One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

Kinematic model of planting mechanism. by Jian Kang (118630)

“…Third, we formulated a multi-objective optimization problem to maintain consistent planting depth, seedling uprightness, and adequate trajectory height across three spacing settings (350, 400 and 450 mm) and solved it with a multi-objective genetic algorithm (MOGA) to obtain Pareto-optimal structural parameters. …”

Video_1_Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling.MP4 by Binbin Su (640741)

Synthetic walking data results. by Keenon Werling (17512156)

“…We have published the algorithm as an open source cloud service at <a href="https://addbiomechanics.org" target="_blank">AddBiomechanics.org</a>, which is available at no cost and asks that users agree to share processed and de-identified data with the community. …”

Video_1_Myocardial strain analysis of echocardiography based on deep learning.MP4 by Yinlong Deng (14268449)

“…We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. …”

Video_2_Myocardial strain analysis of echocardiography based on deep learning.MP4 by Yinlong Deng (14268449)

“…We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. …”

Data_Sheet_1_Myocardial strain analysis of echocardiography based on deep learning.docx by Yinlong Deng (14268449)

“…We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. …”

Akt1 prodigiosin docking and dynamic molecular by Fares ELGHALI (18467655)

“…Long-range electrostatic interactions were calculated using the particle-mesh Ewald (PME) algorithm, with a real space cutoff of 1.2 nm. (8) Initial Velocities Assignment: Initial velocities of particles were assigned based on Maxwell distributions to set the system in motion. (9) Molecular Dynamics Simulation: tow simulations have been performed between 10 and 100 nanosecond (ns) with sampling of each 5 ns, MD simulation was conducted to observe and analyze the system's behavior over an extended period. (10) Analysis: Various parameters including root mean square deviations (RMSD), residue root means square fluctuation (RMSF), number of hydrogen bonds (HB), radius of gyration (Rg) were calculated to analyze the system's dynamics and interactions. …”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …”