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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 2860

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Prof. Dr. Zong Woo Geem

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Department of Smart City, Gachon University, Seongnam, Republic of Korea
Interests: computational intelligence; engineering optimization; harmony search
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Special Issue Information

Dear Colleagues,

This Special Issue invites state-of-the-art research on intelligent systems. It will also include selected papers from the conference of the 26th International Symposium on Advanced Intelligent Systems (ISIS 2025, https://isis-kiis.org/), which will be held at Cheongju, South Korea on 6–9 November 2025. The topics of the contributed papers will include various intelligent techniques and their real-world applications.

Prof. Dr. Zong Woo Geem
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Keywords

computational intelligence
generative AI
theory of AI
applications of AI
machine learning
fuzzy system
evolutionary and optimization algorithms
soft computing
data mining

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Advances in Intelligent Systems in Applied Sciences (13 articles)

Published Papers (6 papers)

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Research

34 pages, 6959 KB

Open AccessArticle

Warehouse Mobile Robot Path Planning Performance Sensitivity to the Neighbor Radius Parameter

by Jihong Jeong and Jin-Woo Jung

Appl. Sci. 2026, 16(8), 3941; https://doi.org/10.3390/app16083941 - 18 Apr 2026

Viewed by 213

Abstract

Many RRT*-based sampling path planning algorithms consider neighboring nodes around a newly added node. The neighbor radius parameter

r_{n e a r}

determines which nodes are included. The performance of RRT*-based algorithms can vary significantly with

r_{n e a r}

. [...] Read more.

Many RRT*-based sampling path planning algorithms consider neighboring nodes around a newly added node. The neighbor radius parameter

r_{n e a r}

determines which nodes are included. The performance of RRT*-based algorithms can vary significantly with

r_{n e a r}

. This variation can weaken generalization across environments. This paper quantitatively analyzes the effect of

r_{n e a r}

on performance in sampling-based path planning for mobile robots in a warehouse environment. We evaluate RRT*-based algorithms by varying

r_{n e a r}

. We then select the heuristic chosen

r_{n e a r}

for each algorithm and compare the algorithms under the same conditions. Experiments are conducted in a warehouse environment with a fixed start position and five goal positions. Performance is evaluated using planning time, path length, and cumulative change in turning angle. Lower values indicate better performance for all three metrics. Based on the experimental results, we derive a heuristic value of

r_{n e a r}

for each case. We also identify algorithm characteristics in computational efficiency and path quality under the heuristically chosen parameter settings. The final goal of this study is to provide quantitative evidence for selecting

r_{n e a r}

in warehouse applications. We also present guidelines for parameter setting and algorithm selection for RRT*-based sampling path planning. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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32 pages, 1293 KB

Open AccessArticle

Early Detection of Re-Identification Risk in Multi-Turn Dialogues via Entity-Aware Evidence Accumulation

by Yeongseop Lee, Seungun Park and Yunsik Son

Appl. Sci. 2026, 16(8), 3680; https://doi.org/10.3390/app16083680 - 9 Apr 2026

Viewed by 449

Abstract

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is attributed to its originating conversational participant, and evidence is accumulated in entity-isolated subgraphs that prevent cross-entity contamination. The system signals re-identification onset

t_{pred}

at the earliest turn where combination-based rules grounded in the uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the gated NER configuration leads on primary timeliness (OW@5

= 73.4 %

, MAE

= 1.357

turns); the full system achieves OW@5

= 70.7 %

with MAE

= 2.442

turns as an alternative operating mode for ambiguity-sensitive disclosure patterns. We further evaluate behavior on a 300-record mutation stress set, test RULE_B on the ABCD external corpus, and supplement RULE_A evaluation with both a proxy-labeled transfer analysis on PersonaChat and a manual annotation study on 151 Switchboard dialogues. The reported results should be interpreted as an initial empirical reference point rather than a sufficient endpoint for autonomous runtime enforcement. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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25 pages, 14256 KB

