The classification algorithm flow chart for binary classification among NC, EMCI, LMCI and AD groups. by Qian Zhang (49333)

Homomorphic binary tree. by Jianhua He (341366)

Key steps in neurodegeneration detection algorithm. by Andrew S. Clark (16510496)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

For each classification algorithm, the classification accuracies for binary subject groups NC versus EMCI, NC versus LEMC, EMCI versus LEMC, EMCI versus AD, LMCI versus AD, AD vers... by Qian Zhang (49333)

Effect of different randomization constraints on pattern detection. by Zachary P. Neal (9946331)

Region-specific variable importance. by Katherine E. Overly (10789206)

“…We investigated the use of ensemble species distribution modeling (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using occurrence data and terrain attributes derived from bathymetric datasets at different resolutions, we developed species distribution models unique to each sampling region (west, northeast, and southeast Puerto Rico) using seven different algorithms. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

Data Sheet 1_Development of a two-stage depression symptom detection model: application of neural networks to twitter data.docx by Faye Beatriz Tumaliuan (20394737)

“…The first stage, with a binary output classifier, can detect tweets with “Depression Symptom” or “No Symptom” categories with an accuracy of 0.91 and F1-score of 0.90. …”

Data_Sheet_1_DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads.docx by Jinxin Dong (9229511)

“…The major principle of the proposed method is that it first extracts read depth and mapping quality signals, then uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to find the possible TD regions. …”

Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

“…(K) Using the local threshold, each new image is binarized and then subtracted from the first image as shown in panel (J, bottom). If the identical blob that was detected in panel J (bottom) is found in any of the new subtracted binary images (cyan arrow), the animal is considered as having left its original position, and the algorithm continues. …”

Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…Both the SVM model with a linear kernel and the one with an RBF kernel achieved identical results. Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”

Fig 3 - by Ying Zhou (25031)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…<p dir="ltr">The first algorithm for segmentation and localization (see PathOlOgics_script_1; segment & localize using a pen) relied on manually tracing the borders of each cell using a digital pen tool on a big touchscreen display showing source images/patches. …”

Models and Dataset by M RN (9866504)

“…</p><p dir="ltr"><br></p><p dir="ltr"><b>RAO (Rao Optimization Algorithm):</b><br>RAO is a parameter-less optimization algorithm that updates solutions based on simple arithmetic operations involving the best and worst individuals in the population. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”

Table 1_Non-obtrusive monitoring of obstructive sleep apnea syndrome based on ballistocardiography: a preliminary study.docx by Biyong Zhang (20906192)

“…Applying fast change-point detection to first isolate apnea-suspected episodes would allow for processing only those suspected episodes for further feature extraction and OSAS severity classification. …”