Applied Intelligence

Berth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation ...

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if ...

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death ...

Many machine learning methods and models have been proposed for multivariate data regression and classification in recent years. Most of them are supervised learning methods, which require a large number of labeled data. Moreover, current methods ...

Clickbait is one of the form of false content, purposely designed to attract the user’s attention and make them curious to follow the link and read, view, or listen to the attached content. The teaser aim behind this is to exploit the curiosity ...

Dominance resistance is a challenge for Pareto-based multi-objective evolutionary algorithms to solve the high-dimensional optimization problems. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) still has such disadvantage even though it ...

Ever since the success of naive Bayes (NB) in achieving excellent classification performance and the least computational overhead, more and more researchers have focused their attention on the Bayesian network classifiers (BNCs). Among numerous ...$\mathcal{}$$\mathcal{}$$\mathcal{}$

Aspect-based sentiment analysis is a challenging subtask of sentiment analysis, which aims to identify the sentiment polarities of the given aspect terms in sentences. Previous studies have demonstrated the remarkable progress achieved by memory ...

The goal of sentence matching is to determine the semantic relation between two sentences, which is the basis of many downstream tasks in natural language processing, such as question answering and information retrieval. Recent studies using ...

As most of Multi-Objective Evolutionary Algorithms (MOEAs) scale quite poorly when the number of objective functions increases, new strategies have been proposed to face this limitation. Considered one of the most well-succeeded examples of such ...

The hardware platform is a significant consideration in efficient CNN model design. Most lightweight networks are based on GPUs and mobile devices. However, they are usually not efficient nor fast enough for desktop CPU platforms. In this paper, ...

To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using ...

The multistep-ahead prediction of chlorophyll provides an effective means for early warning of red tide. However, since multistep-ahead forecasting presents challenges, such as vague interactive relationships among ocean factors, long-term ...

Identifying influential nodes in complex networks is an open and challenging issue. Many measures have been proposed to evaluate the influence of nodes and improve the accuracy of measuring influential nodes. In this paper, a new method is ...

Aspect-based sentiment analysis aims to predict the sentiment polarity of each specific aspect term in a given sentence. However, the previous models ignore syntactical constraints and long-range sentiment dependencies and mistakenly identify ...

Array imperfections severely degrade the performance of most physics-driven direction-of-arrival (DOA) methods. Deep learning-based methods do not rely on any assumptions, can learn the latent data features of a given dataset, and are expected to ...

A multi-agent system (MAS) is expected to be applied to various real-world problems where a single agent cannot accomplish given tasks. Due to the inherent complexity in the real-world MAS, however, manual design of group behaviors of agents is ...

This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still lead to unexpected ghosting, blurred, edges as well as blocking effects ...

With the popularization of information technology and the acceleration of the people’s pace of life, the takeout food industry is prevailing. The choice of order allocation mode plays an important role in order delivery efficiencies. This paper ...

Recently, object detection has been a vital aspect in the vision community, while scale variation of objects in images or videos usually brings challenge for performance improvement. To combat this problem, conventional paradigms generally adopt ...

Label flipping attack is a poisoning attack that flips the labels of training samples to reduce the classification performance of the model. Robustness is used to measure the applicability of machine learning algorithms to adversarial attack. ...

Multi-relation Question Answering is an important task of knowledge base over question answering (KBQA), multi-relation means that the question contains multiple relations and entity information, so it needs to use the fact triples in the ...

Inspired by biological immune systems, the field of artificial immune system (AIS), particularly the negative selection algorithm (NSA), has been proved effective in solving computational problems. However in practical applications, NSA still ...

A novel technique is proposed for maintaining the diversity of sample features and modeling imbalanced datasets in multi-channel time-varying signal classification. The RBF-LVQPNN consists of a time-varying signal input layer, an RBF process ...

Quantization, which involves bit-width reduction, is considered as one of the most effective approaches to rapidly and energy-efficiently deploy deep convolutional neural networks (DCNNs) on resource-constrained embedded hardware. However, bit-...

Particle Swarm Optimization (PSO) is a computational method in which a group of particles moves in search space in search of an optimal solution. During this movement, each particle updates its position and velocity with its best previous position ...

Text style transfer, the aim of which is to convert a specific style in a given sentence to another target style while maintaining the style-independent content information of the original sentence, can face challenges when applied to non-parallel ...

With the recent significant growth of virtualization and cloud services, the data center network (DCN) as the underlying infrastructure is more important. The increasing and changing volume of workloads highlights critical issues such as load ...

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a ...

Cooperative control is currently a challenging topic of crowded unmanned underwater vehicle (UUV) swarm. However, individual behavior conflict and chain-avalanche collision involved in this swarm are easily triggered due to the fluctuations and ...