Diffusion-weighted MRI tractography combining with fiber clustering is a typical approaches of white matter tract segmentation. However, due to the anatomical accuracy of the diffusion-weighted images (DWI) and complicated processing procedure, this ...

With the rapid economic development and increasingly frequent language exchanges in Tibet, the traditional statistical machine translation methods are faced with problems such as lack of data and over-fitting of training, resulting in poor translation ...

Subject to many factors such as material properties, load, chloride ion, carbonization, abrasion, alkali aggregate reaction, test error and so on, the freeze-thaw degradation process is of high uncertainties, which means great difficulty in modeling and ...

Fraud detection is the focus of research in Internet financial domain. The methods of both expert rules and machine learning detect fraud by seeking commonness among groups and individual differences between normal and abnormal transactions. However, ...

Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, ...

It has been a key issue for solving problems efficiently by machine learning models with code categorical features. The state-of-the-art one-hot coding is a widely accepted method to convert the categorical features into numerical values. However, it ...

With the rapid development of Internet finance, credit scoring has played a significant role in peer to peer lending platforms. However, the massive and high-dimensional characteristics of the credit data make it difficult to directly build the credit ...

With the rapid development of online open courses, it has become a hot issue in educational research to use learning analysis technology to explore online learning characteristics and provide support for the improvement of teaching methods and academic ...

Given examinees' performance (i. e., scores) on each problem, cognitive diagnosis models can discover the latent characteristics of examinees. Traditional cognitive diagnosis models require teachers to provide scores in time. Thus we can hardly apply ...

Massive Open Online Courses (MOOCs) have played an increasingly crucial role in education, but the high dropout rate problem is great serious. Predicting whether students will dropout has attracted considerable attention. Current methods mainly depend on ...

Few-shot learning, which learns novel concept from very few samples given some base categories that have sufficient training samples, is a significant yet practical task since plenty of real-world categories have merely very limited samples. The main ...

Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years because it can liberate the labor force of annotating data. However, in many cases, the target data is scarce because it is difficult to obtain, at this time, the ...

Retinal vessel segmentation is indispensable for ophthalmic computer-aided diagnosis (CAD) and large-scale automatic detection system of retinal diseases. Recently, various convolutional neural networks (CNN) have been applied in retinal vessel ...

Question answering (QA) is one of the most significant tasks in artificial intelligence (AI) that not yet solved completely. It is a task which requires building data models capable of providing answers to questions expressed in human language. Full ...

Recent advance of deep recurrent neural network has brought significant improvements for time series prediction of financial market data. In this paper we present the design, implementation and evaluation of an end-to-end deep learning based trading ...

Online panoramic video has recently gained enormous popularity. Tile-based adaptive streaming is a promising method to deliver a panoramic video for the sake of bandwidth saving. However, it's challenging to estimate the user's field of view (FoV) and ...

The improvement of traffic flow prediction accuracy is of great significance for an Intelligent Transportation System. However, most current prediction methods are based on the complete or relatively complete datasets. However, complete traffic datasets ...

In this paper, we propose an attack detection framework in the Internet of Things (IoT) devices. The framework applies a data-centric method to process the energy consumption data and classify the attack status of the monitored device. We implement the ...

Unmanned scene recognition means that unmanned vehicles can collect environmental data from equipped sensors and make decisions through algorithms, in which deep learning has become one of key technologies. Especially, with the discovery of adversarial ...