The sample images of the ship dataset. by Manlin Zhu (21354237)

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”

(a) the AFF module; (b) the iAFF module. by Manlin Zhu (21354237)

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”

The overall structure of YOLOv10. by Manlin Zhu (21354237)

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”

(a) the C2f module; (b) the C2f_iAFF module. by Manlin Zhu (21354237)

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”

The specific configuration of the ship dataset. by Manlin Zhu (21354237)

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”

The testing results of YOLO-HPSD. by Manlin Zhu (21354237)

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”

Changed differential alternative splicing events (DASEs) patterns between PCOS and Normal granulosa cells. by Linlin Yang (737661)

“…(C) Cluster and GO enrichment of genes that contained differential alternative splicing. The K-means algorithm was used for clustering. To ensure the reproducibility of the results, we set the random seed to 2024 and determined the optimal number of clusters to be 5 based on the elbow method. …”

Reference frame [36]. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

Parameter values [34]. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

Data Sheet 1_Using reinforcement learning in genome assembly: in-depth analysis of a Q-learning assembler.pdf by Kleber Padovani (10198918)

“…We expand upon the previous approach found in the literature to solve this problem by carefully exploring the learning aspects of the proposed intelligent agent, which uses the Q-learning algorithm. We improved the reward system and optimized the exploration of the state space based on pruning and in collaboration with evolutionary computing (>300% improvement). …”

Proposed Hybrid (NNPID+FPID) Control Scheme. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

Control scheme block diagram [34]. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

3D Plot for helical trajectory tracking. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

PID gain plot for <i>ϕ</i> dynamics. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

Fuzzy Rules for and [43]. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

PID gain plot for y dynamics. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

PID gain plot for <i>ψ</i> dynamics. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

Table 1_Predicting the risk of metabolic-associated fatty liver disease in the elderly population in China: construction and evaluation of interpretable machine learning models.doc... by Yingxin Zeng (5119190)

“…Predictive models were constructed using 10 ML algorithms, and model performance was evaluated based on discriminative ability, calibration ability, and clinical utility. …”

Structure of fuzzy PID controller [34]. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”

Summary of methodology. by Nigatu Wanore Madebo (22146141)

“…The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. …”