| Integrating features for accelerometer-based activity recognition ÇB Erdaş, I Atasoy, K Açıcı, H Oğul Procedia Computer Science 98, 522-527, 2016 | 110 | 2016 |
| Parkinson's disease monitoring from gait analysis via foot-worn sensors T Aşuroğlu, K Açıcı, ÇB Erdaş, MK Toprak, H Erdem, H Oğul Biocybernetics and Biomedical Engineering 38 (3), 760-772, 2018 | 71 | 2018 |
| A random forest method to detect Parkinson’s disease via gait analysis K Açıcı, ÇB Erdaş, T Aşuroğlu, MK Toprak, H Erdem, H Oğul Engineering Applications of Neural Networks: 18th International Conference …, 2017 | 61 | 2017 |
| Neurodegenerative disease detection and severity prediction using deep learning approaches ÇB Erdaş, E Sümer, S Kibaroğlu Biomedical Signal Processing and Control 70, 103069, 2021 | 34 | 2021 |
| Human activity recognition by using different deep learning approaches for wearable sensors ÇB Erdaş, S Güney Neural Processing Letters 53 (3), 1795-1809, 2021 | 28 | 2021 |
| A deep LSTM approach for activity recognition S Güney, ÇB Erdaş 2019 42nd International Conference on Telecommunications and Signal …, 2019 | 27 | 2019 |
| CNN-based severity prediction of neurodegenerative diseases using gait data Ç Berke Erdaş, E Sümer, S Kibaroğlu Digital Health 8, 20552076221075147, 2022 | 23 | 2022 |
| A machine learning-based approach to detect survival of heart failure patients ÇB Erdaş, D Ölçer 2020 Medical Technologies Congress (TIPTEKNO), 1-4, 2020 | 17 | 2020 |
| HANDY: A benchmark dataset for context-awareness via wrist-worn motion sensors K Açıcı, ÇB Erdaş, T Aşuroğlu, H Oğul Data 3 (3), 24, 2018 | 17 | 2018 |
| Detection of cataract, diabetic retinopathy and glaucoma eye diseases with deep learning approach G ARSLAN, ÇB Erdaş Intelligent Methods In Engineering Sciences 2 (2), 42-47, 2023 | 16 | 2023 |
| A fully automated approach involving neuroimaging and deep learning for Parkinson’s disease detection and severity prediction ÇB Erdaş, E Sümer PeerJ Computer Science 9, e1485, 2023 | 12 | 2023 |
| T4SS effector protein prediction with deep learning K Açıcı, T Aşuroğlu, ÇB Erdaş, H Oğul Data 4 (1), 45, 2019 | 12 | 2019 |
| Neurodegenerative diseases detection and grading using gait dynamics ÇB Erdaş, E Sümer, S Kibaroğlu Multimedia tools and applications 82 (15), 22925-22942, 2023 | 10 | 2023 |
| Texture of activities: exploiting local binary patterns for accelerometer data analysis T Aşuroğlu, K Açici, ÇB Erdaş, H Oğul 2016 12th International Conference on Signal-Image Technology & Internet …, 2016 | 9 | 2016 |
| A deep learning method to detect Parkinson’s disease from MRI slices ÇB Erdaş, E Sümer SN Computer Science 3 (2), 120, 2022 | 7 | 2022 |
| Detection and differentiation of COVID-19 using deep learning approach fed by x-rays ÇB Erdaş, D Ölçer International Journal of Applied Mathematics Electronics and Computers 8 (3 …, 2020 | 6 | 2020 |
| A deep learning-based approach to detect neurodegenerative diseases ÇB Erdaş, E Sümer 2020 Medical Technologies Congress (TIPTEKNO), 1-4, 2020 | 5 | 2020 |
| Enhancing Skin Disease Diagnosis Through Deep Learning: A Comprehensive Study on Dermoscopic Image Preprocessing and Classification ENH Kırğıl, ÇB Erdaş International Journal of Imaging Systems and Technology 34 (4), e23148, 2024 | 3 | 2024 |
| CNN‐Based Neurodegenerative Disease Classification Using QR‐Represented Gait Data ÇB Erdaş, E Sümer Brain and Behavior 14 (10), e70100, 2024 | 2 | 2024 |
| Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning E Kıran Yenice, C Kara, ÇB Erdaş Eye 38 (13), 2644-2648, 2024 | 2 | 2024 |
