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Publication Metadata only Forecasting Electricity Consumption Using Deep Learning Methods with Hyperparameter Tuning, Hiperparametre Ayarl Derin Orenme Yontemleri ile Elektrik Tuketiminin Tahmini(Institute of Electrical and Electronics Engineers Inc., 2020) Ayvaz, Serkan; Onur Arslan; Ayvaz, Serkan, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Onur Arslan, null, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this study, it is tried to estimate one-day electricity consumption by using deep learning methods with a dataset which includes the change in time-dependent electricity consumption. After explaining the time series components and machine learning concepts, general information about previous studies on electricity consumption estimation is given. Since the dataset used is a time series, all the features are emphasized in detail and necessary operations like resample and differencing are performed before proceeding to the modeling. Tuning was applied on hyperparameters which significantly affect the performance of the algorithms used in the modeling stage and the most suitable parameters were searched for each method. Then the best results were compared with each other and the method with the lowest error rate was determined. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(Institute of Electrical and Electronics Engineers Inc., 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Jeny, Afsana Ahsan, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Sakib, Abu Noman Md, Department of Cse, Khulna University of Engineering and Technology, Khulna, Bangladesh; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Lima, Khadija Akter, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Ahmed, Ikhtiar, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, American University of Malta, Cospicua, MaltaSkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only The analysis of feature selection with machine learning for indoor positioning(Institute of Electrical and Electronics Engineers Inc., 2021) Aydin, Hurkan M.; Ali, Muhammad Ammar; Gelal, Ece; Aydin, Hurkan M., Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ali, Muhammad Ammar, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyIndoor positioning is useful in various venues including warehouses, convention centers, malls, airports, nursing homes. In these scenarios, reducing the complexity of location estimation both improves responsiveness and helps to elongate battery life of the mobile device. In this work, we carry out a detailed analysis of the impact of Principal Component Analysis (PCA) on the computational complexity and accuracy with different machine learning algorithms on a large data set containing 520 APs. We compare the algorithms' training and testing times, as well as their accuracies in the presence and absence of PCA. Our results show that (i) PCA significantly reduces both the training and testing times for classification and regression using k-nearest neighbor (kNN) and support vector machine (SVM) algorithms while preserving if not improving accuracy, (ii) PCA slightly improves the training/testing times for regression using multi-layer perceptron (MLP), (iii) random forest (RF) does not perform well with PCA. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only A dynamic recurrent neural networks-based recommendation system for banking customers, Bankacilik müşterileri için dinamik tekrarlayan sinir aǧlari tabanli bir tavsiye sistemi(Institute of Electrical and Electronics Engineers Inc., 2021) Hasan, Avci; Sakar, C. Okan; Hasan, Avci, Bilgisayar Mühendisliõi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Bilgisayar Mühendisliõi Bölümü, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn recent years, machine learning approaches are replacing traditional methods in many industries. In parallel with these developments, marketing operations in banking started to be supported with machine learning based applications. In this study, a highly applicable product recommendation approach for banking customers was implemented using deep learning techniques. For this purpose, the dynamic recurrent neural network (DREAM) architecture, which was previously applied to e-commerce data, has been applied to banking customer data for recommendation system design. Comparative experiments with long short term memory and multilayer perceptron-based solutions have shown that the DREAM based recommendation approach has significant potential to be used in product proposal in the banking sector. