MLMI

Accurate distributed photovoltaic load forecasting can effectively assist the overall power dispatch and then reduce the risk of grid operation. Different from the existing research, we deeply mine the clustering characteristics of load in the time ...

Narrow-based Exchange-Traded Funds (ETFs) offer significant diversification compared to stock market investments while maintaining a reasonable level of predictability through asset combinations and industry dependencies, rendering them ideal for the ...

In order to solve the problem of short term power load forecasting and improve the accuracy of load forecasting in distribution station area, a short term power load forecasting model integrating multiple temporal series characteristics is proposed in ...

Domain generalization requires that models trained only with source domain data also perform well on unknown target domain data, which requires that existing models trained with source domain have good generalization performance. Compared with semi-...

Diabetes is an irreversible, chronic metabolic disease, and is now the third most important non-communicable disease threatening human health. Therefore, early diagnosis of diabetes is essential. In this paper, after preprocessing the Pima Indian ...

With the rapid development of deep recommendation models, Multitask Learning (MTL) enables models to have better generalization ability in the learning process by extracting relevant information from different tasks. Although many studies have focused on ...

Fairness-aware classification has emerged as a focal point in a wide range of machine learning applications. To address fairness issues such as demographic parity and disparate mistreatment, researchers have developed fair classifiers. A common approach ...

This study delved into the realm of sports analytics, employing machine learning techniques to predict the outcomes of NBA games based on player performance and team statistics. Through meticulous data collection, filtering, and model comparison, we ...

Complex networks are composed of nodes and complex relationships between nodes, which are the abstraction of various complex systems in the real world. Community structure is a remarkable characteristic of complex networks.Community detection algorithms ...

Accurate recognition of image targets is a fundamental intelligent perception task and extracting effective features is the prerequisite. However, there are still problems with both hand-crafted features and the ones learned by deep neural networks, such ...

Obstacle detection is of great significance for autonomous driving. Accurately identifying obstacles and transmitting obstacle information to the underlying planning and control system is essential for effective and safe autonomous driving. Obstacle ...

Epilepsy, a chronic brain disease with recurrent attacks, is the most common neurological disease in humans. It is extremely important for patients with epilepsy to accurately and timely diagnose seizures. A new method based on Electroencephalogram (EEG) ...

Accurate disease severity estimation is vital for fruit tree disease prevention and control. Most of the existing image processing methods for fruit tree disease severity estimation are based on a single leaf evaluation, and mostly, are done in ...

In high-risk construction environments, ensuring safety is paramount. Accidents, often resulting in casualties, underscore the importance of wearing helmets correctly. To tackle the challenges posed by small, traditional targets and the complexity of ...

With the improvement in detector performance, the Kalman Filter can estimate object trajectories based on the basic assumption of short-term linear motion. However, in scenarios with severe object occlusion, this estimation may suffer from significant ...

Detecting anomalous subsequences plays a vital role in time series analysis. Traditional approaches often rely on sliding windows to extract subsequences and identify anomalies. However, these methods encounter two difficulties: i) they are unable to ...

In order to solve the problems of limited feature expression ability and low segmentation accuracy of the lesion segmentation network, CGM-UNet based on Coordinate Attention (CA), Multi-Head Self-Attention (MHSA) and Global Pyramid Guidance (GPG) was ...

The field of atmospheric imaging has continually grappled with the complex task of accurate contrail detection, largely due to the intricate and variable nature of contrail formations within diverse atmospheric environments. Addressing this challenge, ...

Wi-Fi is a widely used technology worldwide but can also be a source of insecurity. One of the most common forms of attack is the evil twin, where an attacker creates a fake Wi-Fi network with the same name as a legitimate network. Machine learning ...

With the continuous development of information technology, the popularity of intelligent devices not only provides great convenience to people's life, but also provides prerequisites for cyber attacks. Deep learning-based cyber attack detection methods ...

Border Gateway Protocol (BGP) serves as a path vector protocol that manages network reachability information among Autonomous Systems (AS), is critical to the stability and reliability of the Internet. The single approach in previous studies fails to ...

Accurate and robust segmentation of surgical instrument parts is urgently required by AI enhanced intelligent surgery and automatic surgery skill evaluation. However, it is still a challenging problem due to the multiple small areas of frontal clasper ...

At urban intersections, traffic congestion often occurs. Currently, most of the traffic signal lights in China adopt fixed timing schemes, lacking intelligence, which leads to severe traffic congestion in cities. This study focuses on continuous linear ...

Within the grassland ecosystem, grazing has the most significant impact. A reasonable grazing strategy must account for both the income of pastoralists and the capacity of the grazing pasture to achieve sustainable development. To ensure the virtuous and ...

Generative adversarial networks (GANs) have seen significant research interest over the past decade, yet core issues of training instability and mode collapse persist. This work proposes SwarmGAN, a novel GAN framework incorporating swarm intelligence ...

Graph signal sampling and reconstruction techniques effectively reduce data dimensions while preserving the information and structure contained in networks. The distortion-free reduction of data dimensions not only aids in reducing the computational ...