Browsing by Author "Bisandu, Desmond Bala"
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Item Open Access Analysing the sentiment of air-traveller: a comparative analysis(IJCTE, 2022-03-31) Homaid, Mohammed Salih A; Bisandu, Desmond Bala; Moulitsas, Irene; Jenkins, Karl W.Airport service quality is considered to be an indicator of passenger satisfaction. However, assessing this by conventional methods requires continuous observation and monitoring. Therefore, during the past few years, the use of machine learning techniques for this purpose has attracted considerable attention for analysing the sentiment of the air traveller. A sentiment analysis system for textual data analytics leverages the natural language processing and machine learning techniques in order to determine whether a piece of writing is positive, negative or neutral. Numerous methods exist for estimating sentiments which include lexical-based methodologies and directed artificial intelligence strategies. Despite the wide use and ubiquity of certain strategies, it remains unclear which is the best strategy for recognising the intensity of the sentiments of a message. It is necessary to compare these techniques in order to understand their advantages, disadvantages and limitations. In this paper, we compared the Valence Aware Dictionary and sentiment Reasoner, a sentiment analysis technique specifically attuned and well known for performing good on social media data, with the conventional machine learning techniques of handling the textual data by converting it into numerical form. We used the review data obtained from the SKYTRAX website for each airport. The machine learning algorithms evaluated in this paper are VADER sentiment and logistic regression. The term frequency-inverse document frequency is used in order to convert the textual review data into the resulting numerical columns. This was formulated as a classification problem, whereby the prediction of the algorithm was compared with the actual recommendation of the passenger in the dataset. The results were analysed according to the accuracy, precision, recall and F1-score. From the analysis of the results, we observed that logistic regression outperformed the VADER sentiment analysis.Item Open Access A deep BiLSTM machine learning method for flight delay prediction classification(Embry-Riddle Aeronautical University, 2023-09-25) Bisandu, Desmond Bala; Moulitsas, IreneThis paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features to train and test the models. The performance evaluation of the models and Confusion matrix shows that BiLSTM outperforms the LSTM model. In evaluating the models using the Mathews Correlation Coefficient (MCC), the BiLSTM model offers a better correlation of 0.99 between the original and predicted classes. Our experiment shows that for predicting flight delays, the BiLSTM model takes advantage of the forward and backward hidden sequences and the deep neural network for performance exploration and exploitation to achieve high accuracy, recall, and F1-Score. Our findings suggest that the BiLSTM model can effectively predict flight delays and provide valuable information for airlines, passengers, and airport managers.Item Open Access A deep feedforward neural network and shallow architectures effectiveness comparison: Flight delays classification perspective(ACM, 2021-11-22) Bisandu, Desmond Bala; Homaid, Mohammed Salih A; Moulitsas, Irene; Filippone, SalvatoreFlight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector Machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector Machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector Machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher accuracy in a binary classification of flight delays. Finally, we recommended further studies on the applicability of the Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the model’s performance.Item Open Access A hybrid ensemble machine learning approach for arrival flight delay classification prediction using voting aggregation technique(AIAA, 2023-06-08) Bisandu, Desmond Bala; Moulitsas, IreneThe number of flights keeps increasing with the development of civil aviation, and flight delays have become a severe issue of concern to the aviation industry. The need for reliable flight delay classification and prediction cannot be overemphasised because of its importance. In this research, we classify arrival flight delay using a hybrid ensemble machine learning algorithm based on voting aggregation. Each ensemble’s architecture consists of four supervised machine learning algorithms: Logistic Regression, Decision Tree, Support Vector Classifier and Random Forest. We conducted a comparative experiment using the United States Bureau of Statistics dataset to verify the efficacy of our proposed approach against other benchmark approaches. We employ multiple evaluation metrics to check the performance of our proposed model comprehensively. Accuracy, Recall, Precision, F1-Score, AUC Score, and ROC curve. Our experimental results show that our model outperforms the benchmark techniques on the binary classification of flight delays task. Hence, the hybrid ensemble model presented in this research can be used as a decision-support model to improve the flight scheduling management system design to assist the airline and airport operation managers and passengers with improved travel itineraries management.Item Open Access Illuminating the neural landscape of pilot mental states: a convolutional neural network approach with Shapley Additive explanations interpretability(MDPI, 2023-11-11) Alreshidi, Ibrahim; Bisandu, Desmond Bala; Moulitsas, IrenePredicting pilots’ mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states—channelised attention, diverted attention, startle/surprise, and normal state—in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model’s interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection.Item Open Access Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm(Elsevier, 2024-01-31) Bisandu, Desmond Bala; Moulitsas, IreneAccurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness.Item Open Access Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction(Springer, 2022-01-30) Bisandu, Desmond Bala; Moulitsas, Irene; Filippone, SalvatoreThe importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment.