Aims and scope

Soft Computing disseminates significant results in the foundations, methodologies, and applications of soft computing. The journal promotes integrating theoretical and practical findings into everyday and advanced applications while connecting soft computing with other disciplines.

It focuses on system solutions based on paradigms such as evolutionary algorithms, genetic programming, swarm intelligence, neural networks, fuzzy systems, Bayesian networks, and chaos theory.

The journal fosters comparisons, extensions, and new applications across disciplines, offering an international forum for researchers and practitioners in this rapidly evolving field and serving as a unifying platform.

Soft Computing - Section Foundation, Algebraic and Analytical Methods in Soft Computing

The Section Foundation, Algebraic and Analytical Methods in Soft Computing welcomes original and significant contributions to the mathematical and logical foundations of soft computing and related areas.

The Section is a forum that aims at promoting, circulating, and stimulating the research on mathematical foundations of soft computing intended in a broad sense as a method for dealing with non-crisp information, for instance, characterized by the presence of vagueness, imprecisions, high complexity, or uncertainty. Relevant topics include, but are not limited to:

• Algebra and algebraic logic,
• Computational paradigms and computational complexity,
• Description logic, temporal logic, dynamic logic, and modal logic,
• Domain theory and type theory,
• Fuzzy logic, fuzzy set theory, and many-valued logic,
• Substructural logic,
• Probability logic, belief functions, etc.

Soft Computing – Section Application of Soft Computing

The Section Application of Soft Computing aims to promote and stimulate research in the field of the development of enhanced computational systems based on innovative or consolidated soft computing methodologies such as fuzzy logic, neural networks, and evolutionary algorithms. Under this perspective, particular attention is given to hybrid and agent-based systems, i.e., computational frameworks that use different soft computing approaches in a synergic way to face the intrinsic complexities characterizing real environments and exploit imprecise and vague information that strongly characterizes real-world scenarios. As a consequence, this journal section seeks original research contributions with a focus on applications belonging but not limited to, the following areas:

• Computer networks,
• Data mining,
• Image and video processing,
• Intelligence agents,
• Machine learning,
• Pattern recognition,
• Robotics,
• Web intelligence.

Soft Computing – Section Fuzzy Systems and their Mathematics

The Section Fuzzy Systems and their Mathematics focuses on contributions handling uncertainty and imprecision by extending classical set theory and logic. Applications span control systems, decision-making, pattern recognition, and optimization. We invite researchers, academics, and practitioners to submit original manuscripts exploring the mathematics and applications of fuzzy systems. Contributions highlighting advancements in modeling complex phenomena, decision-making, control systems, pattern recognition, and optimization are particularly welcome. This journal seeks papers that integrate fuzzy systems with machine learning and optimization techniques, emphasizing computational efficiency and adaptability. Topics of interest include, but are not limited to: 

• Fuzzy set theory and logic,
• Handling uncertainty and imprecision,
• Mathematical modeling of fuzzy systems,
• Applications in control systems,
• Decision-making processes using fuzzy logic,
• Pattern recognition with fuzzy inference,
• Optimization techniques in fuzzy environments,
• Integration of fuzzy systems with machine learning,
• Adaptive and scalable fuzzy algorithms,
• Real-world case studies in fuzzy system applications.

Soft Computing – Section Mathematical Methods in Data Science

The Section Mathematical Methods in Data Science Mathematical welcomes original and significant contributions to the methods that form the backbone of data science, providing theoretical foundations for modeling, analysis, and interpretation of data. This journal invites researchers, practitioners, and academics to submit original research and review articles exploring the role of mathematical methods in data science. Papers addressing advancements in graph theory, numerical methods, information theory, and mathematical programming are highly encouraged. Contributions highlighting innovative methodologies, theoretical breakthroughs, and practical applications to solve complex data science problems are also of particular interest. Topics of interest include, but are not limited to: 

• Mathematical foundations of data science,
• Graph theory and network analysis,
• Numerical methods in data analysis,
• Information theory and entropy,
• Optimization techniques in data science,
• Machine learning and mathematical modeling,
• Data-driven decision-making,
• Computational efficiency in data methods,
• Statistical analysis in large datasets.

