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- MakaleAçık Erişim2023Scopus SJRQ1DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES
Utku, Anil
The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.
AtıfScopus9OpenAlex11
İncele - MakaleAçık Erişim2022Scopus SJRQ1DEEP LEARNING BASED A COMPREHENSIVE ANALYSIS FOR WASTE PREDICTION
Utku, Anıl; Kaya, Sema Kayapinar
In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTM-based deep learning method is more successful in the waste prediction problem than the other compared models.
AtıfScopus13OpenAlex13İncele - MakaleAçık Erişim2022Scopus SJRQ1MULTI-LAYER PERCEPTRON BASED TRANSFER PASSENGER FLOW PREDICTION IN ISTANBUL TRANSPORTATION SYSTEM
Utku, Anıl; Kaya, Sema Kayapınar
Predicting passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.
AtıfScopus27OpenAlex25İncele - MakaleAçık Erişim2020Evaluation of the effect of COVID-19 on countries' sustainable development level: A comparative MCDM framework
Kaya, Sema Kayapinar
The extent of the outbreak of coronavirus disease (COVID-19) had a major impact on health, social life, economic and environmental activities in almost every country over the world. It has disrupted the sustainable development of countries and brought many uncertainties for their future capabilities. In this study, the effects of the COVID-19 on OECD countries' sustainable development were investigated, and the sustainable development performance of the countries was evaluated by the MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) method. Data for the second quarter of 2020 and the same quarter of the previous year is considered. Then, the results obtained by the MAIRCA method were compared with two different multi-criteria decision-making (MCDM) methods called MABAC (Multi‐Attributive Border Approximation Area Comparison) and WASPAS (Weighted Aggregated Sum Product Assessment). The effectiveness and validity of the results obtained from these methods were tested with Spearman's correlation coefficient. Finally, to examine the effect of COVID-19 on the indicators of sustainable development, a non-parametric Wilcoxon signed-rank test was applied. As a result, it was concluded that COVID-19 negatively affected the sustainable development of countries. However, sustainable development performances of developed countries have been observed to be better than developing countries.
AtıfScopus49OpenAlex56İncele - DergiOperational Research in Engineering Sciences: Theory and Applications
- DergiDecision Making: Applications in Management and Engineering