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Publication Open Access Crop yield prediction using machine learning: A systematic literature review(Elsevier B.V., 2020) van Klompenburg, Thomas; Kassahun, Ayalew; Catal, Cagatay; van Klompenburg, Thomas, Information Technology Group, Wageningen University & Research, Wageningen, Netherlands; Kassahun, Ayalew, Information Technology Group, Wageningen University & Research, Wageningen, Netherlands; Catal, Cagatay, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyMachine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved 567 relevant studies from six electronic databases, of which we have selected 50 studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN). © 2020 Elsevier B.V., All rights reserved.Publication Metadata only Distracted driver detection by combining in-vehicle and image data using deep learning(Elsevier Ltd, 2020) Ömerustaoğlu, Furkan; Sakar, C. Okan; Kar, Gorkem; Ömerustaoğlu, Furkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kar, Gorkem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyDistracted driving is among the most important reasons for traffic accidents today. Recently, there is an increasing interest in building driver assistance systems that detect the actions of the drivers and help them drive safer. In these studies, although some distinct data types such as the physical conditions of the driver, audio and visual features, car information are used, the main data source is the images of the driver that include the face, arms, and hands taken with a camera placed inside the car. In this work, we propose to integrate sensor data into the vision-based distracted driver detection model to improve the generalization ability of the system. With this purpose, we created a new data set that includes driver images and sensor data collected from real-world drives. Then, we constructed a two-stage distracted driving detection system to detect nine distracted behaviors. In the first stage, vision-based Convolutional Neural Network (CNN) models were created by transfer learning and fine-tuning methods. In the second stage, Long-Short Term Memory-Recurrent Neural Network (LSTM-RNN) models were created using sensor and image data together. We evaluate our system by two different fusion techniques and show that integrating sensor data to image-based driver detection significantly increases the overall performance with both of the fusion techniques. We also show that the accuracy of the vision-based model increases by fine-tuning the pre-trained CNN model using a related public dataset. © 2020 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 Open Access CataractNet: An automated cataract detection system using deep learning for fundus images(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Islam, Md Baharul; Sadeghzadeh, Arezoo; Rahman, Saimunur; Junayed, Masum Shah, Daffodil International University, Dhaka, Bangladesh, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Daffodil International University, Dhaka, Bangladesh, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, Malta; Sadeghzadeh, Arezoo, Daffodil International University, Dhaka, Bangladesh; Rahman, Saimunur, Commonwealth Scientific and Industrial Research Organisation, Canberra, AustraliaCataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Struc, V.; Ivanovska, M.; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, MaltaWith the emergence of the global epidemic of COVID-19, face recognition systems have achieved much attention as contactless identity verification methods. However, covering a considerable part of the face by the mask poses severe challenges for conventional face recognition systems. This paper proposes an automated Masked Face Recognition (MFR) system based on the combination of a mask occlusion discarding technique and a deep-learning model. Initially, a pre-processing step is carried out in which the images pass three filters. Then, a Convolutional Neural Network (CNN) model is proposed to extract the features from unoccluded regions of the faces (i.e., eyes and forehead). These feature maps are employed to obtain covariance-based features. Two extra layers, i.e., Bitmap and Eigenvalue, are designed to reduce the dimension and concatenate these covariance feature matrices. The deep covariance features are quantized to codebooks combined based on Bag-of-Features (BoF) paradigm. Finally, a global histogram is created based on these codebooks and utilized for training an SVM classifier. The proposed method is trained and evaluated on Real-World-Masked-Face-Recognition-Dataset (RMFRD) and Simulated-Masked-Face-Recognition-Dataset (SMFRD) achieves an accuracy of 95.07% and 92.32 %, respectively, showing its competitive performance compared to the state-of-the-art. Experimental results prove that our system has high robustness against noisy data and illumination variations. © 2025 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 Open Access Three-Stream 3D deep CNN for no-Reference stereoscopic video quality assessment(Elsevier B.V., 2022) Imani, Hassan; Islam, Md Baharul; Arica, Nafiz; Imani, Hassan, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, Turkey; Arica, Nafiz, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, TurkeyConvolutional Neural Networks (CNNs) have achieved great success in learning computer vision tasks, particularly 3D CNNs, for extracting spatio-temporal features from the given videos. However, 3D CNNs have not been well-examined for the Stereoscopic Video Quality Assessment (SVQA). To our best knowledge, most of the state-of-the-art methods used the traditional hand-crafted feature extraction methods for the SVQA. Very few methods used the power of deep learning for SVQA, and they just considered the spatial information, ignoring the disparity and motion information. In this paper, we propose a No-Reference (NR) deep 3D CNN architecture that jointly focuses on spatial, disparity, and temporal information between consecutive frames. A 3-Stream 3D CNN, shortly 3S-3DCNN, by performing 3D CNNs, extracts features from spatial, motion, and depth channels to estimate the stereo video's quality. It captures the degradations in the quality of the stereoscopic video in multiple dimensions. Firstly, the scene flow, which is the joint prediction of the optical flow and stereo disparity, is calculated. Then, the spatial information, optical flow, and disparity map of a given video are used as input to the 3S-3DCNN model. The extracted features are concatenated and utilized as inputs to the fully connected layers for doing the regression. We split the input videos into cube patches for data augmentation and remove the cubes that confuse our model from the training and testing sets. Two standard stereoscopic video quality assessment benchmarks of LFOVIAS3DPh2 and NAMA3DS1-COSPAD1 were used to evaluate our method. Experimental results show that our 3S-3DCNN method's objective score significantly correlates with the subjective SVQ scores in multiple video datasets. The RMSE for NAMA3DS1-COSPAD1 dataset is 0.2757, which outperforms other methods by a large margin. The SROCC value for the blur distortion of the LFOVIAS3DPh2 dataset is more than 98%, indicating that the 3S-3DCNN is consistent with human visual perception. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Performance Assessment of Physiotherapy and Rehabilitation Exercises with Deep Learning, Derin Öǧrenme ile Fizyoterapi ve Rehabilitasyon Egzersizleri için Performans Deǧerlendirme(Institute of Electrical and Electronics Engineers Inc., 2022) Aytutuldu, Ilhan; Aydin, Tarkan; Aytutuldu, Ilhan, Bilgisayar Mühendisliǧi Bölümü, Gebze Teknik Üniversitesi, Gebze, Turkey; Aydin, Tarkan, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, TurkeyPhysiotherapy and rehabilitation process is critical for patients in postoperative recovery or the treatment of a wide variety of musculoskeletal disorders. However, providing access to a clinician for each rehabilitation session is a heavy burden and high cost to individuals. Also, it is very important to remotely check whether the exercises are performed correctly, especially during periods of lock-down due to the current pandemic, to provide motivation in the rehabilitation progress of the patients and to ensure that the recommended exercises contribute to the treatment. In this study, deep learning-based performance assessment of rehabilitation exercises has been proposed by using RGB videos obtained with low cost off-the-shelf cameras instead of high-cost, hard-to-reach depth cameras or wearable contact sensors. The proposed deep learning (DL) network models, PtConvNet, PtHybNet and PtBiLSTM, utilize three dimensional (3D) skeletal joint positions of patients extracted from exercise videos. Performance scores given by the physiotherapists have been used as the ground-truth in the training of the framework. We showed that the performance estimates of the learning models reliably follow the actual values and that the DL models confirm the ability to evaluate rehabilitation exercises. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network(Institute of Electrical and Electronics Engineers Inc., 2022) Alimanov, Alnur; Islam, Md Baharul; Alimanov, Alnur, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, TurkeyMedical imaging plays a significant role in detecting and treating various diseases. However, these images often happen to be of too poor quality, leading to decreased efficiency, extra expenses, and even incorrect diagnoses. Therefore, we propose a retinal image enhancement method using a vision transformer and convolutional neural network. It builds a cycle-consistent generative adversarial network that relies on unpaired datasets. It consists of two generators that translate images from one domain to another (e.g., low- to high-quality and vice versa), playing an adversarial game with two discriminators. Generators produce indistinguishable images for discriminators that predict the original images from generated ones. Generators are a combination of vision transformer (ViT) encoder and con-volutional neural network (CNN) decoder. Discriminators include traditional CNN encoders. The resulting improved images have been tested quantitatively using such evaluation metrics as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and qualitatively, i.e., vessel segmentation. The proposed method successfully reduces the adverse effects of blurring, noise, illumination disturbances, and color distortions while signifi-cantly preserving structural and color information. Experimental results show the superiority of the proposed method. Our testing PSNR is 31.138 dB for the first and 27.798 dB for the second dataset. Testing SSIM is 0.919 and 0.904, respectively. The code is available at https://github.com/AAleka/Transformer-Cycle-GAN © 2023 Elsevier B.V., All rights reserved.Publication Metadata only Audio Speech Signal Analysis for Early Autism Spectrum Disorder Detection(Institute of Electrical and Electronics Engineers Inc., 2023) Jaby, Assil; Islam, Md Baharul; Jaby, Assil, Computer Vision Lab, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, Florida Gulf Coast University, Fort Myers, United StatesAutism Spectrum Disorder (ASD) is a neurodevel-opmental disorder that affects social interaction, communication, and behavior. Recent studies have shown that vocal and auditory features can be used to identify people with ASD. However, existing approaches are limited by their subjective nature, reliance on expert interpretation, and the laborious process of data gathering. This study presents a deep learning-based technique for ASD detection, which utilizes a hybrid vision transformer and convolutional neural network (CNN) architecture. The Swin transformer extracts high-level features and attention maps from audio samples, which the CNN uses as input. We trained and evaluated our model using audio samples from both ASD and typically developing (TD) children, achieving competitive accuracy in distinguishing between the two groups. Our findings suggest that the proposed method has the potential to complement existing diagnostic tools and improve early ASD detection in children. Moreover, our results indicate that deep learning-based techniques and standard diagnostic tools can offer a reliable and objective approach to detecting ASD. The suggested model could be implemented in clinical settings as a screening tool to aid in the early diagnosis of ASD. © 2023 Elsevier B.V., All rights reserved.