Texas Rio Grande Valley Red–crowned Parrot predicted nest site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

Sample classes from the HMDB51 dataset. by Yasir Khan Jadoon (21433231)

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. …”

Flowchart for developed MAOA. by P. M. Vijayan (17095147)

“…The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). …”

Representation of autoencoder with DBN. by P. M. Vijayan (17095147)

“…The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). …”

Threats in MQTT set. by P. M. Vijayan (17095147)

“…The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). …”

Features of MQTT set. by P. M. Vijayan (17095147)

“…The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). …”

Architectural view of smart home atmosphere. by P. M. Vijayan (17095147)

“…The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). …”

Genetic algorithm iteration data chart. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”

Table1_A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm.DOCX by Xianjing Zhong (18067297)

Sample classes from UCF101 dataset [40]. by Yasir Khan Jadoon (21433231)

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. …”

Pest dataset details. by Ruikang Xu (18778060)

“…Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. Finally, to further improve our detection results, we incorporated non-maximum suppression (Soft NMS) and Cascade R-CNN’s cascade structure into the optimization process to ensure more accurate and reliable prediction results. …”

T2-Flair model training process using all data. by Fengning Liang (20927493)

T2-Flair model training process using all data. by Fengning Liang (20927493)

S1 Data - by Zhengyu Xu (8550660)

Table_1_PSO-XnB: a proposed model for predicting hospital stay of CAD patients.XLSX by Geetha Pratyusha Miriyala (18500457)

S10 Data - by Zhenjing Xie (18196063)

“…The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.…”

Accuracy rate table. by Zhenjing Xie (18196063)

“…The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.…”

S2 File - by Zhenjing Xie (18196063)

“…The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.…”

S8 Data - by Zhenjing Xie (18196063)

“…The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.…”

S5 Data - by Zhenjing Xie (18196063)

“…The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.…”