Real-time algorithms have to adapt and adjust to new incoming patterns to provide timely
and accurate responses. This paper presents a new distributed forecasting algorithm for …

Compared to models pretrained on ImageNet, the segment anything model (SAM) has been
trained on a massive segmentation corpus, excelling in both generalization ability and …

Traditional classification tasks suffer from the class-imbalanced problem, where some
classes far outnumber others. To address this issue, existing class-imbalanced learning …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and
classify known-class samples. Most of the existing OSR methods are inductive methods …

Robust support vector machine (RSVM) using ramp loss provides a better generalization
performance than traditional support vector machine (SVM) using hinge loss. However, the …

The interconnection of instruments (ie, actuators and sensors) networked together for
industrial applications brings about the Industrial Internet of Things (IIoT). This connectivity …

The generalization performance of “All-in-one” Multi-class SVM (AIO-MSVM) based on
uniformly ergodic Markovian chain (ueMc) samples is considered. We establish the fast …

With the advent of multimedia, social media, and the Internet of Things, an unprecedented
volume of data is being generated at a remarkable speed. Therefore, the application of data …

Optimizing crop production is essential for sustainable agriculture and food security. This
study presents the AgriFusion Model, an advanced ensemble‐based machine learning …