Prediction of warning level in aircraft accidents using data mining techniques

Christopher, A. B. Arockia; Balamurugan, S.

doi:10.1017/s0001924000009623

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…Feature selection is an important step for the analysis of standardized data, to enable our ML algorithms to train faster and overcome the problem of overfitting. There have been various approaches for the feature selection used in ML related problems including principal component analysis [42], information gain [55], chi-square test [56], [57] and many more. One of the key motivations of utilizing chi-square test in our approach is the way it ranks features based on statistical significance indicating the dependency between the current feature and the target class [57].…”

Section: Feature Selectionmentioning

confidence: 99%

IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

Khalaf

Alaskar²,

Hussain

et al. 2020

IEEE Access

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River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively. INDEX TERMS Internet of Things, ensemble machine learning, flood sensor data, long-short term memory.

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Section: Feature Selectionmentioning

confidence: 99%

IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

Khalaf

Alaskar²,

Hussain

et al. 2020

IEEE Access

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“…It is indicated an essential improvement of inefficient performance and accuracy from the traditional Naïve Bayes classifier utilizing on specific Data Repository called UCL. [14,43] applied a large-scale mood analysis detection for document classification based on social media text. The authors attempted to structure their paper into three main stages; firstly, addressing the main issue related to feature selection, feature extraction, and classification method of mood in internet resources.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Mood Detection Based on Arabic Text Documents using Machine Learning Methods

Hussein¹

2020

IJATCSE

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Document text classification is utilized for information feature extraction and retrieval as the primary source of digitizing the written information using text classification techniques. Text classification can provide much more information by analyzing the text using machine learning methods. One of the practical applications of text classification is mood detection using machine learning algorithms. Machine learning algorithms allow a practical and beneficial platform for analyzing and detect mood from the text documents. However, there are few applications to analyze the text in Arabic with high accuracy and especially detecting mood using Arabic text documents, messages, or blogs. The main objective of using machine learning algorithms is to detect the accurate mood target value to the given messages. This study focuses on four mood classes (Happy, Sad, Angry, fear). The text analyzed for this study were gathered from some social media and internet blogs. Three kinds of techniques have been identified based on machine learning approaches: Naïve Bays algorithm, k-Nearest Neighbors (KNN) algorithm, and Support Vector Machine (SVM) algorithm. The text was further analyzed for feature selection related to text mining, feature correlation analysis, and information gain. Lastly, splitting the text into training and testing sets for possible models using robust classifiers. After running the selected classifiers for our study, the results showed that Naïve bays classier had the highest achievement in terms of accuracy. Naïve Bays classifiers received 70%, support vector machine classifiers obtained 68.33%, and k-Nearest Neighbors (KNN) algorithm yield 51.67%.

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“…The classification algorithms such as Decision Tree (DT), K-nearest neighbour (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and NB are used to predict the warning level of the component as the classification attribution. We have explored the use of different classification techniques on aviation components data (Christopher and Balamurugan, 2014) (8) .…”

Section: Introductionmentioning

confidence: 99%

Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

Anderson¹,

Kumar²,

Christopher³

2019

Aeronaut. j.

Self Cite

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Data mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

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Prediction of warning level in aircraft accidents using data mining techniques

Cited by 11 publications

References 14 publications

IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

Mood Detection Based on Arabic Text Documents using Machine Learning Methods

Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

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