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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 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 Anterior Segment Eye Abnormality Detection(Association for Computing Machinery, 2023) Corbaci, Tolga; Corbaci, Tolga, School of Medicine, Bahçeşehir Üniversitesi, Istanbul, TurkeyVision is the most critical sense helping us to understand the world around us. Ophthalmology is an area of medicine that deals with the eye and vision. In many remote areas, people do not have access to ophthalmologists, and many go blind for preventable reasons. Awareness about eye health and early diagnosis is essential in eye health to prevent blindness. An artificial intelligence (AI) algorithm that can quickly detect eye disease is valuable and necessary. Anterior segment eye images are essential and easily obtained without additional equipment. In this study, I aimed to build an artificial intelligence algorithm to detect eye diseases from mobile photographs. I extracted and combined anterior segment eye photos from various publicly available datasets and labeled 3938 images as Normal (healthy) and 1094 images as Abnormal (unhealthy). I increased the data diversity by augmenting it with random flips and rotations: and then prepared it for AI training. I re-trained the algorithms trained in ImageNet Visual Recognition Challenge with the transfer learning method. I compared custom and pre-trained models. After evaluating the performance of the models with the test set, 98% accuracy and 97% F1 score were obtained with the Inception-ResNetV2 model. © 2023 Elsevier B.V., All rights reserved.Publication Metadata only Image Processing and Machine Learning Techniques for Chagas Disease Detection and Identification(Springer Science and Business Media Deutschland GmbH, 2024) Rada, Lavdie; Azzawi, Inass; Kumar, Preet; Brito-Loeza, Carlos; Karabaǧ, Cefa; Reyes-Aldasoro, Constantino Carlos; Yap, M.H.; Kendrick, C.; Behera, A.; Cootes, T.; Zwiggelaar, R.; Rada, Lavdie, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Azzawi, Inass, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kumar, Preet, University of the West of England, Bristol, United Kingdom; Brito-Loeza, Carlos, Universidad Autónoma de Yucatán, Merida, Mexico; Karabaǧ, Cefa, School of Computing and Digital Media, London Metropolitan University, London, United Kingdom; Reyes-Aldasoro, Constantino Carlos, School of Science & Technology, University of London, London, United KingdomChagas disease, caused by the Trypanosoma cruzi parasite, poses a significant health threat, particularly in Latin America, with millions affected globally. This research introduces a novel approach using deep learning techniques for the automated detection of Trypanosoma cruzi in blood smear images provided by Zoonoses Laboratory (CIR) in Mexico. Advanced deep learning architectures like Faster RCNN, RetinaNet, YOLOv8, and FCOS have been adapted, trained, and compared with each other in terms of the detection accuracy of each image. Our selection of those models is based on their ability to swiftly and accurately detect anomalies, measured through rigorous assessment using pivotal metrics like Mean Average Precision (mAP) across varying Intersection over Union (IoU) thresholds. Notably, the YOLOv8 model has showcased outstanding performance, boasting a remarkable mAP score of 0.951 for parasite detection and localisation. Specifically, YOLOv8 outperforms with a leading mAP of 0.951 at 50% IoU and maintains commendable precision with a score of 0.594 for IoU thresholds ranging from 50% to 95%. This research reduces dependence on skilled manual analysis holding a significant implications for healthcare in Chagas-affected regions by providing a rapid, automated solution to disease detection. This work has the potential to revolutionise diagnostics in resource-limited settings. Moreover, the models’ adaptability to other parasitic infections enhances their global health impact. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only Consumer Preference Estimation Using EEG Signals and Deep Learning, EEG Sinyalleri ve Derin Öğrenme Kullanılarak Tüketici Beğeni Durum Kestirimi(Institute of Electrical and Electronics Engineers Inc., 2024) Ceylan, Burak Gor; Cekic, Yalcin; Akan, Aydin I.; Ceylan, Burak Gor, Istanbul University-Cerrahpasa, Istanbul, Turkey; Cekic, Yalcin, Istanbul University-Cerrahpasa, Istanbul, Turkey; Akan, Aydin I., Istanbul University-Cerrahpasa, Istanbul, TurkeyEmotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only Liking State Estimation Using Time-Frequency Image Representation of EEG Signals, EEG Sinyallerinin Zaman-Frekans Imge G sterimleri Kullanilarak Begeni Durum Kestirimi(Institute of Electrical and Electronics Engineers Inc., 2025) Ceylan, Burak Gor; Cekic, Yalcin; Akan, Aydin I.; Ceylan, Burak Gor, Biyomedikal Mühendisliǧi, İstanbul Yeni Yüzyıl Üniversitesi, Zeytinburnu, Turkey; Cekic, Yalcin, Mekatronik Mühendisliǧi, Bahçeşehir Üniversitesi, Istanbul, Turkey; Akan, Aydin I., Elektrik ve Elektronik Mühendisliği Bölümü, Izmir Ekonomi Üniversitesi, Izmir, TurkeyIn recent years, there has been a significant increase in research on emotion and preference state estimation. In this study, a preference prediction method is proposed for use in neuromarketing studies by utilizing time-frequency (TF) energy distribution images derived from electroencephalogram (EEG) signals. EEG signals recorded while participants watched commercials from two different automobile brands were evaluated using deep learning techniques to estimate their preference states. After viewing the advertisements, participants were shown selected visual segments from the commercials (e.g., front view, dashboard, etc.) and asked to rate their preferences on a scale from 1 to 5. The EEG signals corresponding to these segments were transformed into two-dimensional RGB-scaled scalogram/spectrogram images using Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT). Using deep learning models, the proposed method achieved maximum classification accuracies of 86.61% and 87.26%, respectively. © 2025 Elsevier B.V., All rights reserved.Publication Metadata only A Comparative Study of Hybrid Recommender Systems: Integrating Collaborative Filtering and Transformer-Based Models for Cold-Start and Popularity Bias Mitigation(Institute of Electrical and Electronics Engineers Inc., 2025) Yilmaz, Esin Secil; Duru, Ismail; Yilmaz, Esin Secil, Samgen, Bahçeşehir University, Berlin, Germany; Duru, Ismail, Bahçeşehir Üniversitesi, Istanbul, TurkeyRecommender systems play a crucial role in delivering personalized content, however, they face challenges such as cold-start problems (CSP), data sparsity, and popularity bias (PB). This study shows a mixed method that combines collaborative filtering (CF) methods such as singular value decomposition++ (SVD++) and autoencoder-based model (AEM) with transformer-based models (TBM) such as E5-Large to improve recommendations. The hybrid model performs better on Amazon Gift Card Dataset (AGCD) than the standalone CF and Content-Based Filtering (CBF) methods in terms of precision (0.500) and recall (0.556). Although standalone SVD++ achieves the lowest RMSE (0.3645) and MAE (0.1468) due to its focus on interaction patterns, the hybrid model balances these metrics (RMSE=0.4258, MAE=0.1585) with its ability to mitigate CSP and PB through semantic embeddings and fusion of latent features. The results show that the hybrid framework is strong enough to deal with sparsity and bias while still being very accurate. This shows how important it is to combine deep learning with traditional recommendation. © 2025 Elsevier B.V., All rights reserved.