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4. Serrate RNA Effector Molecule (SRRT) Is Associated with Prostate Cancer Progression and Is a Predictor of Poor Prognosis in Lethal Prostate Cancer.

Author

Gamallat, Yaser, Choudhry, Muhammad, Li, Qiaowang, Rokne, Jon, Alhajj, Reda, Abdelsalam, Ramy, Ghosh, Sunita, Arbet, Jaron, Boutros, Paul, and Bismar, Tarek

Subjects
ARS2/SRRT (arsenite-resistance protein 2/serrate RNA effector molecule), ATM, ERG, PTEN, TP53, lethal prostate cancer
Abstract

Arsenite-resistance protein 2, also known as serrate RNA effector molecule (ARS2/SRRT), is known to be involved in cellular proliferation and tumorigenicity. However, its role in prostate cancer (PCa) has not yet been established. We investigated the potential role of SRRT in 496 prostate samples including benign, incidental, advanced, and castrate-resistant patients treated by androgen deprivation therapy (ADT). We also explored the association of SRRT with common genetic aberrations in lethal PCa using immunohistochemistry (IHC) and performed a detailed analysis of SRRT expression using The Cancer Genome Atlas (TCGA PRAD) by utilizing RNA-seq, clinical information (pathological T category and pathological Gleason score). Our findings indicated that high SRRT expression was significantly associated with poor overall survival (OS) and cause-specific survival (CSS). SRRT expression was also significantly associated with common genomic aberrations in lethal PCa such as PTEN loss, ERG gain, mutant TP53, or ATM. Furthermore, TCGA PRAD data revealed that high SRRT mRNA expression was significantly associated with higher Gleason scores, PSA levels, and T pathological categories. Gene set enrichment analysis (GSEA) of RNAseq data from the TCGA PRAD cohort indicated that SRRT may play a potential role in regulating the expression of genes involved in prostate cancer aggressiveness. Conclusion: The current data identify the SRRTs potential role as a prognostic for lethal PCa, and further research is required to investigate its potential as a therapeutic target.

Published
2023

6. AutoCOR: Autonomous Condylar Offset Ratio Calculator on TKA-Postoperative Lateral Knee X-ray

Author

Cakmak, Gulsade Rabia, Hamamci, Ibrahim Ethem, Yilmaz, Mehmet Kursat, Alhajj, Reda, Azboy, Ibrahim, and Ozdemir, Mehmet Kemal

Subjects
Computer Science - Computer Vision and Pattern Recognition, 92C55 (Primary)
Abstract

The postoperative range of motion is one of the crucial factors indicating the outcome of Total Knee Arthroplasty (TKA). Although the correlation between range of knee flexion and posterior condylar offset (PCO) is controversial in the literature, PCO maintains its importance on evaluation of TKA. Due to limitations on PCO measurement, two novel parameters, posterior condylar offset ratio (PCOR) and anterior condylar offset ratio (ACOR), were introduced. Nowadays, the calculation of PCOR and ACOR on plain lateral radiographs is done manually by orthopedic surgeons. In this regard, we developed a software, AutoCOR, to calculate PCOR and ACOR autonomously, utilizing unsupervised machine learning algorithm (k-means clustering) and digital image processing techniques. The software AutoCOR is capable of detecting the anterior/posterior edge points and anterior/posterior cortex of the femoral shaft on true postoperative lateral conventional radiographs. To test the algorithm, 50 postoperative true lateral radiographs from Istanbul Kosuyolu Medipol Hospital Database were used (32 patients). The mean PCOR was 0.984 (SD 0.235) in software results and 0.972 (SD 0.164) in ground truth values. It shows strong and significant correlation between software and ground truth values (Pearson r=0.845 p<0.0001). The mean ACOR was 0.107 (SD 0.092) in software results and 0.107 (SD 0.070) in ground truth values. It shows moderate and significant correlation between software and ground truth values (Spearman's rs=0.519 p=0.0001412). We suggest that AutoCOR is a useful tool that can be used in clinical practice., Comment: 9 pages

Published
2022

7. Twitter Credibility Score for Preventing Fake News Dissemination on Twitter

Author

Taher, Hamza, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, Özyer, Sibel Tarıyan, editor, and Kaya, Buket, editor

Published
2023
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8. Detecting Trending Topics and Influence on Social Media

