Under the background of big data and Internet finance, quantitative investment is becoming
more and more critical, and the prediction of the stock price has become the focus of …

Purpose The purpose of this paper is to provide reference for researchers by reviewing the
research advances and trend of agricultural product price forecasting methods in recent …

Deep learning is widely used to automate processes, improve performance, detect patterns,
and solve problems. Thus, applications of deep learning are limitless. Particle swarm …

The existing grey wolf optimization algorithm has some disadvantages, such as slow
convergence speed, low precision and so on. So this paper proposes a grey wolf …

Existing communication networks have inherent limitations in translation theory and adapt to
address the complexity of repairing new remote applications at the highest possible level …

Abstract Extreme Learning Machine (ELM) is swiftly gaining popularity as a way to train
Single hidden Layer Feedforward Networks (SLFN) for its attractive properties. ELM is a fast …

针对基本灰狼算法易陷入局部最优, 未考虑个体自身经验等问题, 本文提出一种基于Tent
映射的混合灰狼优化算法(grey wolf optimization algorithm based on particle swarm …

The lateral stability is the crucial feature in a distributed drive electronic vehicle (DDEV). A
high speed DDEV in a sharp turn may lose the lateral stability when it encounters fast varied …

With the rapid development of economic globalization, the trade of various countries has
become increasingly close and thus the rapid growth of container transportation business …

The fault prediction problem for modern industrial equipment is a hot topic in current
research. So, this paper first proposes a dynamic cuckoo search algorithm. The algorithm …