identify flood-affected areas with image classification. The proposed integrated approach can be used to deliver relief on a … system uses a combined convolutional neural network (CNN) and sorting algorithm. The Inception v3 and DenseNet CNN approach … classification. The Inception v3 and DenseNet architectures achieve 83% and 81% accuracy in our self-made flood level dataset …

Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS …, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which …

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

portals on social media. Seven classification algorithms of Gradient Boosting, Random Forest, K-Nearest Neighbour, Logistic … Regression, Neural Network, Support Vector Machine, and Naïve Bayes are used to build the classification model. In addition, four … combinations of the above-mentioned methods are used to construct an ensemble learning classification model. The performance …

, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification …. Furthermore, the study examines the impact of imputed values on classification algorithms, including k-Nearest Neighbors (kNN …

Factors Analysis and Classification System (HFACS) to investigate whether the AI agent is capable of conducting aviation … sociotechnical ecosystem. Machine Learning (ML) models are capable and could excel on accident report classification tasks within the …

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