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• A new method for extracting body temperature of caged chickens was proposed.
• An improved YOLOv8n-mvc model was developed for automatic detection of ROI.
• The images of visible and infrared thermal were matched to extract ...

Severe diseases in chickens present substantial risks to poultry husbandry industry. Notably, alterations in body temperature serve as critical clinical indicators of these diseases. Consequently, timely and accurate monitoring of body ...

• Integrated multi-source domain dynamic adaptive transfer learning is built for machining quality prediction.
• Domain similarity-sample double matching multi-source domain integration is designed to integrate knowledge.
• The combined ...

Machining quality prediction of multi-feature parts has been a challenging problem because of small dataset and inconsistent quality data distribution with respect to each machining feature. Transfer learning that leverages knowledge of one task ...

Image deraining with unpaired data has been a challenging problem. Previous methods suffer from either the color distortion artifacts, due to the pixel-level cycle consistency loss, or the time-consuming training process. To address these ...

• We propose a rain removal framework that generates clean images without paired data.
• We propose a method to remove rain by generating rain images using masks as a guide.
• We propose a contrastive learning generator to maintain ...

Fine-grained vital sign monitoring in emergency care is crucial for accurately assessing patient conditions, predicting disease progression, and formulating effective rescue plans. In non-hospital settings, limited equipment often necessitates manual ...

The real-time and precise identification of talent teams can offer essential support for talent development and augmentation. It plays a key role in efficiently and precisely assembling professional talent and constitutes a significant research focus ...

The multiple machining features of parts with thin-wall structural features are spatially distributed and arranged, resulting in mutual influence of machining feature quality at different positions. In addition, the differences in cutting forces ...

Trajectory mapping onto a road network is a complex yet important task. This is because, in the presence of circular sections, Y-shaped intersections, and sections with elevated overlaps with the ground, the conditions of road networks become ...

Label distribution learning (LDL) is an emerging framework in machine learning. Fuzzy mutual information is mutual information under a fuzzy environment and plays an important role in handling uncertainty. This paper explores feature selection ...

• The direct-seeding path for rice covers the entire field.
• Rice direct-seeding path includes the encircling path around the field blocks.
• Encircling path planning algorithm is applicable to different shapes fields.
• The path ...

Path planning is one of the key technologies that determines the efficiency and quality of field operations using autonomous agricultural machinery. To date, there has been extensive research on global coverage path planning for unmanned ...

Humans engage in causal inference almost every day, however, the term ‘causation’ is still quite ambiguous, and few AI systems provide a comprehensive and satisfactory solution to causal inference. In this paper, we adopt the primary meaning of ...

Most existing RF-based vital sign monitoring systems either assume that a human subject is stationary or discard measurements when motion is detected in order to output reliable respiration rates and heart rates. Such an assumption greatly limits the ...

Image fusion plays a crucial role in enhancing the quality and accuracy of semantic segmentation, which is essential for autonomous driving systems. By merging information from multiple imaging sensors or modalities, such as infrared and visible ...

The Yellow Alert program was proposed by the California Department of Transportation to establish a system designed to coordinate public alerts following a major injury or fatality producing hit-and-run collision. Sufficient vehicle information ...

Multi-label feature selection based on fuzzy rough sets, as a key step of multi-label data preprocessing, has been widely concerned by scholars in recent years. Most of the existing multi-label feature selection algorithms directly treat labels ...

• Described the current status of land-leveling implements.
• Analyzed the current status of laser-controlled leveling technology.
• Analyzed the current status of GNSS-control leveling technology.
• Analyzed the benefits of the ...

Land leveling technology is necessary for land cultivation and is an important support for sustainable agricultural development. First, this paper reviews the current status of land-leveling implements, including dry-land and paddy-field leveling ...

In high-dimensional data modeling, variable selection plays a crucial role in improving predictive accuracy and enhancing model interpretability through sparse representation. Unfortunately, certain variable selection methods encounter challenges ...

In today's university era, reforming the English teaching model has become a major research topic for researchers. Based on this, this paper adopts a hierarchical and progressive model construction method to further explore the reform model of university ...

Multi-view K-means clustering successfully generalizes K-means from single-view to multi-view, and obtains excellent clustering performance. In every view, it makes each data point close to the center of the corresponding cluster. However, multi-view K-...

Hybrid data lead to overfitting in machine learning models, which may reduce the accuracy of classification. Feature selection can not only reduce the computational cost of processing hybrid data but also improve the accuracy of classification. ...

• Mutual information entropy is presented to measure the uncertainty of a hybrid information system.
• Max-relevance and minimal redundancy model (MRMR-model) is put forward.
• A feature selection algorithm (denoted as MRMR) is designed.

Feature selection can remove data noise and redundancy and reduce computational complexity, which is vital for machine learning. Because the difference between nominal attribute values is difficult to measure, feature selection for hybrid information ...