Category: 7. Maths

YOLOv11n-Seg base model

In this study, the YOLOv11 algorithm from the YOLO family was selected as the base model. YOLOv11 includes five variants with different parameter sizes: n, s, m, l, and x¹². Among these, YOLOv11n has the fewest parameters…

Overall architecture

In this section, we provide a detailed introduction to our proposed anomaly detection method, HADNet. HADNet is a sophisticated detection technology that operates in hyperbolic space and is specifically designed for…

Training, testing, and forecasting

As can be seen from Table 3, the only BPNN models do not perform well with R² not that large. According Standard for hydrological information and hydrological forecasting (GB/T 22,482–2008)²⁸, as the ratio…

Existing models of multimodal sentiment analysis utilize either early fusion, which concatenates features of individual modalities, or late fusion, which computes the average prediction from each modality. Traditional methods still struggle to…

Framework overview

The intelligent rendering framework for mirror painting consists of four main modules: input processing, feature extraction, intelligent rendering, and user interaction optimization. The primary goal of this framework is to…

Data source and preprocessing

This study used data from the CVAR online database, a part of MedEffect Canada¹³. This program is designed to manage data on health products and their adverse reactions for the primary benefit of the consumer. Health…

This section proposes an effective method to address the temporal dependency problem and the “many-to-one” problem in sensor fusion semantic segmentation, focusing on reducing the accumulated error and improving the mapping accuracy of depth…

The proposed methodology combines dual-stream feature extraction with the use of textual representation learning and graph-based social context modeling for enhanced fake news detection. The workflow of proposed study is given in Fig. 1. It…

This study explores various deep learning models, including AlexNet, MobileNet, ResNet, SqueezeNet, and ConvNeXt, which are tailored for tasks like plant disease detection and classification. These models were chosen for their ability to handle…

Experimental setup

The proposed medical image encryption algorithm is validated through experimental analysis of a DICOM dataset consisting of 100 Gy-scale DICOM images with a size of 256 × 256 pixels. The simulation analysis is carried out…