Frequency-dependent pre- and post- synaptic plasticity in rate-based models. حسب Toviah Moldwin (10866708)

الموضوعات:

Different mechanisms for homeostatic plasticity. حسب Toviah Moldwin (10866708)

الموضوعات:

Possible plasticity rules for presynaptic input- and spike-dependent- calcium. حسب Toviah Moldwin (10866708)

الموضوعات:

The calcitron model and calcium-based plasticity rules. حسب Toviah Moldwin (10866708)

الموضوعات:

Supplementary file 1_Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.docx حسب Xiaohui Guo (131711)

Data Sheet 1_Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms.pdf حسب Li Yang (6520)

Table 3_Machine learning-based prediction of clinical outcomes in cervical cancer using routine hematological indices: development and web implementation.xlsx حسب Gaigai Bai (13173289)

"…A panel of hematological indices was evaluated, including inflammatory markers, coagulation parameters, and metabolic indicators. Machine learning (ML) algorithms innovatively integrated with traditional regression were employed for feature selection and model development. …"

Table 5_Machine learning-based prediction of clinical outcomes in cervical cancer using routine hematological indices: development and web implementation.xlsx حسب Gaigai Bai (13173289)

"…A panel of hematological indices was evaluated, including inflammatory markers, coagulation parameters, and metabolic indicators. Machine learning (ML) algorithms innovatively integrated with traditional regression were employed for feature selection and model development. …"

Table 4_Machine learning-based prediction of clinical outcomes in cervical cancer using routine hematological indices: development and web implementation.xlsx حسب Gaigai Bai (13173289)

"…A panel of hematological indices was evaluated, including inflammatory markers, coagulation parameters, and metabolic indicators. Machine learning (ML) algorithms innovatively integrated with traditional regression were employed for feature selection and model development. …"

Mean values of different benchmark functions. حسب Jalal- ud-Din (21474307)

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Detail of benchmark functions comparison. حسب Jalal- ud-Din (21474307)

الموضوعات:

Average execution time (in seconds) of different benchmark functions. حسب Jalal- ud-Din (21474307)

الموضوعات:

Stander deviation values of different benchmark functions. حسب Jalal- ud-Din (21474307)

الموضوعات:

Comparison of different crossover operators. حسب Jalal- ud-Din (21474307)

الموضوعات:

Video 2_Replicating associative learning of rodents with a neuromorphic robot in an open-field arena.mp4 حسب Tianze Liu (13922196)

Video 1_Replicating associative learning of rodents with a neuromorphic robot in an open-field arena.mp4 حسب Tianze Liu (13922196)

Data Sheet 1_Machine learning-based prediction of clinical outcomes in cervical cancer using routine hematological indices: development and web implementation.docx حسب Gaigai Bai (13173289)

"…A panel of hematological indices was evaluated, including inflammatory markers, coagulation parameters, and metabolic indicators. Machine learning (ML) algorithms innovatively integrated with traditional regression were employed for feature selection and model development. …"

<b>Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</b> حسب Yize Wang (19535173)

"…<p dir="ltr">The farm-level yaw control optimization algorithm Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</p>…"

Accuracy of different electrical and optical neural networks for various input noise levels. حسب Reyhane Ahmadi (14857363)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional حسب Ryan Stocks (16867476)

"…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. …"