MLMI

In 2020 Scaled-YOLOv4 was introduced. It is one of the best object detection models outclassing its peers in MS COCO test-dev. In this study, the proponents used Scaled-YOLOv4 as their object detection model. The model will be used in the environment of ...

Federated learning (FL) is an emerging distributed machine learning method that collaboratively trains a universal model among clients while maintaining their data privacy. Recently, several efforts attempt to introduce vision transformer (ViT) models ...

Employee attrition has long been a problem that troubles firms. Traditional machine learning algorithms have demonstrated superior performance on the employee attrition prediction problem. However, many previous studies have ignored the imbalance of ...

Accurate forecasting of volatility is critical for options trading, as option prices are directly related to the volatility of the underlying product. However, industry-leading pricing algorithms never stop evolving. In order to create better trading ...

Effective credit risk prediction is critical for commercial banks to actively manage their lending book and reduce negative impact from potential credit losses. In a benign credit markets where default rates are low, the datasets for customers’ credit ...

Rate prediction has always been the focus of research in Recommender Systems (RSs), and deep learning based models have achieved remarkable results in this field. Previous works use techniques like FNN to explore new cross features and output the final ...

With the development of network technology, the music recommendation system has also developed rapidly, and the online music platform has become the first choice for people to listen to music. However, music recommendation systems also face some ...

Today, text information includes all the information in the form of natural language text, among which text information occupies an important position in life and becomes an important part of people's use of social information resources. The main ...

Disaster weather greatly affects the quality and yield of fruit trees. Improving the ability of wind speed simulation and prediction enable fruit farmers to prepare for meteorological disasters in advance and reduce the losses of fruit farmers. Aiming ...

Although Hamiltonian Neural Network (HNN) achieve good accuracy for various numeral solvers, if theoretic results of Hamiltonian systems are applicable to HNN, it is paramount to find the exactly symplectic map in discrete time. In this paper, consider ...

Arrhythmia is a group of common diseases related to irregular heart rate. Accurate classification of electrocardiogram is very important for college students to detect heart disease. Experts will spend a major expenditure of time and effort in the ...

Aiming at the problem that the air target trajectory is complex and changeable, and the prediction accuracy is not high, which leads to the inaccurate prediction of the air target trajectory, a PSO-GWO -RBF neural network based air target trajectory ...

The minimum vertex covering problem is one of the classical NP-Hard problems in combinatorial optimization, which has been widely used in practical problems. The existing approximation algorithms for finding the minimum vertex cover set of a general ...

After the human society entered the network age, with the continuous development of information technology, the amount of data generated and collected in various production activities continued to increase, which prompted a new change in the field of ...

This paper proposes a method for fast residual network learning features to enhance the model effect, which can effectively improve the effect of person re-identification. Unlike most other person re-identification methods, the proposed model is more ...

A Great progress of Neural Machine Translation (NMT) tasks has been achieved by the transformer models in recent years, which is largely owing to the careful design of multi-head attention and feed-forward neural network layers in its encoder-decoder ...

For natural language processing, text similarity calculation has proven a difficult task, especially in certain fields. BERT [1] models have been increasingly popular for solving text similarity calculation problems in recent years. Although the BERT ...

Recently, Chinese named entity recognition has attracted a lot of attention. Most of the work utilizes words matching with lexicon which integrates potential word information with lattice structure or graph structure. Although existing approaches have ...

The advent of the big data era has brought unprecedented data to machine translation, which is particularly important for data-driven models and algorithms. The degree of public quotation of source text is becoming more and more difficult to understand, ...

The loss rate of cotton pickers is an important indicator to measure the quality of cotton pickers, which is directly the vital interests of cotton farmers. To obtain the loss rate of cotton pickers in real-time, this paper takes the cotton field images ...

Estimating uncertainty for deep networks is a good way to assess the reliability of prediction results and helps self-driving cars avoid traffic accidents. Compared to single RGB or thermal image segmentation, fusing RGB and thermal adds more sources of ...

Aircraft Glass Canopy (AGC) is an important part of human-aircraft interface, and efficient and real-time AGC surface defect detection has become a prerequisite for pilots to perceive the external field of vision normally. While existing deep learning ...

Most traditional goods recommendation algorithm have difficulty in capturing higher-order information about interaction, while heterogeneous graph neural networks can capture complex topological information and preserve the heterogeneous information of ...

With the development of science and technology, transparent parts are almost used in various industries and play an indispensable role. It is meaningful to measure the surface quality of transparent parts. To improve the detection accuracy of surface ...

For attacks on systems during operation, the high false positive rate of traditional machine learning models is no longer able to detect cyber attacks with high accuracy. In this paper, we propose a cyber attacks behavior detection model based on ...

The influence of user behavioral diversity and multi-source information on dissemination behavior has been neglected in the current research in social networks. Therefore, the multi-source information dissemination in social networks from the micro ...

With the development of Internet technology, open networks and standardized protocols bring more potential security threats. How to propose effective identification and prevention methods for some typical malicious program attacks is an important issue ...

Very large-scale (VLS) deep learning models are capable of generating meaningful code snippets, yet the performance drops dramatically when the coding task becomes more complex. Although fully neural approaches have been proposed to solve this problem, ...

Reading is one of the first things humans learn to improve their comprehension, vocabulary, and imagination. Determining what to read for your level will be difficult, but forcing you to understand a high text level is more complex. It can result in not ...

The traditional swarm UAV system technology based on the central node and trust environment is mainly divided into two parts: target allocation and collaborative guidance law design. This kind of design method cannot guarantee the safety and stability ...