Intraoperative facial and intraoral photographs of the case with mandibular third molar extraction. حسب Lijuan Liu (136685)

Third. حسب Cuicui Liu (1496260)

Posttreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. حسب Lijuan Liu (136685)

Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. حسب Lijuan Liu (136685)

Superimposition of the cephalometric tracings obtained before and after treatment of case with mandibular third molar extraction. حسب Lijuan Liu (136685)

Comparison of pretreatment cephalometric variables between premolar extraction and mandibular third molar extraction groups. حسب Lijuan Liu (136685)

Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups. حسب Lijuan Liu (136685)

Comparison of treatment effects between premolar extraction and mandibular third molar extraction groups. * p < 0.05; ** p < 0.01. حسب Lijuan Liu (136685)

Dynamics of net spillage in the third stage (post epidemic). حسب Cuicui Liu (1496260)

CNN Model Architecture. حسب Mudhafar Jalil Jassim Ghrabat (22177655)

الموضوعات:

Changes in total spillage during the third stage (post epidemic). حسب Cuicui Liu (1496260)

Flowchart of Proposed SMO_CNN. حسب Mudhafar Jalil Jassim Ghrabat (22177655)

الموضوعات:

CNN Model Layers Summary. حسب Mudhafar Jalil Jassim Ghrabat (22177655)

الموضوعات:

Training-induced changes in bipedal stance. حسب Cristina Dolciotti (16317330)

Consolidation of training-induced changes in unipedal stance: 95% Area, Y Sway Amplitude and LFS. حسب Cristina Dolciotti (16317330)

Consolidation of training-induced changes in unipedal stance: Path Length, X Sway Amplitude and Velocity. حسب Cristina Dolciotti (16317330)

Dynamics of pairwise net spillovers between sectors in the third stage (post epidemic). حسب Cuicui Liu (1496260)

Performance Results of Proposed SMO_CNN Model. حسب Mudhafar Jalil Jassim Ghrabat (22177655)

الموضوعات:

Validation of child labor measures using third-party data, households with all children in school. حسب Guilherme Lichand (11410286)

CNN framework. حسب Sajid Mehmood (6689843)

"…We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. …"