One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

3D visualization of the Rastrigin function from the CEC 2019 benchmark test functions. by Mahmood A. Jumaah (22272371)

Image_1_A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.JPEG by Rujia Miao (12075653)

Table_1_A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.DOC by Rujia Miao (12075653)

Control parameters of the SOMA algorithm. by Quoc Bao Diep (18452207)

“…Our method enables UAVs to autonomously generate safe and efficient paths in real-time, adapt to dynamic changes, and scale to large swarms without relying on centralized control. …”

DataSheet1_Optimal capacity configuration of the wind-storage combined frequency regulation system considering secondary frequency drop.docx by Dongdong Li (123985)

“…<p>With wind power integrated into the power system on a large scale, the system has become vulnerable to the frequency stability issue. …”

Use of a Droplet Platform To Optimize Pd-Catalyzed C–N Coupling Reactions Promoted by Organic Bases by Lorenz M. Baumgartner (7053485)

“…The single-phase nature of these reaction conditions facilitates their implementation in continuous flow systems, high-throughput optimization platforms, and large-scale applications. …”

<b>Spatial modeling of gully density on the Qinghai-Tibet Plateau: Application of hyperparameter optimization in interpretable machine learning</b> by Zhoujiang Liu (22741966)

“…Various machine learning models were used, and different hyperparameter optimization algorithms were selected to train the models to obtain the best model. …”

DataSheet1_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

“…The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. …”

Table1_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.xlsx by Pengyang Zhang (11256688)

“…The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. …”

DataSheet3_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

“…The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. …”

DataSheet2_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

“…The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. …”

Table2_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.docx by Pengyang Zhang (11256688)

“…The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. …”

DataSheet4_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

“…The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. …”

Table_1_Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.DOCX by Hasan Zulfiqar (12117255)

Extraction and expression of architectural color. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Basic color value distribution map of the street. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

SegNet architecture. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Overview of workflow. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Descriptive statistics for the volunteers. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”