Table 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Table 4_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Image 3_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Table 8_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Table 3_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Image 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.tiff حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Table 5_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

Table 7_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx حسب Benedictor Alexander Nguchu (9984371)

"…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …"

High-Precision Landing on a Moving Platform Based on Drone Vision Using Yolo Algorithm حسب Satoshi Suzuki (20437556)

"…To overcome the challenge of the flight altitude is too high to detect the landing target, this paper fi rst detects large-volume targets, followed by the precise identifi cation of smaller targets, achieving enhanced recognition accuracy and speed through an improved YOLOv8 OBB algorithm. To maintain the UAV’s safety and stability throughout the landing process, This paper applies a position control approach using a reference model based sliding mode controller (RMSMC). …"

Example of the representation flag based on principal component analysis (PCA) and Q-statistics. حسب José L. Safanelli (20552654)

الموضوعات:

Pseudocode for machine learning models. حسب Jingru Dong (14076094)

الموضوعات:

Model validation results. حسب Tanmoy Bhowmik (1776838)

الموضوعات:

Comparison of the performance of the different models in the BRFSS _ 2015 data set. حسب Lang Deng (5681033)

الموضوعات:

Performance comparison of different models of the BRFSS_2015 data set. حسب Lang Deng (5681033)

الموضوعات:

DataSheet1_Barrier-free tomato fruit selection and location based on optimized semantic segmentation and obstacle perception algorithm.docx حسب Lingli Zhou (539752)

"…To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. …"

Flow chart of Novel enhanced DBSCAN based model. حسب Shahneela Pitafi (20454775)

الموضوعات:

Stage 2 Interviews: Investigating models for the development of online educational interventions حسب Glen Shearer (9834266)

Routing policy based on path satisfaction. حسب Yang Yu (4292)

"…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. …"

A robust privacy-preserving online monitoring algorithm for disease surveillance حسب Miaomiao Yu (519470)

"…To achieve disease surveillance in a timely and privacy-preserving manner, we develop an online monitoring algorithm based on differential privacy. …"

Pareto optimal front result of MOCOA. حسب Hang Zhao (143592)

الموضوعات: "…crayfish optimization algorithm…"