Author

Onaran, Miray, Yılmaz, Ceren, Gülten, Feridun Cemre, Özyer, Tansel, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, Özyer, Sibel Tarıyan, editor, and Kaya, Buket, editor

Published
2023
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9. Parallelized Cyber Reconnaissance Automation: A Real-Time and Scheduled Security Scanner

Author

Malkawi, Malek, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, Özyer, Sibel Tarıyan, editor, and Kaya, Buket, editor

Published
2023
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12. Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images

Author

Sarhan, Abdullah, Rokne, Jon, Alhajj, Reda, and Crichton, Andrew

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The vascular structure of blood vessels is important in diagnosing retinal conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of these vessels can help in detecting retinal objects such as the optic disc and optic cup and hence determine if there are damages to these areas. Moreover, the structure of the vessels can help in diagnosing glaucoma. The rapid development of digital imaging and computer-vision techniques has increased the potential for developing approaches for segmenting retinal vessels. In this paper, we propose an approach for segmenting retinal vessels that uses deep learning along with transfer learning. We adapted the U-Net structure to use a customized InceptionV3 as the encoder and used multiple skip connections to form the decoder. Moreover, we used a weighted loss function to handle the issue of class imbalance in retinal images. Furthermore, we contributed a new dataset to this field. We tested our approach on six publicly available datasets and a newly created dataset. We achieved an average accuracy of 95.60% and a Dice coefficient of 80.98%. The results obtained from comprehensive experiments demonstrate the robustness of our approach to the segmentation of blood vessels in retinal images obtained from different sources. Our approach results in greater segmentation accuracy than other approaches., Comment: Accepted by ICPR. arXiv admin note: text overlap with arXiv:2010.00583

Published
2020

13. Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Author

Sarhan, Abdullah, Al-KhazÁly, Ali, Gorner, Adam, Swift, Andrew, Rokne, Jon, Alhajj, Reda, and Crichton, Andrew

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78\% and a Dice coefficient of 94.73\% for a disc segmentation from a retinal image in 0.03 seconds. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources., Comment: Accepted by ACCV 2020

Published
2020

14. A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Author

Islam, Md. Milon, Karray, Fakhri, Alhajj, Reda, and Zeng, Jia

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19., Comment: 18 pages, 2 figures, 4 Tables

Published
2020
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15. Covid-19 and Vaccine Tweet Analysis

Author

Alp, Eren, Gergin, Bedirhan, Eraslan, Yiğit Ahmet, Çakmak, Mert Can, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, and Özyer, Tansel, editor

Published
2022
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17. Convex Hull in Brain Tumor Segmentation

Author

Sailunaz, Kashfia, Bestepe, Deniz, Alhajj, Sleiman, Özyer, Tansel, Rokne, Jon, Alhajj, Reda, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mahmud, Mufti, editor, He, Jing, editor, Vassanelli, Stefano, editor, van Zundert, André, editor, and Zhong, Ning, editor

Published
2022
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18. Weather Big Data Analytics: Seeking Motifs in Multivariate Weather Data

Author

Xylogiannopoulos, Konstantinos F., Karampelas, Panagiotis, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, Morselli, Carlo, Advisory Editor, Çakırtaş, Mehmet, editor, and Ozdemir, Mehmet Kemal, editor

Published
2021
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19. Simplifying E-Commerce Analytics by Discovering Hidden Knowledge in Big Data Clickstreams

Author

Xylogiannopoulos, Konstantinos F., Karampelas, Panagiotis, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Birinci, Şuayip, editor, and Kawash, Jalal, editor

Published
2020
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20. User’s Research Interests Based Paper Recommendation System: A Deep Learning Approach

Author

Bulut, Betül, Gündoğan, Esra, Kaya, Buket, Alhajj, Reda, Kaya, Mehmet, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Birinci, Şuayip, editor, and Kawash, Jalal, editor

Published
2020
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21. Crowd Behavior Modeling in Emergency Evacuation Scenarios Using Belief-Desire-Intention Model

Author

Şahin, Coşkun, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Birinci, Şuayip, editor, and Kawash, Jalal, editor

Published
2020
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25. Global impact on human obesity – A robust non-linear panel data analysis.

