CHIGA's impact on defect prediction performance حسب Sheunopa Charumbira (21011003)

"…The results of our investigation indicate that CHIGA performs competitively across various combinations of datasets and classification algorithms. Notably, CHIGA reduces the performance fluctuation rate by an average of 45.3\% when compared to nine established metric selection techniques. …"

Presentation 1_Development of an AI-driven digital assistance system for real-time safety evaluation and quality control in laparoscopic liver surgery.pptx حسب Zi-Yang Peng (9616239)

"…The programmatic phase classification for laparoscopic hemi-hepatectomy reached an average accuracy of 91% (p<0.001), enabling a correct recognition of surgical events. …"

Boundary peeling: An outlier detection method حسب Sheikh Arafat (21075406)

"…We introduce Boundary Peeling, an unsupervised outlier detection algorithm. Boundary Peeling uses the average signed distance from iteratively peeled, flexible boundaries generated by one-class support vector machines to flag outliers. …"

Schematic of the optimized EDLNet for pneumonia prediction. حسب Shuvo Biswas (21728782)

"…<p>GAP indicates GlobalAveragePooling Layer, RF indicates Random Forest feature optimizer and SVM indicates Support Vector Machine algorithm for Pneumonia classification.…"

Gradient Boosting Trees Machine Learning Approach to Predict the Mutation Types in CaSR. حسب Aylin Bircan (20176049)

"…Contributions of Shapley values for type of pathogenicity classification to the model output for XGBoost. aa0: the amino acid found in the human CaSR, aa1: substituted amino acid, AF: average flexibility, TMT: TM tendency, ZP: Zimmerman polarity, B: BLOSUM62, AWR: atomic weight ratio, TM: transmembrane domain. …"

Supplementary Material for: Deep Learning-Based Detection of Ocular Surface Squamous Neoplasia from Ocular Surface Images حسب Rehman O. (20602319)

"…Deep learning (DL) algorithms were applied for ternary classification of the SL images (OSSN, OOSD and normal). …"

Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx حسب Varun Sendilraj (19732510)

"…For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Data Sheet 1_Predicting the prognosis of patients with sudden sensorineural hearing loss by analyzing the audiometric curve of the unaffected ear.zip حسب Xuan Sun (619836)

"…Regression analysis shows that Cluster Y patients achieve an average improvement in hearing threshold post-treatment that is 6.52 dB higher than that of Cluster X. …"

Facial Expression-Driven Rehabilitation Robotics: A Machine Learning Approach with Stretchable Sensors حسب Refat Chowdhury Mohammad Masum (19796934)

"…To address these issues, we developed a low-cost, flexible sensor system capable of detecting subtle facial muscle deformations and translating them into control commands via a Random Forest machine learning algorithm. Our system classifies four emotional states — Neutral, Happy, Sad, and Disgust — with a classification accuracy of 92.4\%. …"

CIAHS-Data.xls حسب Yingchang Li (22195585)

"…For this purpose, we employed the Natural Breaks classification method to reclassify factor values. This method identifies inherent natural grouping points within the data through the Jenks optimization algorithm, maximizing between-class differences while minimizing within-class differences37. …"

The dataset of main grain land changes in China over 1985–2020 حسب Shidong Liu (11590769)

"…Here, we developed the change map of MGL with resolution 30m in China for the period 1985–2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. …"

An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach حسب Sanaa Badr (20628838)

"…The stratified 3-fold cross-validation results show that the SVM model obtained the highest average accuracy (95.83%) and demonstrated the best overall performance, closely followed by the ANN and LR models with an average accuracy equal to 91.67%. …"

Data Sheet 1_Recognition of brain activities via graph-based long short-term memory-convolutional neural network.pdf حسب Yanling Yang (427399)

"…</p>Results<p>The results demonstrated that the proposed GLCNet outperformed other models with the average accuracies of 78.65% and 65.8% for two classification and four classification on the MEG-BCI dataset, respectively.…"

A multi-paradigm and longitudinal EEG dataset including the “sixth-finger” and “affected-hand” motor imagery of stroke patient حسب Zhuang Wang (22542833)

"…Preliminary analysis using classical machine learning algorithms (CSP+SVM and CSP+LDA) demonstrated an average classification accuracy between the two MI paradigms maintained at approximately 74% ~ 76%. …"

Parameter estimates of mixed generalized Gaussian distribution for modelling the increments of electroencephalogram data حسب Zoe Salinger (19090613)

"…Statistical analyses of EEG data includes classification and prediction using arrays of EEG features, but few models for the underlying stochastic processes have been proposed. …"