Base-case solution. by Xu Wang (28596)

Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis. by Richard Porwancher (11413931)

Model-based sex classification accuracies. by Kevin J. Wischnewski (21354521)

Confusion matrix: Describes the two errors based on ground truth and predicted values of the SCP in the frame. by Sukhvinder Kaur (20297968)

The overall framework of the pest detection network is based on the combination of Cascade RCNN and Swin Transformer. by Ruikang Xu (18778060)

A Graphical representation of the MetaNovo algorithm applied for sequence database filtration. by Matthys G. Potgieter (16384775)

Generalized Energy-Based Fragmentation Approach for the Electronic Emission Spectra of Large Systems by Jiahui Du (678756)

“…Furthermore, we also employ the TOP algorithm for tracking the ESs in their geometry optimizations at the time-dependent density functional theory (TDDFT) level. …”

Structures of swin transformer block. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

Pest dataset details. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

Data augmentation. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

Original and expanded datasets explained. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

Effect of adding different modules on mAP_0.5. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

Copy and paste. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

Pictures of some farmland pests. by Ruikang Xu (18778060)

“…Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. …”

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

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

Table 6_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

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

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

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