Hasan Ogul

• Formoe, Lars; Mygind, Dan Bruun; Løkke, Espen & Ogul, Hasan (2023). Unsupervised Learning for Automatic Speech Recognition in Air Traffic Control Environment. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14102, p. 239–248. doi: 10.1007/978-3-031-40498-6_21.
• Ogundokun, Roseline Oluwaseun; Misra, Sanjay; Akinrotimi, Akinyemi Omololu & Ogul, Hasan (2023). MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. Sensors. ISSN 1424-8220. 23(2). doi: 10.3390/s23020656. Show summary

  Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.

• Aşuroğlu, Tunç & Ogul, Hasan (2022). A deep learning approach for parkinson’s disease severity assessment. Health and Technology. ISSN 2190-7188. 12(5), p. 943–953. doi: 10.1007/s12553-022-00698-z.
• Dede Şener, Duygu; Ogul, Hasan & Basak, Selen (2022). Text-based experiment retrieval in genomic databases. Journal of information science. ISSN 0165-5515. doi: 10.1177/01655515221118670.
• Bayrak, Tuncay; Çetin, Zafer; Saygılı, E. İlker & Ogul, Hasan (2022). Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach. Medical and Biological Engineering and Computing. ISSN 0140-0118. 60(10), p. 2877–2897. doi: 10.1007/s11517-022-02641-w.
• Assidicky, Ridho; Tokat, Unal Metin; Tarman, Ibrahim Oguzhan; Saatci, Ozge; Ersan, Pelin Gulizar & Raza, Umar [Show all 10 contributors for this article] (2022). Targeting HIF1-alpha/miR-326/ITGA5 axis potentiates chemotherapy response in triple-negative breast cancer. Breast Cancer Research and Treatment. ISSN 0167-6806. 193(2), p. 331–348. doi: 10.1007/s10549-022-06569-5.
• Paker, Ahmet & Ogul, Hasan (2021). A Deep Learning Model for MicroRNA-Target Binding. In Elloumi, Mourad (Eds.), Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications. Springer. ISSN 978-3-030-71675-2. p. 61–79. doi: doi.org/10.1007/978-3-030-71676-9_3.
• Chand, Dipesh & Ogul, Hasan (2021). A Framework for Lecture Video Segmentation from Extracted Speech Content. In Kovacs, Levente (Eds.), IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-8054-0. p. 000299–000304. doi: 10.1109/SAMI50585.2021.9378632. Show summary

  Increasing demand for lecture videos in digital libraries has raised the challenge of automatic annotation of lecture content for effective navigation of lectures by users. One direction is the prior segmentation of lecture videos to simplify several applications such as indexing, keyword spotting, and targeted search. In this study, we present a lecture video segmentation framework based on the speech content of the instructors. The framework is built upon a model that extracts textual and acoustic features from speech and uses them to identify topical segment boundaries of the lecture video. To evaluate our proposed model, we collected our own dataset containing a diverse set of 37 lecture videos and also manually created ground truth. The performance was measured by using metrics like Precision, Recall, and F1 Score and obtained 0.69, 0.58, and 0.63 respectively. We also compared our model with some previously known similar models where our model outperformed others. The overall results of the study are presented as a lecture video segmentation model, integrating various tools and techniques, and showing promising performance. Findings can be used further for research in content-based search and retrieval using speech content.

• Balci, Tolgay & Ogul, Hasan (2021). Predicting Bank Return on Equity (ROE) using Neural Networks. In Kovacs, Levente (Eds.), IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-8054-0. p. 000279–000286. doi: 10.1109/SAMI50585.2021.9378636.
• Bayrak, Tuncay & Ogul, Hasan (2021). Computer-aided diagnosis of sleep apnea using gene expression. Health and Technology. ISSN 2190-7188. 11, p. 941–952. doi: 10.1007/s12553-021-00557-3.
• Aşuroğlu, Tunç & Ogul, Hasan (2021). A Deep Learning Approach for Sepsis Monitoring via Severity Score Estimation. Computer Methods and Programs in Biomedicine. ISSN 0169-2607. 198. doi: 10.1016/j.cmpb.2020.105816.
• Agziyagli, Veysel Sercan & Ogul, Hasan (2020). Multi-level lecture video classification using text content. In Adamov, Abzetdin (Eds.), 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT): Conference proceedings. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7386-3. doi: 10.1109/AICT50176.2020.9368692.
• Chand, Dipesh & Ogul, Hasan (2020). Content-Based Search in Lecture Video: A Systematic Literature Review. In Huang, May; Gokhale, Anu & Zhang, Sen (Ed.), 3rd International Conference on Information and Computer Technologies (ICICT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7283-5. p. 169–176. doi: 10.1109/ICICT50521.2020.00034.
• Cabıoğlu, Çağrı & Ogul, Hasan (2020). Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12108, p. 716–726. doi: 10.1007/978-3-030-45385-5_64. Show summary

  Breast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are obtained by using the temperature difference of regions without giving radiation by the thermal camera. In this study, we present methods for computer aided diagnosis of breast cancer using thermal images. To this end, various Convolutional Neural Networks (CNNs) have been designed by using transfer learning methodology. The performance of the designed nets was evaluated on a benchmarking dataset considering accuracy, precision, recall, F1 measure, and Matthews Correlation coefficient. The results show that an architecture holding pre-trained convolutional layers and training newly added fully connected layers achieves a better performance compared with others. We have obtained an accuracy of 94.3%, a precision of 94.7% and a recall of 93.3% using transfer learning methodology with CNN.

