Turgay Celik

• Moghimi, Armin; Welzel, Mario; Celik, Turgay & Schlurmann, Torsten (2024). A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery. IEEE Access. ISSN 2169-3536. 12, p. 52067–52085. doi: 10.1109/ACCESS.2024.3385425.
• Ahsan Ashraf, Muhammad & Celik, Turgay (2024). Evaluating radiofrequency electromagnetic field exposure in confined spaces: a systematic review of recent studies and future directions. Radiation Protection Dosimetry. ISSN 0144-8420. 200(6), p. 598–616. doi: 10.1093/rpd/ncae045.
• Moghimi, Armin; Sadeghi, Vahid; Mohsenifar, Amin; Celik, Turgay & Mohammadzadeh, Ali (2024). LIRRN: Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images. Sensors. ISSN 1424-8220. 24(7). doi: 10.3390/s24072272.
• Essa, Saadia Gutta; Celik, Turgay & Human-Hendricks, Nadia Emelia (2023). Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review. IEEE Access. ISSN 2169-3536. 11, p. 48392–48409. doi: 10.1109/ACCESS.2023.3276439. Full text in Research Archive Show summary

  Artificial intelligence (AI) approaches have been used in personalised adaptive education systems to overcome the limitations of statically determined learning styles (LSs). These approaches utilise algorithms from machine learning (ML) to tackle the challenge of personalising e-learning by mapping students’ behavioural attributes to a particular LS automatically and dynamically to optimise the individual learning process. Motivated by the many influential studies in this field and the current developments in ML and AI, a comprehensive systematic literature review was conducted from 2015 to 2022. Influential scientific literature was analysed to identify the emerging trends and gaps in the literature in terms of LS models and possible ML techniques employed for personalised adaptive learning platforms. The outcomes of this paper include a review and analysis of the current trends of this emerging field in terms of the applications and developments in using ML approaches to implement more intelligent and adaptive e-learning environments to detect learners’ LSs automatically for enhancing learning. In addition, the following issues were also investigated: the platforms that stimulated research; identifying LS models utilised in e-learning; the evaluation methods used; and the learning supports provided. The results indicated an increasing interest in using artificial neural network approaches to identify LSs. However, limited work has been conducted on the comparison of deep learning methods in this context. The findings suggest the need to consider and stimulate further empirical investigation in documenting the adoption and comparison of deep learning algorithms in classifying LSs to provide higher adaptability.

• Mabokela, Koena Ronny; Celik, Turgay & Raborife, Mpho (2023). Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape. IEEE Access. ISSN 2169-3536. 11, p. 15996–16020. doi: 10.1109/ACCESS.2022.3224136. Full text in Research Archive Show summary

  Sentiment analysis automatically evaluates people’s opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis techniques for under-resourced languages and the use of high-resourced languages to develop resources for low-resource languages. The ultimate goal is to identify appropriate strategies for future investigations. We report over 35 studies with different languages demonstrating an interest in developing models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for sentiment analysis. Our focus is to critically compare methods, employed datasets and identify research gaps. This study contributes to theoretical literature reviews with complete coverage of multilingual sentiment analysis studies from 2008 to date. Furthermore, we demonstrate how sentiment analysis studies have grown tremendously. Finally, because most studies propose methods based on deep learning approaches, we offer a deep learning framework for multilingual sentiment analysis that does not rely on the machine translation system. According to the meta-analysis protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the sentiment analysis performance. Therefore, deep learning methods are recommended for the development of multilingual sentiment analysis for under-resourced languages.

• Wanyonyi, David & Celik, Turgay (2022). Open-Source Face Recognition Frameworks: A Review of the Landscape. IEEE Access. ISSN 2169-3536. 10, p. 50601–50623. doi: 10.1109/ACCESS.2022.3170037. Full text in Research Archive
• Moghimi, Armin; Celik, Turgay & Mohammadzadeh, Ali (2022). Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images. International Journal of Remote Sensing. ISSN 0143-1161. 43(11), p. 3927–3956. doi: 10.1080/01431161.2022.2102951. Show summary

  In some remote sensing applications, such as unsupervised change detection, bitemporal multispectral images must be first aligned/harmonized radiometrically. For doing so, Many Relative Radiometric Normalization (RRN) algorithms exist; however, most suffer from misregistration problems and can only operate on geo/co-registered image pairs, while unregistered multispectral pairs are required. To tackle this situation, keypoint-based RRN methods were introduced, which can radiometrically calibrate unregistered/registered image pairs using keypoint matching algorithms. However, they ignore the spatial and spectral characteristics of spectral bands of input images, resulting in potential RRN errors. They also employ a linear mapping function for RRN modelling, which can not handle non-linear radiometric distortions. To address these limitations, this paper proposes a robust algorithm for RRN of bitemporal multispectral images, using a new extension of SURF detector for multispectral images, namely the Weighted Spectral Structure Tensor SURF (WSST-SURF), and a flexible Switching Regression (SR) model. Taking advantage of the tensor theory, WSST-SURF efficiently preserves both spatial and spectral information distributed over all bands of multispectral images for keypoint detection, resulting in extracting reliable inliers (or keypoints) for RRN. An adaptive SR model is introduced based on the normalized mutual information, accurately approximating a linear/non-linear relationship between inliers in multispectral images. Six unregistered multispectral image pairs captured by inter/intra remote sensing sensors were employed to validate the efficacy of the proposed method. The results indicate that adopting spectral tensor-based SURF methods in the RRN process exhibits better local and global performance than using the original SURF. Furthermore, the proposed method outperforms the existing conventional RRN methods in terms of accuracy and visual quality, indicating its competence for RRN of bitemporal multispectral images with high illumination, viewpoint, and scale differences.

• Dastile, Xolani; Celik, Turgay & Vandierendonck, Hans (2022). Model-Agnostic Counterfactual Explanations in Credit Scoring. IEEE Access. ISSN 2169-3536. 10, p. 69543–69554. doi: 10.1109/ACCESS.2022.3177783. Full text in Research Archive
• Zhao, Yitao; Celik, Turgay; Liu, Nanqing & Li, Heng-Chao (2022). A Comparative Analysis of GAN-Based Methods for SAR-to-Optical Image Translation. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. 19. doi: 10.1109/LGRS.2022.3177001. Show summary

  Unlike optical sensors, synthetic aperture radar (SAR) sensors acquire images of the Earth’s surface with all-weather and all-time capabilities, which is vital in a situation such as a disaster assessment. However, SAR sensors do not offer as rich visual information as optical sensors. SAR-to-optical image-to-image translation generates optical images from SAR images to benefit from what both imaging modalities have to offer. It also enables multisensor image analysis of the same scene for applications such as heterogeneous change detection. Various architectures of generative adversarial networks (GANs) have achieved remarkable image-to-image translation results in different domains. Still, their performances in SAR-to-optical image translation have not been analyzed in the remote-sensing domain. This letter compares and analyzes the state-of-the-art GAN-based translation methods with open-source implementations for SAR-to-optical image translation. The results show that GAN-based SAR-to-optical image translation methods achieve satisfactory results. However, their performances depend on the structural complexity of the observed scene and the spatial resolution of the data. We also introduce a new dataset with a higher resolution than the existing SAR-to-optical image datasets and release implementations of GAN-based methods considered in this letter to support the reproducible research in remote sensing.

• Yavariabdi, Amir; Kusetogullari, Huseyin; Celik, Turgay; Thummanapally, Shivani; Rijwan, Sakib & Hall, Johan (2022). CArDIS: A Swedish Historical Handwritten Character and Word Dataset. IEEE Access. ISSN 2169-3536. 10, p. 55338–55349. doi: 10.1109/ACCESS.2022.3175197. Full text in Research Archive Show summary

  This paper introduces a new publicly available image-based Swedish historical handwritten character and word dataset named C haracter Ar kiv D igital S weden (CArDIS) ( https://cardisdataset.github.io/CARDIS/ ). The samples in CArDIS are collected from 64, 084 Swedish historical documents written by several anonymous priests between 1800 and 1900. The dataset contains 116, 000 Swedish alphabet images in RGB color space with 29 classes, whereas the word dataset contains 30, 000 image samples of ten popular Swedish names as well as 1, 000 region names in Sweden. To examine the performance of different machine learning classifiers on CArDIS dataset, three different experiments are conducted. In the first experiment, classifiers such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbor (k-NN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Random Forest (RF) are trained on existing character datasets which are Extended Modified National Institute of Standards and Technology (EMNIST), IAM and CVL and tested on CArDIS dataset. In the second and third experiments, the same classifiers as well as two pre-trained VGG-16 and VGG-19 classifiers are trained and tested on CArDIS character and word datasets. The experiments show that the machine learning methods trained on existing handwritten character datasets struggle to recognize characters efficiently on the CArDIS dataset, proving that characters in the CArDIS contain unique features and characteristics. Moreover, in the last two experiments, the deep learning-based classifiers provide the best recognition rates.

• Moghimi, Armin; Mohammadzadeh, Ali; Celik, Turgay; Brisco, Brian & Amani, Meisam (2022). Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies. Remote Sensing. ISSN 2072-4292. 14(8). doi: 10.3390/rs14081777. Full text in Research Archive Show summary

  Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining the spectral correlation, spectral angle mapper (SAM), and Chebyshev distance. This index was then categorized into three groups of changed, unchanged, and uncertain classes using a fast multiple thresholding technique. In the fine stage, the subject image was first segmented into different clusters by the histogram-based fuzzy c-means (HFCM) algorithm. The optimal PIFs were then selected from unchanged and uncertain regions using each cluster’s bivariate joint distribution analysis. In the RRN modeling step, two normalized subject images were first produced using the robust linear regression (RLR) and cluster-wise-RLR (CRLR) methods based on the clustered PIFs. Finally, the normalized images were fused using the Choquet fuzzy integral fusion strategy for overwhelming the discontinuity between clusters in the final results and keeping the radiometric rectification optimal. Several experiments were implemented on four different bi-temporal satellite images and a simulated dataset to demonstrate the efficiency of the proposed method. The results showed that the proposed method yielded superior RRN results and outperformed other considered well-known RRN algorithms in terms of both accuracy level and execution time.

• Mokoena, Tshepiso; Celik, Turgay & Marivate, Vukosi (2022). Why is this an anomaly? Explaining anomalies using sequential explanations. Pattern Recognition. ISSN 0031-3203. 121. doi: 10.1016/j.patcog.2021.108227. Show summary

  In most applications, anomaly detection operates in an unsupervised mode by looking for outliers hoping that they are anomalies. Unfortunately, most anomaly detectors do not come with explanations about which features make a detected outlier point anomalous. Therefore, it requires human analysts to manually browse through each detected outlier point’s feature space to obtain the subset of features that will help them determine whether they are genuinely anomalous or not. This paper introduces sequential explanation (SE) methods that sequentially explain to the analyst which features make the detected outlier anomalous. We present two methods for computing SEs called the outlier and sample-based SE that will work alongside any anomaly detector. The outlier-based SE methods use an anomaly detector’s outlier scoring measure guided by a search algorithm to compute the SEs. Meanwhile, the sample-based SE methods employ sampling to turn the problem into a classical feature selection problem. In our experiments, we compare the performances of the different outlier- and sample-based SEs. Our results show that both the outlier and sample-based methods compute SEs that perform well and outperform sequential feature explanations.

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• Moghimi, Armin; Mohammadzadeh, Ali; Celik, Turgay; Brisco, Brian & Amani, Meisam (2022). Correction: Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies(Remote Sens., (2022), 14, (1777), 10.3390/rs14081777). Remote Sensing. ISSN 2072-4292. 14(22). doi: 10.3390/rs14225898.

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