Exploration of the Evolution of Airport Ground Delay Programs

2018 ◽  
Vol 2672 (23) ◽  
pp. 71-81 ◽  
Author(s):  
Kexin (May) Ren ◽  
Amy M. Kim ◽  
Kenneth Kuhn
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Capacity Utilization ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Lower Efficiency ◽  
Weather Forecasts ◽  
Learning Techniques ◽  
Predictability Score ◽  
Ground Delay

This study introduces a novel method of merging disparate but complementary datasets and applying machine learning techniques to ground delay program (GDP) data. More specifically, it aims to characterize GDPs with respect to changing weather forecasts, GDP plan parameters, and operational performance. The analysis aims to gain insights into GDP usage patterns (implementation and revisions), with respect to these key dimensions. It also aims to gain insights into how GDP cancelations and revisions correlate with operational efficiency and predictability. The results could be used to help traffic managers and air carriers understand complex patterns in the evolution of GDPs, so that they might, for example, better anticipate or even plan a response to a change in weather conditions. The focus is on GDPs at Newark Liberty International Airport (EWR), from 2010 through 2014. A master dataset was generated by merging several datasets on GDPs, weather forecasts, and individual flight information. Several scenarios of GDP evolution were then identified by reducing the dimensionality of the master GDP dataset, then applying cluster analysis on the lower dimensional data. It was found that GDPs at EWR can be categorized into 10 types based on weather forecasts, realized weather, GDP scope, arrival rates, and duration. The characteristics of these 10 GDP clusters were further explored by examining the relationships between GDP scenarios and their performance. It was found that GDPs under stable, low-severity weather and with large scope may score higher on the efficiency metric than expected. When GDPs called in the same weather conditions have high program rates, medium durations, and narrow scopes, capacity utilization was higher than expected—less affected flights lead to fewer cancelations and more arrivals (albeit delayed), and therefore, higher capacity utilization. Results also suggest that program rates are set more conservatively than needed for some poor weather conditions that end earlier than expected. GDPs with fewer revisions were associated with a higher predictability score but lower efficiency score. These findings can provide greater insights and knowledge about GDPs for future planning purposes. More specifically, the findings could, for example, be used to support discussion around, or even future guidance regarding, how to set and adjust GDP program rates. In future work additional data could be utilized to provide a more comprehensive operational picture of GDPs, and a wider range of performance metrics could be considered. It is also recommended that the patterns of how GDPs evolve over their lifetimes be further explored using other machine learning techniques that may provide new and useful insights.

scholarly journals Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

2018 ◽  
Vol 11 (1) ◽  
pp. 105 ◽  
Author(s):  
Syed Abidi ◽  
Mushtaq Hussain ◽  
Yonglin Xu ◽  
Wu Zhang
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Teaching And Learning ◽  
Performance Metrics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Tutoring System ◽  
Learning Techniques

Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

scholarly journals Blue Skies: A Methodology for Data-Driven Clear Sky Modelling

2017 ◽  
Author(s):  
Kartik Palani ◽  
Ramachandra Kota ◽  
Amar Prakash Azad ◽  
Vijay Arya
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Weather Conditions ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Clear Sky ◽  
Learning Techniques ◽  
Atmospheric Data ◽  
Average Irradiance ◽  
Photo Voltaic

One of the major challenges confronting the widespread adoption of solar energy is the uncertainty of production. The energy generated by photo-voltaic systems is a function of the received solar irradiance which varies due to atmospheric and weather conditions. A key component required for forecasting irradiance accurately is the clear sky model which estimates the average irradiance at a location at a given time in the absence of clouds. Current methods for modelling clear sky irradiance are either inaccurate or require extensive atmospheric data, which tends to vary with location and is often unavailable. In this paper, we present a data-driven methodology, Blue Skies, for modelling clear sky irradiance solely based on historical irradiance measurements. Using machine learning techniques, Blue Skies is able to generate clear sky models that are more accurate spatio-temporally compared to the state of the art, reducing errors by almost 50%.

scholarly journals Combining Linear Regression and Machine Learning Approaches to Identify Consensus Variables Related to Optimum Sweetpotato Transplanting Date