Author

Munir, Mubbasher, Zakaria, Zahrahtul Amani, Baig, Atif Amin, Mohamad, Mumtazimah Binti, Arshed, Noman, and Alhajj, Reda

Abstract

Purpose: Recent studies in economics showed that humans are bounded rational. This being consumers, they are not perfect judges of what matters for the standard of living. While with a marked increase in economic and social wellbeing, there is a consistent rise in obesity levels, especially in the developed world. Thus, this study intends to explore the empirical and socio-economic antecedents of human obesity across countries using six global indexes. Methods: This study used the data of 40 countries between 1975 to 2018 and used the Panel FGLS Regression with the quadratic specification. Findings: The results showed that health and food indicators increase global human obesity, environment and education indicators decrease global human obesity, and economic and social indicators follow an inverted U-shaped pattern in affecting global human obesity. Originality: Previous studies have used infant mortality and life expectancy as the major health indicator in determining the standard of living while overlooking global human obesity as a major deterrent to welfare. This study has provided a holistic assessment of the causes of obesity in global contexts. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Global human obesity and political globalization; asymmetric relationship through world human development levels.

Author

Munir, Mubbasher, Zakaria, Zahrahtul Amani, Alhajj, Reda, Mohamad, Mumtazimah Binti, Baig, Atif Amin, and Arshed, Noman

Abstract

Purpose - Political globalization is a crucial and distinct component of strengthening global organizations. Obesity is a global epidemic in a few nations, and it is on the verge of becoming a pandemic that would bring plenty of diseases. This research aims to see how the political globalization index affects worldwide human obesity concerning global human development levels. Methods- To assess any cross-sectional dependence among observed 109 nations, the yearly period from 1990 to 2017 is analyzed using second generation panel data methods. KAO panel cointegration test and Fully Modified Least Square model were used to meet our objectives. Finding- Low level of political globalization tends to increase global human obesity because countries cannot sway international decisions and resources towards them. While the high level of political globalization tends to reduce obesity because it can control and amends international decisions. For the regression model, a fully modified Least Square model was utilized. The study observed that the R squared values for all models are healthy, with a minimum of 87 percent variables explaining differences in global obesity at the country level. Originality: There is very important to tackle the globalization issue to reduce global human obesity. With the simplicity of dietary options and the amount of physical labour they undergo in their agricultural duties, an increase in rural population percentage tends to lower the average national obesity value. [ABSTRACT FROM AUTHOR]

Published
2024
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27. H-OCS: A Hybrid Optic Cup Segmentation of Retinal Images

Author

Sarhan, Abdullah, Rokne, Jone, Alhajj, Reda, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tsapatsoulis, Nicolas, editor, Panayides, Andreas, editor, Theocharides, Theo, editor, Lanitis, Andreas, editor, Pattichis, Constantinos, editor, and Vento, Mario, editor

Published
2021
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28. Utilizing Digitized Surveys for Data Collection: The Case of Developing Countries

Author

Sarhan, Abdullah, Addam, Omar, Rokne, Jone, Alhajj, Reda, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Kapoor, Supriya, editor, and Bhatia, Rahul, editor

Published
2021
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29. Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Author

Sarhan, Abdullah, Al-Khaz’Aly, Ali, Gorner, Adam, Swift, Andrew, Rokne, Jon, Alhajj, Reda, Crichton, Andrew, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ishikawa, Hiroshi, editor, Liu, Cheng-Lin, editor, Pajdla, Tomas, editor, and Shi, Jianbo, editor

Published
2021
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30. Emergency Detection and Evacuation Planning Using Social Media

Author

Şahin, Coşkun, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Özyer, Tansel, editor, and Bakshi, Sambit, editor

Published
2019
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31. Social Network Analysis to Combat Terrorism: 2015 Paris Attacks

Author

Gupta, Animesh, Özyer, Tansel, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Özyer, Tansel, editor, and Bakshi, Sambit, editor

Published
2019
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32. Detecting Canadian Internet Satisfaction by Analyzing Twitter Accounts of Shaw Communications : Analyzing Twitter Accounts of Shaw Communications

Author

Cressy, Satara, Pham, B. L., Wright, Hannah, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, and Kaya, Mehmet, editor

Published
2019
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38. Recommender System for Product Avoidance

Author

Dhaliwal, Manmeet, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Moshirpour, Mohammad, editor, and Far, Behrouz H., editor

Published
2018
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39. Temporal Dependency Between Evolution of Features and Dynamic Social Networks

Author

Sailunaz, Kashfia, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Moshirpour, Mohammad, editor, and Far, Behrouz H., editor

Published
2018
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40. Finding Influential Factors for Different Types of Cancer: A Data Mining Approach