• Erdogan, Burak & Ogul, Hasan (2020). Objective Pain Assessment Using Vital Signs. Procedia Computer Science. ISSN 1877-0509. 170, p. 947–952. doi: 10.1016/j.procs.2020.03.103. Full text in Research Archive
• Baldominos, Alejandro; Puello, Adrián; Ogul, Hasan; Aşuroğlu, Tunç & Colomo-Palacios, Ricardo (2020). Predicting infections using computational intelligence – A systematic review. IEEE Access. ISSN 2169-3536. 8, p. 31083–31102. doi: 10.1109/ACCESS.2020.2973006. Full text in Research Archive
• Baldominos, Alejandro; Ogul, Hasan; Colomo-Palacios, Ricardo; Sanz-Moreno, José & Gómez-Pulido, José Manuel (2020). Infection Prediction using Physiological and Social Data in Social Environments. Information Processing & Management. ISSN 0306-4573. 57(3). doi: 10.1016/j.ipm.2020.102213. Show summary

  The ability of detecting infections at an early stage in clinical environments is an important clinical problem. When an infection is not diagnosed on time, it may not only affect the health of the infected patient, but also spread and infect other people. In this paper, we propose the development of a clinical decision support system (CDSS) for diagnosing infections using clinical signals from patients. This system is designed to be able to cope with small amounts of data (a single record per day and patient), making it convenient for environments under strict constraints (such as low resources or bad connectivity). Additionally, we have incorporated data from external sources, in order to enrich the quality of the models. In particular, we have considered social data arising from web searches, retrieved from Google Trends, as well as weather data.

• Baldominos, Alejandro; Ogul, Hasan & Colomo-Palacios, Ricardo (2019). Infection diagnosis using biomedical signals in small data scenarios. IEEE International Symposium on Computer-Based Medical Systems. ISSN 2372-9198. p. 38–43. doi: 10.1109/CBMS.2019.00018. Show summary

  Being able to detect an infection at an early stage is a clinical problem of the utmost importance. An infection not diagnosed on time might not only severely affect the infected patient's health, but also to spread and start a focus of contagion to other people. In this paper, we propose a clinical decision support system to automatically diagnose infections using physiological signals from the patients. The focus of the system is put on being able to deal with very small amounts of data (one aggregated record per patient and day), which eases the potential of the system in environments with low resources. Data has been acquired between April 2018 and January 2019 in two nursing homes in Spain, where nurses had also tested patients for infections. Machine learning models have then been created by aggregating measurements from days prior to the infection (lead) and after the infection started (lag) in order to generate features. The best model attained reports an AUROC of 0.722, using data from up to two days after the infection started. Interestingly, an AUROC of up to 0.692 is achieved when infection prognosis is considered; i.e., using only measurements prior to the manual recording of the infection to compose the dataset.

• Paker, Ahmet & Ogul, Hasan (2019). mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 1062, p. 38–44. doi: 10.1007/978-3-030-27684-3_6.
• Acici, Koray; Asuroglu, Tunc; Erdas, Cagatay Berke & Ogul, Hasan (2019). T4SS Effector Protein Prediction with Deep Learning. Data. ISSN 2306-5729. 4(1). doi: 10.3390/data4010045. Full text in Research Archive
• Bayrak, Tuncay & Ogul, Hasan (2019). A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering. Current Bioinformatics. ISSN 1574-8936. 14(6), p. 480–490. doi: 10.2174/1574893614666190126144139.
• Haberal, Ismail & Ogul, Hasan (2019). Prediction of Protein Metal Binding Sites Using Deep Neural Networks. Molecular Informatics. ISSN 1868-1743. 38(7). doi: 10.1002/minf.201800169.
• Ogul, Hasan; Baldominos, Alejandro; Aşuroğlu, Tunç & Colomo-Palacios, Ricardo (2019). On Computer-Aided Prognosis of Septic Shock from Vital Signs. In Rodriguez, Alejandro (Eds.), 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS): Proceedings of a meeting held 5-7 June 2019, Cordoba, Spain. IEEE conference proceedings. ISSN 978-1-7281-2286-1. p. 87–92. doi: https:/doi.org/10.1109/CBMS.2019.00028. Show summary

  Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.

• Ogul, Hasan (2018). ALoT: A Time-Series Similarity Measure Based on Alignment of Textures. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11314 LNCS, p. 576–585. doi: 10.1007/978-3-030-03493-1_60. Show summary

  Inferring the similarity between two time-series signals is a key step in several data analysis tasks for a variety of engineering applications. In this paper, we introduce a novel elastic similarity measure (ALoT) based on the alignment of textures, instead of observed values, extracted from input time-series signals. To obtain the texture information, Local Binary Patterns are adapted for one-dimensional signals. According to experiments performed on a large number of benchmark time-series classification datasets, the proposed method achieves higher accuracy than current pairwise similarity measures for several cases in a 1-Nearest Neighbor classification setup.

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• Ogul, Hasan & Moræus, Jonas Aamodt (2024). 2024 4th International Conference on Applied Artificial Intelligence (ICAPAI). IEEE conference proceedings. ISBN 979-8-3503-4976-4. 240 p.
• Ogul, Hasan; Aamodt, Jonas Moræus; Misra, Sanjay & Kjølerbakken, Kai Morgan (2024). 2024 4th International Conference on Applied Artificial Intelligence (ICAPAI). IEEE conference proceedings. ISBN 979-8-3503-4976-4. 300 p.

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• Gunel, A; Guler, U; Erkan, B; Idrisoglu, H; Mazlum, V & Salih, B [Show all 8 contributors for this article] (2022). Benchmarking proteome of extracellular vesicles from plasma of fALS-associated SOD1 mutant patients and SOD1 mutant healthy controls. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration. ISSN 2167-8421. 23, p. 102–102. doi: 10.1080/21678421.2022.2120682.

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