| Issue Date | Title | Author(s) |
| 27-Aug-2024 | Accurate COVID-19 detection using full blood count data and machine learning | Yang, R; Chen, D; Yang, Q; Qiu, Y; Wang, F |
| 10-May-2024 | Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach | Starodubov, D; Danishvar, S; Abu Ebayyeh, AARM; Mousavi, A |
| 29-Mar-2022 | Boosting Iris Recognition by Margin-Based Loss Functions | Alinia Lat, R; Danishvar, S; Heravi, H; Danishvar, M |
| 12-Apr-2021 | Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks | Ahmed, HOA; Nandi, AK |
| 7-Jul-2022 | The Current State of the Art in Deep Learning for Image Classification: A Review | Byerly, A; Kalganova, T; Ott, R |
| 19-Sep-2020 | Deep learning in the construction industry: A review of present status and future innovations | Akinosho, TD; Oyedele, L; Bilal, M; Ajayi, A; Delgado, M; Akinade, O; Ahmed, A |
| 2-Apr-2020 | DO-U-Net for Segmentation and Counting: Applications to Satellite and Medical Images | Overton, T; Tucker, A |
| 2022 | Homogeneous vector capsules and their application to sufficient and complete data | Byerly, Adam D. |
| 6-Oct-2022 | A Machine Learning Based Model for Monitoring of Composites' Drilling-Induced Defects During Assembly Production Using Terahertz Imaging Data | Amini, A; Gan, TH |
| 18-Apr-2024 | Research and development of image processing algorithms for effective recognition of various gestures in real time | Boyko, N; Telishevskyi, P; Argyroudis, S |
| 27-Mar-2023 | A Review of Techniques on Gait-Based Person Re-Identification | Rahi, B; Li, M; Qi, M |
| 30-Nov-2024 | Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks | Geravanchizadeh, M; Shaygan Asl, A; Danishvar, S |
| 2023 | Skin Lesion Diagnosis Using Convolutional Neural Networks | Nunez, D; Li, Y |
| 11-Aug-2022 | Waveguide quality inspection in quantum cascade lasers: A capsule neural network approach | Abu Ebayyeh, AARM; Mousavi, A; Danishvar, S; Blaser, S; Gresch, T; Landry, O; Müller, A |
