ML algorithms in cyber security. حسب Abdullah Asım Yılmaz (20715311)

Flowchart of DQN algorithm. حسب Jiandong Qiu (20389944)

Comparison of different algorithms. حسب Jiandong Qiu (20389944)

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. حسب Abdullah Asım Yılmaz (20715311)

CSSI Elements: Multi-GPU and Network Modeling and Simulation in SST حسب Kishwar Ahmed (21792083)

Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction حسب Mohamad A. Raja (19640297)

الموضوعات:

Performance of machine learning algorithms. حسب Wenxue Liu (6877754)

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

الموضوعات:

Homophilic vs. حسب Caesar Tawfeeq (21087272)

الموضوعات:

Main code. حسب Jingguo Bi (21439766)

Risk element category diagram. حسب Yao Hu (3479972)

Comparison of Q-learning and DQN. حسب Jiandong Qiu (20389944)

Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements. حسب Martina Bukač (20161460)

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …"