Joint Detection of Change Points in Multichannel Single-Molecule Measurements by Hugh Wilson (11797444)

Subjects:

The main workflow of the proposed algorithm. by Xin Li (51274)

The detected blocks. by Xin Li (51274)

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

“…The resulting binary image is then closed with a vertical structuring element to connect points where no local maxima is detected. …”

Illustrations of fovea detection procedure. The typical case (left) and the abnormal case (right) with ILMs (black lines), fitted lines (red lines), intersection points (blue verti... by Rinrada Tanthanathewin (18611171)

AUC score comparison table for all the deep learning algorithms. by Khandaker Mohammad Mohi Uddin (13124356)

Comparative overview of all DL and ML algorithms parameters in used in this study. by Khandaker Mohammad Mohi Uddin (13124356)

Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing... by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

Nonparametric Distribution Regression and Change Point Detection in High-Dimensions by Shivam Kumar (17808571)

“…First, we introduce the Functional Regression Binary Segmentation (FRBS) algorithm for change-point detection in the slope function when predictors are functions and responses are scalars. …”

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

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

MERs and their Fourier descriptor figure. by Xin Li (51274)

Supportive boundary lines and related results. by Xin Li (51274)

Automatic Pre-Processing (APP). by Yuxiang Wu (748543)

Recognition of each neuron’s digital identity. by Yuxiang Wu (748543)

Bootstrap-based inference for multiple mean-variance changepoint models by Yang Li (7082)

“…This method integrates the weighted bootstrap with the Sequential Binary Segmentation (SBS) algorithm. Not only does our technique pinpoint the location and number of change points, but it also determines the type of change for each estimated point, specifying whether the change occurred in the mean, variance, or both. …”

Table_2_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We also used a supervised template-based detection algorithm (binary point matching) to evaluate the efficacy of automated detection for titi duets in audio recordings using linear arrays of ARUs within a ~2 km² area. …”

Table_3_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We also used a supervised template-based detection algorithm (binary point matching) to evaluate the efficacy of automated detection for titi duets in audio recordings using linear arrays of ARUs within a ~2 km² area. …”

Table_1_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We also used a supervised template-based detection algorithm (binary point matching) to evaluate the efficacy of automated detection for titi duets in audio recordings using linear arrays of ARUs within a ~2 km² area. …”

DataSheet_1_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We also used a supervised template-based detection algorithm (binary point matching) to evaluate the efficacy of automated detection for titi duets in audio recordings using linear arrays of ARUs within a ~2 km² area. …”