Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Utku, Anıl(8)
- Can, Umit(4)
- Utku, Anil(4)
- Gul, Muhammet(2)
- Kaya, Sema Kayapinar(2)
- Acharya, U. Rajendra(1)
- Akcayol, M. Ali(1)
- Akcayol, Muhammet Ali(1)
- Alpsülün, Mustafa(1)
- Amoozegar, Azadeh(1)
- Aslan, Serpil(1)
- Bakay, Ayşenur(1)
- Baloglu, Ulas Baran(1)
- Balıkçı, Hasan Celal(1)
- Baran, Mateusz(1)
- Baris, Veysel Karani(1)
- Barut, Abdulkadir(1)
- Can, U.(1)
- Celik, Erkan(1)
- Celik, Gulbahar(1)
- Ciger, Emel(1)
- Doğan, Kıvanç(1)
- Ertarğın, Merve(1)
- Fuat Guneri, Ali(1)
- Gunal, Murat M.(1)
- Hüseyin Bolat(1)
- Kangal, Evrim Ersin(1)
- Kaya, Sema Kayapınar(1)
- Khatti, Jitendra(1)
- Król, Małgorzata(1)
- Kutlu, Banu(1)
- Niedźwiecki, Michał(1)
- Pekel, Engin(1)
- Pilatin, Abdulmuttalip(1)
- Polat, Alper(1)
- Polat, Berivan Yılmazer(1)
- Pławiak, Paweł(1)
- Rzecki, Krzysztof(1)
- Salti, Mehmet(1)
- Serin, Faruk(1)
Yayınlar
- 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.
AtıfScopus13OpenAlex13
İncele - 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 - MakaleYalnız künye2020Scopus SJRQ1Emergency department network under disaster conditions: The case of possible major Istanbul earthquake
Gul, Muhammet; Fuat Guneri, Ali; Gunal, Murat M.
Emergency departments (EDs) provide health care services to people in need of urgent care. Their role is remarkable when extraordinary events that affect the public, such as earthquakes, occur. In this paper, we present a hybrid framework to evaluate earthquake preparedness of EDs in cities. Our hybrid framework uses artificial neural networks (ANNs) to estimate number of casualties and discrete event simulation (DES) to analyse the effect of surge in patient demand in EDs, after an earthquake happens. At the core of our framework, Earthquake Time Emergency Department Network Simulation Model (ET-EDNETSIM) resides which can simulate patient movements in a network of multiple and coordinated EDs. With the design of simulation experiments, different resource levels and sharing rules between EDs can be evaluated. We demonstrated our framework in a network of five EDs located in a region of which is estimated to have the highest injury rate after an earthquake in Istanbul, Turkey. Results of our study contributed to the planning for expected earthquake in Istanbul. Simulating a network of EDs extends the individual ED studies in the literature and furthermore, our hybrid framework can help increase earthquake preparedness in cities around the world. On the methodological side, the use of ANN, which is a member of machine learning (ML) algorithms family, in our hybrid framework also shows the close links between ML and DES.
AtıfScopus30WoS31OpenAlex40
İncele - MakaleYayıncıda açık erişim2018Scopus SJRQ2Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS
Rzecki, Krzysztof; Sośnicki, Tomasz; Baran, Mateusz; Niedźwiecki, Michał; Król, Małgorzata; Łojewski, Tomasz; Acharya, U. Rajendra; Yildirim, Özal; Pławiak, Paweł
Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.
AtıfScopus39
İncele - MakaleAçık Erişim2018Heartbeat type classification with optimized feature vectors
Yıldırım, Özal; Baloglu, Ulas Baran
In this study, a feature vector optimization based method has been proposed for classification of the heartbeat types. Electrocardiogram (ECG) signals of five different heartbeat type were used for this aim. Firstly, wavelet transform (WT) method were applied on these ECG signals to generate all feature vectors. Optimizing these feature vectors is provided by performing particle swarm optimization (PSO), genetic search, best first, greedy stepwise and multi objective evoluationary algorithms on these vectors. These optimized feature vectors are later applied to the classifier inputs for performance evaluation. A comprehensive assessment was presented for the determination of optimized feature vectors for ECG signals and best-performing classifier for these optimized feature vectors was determined.
AtıfScopus7OpenAlex7
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