Deep learning models for forecasting aviation demand time series

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

Systematic review of passenger demand forecasting in aviation industry

Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.

Multiclass sentiment analysis on COVID-19-related tweets using deep learning models

COVID-19 is an infectious disease with its first recorded cases identified in late 2019, while in March of 2020 it was declared as a pandemic. The outbreak of the disease has led to a sharp increase in posts and comments from social media users, with a plethora of sentiments being found therein. This paper addresses the subject of sentiment analysis, focusing on the classification of users’ sentiment from posts related to COVID-19 that originate from Twitter. The period examined is from March until mid-April of 2020, when the pandemic had thus far affected the whole world. The data is processed and linguistically analyzed with the use of several natural language processing techniques. Sentiment analysis is implemented by utilizing seven different deep learning models based on LSTM neural networks, and a comparison with traditional machine learning classifiers is made. The models are trained in order to distinguish the tweets between three classes, namely negative, neutral and positive.