HortScience ◽  
2010 ◽  
Vol 45 (4) ◽  
pp. 684-686 ◽  
Author(s):  
Arthur Villordon ◽  
Christopher Clark ◽  
Tara Smith ◽  
Don Ferrin ◽  
Don LaBonte
Keyword(s):  
Machine Learning ◽  
Stepwise Regression ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Root Yield ◽  
Weather Forecasts ◽  
Regression Methods ◽  
Learning Techniques ◽  
Leave One Out ◽  
Fold Cross Validation

Forward and stepwise regression methods identified variables related to the influence of transplanting date on yield of U.S. #1 sweetpotatoes. The variables were mean minimum soil temperature 5 days after transplanting (DAT), wind direction at transplanting, and accumulated heat units (growing degree-days) 5 DAT. Machine learning techniques identified the same variables using leave-one-out and k-fold cross-validation methods. Growers and crop consultants, in collaboration with knowledge workers, can use this information in conjunction with public and subscription-based weather forecasts to further optimize transplanting date determination and for making risk-averse decisions. These results help to underscore the importance of consistent transplant establishment as one of the determinants of storage root yield in sweetpotatoes.

scholarly journals A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

2018 ◽  
Vol 22 ◽  
Author(s):  
Manojit Chattopadhyay ◽  
Rinku Sen ◽  
Sumeet Gupta
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Meta Analysis ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Business Organizations ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Network Technologies

Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

scholarly journals Deep forest model for diagnosing COVID-19 from routine blood tests

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maryam AlJame ◽  
Ayyub Imtiaz ◽  
Imtiaz Ahmad ◽  
Ameer Mohammed
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Performance Metrics ◽  
Laboratory Data ◽  
Machine Learning Techniques ◽  
Layer By Layer ◽  
Infected People ◽  
Global Pandemic ◽  
Learning Techniques ◽  
Deep Forest

AbstractThe Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

scholarly journals Machine Learning Approach

2022 ◽  
pp. 349-366
Author(s):  
Roopashree S. ◽  
Anitha J. ◽  
Madhumathy P.
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Medicinal Herbs ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Domain Experts ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Insufficient Knowledge ◽  
Complete Framework

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

scholarly journals Cardiac Disease Prediction using Supervised Machine Learning Techniques.

2022 ◽  
Vol 2161 (1) ◽  
pp. 012013
Author(s):  
Chiradeep Gupta ◽  
Athina Saha ◽  
N V Subba Reddy ◽  
U Dinesh Acharya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cardiac Disease ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Techniques ◽  
Learning Techniques

Abstract Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

scholarly journals The Practice of Implementing ML Service into an Internet Business Application

2021 ◽  
Vol 24 ◽  
pp. 8-14
Author(s):  
Pavels Osipovs
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Performance Metrics ◽  
Development Stage ◽  
Machine Learning Techniques ◽  
Practical Application ◽  
Learning Techniques ◽  
Business Application ◽  
Internet Business

Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.

scholarly journals Machine Learning Approach

2021 ◽  
pp. 214-231
Author(s):  
Roopashree S. ◽  
Anitha J. ◽  
Madhumathy P.
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Medicinal Herbs ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Domain Experts ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Insufficient Knowledge ◽  
Complete Framework

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

scholarly journals Heart Attack Prediction by using Machine Learning Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 1577-1580
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Analysis ◽  
Missing Data ◽  
Heart Attack ◽  
Performance Metrics ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Light Weight ◽  
Learning Techniques

Heart disease is most common now a days and it is a very serious problem. Machine learning provides a best way for predicting heart disease. The aim of this paper is to develop simple, light weight approach for detecting heart disease by machine learning techniques. Machine learning can be implemented in heart disease prediction. In this paper different machine learning techniques have been used and it compares the result using various performance metrics. This study aims to perform comparative analysis of heart disease detection using publicly available dataset collected from UCI machine learning repository. There are various datasets available such as Switzerland dataset, Hungarian dataset and Cleveland dataset. Here Cleveland dataset is used which is having 303 records of patients along with 14 attributes are used for this study and testing. These datasets are preprocessed by removing all the noisy and missing data from the dataset. And then the preprocessed dataset are used for analysis. In this study six different machine learning techniques were used for comparison based on various performance metrics. The analysis shows that out of six techniques SVM gives the best result with 89.34%. A GUI is developed for the prediction of heart disease.

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