Applied Soft Computing

Feature selection aims to choose a subset of features with minimal feature-feature correlation and maximum feature-class correlation, which can be considered as a multi-objective problem. Grey wolf optimization mimics the leadership ...

• A multi-objective grey wolf optimization for feature selection is proposed.
• A ...

Time series is mostly with a chaotic nature and non-stationary characteristic in real-word, which makes it difficult to be modeled and predicted accurately. To solve this problem, we introduce a novel self-organizing modular neural ...

• EMD is used to decompose time series based on time characteristic dynamically, which eliminates the drawback of some subnetworks with few training data and ...

As an important expanded quantification fuzzy rough set model, the local fuzzy rough set model is used to measure relative quantitative information between the fuzzy similarity classes and the basic concept. Although many studies have ...

• Relations between similarity classes and fuzzy sets are discussed more comprehensively.

Industrial process data are naturally in the form of complex time-series with high nonlinearities and dynamics. Long short-term memory (LSTM) networks are suitable for developing prediction models to handle nonlinear and dynamic ...

• An adaptive learning algorithm based on l2-norm regularization is introduced for parameter adjustment.

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-...

• An asynchronous parallel framework is designed to boost particle swarm optimization.

The widespread use of plastic products worldwide generates an enormous amount of waste. Plastic is a substance that is not biodegradable and is permanent in the environment. The environment and living beings are negatively affected by ...

• Dombi t-conorm and t-norm operations under the IVSF environment are introduced.

Instant delivery is an important part of urban logistics distribution, which realizes point-to-point distribution between merchants and customers. During the peak period of orders, instant delivery is a large-scale variable NP-hard ...

• A knowledge base is established by the order priority knowledge and the feature knowledge of feasible scheme.

Metaheuristic algorithms are currently being studied in depth by many scholars, and it is an important task to improve the learning and adaptive capabilities of the algorithms so that they can be of great use in a wide range of ...

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• Analyze MRFO and propose corresponding strategies.
• Designed progressive ...

This paper presents a system for multi-label classification of text data processed in Call/Contact Centre (CC) systems. The solution presented herein constitutes a significant innovation and an advantage in relation to the solutions ...

• Development of a multi-label classification method for the Contact Center industry.

Zeroing neural network (ZNN) is a powerful tool in designing suitable control schemes since it is a systematic approach. It has been used in fields like robot manipulator control and tracking control, but few researchers have ...

• This study provides an insight into the application of ZNN in networked control systems.

Forest fires are an abrupt and highly destructive meteorological disaster that can occur in all regions of the world, resulting in significant ecological, economic and social losses. Moreover, the causes of forest fire disasters are ...

• Multi-granularity T-spherical fuzzy incomplete information systems are built.
• ...

Air quality prediction is a crucial issue in air pollution control and plays a vital role in environmental preservation and the promotion of sustainable development. A novel air quality prediction framework, referred to as Correlation-...

• Propose a new model CRINet for air quality prediction.
• The proposed model used ...

When solving large-scale many-objective optimization problems (LMaOPs), due to the large number of variables and objectives involved, the algorithm is faced with a very high-dimensional and complex search space, which is difficult to ...

• A universal large-scale many-objective optimization framework based on cultural learning (UCLMO) is proposed.

Big data analytics is important in many businesses that use computing applications like real-time shopping and e-commerce. Big data is employed for promoting goods and improve connectivity betwixt retailers and customers. People today ...

• Big data analytics is used in real-time shopping and e-commerce.
• The data’s are ...

This paper addresses the problem of efficient automation system configuration for mass customization in industrial manufacturing. Due to the various demands from customers, production lines need to adjust the process parameters of the ...

• Reinforcement learning is adopted to mass customization for strip rolling.
• The ...

Skeleton-based action recognition is widely used in varied areas such as human–machine interaction and virtual reality. Benefit from the powerful expression ability to depict structural data, graph convolutional networks (GCNs) have ...

• A multi-stream model Global–Local Motion Fusion Network is proposed.
• Grouping ...

Evolutionary multi-task optimization (EMTO) is a new branch of evolutionary algorithm (EA) that aims to optimize multiple tasks simultaneously within a same problem and output the best solution for each task. EMTO utilizes the ...

• To better organize these respectable research works and inspire future researchers, this paper reviews the related works on EMTO in the following three ...

Predicting long-term stock index prices is a challenging and debatable task. Most of the studies focus on predicting next-day stock prices. However, those are not useful to long-term investors and traders. In this paper, we attempt to ...

• Study tests SVR models to predict long-term prices of global indices.
• OGA-SVR ...

Hesitant fuzzy sets (HFSs) are a powerful tool to describe uncertain and vague information, whose relationship can be analyzed and mined by clustering algorithms. The partitional idea is widely used in clustering analysis for real-...

• Propose a HFKM clustering algorithm based on the mean but not HFA operator.
• ...

Prior studies have noted the importance of ceiling temperature distribution prediction of tunnel fires. To overcome the limitations of precise modeling difficulties for model-driven methods and biased toward the convergence for the ...

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• A hybrid model-driven and data-driven fusion algorithm is developed.
• The ...

The growing demand for machine learning applications in industry has created a need for fast and efficient methods to develop accurate machine learning models. Automated Machine Learning (AutoML) algorithms have emerged as a promising ...

• Fast exploration of large search spaces, identifying key operators for the task.

The data of target detection in remote sensing images are diverse, and the detection results of some categories with a small number of samples are poor. In order to solve this problem, most of the existing methods focus on the category ...

• A loss-based sample selection mechanism is proposed.
• Select between the ...

Prediction of a non-stationary time-series is hard as the frequency components and their amplitudes in the series vary randomly over time. This paper proposes a 2-phase approach for prediction of such non-stationary time-series. The ...

• The paper proposes an intelligent approach for time-series forecasting by Taylor series.

Public institutions, such as law enforcement agencies or health centers, have a vast volume of unstructured text documents, e.g. police reports. Currently, before this data can be shared (e.g. with research institutions), it must go ...

• First tool to combine document anonymization, geoparsing, geocoding & visualization.

The Bitcoin market is firmly positioned as a global asset market. However, due to its extremely high volatility and the lack of a custom Bitcoin trading system, investors find it difficult to establish an effective investment strategy ...

• Build an intelligent Bitcoin trading system for maximum profitability.
• Provided ...

Efficient and stable global optimizers constitute a noteworthy arena of academic study and real-world applications. Since Multi-scale Quantum Harmonic Oscillator Algorithm inspired by the quantum motion for solving optimization ...

• We propose a general framework for a class of quantum-inspired algorithms.
• A ...

Research on human action recognition based on skeletons has received much attention. But most of the research focuses on improving the model’s generalization ability, while ignoring significant efficiency issues. This leads to ...

• A lightweight human action recognition neural network structure.
• Can be quickly ...

With the development of Internet of Things (IoT), Mobile CrowdSensing (MCS) platform will release projects consisting of heterogeneous tasks, requiring participants with different skills to collaborate to develop such systems. In this ...

• Constructing a heterogeneous multi-project multi-task allocation model in MCS.
• ...

The world steel industry is highly dependent on the use of electric arc furnaces (EAFs). The application of the electric arc phenomenon causes many power quality (PQ) problems, such as harmonics or voltage flickering. An adequate EAF ...

• LSTM networks can effectively model electric arc furnace stochastic properties.