Soft Computing – Section Data Analytics and Machine Learning

This Section welcomes original and significant contributions to data analytics and machine learning, which are interconnected disciplines that extract insights and predict patterns from complex datasets. Data analytics examines, cleans, transforms, and interprets data to derive actionable insights. Machine learning builds on this by developing algorithms that learn from data to make predictions or automate decisions without explicit programming. Submissions focusing on supervised, unsupervised, or reinforcement learning are encouraged, along with works on feature engineering, dimensionality reduction, and model evaluation. We welcome research showcasing the application of these methods in areas such as business intelligence, healthcare, finance, and beyond. Topics of interest include, but are not limited to:

• Data analytics techniques,
• Machine learning algorithms,
• Supervised learning,
• Unsupervised learning,
• Reinforcement learning,
• Feature engineering,
• Dimensionality reduction,
• Model evaluation and validation.

Soft Computing – Section Optimization

Section Optimization stimulates researchers and practitioners to submit original contributions on advancements in the field of optimization. Optimization plays a critical role in various fields, such as engineering, economics, data science, and artificial intelligence. In this section are techniques for solving linear and nonlinear programming, integer programming, dynamic programming, and heuristic or metaheuristic approaches. Submissions exploring gradient-based optimization, evolutionary algorithms, and machine learning-based optimization methods are highly encouraged. We welcome papers focusing on real-world applications such as resource allocation, scheduling, decision-making under uncertainty, and systems optimization. Topics of interest include, but are not limited to:

• Linear programming,
• Nonlinear programming,
• Multi-Objective Optimization
• Integer programming,
• Dynamic programming,
• Heuristic and metaheuristic approaches,
• Gradient-based optimization,
• Evolutionary algorithms,
• Machine learning optimization methods.

Soft Computing – Section Soft Computing in Decision Making and Modelling in Economics

Section Soft Computing in Decision Making and Modeling in Economics welcomes researchers to submit original contributions exploring the application of soft computing techniques in decision-making and economic modeling. Soft computing techniques are increasingly applied in decision-making and economic modeling. These approaches handle uncertainty, imprecision, and complex relationships often found in economic systems. Submissions focusing on the development of models that enhance decision-making processes, forecasting, and policy analysis are highly encouraged. We welcome research that showcases innovative solutions for managing economic challenges, such as risk assessment, resource optimization, and market dynamics. Topics of interest include, but are not limited to:

• Soft computing in decision-making,
• Economic modeling with uncertainty,
• Fuzzy logic in economics,
• Risk assessment using soft computing,
• Resource optimization in economic systems,
• Forecasting using soft computing methods,
• Policy analysis with soft computing techniques,
• Complex relationship modeling in economics,
• Market dynamics and soft computing approaches.

Soft Computing – Section Neural Networks

This section invites researchers to submit original contributions focused on advancements in neural networks. Neural networks excel at handling complex, nonlinear relationships and can be applied to various tasks such as classification, regression, image recognition, and natural language processing. Topics of interest include developing and using neural network architectures for classification, regression, image recognition, natural language processing, and time-series analysis. Submissions exploring deep learning, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other specialized architectures are encouraged. Contributions addressing training, optimization, scalability, and real-world application challenges are welcome. Topics of interest include, but are not limited to:

• Classification,
• Regression,
• Image recognition,
• Natural language processing,
• Time-series analysis,
• Deep learning,
• Convolutional neural networks (CNNs),
• Recurrent neural networks (RNNs),
• Training and optimization of neural networks.

Soft Computing – Section Algebraic and Analytical Methods in Soft Computing

This section welcomes original contributions focused on algebraic and analytical methods in soft computing. Algebraic and analytical methods are fundamental to developing soft computing techniques, providing the mathematical tools for modeling uncertainty and imprecision. Again, this section focuses on the development and application of algebraic structures and analytical techniques. Submissions addressing the use of these methods to enhance decision-making, pattern recognition, and system optimization are encouraged. Contributions that explore hybrid approaches combining algebraic and analytical methods are highly valued. Topics of interest include, but are not limited to:

• Algebraic structures (fuzzy sets, lattices, Boolean algebra),
• Analytical methods (differential equations, optimization),
• Soft computing techniques,
• Modeling uncertainty and imprecision,
• Decision-making with algebraic methods,
• Pattern recognition using analytical techniques,
• Hybrid approaches in soft computing,
• System optimization,
• Mathematical foundations for soft computing.

Articles published in Soft Computing (SOCO) support United Nations Sustainable Development Goals (SDGs):

    Affordable and clean energy (SDG 7)
    Industry, innovation and infrastructure (SDG 9)
    Sustainable cities and communities (SDG 11)
    Responsible consumption and production (SDG 12)