Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

Energy consumption at each joint for different methods. by Hedyeh Jafari (9667127)

Hip versus ankle joints torque for different methods. by Hedyeh Jafari (9667127)

Phase portraits comparison of the mentioned methods for the ankle joint. by Hedyeh Jafari (9667127)

Phase portraits comparison of the mentioned methods for the hip joint. by Hedyeh Jafari (9667127)

Illustration of two joints dynamical model of the human body in standing position. by Hedyeh Jafari (9667127)

Evolution of changing weights in the optimization of the MPC controller. by Hedyeh Jafari (9667127)

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. …”

Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. …”

Evolution of changing the COP distance error’s weight (<i>α</i>) inCOP-BC controller. by Hedyeh Jafari (9667127)

Evolution of changing controllers gain in IPD controller. by Hedyeh Jafari (9667127)

Controllers gains. by Hedyeh Jafari (9667127)

“…In this article, we studied the methods based on optimization algorithms that can mimic the performance of the postural sway controller in the upright stance. …”

Parameters bounds and limitations. by Hedyeh Jafari (9667127)

“…In this article, we studied the methods based on optimization algorithms that can mimic the performance of the postural sway controller in the upright stance. …”

Body characteristics. by Hedyeh Jafari (9667127)

“…In this article, we studied the methods based on optimization algorithms that can mimic the performance of the postural sway controller in the upright stance. …”

Table1_Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.DOCX by Åsmund Flobak (789984)

“…We now demonstrate how automated adjustments of model topology and logic equations both can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations, while being less restrained for parts of the model where parameterization is not guided by biological data. …”

Sample image for illustration. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”

Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”

Comparison analysis of computation time. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”

Process flow diagram of CBFD. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”