The endospores formed by strains of type A Clostridium perfringens that produce the C. perfringens enterotoxin (CPE) are known to be more resistant to heat and cold than strains that do not produce this toxin. The high heat resistance of these spores allows them to survive the cooking process, leading to a large number of food-poisoning cases each year. The relative importance of factors contributing to the establishment of heat resistance in this species is currently unknown. The present study examines the spores formed by both CPE+ and CPE− strains for factors known to affect heat resistance in other species. We have found that the concentrations of DPA and metal ions, the size of the spore core, and the protoplast-to-sporoplast ratio are determining factors affecting heat resistance in these strains. While the overall thickness of the spore peptidoglycan was found to be consistent in all strains, the relative amounts of cortex and germ cell wall peptidoglycan also appear to play a role in the heat resistance of these strains.

Novel Approach for Battery Type Determination: A Mere Electrical Alternative

2021 ◽  
Author(s):  
İsmail Can Dikmen ◽  
Teoman Karadağ
Keyword(s):  
Integrated Circuits ◽  
Decision Tree ◽  
Statistical Significance ◽  
High Capacity ◽  
Electrical Energy ◽  
Machine Learning Algorithms ◽  
Battery Management ◽  
Separation Function ◽  
Novel Approach ◽  
Tree Algorithms

Abstract Today, the storage of electrical energy is one of the most important technical challenges. The increasing number of high capacity, high-power applications, especially electric vehicles and grid energy storage, points to the fact that we will be faced with a large amount of batteries that will need to be recycled and separated in the near future. An alternative method to the currently used methods for separating these batteries according to their chemistry is discussed in this study. This method can be applied even on integrated circuits due to its ease of implementation and low operational cost. In this respect, it is also possible to use it in multi-chemistry battery management systems to detect the chemistry of the connected battery. For the implementation of the method, the batteries are connected to two different loads alternately. In this way, current and voltage values ​​are measured for two different loads without allowing the battery to relax. The obtained data is pre-processed with a separation function developed based on statistical significance. In machine learning algorithms, artificial neural network and decision tree algorithms are trained with processed data and used to determine battery chemistry with 100% accuracy. The efficiency and ease of implementation of the decision tree algorithm in such a categorization method are presented comparatively.

scholarly journals An Event Group Based Classification Framework for Multi-variate Sequential Data

2017 ◽  
Vol 21 ◽  
Author(s):  
Chao Sun ◽  
David Stirling
Keyword(s):  
Decision Tree ◽  
Classification Problem ◽  
Sequential Data ◽  
Feature Generation ◽  
Data Types ◽  
Nominal Data ◽  
Classification Framework ◽  
Improved Outcomes ◽  
Tree Algorithms ◽  
Industrial Problem

Decision tree algorithms were not traditionally considered for sequential data classification, mostly because feature generation needs to be integrated with the modelling procedure in order to avoid a localisation problem. This paper presents an Event Group Based Classification (EGBC) framework that utilises an X-of-N (XoN) decision tree algorithm to avoid the feature generation issue during the classification on sequential data. In this method, features are generated independently based on the characteristics of the sequential data. Subsequently an XoN decision tree is utilised to select and aggregate useful features from various temporal and other dimensions (as event groups) for optimised classification. This leads the EGBC framework to be adaptive to sequential data of differing dimensions, robust to missing data and accommodating to either numeric or nominal data types. The comparatively improved outcomes from applying this method are demonstrated on two distinct areas – a text based language identification task, as well as a honeybee dance behaviour classification problem. A further motivating industrial problem – hot metal temperature prediction, is further considered with the EGBC framework in order to address significant real-world demands.

scholarly journals Statistical Analysis of the Factors Affecting Academic Achievement of Undergraduate Students A Case Study of Faculty of Arts and Science Kufrah -Benghazi University

2021 ◽  
Vol 2 (2) ◽  
pp. 50-59
Author(s):  
Muna H. Ali
Keyword(s):  
Academic Achievement ◽  
Undergraduate Students ◽  
Process Data ◽  
Family Learning ◽  
Arithmetic Means ◽  
Factors Affecting ◽  
Determining Factors ◽  
Study Population ◽  
Difference Test

This study examined the factors affecting the academic achievement of undergraduate students at the faculty of Arts and Science Kufrah -Benghazi University as a case study. This study seeks to identify and analyze some determining factors that influence students' academic achievement in the study area. Four factors namely: psychological, family, learning facilities, and economic; were considered. The sample was randomly selected from the study population. A questionnaire was administered to 240 (90 males,150 females) students as a sample of this study. The responses of the students were analyzed to meet the objectives of the study using (SPSS) and displayed in forms and tables. After collecting the required data on the variables of the study, they were encoded to be entered into the computer to extract the statistical results. Statistical methods within the Statistical Package for Social Sciences (SPSS) were used to process data obtained by the field study of the sample. To analyze the data mean difference test is used. Results of arithmetic means of the psychological, family, learning facilities, and economic factors were medium. Furthermore, there were no statistically significant differences in the factors affecting academic achievement among the participants due to some demographic factors such as gender and marital status. following recommendations were made; provide proper learning facilities to the students and also improve the environment of the faculty. Furthermore, the students would perform well if they are properly guided by both their parents and teachers.

scholarly journals Near-Optimal Sparse Neural Trees for Supervised Learning

2021 ◽  
Author(s):  
Tanujit Chakraborty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Tree ◽  
Mathematical Formulation ◽  
Machine Learning Algorithms ◽  
Optimal Decision ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Tree Algorithms

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

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