Particle tracking validation of the S8 algorithm. by Jennifer M. Knighten (15453354)

Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support by Kwame O. Hackman (9289505)

“…We further implemented an anomaly detection and temporal consistency algorithm followed by a changing logic to correct the classification anomalies due to image contamination from the cloud and other sources. …”

Prediction of health disorders in dairy cows monitored with collar based on Binary logistic analysis by Xiaojing Zhou (521545)

“…To verify the feasibility and adaptability of the proposed method, we analyzed data of cows in the same herd (herd 1) not used to construct the model, and cows in another herd (herd 2) with data recorded by the same type of automated system, and led to detection of 75.0%, 64.2%, 74.2%, and 76.9% animals in herd 1 correctly predicted to suffer from metritis, mastitis, lameness, and digestive disorders, respectively. …”

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. …”

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. …”

WEISS Catheter Segmentation in Fluoroscopy Dataset by Evangelos Mazomenos (6761597)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p><br></p><p dir="ltr">In the fifth measurement technique, the numbers of sharp <b>surface projections/protrusions</b> were calculated by initially applying Canny's edge detection algorithm to generate an edge map of the cell mask image. …”

Behavioral and Eye-tracking Data for Adaptive Circuit Dynamics Across Human Cortex During Evidence Accumulation in Changing Environments by Peter Murphy (10141523)

“…The main variable in each file is a matrix called <i>Behav</i> for which each row is a trial and columns are the following:</p> <p>column 1 – the generative distribution used to draw the final sample location on each trial (and thus, the correct response)</p> <p>column 2 – the response given by the participant</p> <p>column 3 – the accuracy of the participant’s response</p> <p>column 4 – response time relative to Go cue</p> <p>column 5 – trial onset according to psychtoolbox clock</p> <p>column 6 – number of times participant broke fixation during trial, according to online detection algorithm</p> <p>Each .mat file also contains a trials*samples matrix (<i>tRefresh</i>) of the timings of monitor flips corresponding to the onsets of each sample (and made relative to trial onset), as provided by psychtoolbox.…”

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

“…The performance of the algorithms was assessed using: (i) the rates of false and correct detection of DIF, (ii) the DIF size and form recovery, and (iii) the bias in the latent variable level estimation. …”