Results 41 to 50 of about 1,347,746 (312)
HHSD: Hindi Hate Speech Detection Leveraging Multi-Task Learning
IEEE Access, 2023 Hate speech is now a frequent occurrence on social media. Recently, the majority of study was devoted to identifying hate speech in languages with abundant resources (e.g., English).
Prashant Kapil +5 more
doaj +1 more source
Thresholding Classifiers to Maximize F1 Score
, 2014 This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare.
Lipton, Zachary Chase +2 more
openaire +2 more sources
Machine learning approaches for anomaly detection of water quality on a real-world data set
Journal of Information and Telecommunication, 2019 Accurate detection of water quality changes is a crucial task of water companies. Water supply companies must provide safe drinking water. Nowadays in different areas, we find sensible sensors which monitor data during the time.
Fitore Muharemi +2 more
doaj +1 more source
Towards an Online Empathy Assisted Counselling Web Application [PDF]
EAI Endorsed Transactions on Context-aware Systems and Applications, 2020 INTRODUCTION: In today's society mental health is becoming increasingly important. As a result, more and more individuals need guidance and counselling.
Aarif Mawani, Lawrence Nderu
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Mean F1 score and collective detection performance.
, 2022 Left. Mean F1 score over the whole image dataset depending on the size of the group of players and quorum. Each combination of group size and quorum was tested 160 times through random selection of gameplays of each image. Right.
Lara García Delgado (12569093) +13 more
core +1 more source
JMIR Medical Informatics, 2020 BackgroundEmerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on ...
Kasthurirathne, Suranga N +5 more
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The Macro-F1 score of the models.
, 2022 The Macro-F1 score of the models.
Yajun Zhang (1543207) +3 more
core +1 more source
Comparison of F1-score between different methods.
, 2022 Comparison of F1-score between different methods.
Qian Liu (135614), Xiyu Liu (1700959)
core +1 more source
Bayesian method for comparing F1 scores in the absence of a gold standard
Journal of Biopharmaceutical StatisticsIn the field of medicine, evaluating the diagnostic performance of new diagnostic methods can be challenging, especially in the absence of a gold standard. This study proposes a methodology for assessing the performance of diagnostic tests by estimating the posterior distribution of the F1 score using latent class analysis, without relying on a gold ...
Jun Tamura +3 more
openaire +3 more sources
Investigations of diabetic retinopathy using deep learning techniques [PDF]
PeerJ Computer ScienceDiabetes is a chronic metabolic disorder that frequently leads to diabetic retinopathy (DR), a major cause of preventable vision loss worldwide. Early DR detection remains challenging due to the subtle appearance of microaneurysms, haemorrhages, and ...
S. Praveen Samuel Washburn, G. Mahendran
doaj +2 more sources