Author

Jahan, Munima, Noughabi, Elham Akhond Zadeh, Far, Behrouz H., Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Moshirpour, Mohammad, editor, and Far, Behrouz H., editor

Published
2018
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41. Predicting Future with Social Media Based on Sentiment and Quantitative Analysis

Author

Sharma, Sahil, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Moshirpour, Mohammad, editor, and Far, Behrouz H., editor

Published
2018
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42. Predicting Implicit Negative Relations in Online Social Networks

Author

Gupta, Animesh, Alhajj, Reda, Rokne, Jon, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Moshirpour, Mohammad, editor, and Far, Behrouz H., editor

Published
2018
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43. Text-Based Analysis of Emotion by Considering Tweets

Author

Sailunaz, Kashfia, Özyer, Tansel, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, and Özyer, Tansel, editor

Published
2018
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44. Dynamic Pattern Detection for Big Data Stream Analytics

Author

Xylogiannopoulos, Konstantinos F., Karampelas, Panagiotis, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Kawash, Jalal, editor, Khoury, Suheil, editor, and Day, Min-Yuh, editor

Published
2018
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45. Timelines of Prostate Cancer Biomarkers

Author

Jurca, Gabriela, Addam, Omar, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Kawash, Jalal, editor, Khoury, Suheil, editor, and Day, Min-Yuh, editor

Published
2018
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46. Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia

Author

Jurca, Gabriela, Addam, Omar, Rokne, Jon, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Kawash, Jalal, editor, Khoury, Suheil, editor, and Day, Min-Yuh, editor

Published
2018
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47. Combining Feature Extraction and Clustering for Better Face Recognition

Author

Afra, Salim, Alhajj, Reda, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Editorial Board Member, Brantingham, Patricia L., Editorial Board Member, Gross, Thilo, Editorial Board Member, Han, Jiawei, Editorial Board Member, Manásevich, Raúl, Editorial Board Member, Masys, Anthony J., Editorial Board Member, Morselli, Carlo, Editorial Board Member, Kaya, Mehmet, editor, Kawash, Jalal, editor, Khoury, Suheil, editor, and Day, Min-Yuh, editor

Published
2018
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50. Recent advances in crack detection technologies for structures: a survey of 2022-2023 literature.

Author

Kaveh, Hessam, Alhajj, Reda, Shah, Pritesh, and Kulkarni, Sanjay

Subjects
TRANSFORMER models, FRACTURE mechanics, INFRASTRUCTURE (Economics), MAINTENANCE costs, MACHINE learning
Abstract

Introduction: Cracks, as structural defects or fractures in materials like concrete, asphalt, and metal, pose significant challenges to the stability and safety of various structures. Addressing crack detection is of paramount importance due to its implications for public safety, infrastructure integrity, maintenance costs, asset longevity, preventive maintenance, economic impact, and environmental considerations. Methods: In this survey paper, we present a comprehensive analysis of recent advancements and developments in crack detection technologies for structures, with a specific focus on articles published between 2022 and 2023. Our methodology involves an exhaustive search of the Scopus database using keywords related to crack detection and machine learning techniques. Among the 129 papers reviewed, 85 were closely aligned with our research focus. Results: We explore datasets that underpin crack detection research, categorizing them as public datasets, papers with their own datasets, and those using a hybrid approach. The prevalence and usage patterns of public datasets are presented, highlighting datasets like Crack500, Crack Forest Dataset (CFD), and Deep Crack. Furthermore, papers employing proprietary datasets and those combining public and proprietary sources are examined. The survey comprehensively investigates the algorithms and methods utilized, encompassing CNN, YOLO, UNet, ResNet, and others, elucidating their contributions to crack detection. Evaluation metrics such as accuracy, precision, recall, F1-score, and IoU are discussed in the context of assessing model performance. The results of the 85 papers are summarized, demonstrating advancements in crack detection accuracy, efficiency, and applicability. Discussion: Notably, we observe a trend towards using modern and novel algorithms, such as Vision Transformers (ViT), and a shift away from traditional methods. The conclusion encapsulates the current state of crack detection research, highlighting the integration of multiple algorithms, expert models, and innovative data collection techniques. As a future direction, the adoption of emerging algorithms like ViT is suggested. This survey paper serves as a valuable resource for researchers, practitioners, and engineers working in the field of crack detection, offering insights into the latest trends, methodologies, and challenges. [ABSTRACT FROM AUTHOR]

Published
2024
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