MLMI

This paper presents strategies to tweak the probability distribution of the data set to bias the training process of a neural network for a better learning outcome. For a real-world problem, provided that the probability distribution of the population ...

With the rapid development of mobile applications, the ride-hailing services such as Uber in America and Didi-taxi in China have been very popular all over the world as they provide convenience to the users. A key factor that makes the ride-hailing ...

Stock traders nowadays attach increasing importance to artificial intelligence and machine learning techniques to construct better-performing stock portfolios. In this paper, a stock-selection model based on support vector machine (SVM) is applied to ...

Notably, all neural network models are trained by using gradient descent, and by far, the most successful approach for machine learning is to use gradient descent. However, this is a greedy algorithm and hits some of the biggest open problems in the ...

Accurate prediction of traffic congestion is an important for people's travel and the building of smart city. However, the inherent non-linear relationships and spatiotemporal autocorrelation remain big challenges. To overcome these issues, we propose a ...

In recent years, with the rapid development of deep learning, computer vision technology based on convolutional neural network (CNN) is widely used in industrial fields. At present, surface defect detection by machine vision is one of the most mature ...

Train automatic stop control (TASC) is one of the key techniques of Automatic train operation (ATO) to achieve high stopping precision. Aiming to improve accurate stopping performance, this paper proposes a novel TASC method based on double deep Q-...

The stock market is a barometer of a country's economic situation. The research on the stock market is always highly valued, and the prediction of short-term stock price trends is the focus of investors. The stock price data not only has time-domain ...

This article studies the performance of different evolutionary strategies for deep reinforcement learning policy optimization. The policy will be centred in an important swarm robotic task: the aggregation of simple robots in the environment. The main ...

Existing approaches for video action recognition using convolutional neural network (CNN) usually suffer from the trade-off between accuracy and complexity. On the one hand, the 2D CNNs have difficulty in modeling the long-term temporal dependencies ...

Machine Learning is a subset of Artificial Intelligence, which is a process of learning from different types of data to make accurate predictions. Data in tourism is various such as Statistics, Photos, Maps, and Texts. Also, each tourism cycle has ...

The human mind is naturally conditioned to assess the confidence of another speaker. Hence, confidence while speaking is crucial for success across most domains and situations. Confidence in speech is a highly useful trait to have when engaged in ...

Behavior design for Automated Guided Vehicles (AGV) systems is an active research area, fundamental for robotics, industrial systems automation. The rise of machine learning neural systems and deep learning make promising results in a multitude of areas ...

Dirichlet Process Mixture (DPM) models tend to produce some major clusters along with many small clusters. These small confusing clusters are highly overlapped with major clusters. As the size of samples increasing without the change of sample ...

Electric business, also called E-business, or digital business, might be a solution to the so-called Chinese “three agricultural problems”. In order to find the absence of specific agricultural software, we carry out the data scratching of agricultural ...

At present, most recurrent neural network models used in text classification are shallow models and have limited ability to express texts especially large scale texts. This paper conducts an empirical study on the use of character-level deep recurrent ...

Intrusion detection is one of the key technologies to ensure the security of cyberspace. In this paper, a detection model of Bi-LSTM, whose powerful serialization modeling function can discover the time series characteristics from network data, combined ...

The Bahl-Cocke-Jelinek-Raviv (BCJR) detector for turbo equalization over coded partial-response channels has a complexity growing exponentially with channel memory length. In this paper, we consider the soft-in/soft-out (SISO) channel detection from a ...

The application of object detection is becoming more and more widely in various fields, including the power industry, of course. And YOLOv3 is one of the most popular algorithms in the field of object detection owing to its high performance and ...

In this paper, we present a Faster R-CNN based object detection scheme to automatically map the parking spaces in a parking lot, instead of manually mapping them. The work addresses an important gap in the recent computer vision based artificial ...

How to efficiently and accurately extract water bodies from remote sensing images is the focus of scholars' research. Current research often does not make full use of the unique multi-band data of remote sensing images. This paper proposes an improved ...

Mahjong is a popular and traditional tile-based game in China, which has a history of several hundred years. In general, Mahjong is played by four players, and each player begins with 13 tiles and changes (i.e., draws and discards) tiles in turn, until ...

The increasing complexity of modern power systems has led to the emergence of large-scale dynamic economic dispatch (DED) problems. To solve a large-scale DED problem with high-dimensional decision variables and various constraints is still a challenge ...

In order to improve the computing performance and security of RFID authentication protocol, an improved lightweight RFID two-way authentication protocol is proposed. With the authorization of a small amount of additional information, the two-way ...

Restarting automata have been introduced as a formal tool to model the analysis by reduction, which is a linguistic technique to analyze sentences of natural languages. In earlier works, we have only studied the nondeterministic version of restarting ...