Recall scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

Subjects:

Algorithm process of BPNN. by Xiaoxiang Cao (12644851)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

CEC2017 test function test results. by Tengfei Ma (597633)

Subjects:

F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

Subjects:

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Iteration curves of different algorithms. by Tengfei Ma (597633)

Subjects:

Flowchart of OP-ZOA algorithm. by Tengfei Ma (597633)

Subjects:

Parameters of the proposed algorithm. by Heba Askr (15572851)

Subjects:

Raw task performance on both experiments, with respect to conditions. by Anis Najar (9745561)

Subjects:

Completion times for different algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Fitness function parameters. by Ankita Sharma (3183054)

Comparison with the BST algorithm for the AF and CST bundles. by Thomas Durantel (20933812)

Subjects: “…orientation distribution function…”

Flow chart diagram of blind quantum algorithm. by Sultan H. Almotiri (14029251)

Subjects:

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

Subjects:

Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)