Aims and scope

Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.

The application areas of interest include but are not limited to:

    Interval Analysis (Real Interval Arithmetics, Complex Interval Arithmetics, Interval Equations, etc.)
    Interval Mathematics (Metric Topology for Intervals, Interval Integrals, Interval Differential Equations, etc.)
    Interval Computation (Matrix Computation with Intervals, Systems of Interval Equations, etc.)
    Fuzzy Sets (Fuzzy Numbers, Extension Principle, Fuzzy Rough Sets, Fuzzy Competence Sets, etc.)
    Fuzzy Systems (Fuzzy Control, Fuzzy Neural Networks, Genetic Fuzzy Systems, Hybrid Intelligent Systems, etc.)
    Fuzzy Logics (Many-Valued Logics, Type-2 Fuzzy Logics, Intuitionistic Fuzzy Logics, etc.)
    Fuzzy Mathematics (Fuzzy Differential Equations, Fuzzy Real Analysis, Fuzzy Topology, Fuzzy Algebra, etc.)
    Fuzzy Optimization (Possibilistic Programming, Fuzzy Linear and Nonlinear Programming, Fuzzy Stochastic Optimization, etc.)
    Fuzzy Statistical Analysis (Fuzzy Random Variables, Fuzzy Regression Analysis, Fuzzy Reliability Analysis, Fuzzy Times Series, etc.)
    Operations Research (Fuzzy Games, Fuzzy Inventory Models, Fuzzy Queueing Theory, Fuzzy Scheduling Problems, etc.)
    Heuristics (Ant Colony Optimization, Artificial Immune Systems, Genetic Algorithms, Particle Swarm Intelligence, Simulated Annealing, Tabu Search, etc.)
    Hybrid Systems (The fusion of Fuzzy Systems and Computational Intelligence)
    Approximate Reasoning (Possibility Theory, Mathematical Theory of Evidence, Fuzzy Common Knowledge, etc.)
    Miscellaneous (Fuzzy Data Mining, Fuzzy Biomedical Systems, Pattern Recognition, Fuzzy Clustering, Information Retrieval, Chaotic Systems, etc.)

Special Issue on Quantum Machine Learning: From Theoretical Foundations to Practical Implementations
Submission Date: 2026-04-03

Description

Quantum computing is transforming the way we process information by leveraging the principles of quantum mechanics. Unlike classical bits, quantum bits (qubits) can exist in multiple states simultaneously, allowing for significantly faster computations. This advancement presents new opportunities across various fields, especially in cybersecurity, where traditional methods struggle to keep up with evolving threats. Quantum Machine Learning (QML) combines quantum computing with artificial intelligence, offering novel ways to detect and prevent cyberattacks. This special issue will explore the potential of QML in strengthening cybersecurity through innovative models, algorithms, and practical applications.

Despite advancements in cybersecurity, digital systems remain vulnerable to increasingly sophisticated threats such as malware, ransomware, and data breaches. Traditional security measures often fall short in handling large-scale cyberattacks, requiring more adaptive and intelligent solutions. QML has the potential to revolutionize cybersecurity by enhancing real-time threat detection, improving anomaly recognition, and developing cryptographic techniques that resist quantum attacks. However, several challenges remain, including the scalability of quantum algorithms, hardware limitations, and the need for optimized quantum-classical hybrid models. Addressing these issues is crucial for the practical implementation of QML in cybersecurity frameworks.

Aligned with the journal's commitment to advancing computational intelligence and soft computing, this special issue will investigate emerging applications of Quantum Machine Learning (QML) in enhancing cybersecurity. As quantum technologies evolve, innovative computational approaches are needed to fully harness QML's potential for detecting and mitigating cyber threats. This issue will feature a mix of theoretical research, experimental studies, and practical applications aimed at addressing current challenges and driving progress in the field. We invite researchers and industry experts to contribute original research, review articles, and case studies that bridge computational intelligence with cybersecurity, ultimately advancing next-generation cyber defense systems powered by QML.

Potential topics include but are not limited to the following:

    Quantum-Inspired AI and Machine Learning for applications in cybersecurity
    Quantum algorithms for advanced malware detection and forensic analysis.
    Enhancing security measures with quantum-inspired algorithms for cyber defense.
    Developing quantum-resistant encryption techniques for secure communications.
    QML for more efficient cyber threat detection and prevention.
    Applying QML for predictive cyber risk modeling in threat intelligence and risk assessment.
    Identifying research gaps and proposing solutions to drive future QML advancements in cybersecurity.
    Post-Quantum Cryptography