Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior … machine learning and zero-shot classification methods for automated abstract screening for systematic reviews. Extensive …

the classification task of data mining where prior outliers preprocessing is necessary. …

spam classification using a fuzzy inference system. Using a rule-based hybrid feature selection technique, we choose the … model for spam classification, and fuzzy logic is employed to categorize each spam class into its severity levels to …

. This research explores content classification through methods such as sentiment analysis, keyword extraction and topic …

assess their severity. This study proposes a cybersecurity risk quantification and classification framework, addressing a …

. Furthermore, the study examines the impact of imputed values on classification algorithms, including k-Nearest Neighbors (kNN …, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification … comparing the performance of imputation techniques using clustering metrics and algorithms (e.g., k-means, Partitioning Around …

images were used in this study. The output of the layered architecture is aggregated to perform the binary classification for … skin cancer. The performance of the trained models is investigated for multiclass and binary classification tasks. The …-based multiclass classification models. …

identify flood-affected areas with image classification. The proposed integrated approach can be used to deliver relief on a … classification. The Inception v3 and DenseNet architectures achieve 83% and 81% accuracy in our self-made flood level dataset …

, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which …