One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

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

Subjects:

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level by Giovanni Nattino (561797)

“…Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. …”

Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

“…Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. …”

The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

“…Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. …”

Comparison of accuracy and DR on UNSW-NB15. by Arshad Hashmi (13835488)

“…Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. …”

Comparison of DR and FPR of UNSW-NB15. by Arshad Hashmi (13835488)

“…Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. …”

LITNET-2020 data splitting approach. by Asmaa Ahmed Awad (16726315)

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. …”

Fig 3 - by Ying Zhou (25031)

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…The missing values were imputed with the minimum intensity value for each specific data set; (b) for samples expressed in both scLRP1+/+ and scLRP1-/- tissue, the filtering process required 2 or more proteins to be detected in both scLRP1+/+ and scLRP1-/- samples. …”

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. …”

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)

“…For both algorithms, the rate of false detection of DIF was close to 5%. …”