A comprehensive study of machine learning methods on diabetic retinopathy classification

Diabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/healthtopics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lower-dimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes. (C) 2021 The Authors. Published by Atlantis Press B.V

Forecasting intermittent demand using the cox process

If a demand has infrequent demand occurrences and irregular demand sizes, then it is intermittent demand. Generally, intermittent demand appears at random, with many time periods having no demand. Owing to peculiar characteristics of intermittent demand, demand forecasting for intermittent demand is especially difficult. There are ad hoc methods developed for intermittent demand forecasting. Since Cox process has shown superior performance for intermittent demand forecasting, we studied forecasting intermittent demand using Cox process in this study. We develop a new method for estimating Cox process intensity which is called Reversed Leven and Segerstedt (RLS) method. Moreover, we propose a novel method which is a Wavelet Transform and Reversed Leven and Segerstedt conjunction model for intermittent demand forecasting using Cox process. Using real data set of 500 kinds of spare parts from an aviation sector company in Turkey, we show that our method produces more accurate forecasts than other intermittent demand forecasting methods using Cox process. The comparison approach has a lead time perspective which is based on lead time ahead demand forecast and lead time demand forecast errors. © 2018 Old City Publishing, Inc

Deep-learning-based short-term electricity load forecasting: A real case application

The rising popularity of deep learning can largely be attributed to the big data phenomenon, the surge in the development of new and novel deep neural network architectures, and the advent of powerful computational innovations. However, the application of deep neural networks is rare for time series problems when compared to other application areas. Short-term load forecasting, a typical and difficult time series problem, is considered as the application domain in this study. One-dimensional Convolutional Neural Networks (CNNs) use is rare in time series forecasting problems when compared to Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), and the efficiency of CNN has been rather remarkable for pattern extraction. Hence, a new method that uses one-dimensional CNNs based on Video Pixel Networks (VPNs) in this study, in which the gating mechanism of Multiplicative Units of the VPNs is modified in some sense, for short term load forecasting. Specifically, the proposed one-dimensional CNNs, LSTM and GRU variants are applied to real-world electricity load data for 1-hour-ahead and 24-hour-ahead prediction tasks which they are the main concerns for the electricity provider firms for short term load forecasting. Statistical tests were conducted to spot the significance of the performance differences in analyses for which ten ensemble predictions of each method were experimented. According to the results of the comparative analyses, the proposed one-dimensional CNN model yielded the best result in total with 2.21% mean absolute percentage error for 24-h ahead predicitions. On the other hand, not a noteworthy difference between the methods was spotted even the proposed one-dimensional CNN method yielded the best results with approximately 1% mean absolute percentage error for 1-h ahead predictions.Q1WOS:0007629763000242-s2.0-8512262726

Using machine learning tools for forecasting natural gas consumption in the province of Istanbul

Commensurate with unprecedented increases in energy demand, awell-constructed forecastingmodel is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.Q1WOS:0004746811000682-s2.0-8506311490