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Stereoscopic Video Quality Assessment Using Modified Parallax Attention Module(Springer Science and Business Media Deutschland GmbH, 2022) Imani, Hassan; Zaim, Selim; Islam, Md Baharul; Junayed, Masum Shah; Durakbasa, N.M.; Gençyılmaz, M.G.; Imani, Hassan, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, Turkey; Zaim, Selim, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, Turkey; Junayed, Masum Shah, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, TurkeyDeep learning techniques are utilized for most computer vision tasks. Especially, Convolutional Neural Networks (CNNs) have shown great performance in detection and classification tasks. Recently, in the field of Stereoscopic Video Quality Assessment (SVQA), 3D CNNs are used to extract spatial and temporal features from stereoscopic videos, but the importance of the disparity information which is very important did not consider well. Most of the recently proposed deep learning-based methods mostly used cost volume methods to produce the stereo correspondence for large disparities. Because the disparities can differ considerably for stereo cameras with different configurations, recently the Parallax Attention Mechanism (PAM) is proposed that captures the stereo correspondence disregarding the disparity changes. In this paper, we propose a new SVQA model using a base 3D CNN-based network, and a modified PAM-based left and right feature fusion model. Firstly, we use 3D CNNs and residual blocks to extract features from the left and right views of a stereo video patch. Then, we modify the PAM model to fuse the left and right features with considering the disparity information, and using some fully connected layers, we calculate the quality score of a stereoscopic video. We divided the input videos into cube patches for data augmentation and remove some cubes that confuse our model from the training dataset. Two standard stereoscopic video quality assessment benchmarks of LFOVIAS3DPh2 and NAMA3DS1-COSPAD1 are used to train and test our model. Experimental results indicate that our proposed model is very competitive with the state-of-the-art methods in the NAMA3DS1-COSPAD1 dataset, and it is the state-of-the-art method in the LFOVIAS3DPh2 dataset. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Analysis of Transfer Learning Models for Face Mask Detection(Institute of Electrical and Electronics Engineers Inc., 2022) Kasap, Özge Yücel; Çakir, Duygu; Paul, R.; Kasap, Özge Yücel, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çakir, Duygu, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe covid-19 outbreak caused a global health crisis, and it still continues to spread rapidly today. Considering that it is very difficult to manually control the use of masks in public places, it is inevitable to turn the process into an automatic detection system. In this study, various Transfer Learning networks in detecting the usage of face masks are analyzed using a large dataset. As a consequence, it can be determined whether the face images recorded in-the-wild and low resolution from various angles are masked / unmasked and whether they are wearing the mask correctly or not. The architecture depths and execution times are also examined to see whether they have a positive effect on the accuracy or not. The results indicate that ResNet50 has a significant success over the rest of the models and the depth of the used model does not guarantee the success nor does it determine the time it takes for training. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Performance Comparison of Oral, Laryngeal and Thoracic Sounds in the Detection of COVID-19 by Employing Machine Learning Techniques, Oral, Larengeal ve Torakal Konuşma Seslerinin COVID-19 Tespitindeki Performanslarinin Yapay Öǧrenme Yöntemleriyle Karşilaştirilmasi(Institute of Electrical and Electronics Engineers Inc., 2022) Gözüacik, Necip; Serbes, Görkem; Kara, Eyup; Atas, Eren; Sakar, C. Okan; Murat Yener, H.; Börekçi, Şermin; Korkmazer, B.; Karaali, Rıtvan; Çeti̇N Kara, Halide Çetin; Gözüacik, Necip, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey, Siemens Advanta Development, Istanbul, Turkey; Serbes, Görkem, Biyomedikal Mühendisliǧi, Yıldız Teknik Üniversitesi, Istanbul, Turkey; Kara, Eyup, Istanbul University-Cerrahpasa, Istanbul, Turkey; Atas, Eren, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Murat Yener, H., Istanbul University-Cerrahpasa, Istanbul, Turkey; Börekçi, Şermin, Istanbul University-Cerrahpasa, Istanbul, Turkey; Korkmazer, B., Istanbul University-Cerrahpasa, Istanbul, Turkey; Karaali, Rıtvan, Istanbul University-Cerrahpasa, Istanbul, Turkey; Çeti̇N Kara, Halide Çetin, Istanbul University-Cerrahpasa, Istanbul, TurkeyCOVID-19 can directly or indirectly cause lung involvements by crossing the upper airways. It is essential to quickly detect the lung involvement condition and to follow up and treat these patients by early hospitalization. In recent COVID-19 diagnosis procedure, PCR testing is applied to the samples taken from the patients and a quarantine period is applied to the patient until the test results are received. As a complement to PCR tests and for faster diagnosis, thin-section lung computed tomography (CT) imaging is used in COVID-19 patients. In this study, it is aimed to develop a method that is as reliable as CT, and compared to CT, less risky, more accessible, and less costly for the diagnosis of COVID-19 disease. For this purpose, first speech and cough sounds from the oral, laryngeal and thoracic regions of COVID-19 patients and healthy individuals were obtained with the multi-channel voice recording system we proposed, the obtained data were processed with machine learning methods and their accuracies in COVID-19 diagnosis were presented comparatively. In our study, the best results were obtained with the features extracted from the cough sounds taken from the oral region. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Fault Estimation for Operational Systems(Institute of Electrical and Electronics Engineers Inc., 2022) Ozkent, Tuncberk; Gelal, Ece; Ozkent, Tuncberk, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyOperational systems are crucial for corporations. A majority of the business processes flow through these systems and even minor downtimes on these systems may cause serious financial consequences. System logs are a promising way for analyzing the behaviors of operational systems. This work investigates fault estimation on a real-life data set derived from the system logs of a large-scale insurance company. Data set consists of operational system indicators like visit duration, connection properties and time of connection collected over four months. Regression and classification algorithms have been used to estimate the impact of the system and environmental parameters on the system response time. The best performance is obtained with the CatBoost classification, which yields 99% accuracy in estimating whether system responds within normal interval. This study assists the operational team in identifying problem scenarios, future improvements may be possible as logs from other operational systems from the company are considered using transfer learning. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only QoE Estimation for the Wi-Fi Edge with Gradient Boosting-based Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Argın, Berke; Demir, Mehmet Özgün; Onalan, Aysun Gurur; Salik, Elif DIlek; Gelal, Ece; Argın, Berke, Lifemote Networks, Istanbul, Turkey; Demir, Mehmet Özgün, Lifemote Networks, Istanbul, Turkey; Onalan, Aysun Gurur, Lifemote Networks, Istanbul, Turkey; Salik, Elif DIlek, Lifemote Networks, Istanbul, Turkey; Gelal, Ece, Bahçeşehir Üniversitesi, Istanbul, TurkeyAn integral part of the Intent-Based Networking paradigm is estimating and improving the end-user quality of experience (QoE). Estimating user experience from the (wide-area) network data alone does not accurately represent the performance at customer premises since Wi-Fi at the edge also significantly affects the perceived QoE. We propose machine learning-based estimation of the end-users' perceived QoE for web browsing and video streaming applications, based on Wi-Fi statistics. We implement support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), XGBoost, and CatBoost algorithms and compare their performance. To the best of our knowledge, our CatBoost-based model yields the highest accuracy to date, 0.92 R2, in estimating the QoE for web browsing based on Wi-Fi statistics. Our experiments also show that the XGBoost-based QoE estimator outperformed the neural network-based model in estimating the QoE for video streaming. Our work demonstrates that network operators can infer the user-perceived QoE in a Wi-Fi network through telemetry data obtained by passive measurements. © 2023 Elsevier B.V., All rights reserved.Publication Metadata only ASD-EVNet: An Ensemble Vision Network based on Facial Expression for Autism Spectrum Disorder Recognition(Institute of Electrical and Electronics Engineers Inc., 2023) Jaby, Assil; Islam, Md Baharul; Ahad, Md Atiqur Rahman; Jaby, Assil, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, American University of Malta, Cospicua, Malta; Ahad, Md Atiqur Rahman, University of East London, London, United KingdomAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects individuals' social interaction, communication, and behavior. Early diagnosis and intervention are critical for the well-being and development of children with ASD. Available methods for diagnosing ASD are unpredictable (or with limited accuracy) or require significant time and resources. We aim to enhance the precision of ASD diagnosis by utilizing facial expressions, a readily accessible and limited time-consuming approach. This paper presents ASD Ensemble Vision Network (ASD-EVNet) for recognizing ASD based on facial expressions. The model utilizes three Vision Transformer (ViT) architectures, pre-Trained on imageNet-21K and fine-Tuned on the ASD dataset. We also develop an extensive collection of facial expression-based ASD dataset for children (FADC). The ensemble learning model was then created by combining the predictions of the three ViT models and feeding it to a classifier. Our experiments demonstrate that the proposed ensemble learning model outperforms and achieves state-of-The-Art results in detecting ASD based on facial expressions. © 2023 Elsevier B.V., All rights reserved.