Aerospace

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https://dspace.lib.cranfield.ac.uk/handle/1826/20713

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Browsing Aerospace by Author "Bala Bisandu, Desmond"

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    ItemOpen Access
    Data supporting: 'Analysing the Sentiment of Air-Traveller: A Comparative Analysis'
    (Cranfield University, 2022-08-31 12:55) Salih A Homaid, Mohammed; Bala Bisandu, Desmond; Moulitsas, Irene; Jenkins, Karl
    Airport service qualityis considered to be an indicator of passenger satisfaction. However, assessingthis by conventional methods requires continuous observation and monitoring.Therefore, during the past few years, the use of machine learning techniquesfor this purpose has attracted considerable attention for analysing thesentiment of the air traveller. A sentiment analysis system for textual dataanalytics leverages the natural language processing and machine learningtechniques in order to determine whether a piece of writing is positive, negativeor neutral. Numerous methods exist for estimating sentiments which includelexical-based methodologies and directed artificial intelligence strategies.Despite the wide use and ubiquity of certain strategies, it remains unclearwhich is the best strategy for recognising the intensity of the sentiments of amessage. It is necessary to compare these techniques in order to understandtheir advantages, disadvantages and limitations. In this paper, we compared theValence Aware Dictionary and sentiment Reasoner, a sentiment analysis techniquespecifically attuned and well known for performing good on social media data,with the conventional machine learning techniques of handling the textual databy converting it into numerical form. We used the review data obtained from theSKYTRAX website for each airport. The machine learning algorithms evaluated inthis paper are VADER sentiment and logistic regression. The termfrequency-inverse document frequency is used in order to convert the textualreview datainto the resulting numerical columns. This was formulated as a classificationproblem, whereby the prediction of the algorithm was compared with the actualrecommendation of the passenger in the dataset. The results were analysedaccording to the accuracy, precision, recall and F1-score. From the analysis ofthe results, we observed that logistic regression outperformed the VADERsentiment analysis.
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    ItemOpen Access
    Prediction of Flight Delay using Deep Operator Network with Gradient-mayfly Optimisation Algorithm
    (Cranfield University, 2024-02-01 08:47) Bala Bisandu, Desmond; Moulitsas, Irene
    Data: This folder contains: - Datasets called Jan_2021_ontime.csv and Nov_2021_ontime.csv were used to obtain the results presented in the journal paper. Source code: This folder contains two files having the instructions on how to run the code and a list of library requirements and folders for each of the ML models as named exactly as contained in the paper which implements the proposed Deep Operator Network with Gradient-mayfly Optimisation Algorithm and all the algorithms presented and validated in the journal paper. Output: This folder contains: - Figures called Figure_1_MAE.png, Figure_1_MAPE.png, Figure_1_RMSE.png, Figure_1_MSE.png, Figure_2_MAE.png,Figure_2_MAPE.png, Figure_2_RMSE.png, Figure_2_MSE.png which shows results from the models based on different train/test ratios, The models are: (A)DBN, (B) Gradient Boosting Classifier, (C) Information Gain-SVM, (D) Multi-Agent Approach, (E) DeepLSTM, (F) SSDCA-based Deep LSTM, (G) DeepONet and (H) Proposed GMOA-based DeepOnet.- Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. - Figures called Figure_1_Prediction_Result_Jan_2021.png and Figure_2_Prediction_Result_Nov_2021.png which are the plots of the prediction results from presented in the journal paper. - 8 csv files called 1 MAE.csv, 1 MAPE.csv, 1 RMSE.csv, 1 MSE.csv, 2 MAE.csv, 2 MAPE.csv, 2 RMSE.csv, 2 MSE.csv, which contains the evaluation results produced by all algorithms presented and validated in the journal paper. - 2 csv files called Delay Prediction_Jan_2021_ontime_Figure_1 and Delay Prediction_Nov_2021_ontime_Figure_2 which contains the prediction results produced by all algorithms presented and validated in the journal paper.