Confusion metrics using LR-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Confusion metrics using MNB-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Confusion metrics using DT-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Confusion metrics using SVM-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Proposed framework for propaganda identification. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Framework for data extraction from Twitter. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

Recent work related to propaganda. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Feature statistical analysis. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Wordcloud of non-propaganda tex. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Classification report based on HaPi. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

5-Fold cross validation. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Labeled dataset with their corresponding lengths. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Five fold cross validation. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Wordcloud of propaganda text. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data by Lijia Wang (314011)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">To assess the consistency, diversity, and complexity of the processed data, the Uniform Manifold Approximation and Projection (UMAP) technique was employed to investigate the structural relationships among the various classes (see PathOlOgics_script_3; UMAP visualizations). …”

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

“…Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. …”

Supplementary materials for PhD thesis 'Characterisation Of The Blazhko Effect In RR Lyrae Stars Using SuperWASP Data' by Paul Greer (22619328)

“…This phase domain analysis also led to the identification of 17 candidates for rare binary objects, due to their sinusoidal O-C curves. 73 objects had quadratic O-C curves, suggesting binary candidates with periods longer than the duration of SuperWASP observations.…”