Open AccessArticle

Handling Multimodality in Pareto Set Estimation via Cluster-Wise Decomposition

by Yuki Suzumura, Yoshihiro Ohta and Hiroyuki Sato

Appl. Sci. 2026, 16(8), 3655; https://doi.org/10.3390/app16083655 - 8 Apr 2026

Viewed by 299

Abstract

Multimodal multi-objective optimization problems often exhibit one-to-many mappings, where multiple distinct variable vectors correspond to the same objective vector. This characteristic makes Pareto set (PS) estimation difficult, as conventional inverse modeling approaches assume a one-to-one correspondence. This study proposes a cluster-wise PS estimation framework in the variable space. Known solutions are partitioned into locally monotonic clusters using oscillation detection with an amplitude threshold, and independent response surface models are constructed for each cluster. By estimating PS solutions from multiple cluster-specific models for a given direction vector, the method preserves multimodal structures that conventional approaches fail to capture. Numerical experiments on the multimodal benchmark problems MMF1–8 and LIRCMOP1–2 demonstrate that the proposed method achieves equal or better HV and IGD values than the conventional method, while improving decision-space approximation as measured by IGDX in most test cases and suppressing the generation of dominated solutions. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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20 pages, 3452 KB

Open AccessArticle

Effectiveness of Experience-Sharing Group Learning in Deep Reinforcement Learning

by Keita Muroya, Makoto Ikeda and Akira Notsu

Appl. Sci. 2026, 16(7), 3250; https://doi.org/10.3390/app16073250 - 27 Mar 2026

Viewed by 370

Abstract

Deep reinforcement learning faces a critical trade-off between computational cost and performance. This study proposes an experience-sharing group-learning framework in which multiple agents with different network sizes collaboratively learn a single task through a shared experience replay memory. Unlike conventional multi-agent approaches that assume homogeneous agents, our method enables agents with different computational capabilities to share experiences, allowing low-performance agents to benefit from high-performance agents’ quality experiences. The proposed method was evaluated in CartPole and Super Mario Bros environments. In CartPole two-agent experiments, the low-performance agent (Agent16, 404 parameters) achieved approximately 2× performance improvement (93.3 to 184.4 steps) through group learning, while the high-performance agent (Agent64, 4676 parameters) maintained comparable performance, though several group conditions fell below the solo 200-step result. Three-agent experiments further improved Agent16 to 196.5 steps with reduced variance. Under step-matched comparisons in Super Mario Bros, the low-capacity agent benefits from experience sharing beyond solo baselines that consume roughly twice as many steps, while the high-capacity agent remains broadly comparable between group and solo. Claims are limited to step-based normalisation. Q-value analysis revealed accelerated early learning, with Q-values increasing by +10.1 (Mario) and +7.7 (Luigi) at 1 million steps. These results demonstrate that experience-sharing group learning can improve learning efficiency for resource-constrained agents under a fixed environment-step budget. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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26 pages, 1262 KB

Open AccessArticle

Sensitivity Analysis of Variational Quantum Classifiers for Identifying Dummy Power Traces in Side-Channel Analysis

by Seungun Park and Yunsik Son

Appl. Sci. 2026, 16(7), 3243; https://doi.org/10.3390/app16073243 - 27 Mar 2026

Viewed by 439

Abstract

The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) for identifying dummy power traces in side-channel analysis (SCA). A controlled benchmarking framework is developed to evaluate training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. To move beyond idealized simulation, hardware-relevant factors, including finite measurement budgets and device noise, are incorporated, and inference robustness under degraded operating conditions is assessed. The results show that VQCs can capture meaningful discriminative patterns in structured side-channel data, although robustness and performance depend strongly on encoding strategy, circuit depth, and measurement conditions. These findings provide an empirical assessment of the potential and limitations of QML for side-channel security and offer practical guidance for future research. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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22 pages, 1123 KB

Open AccessArticle

Balanced Marginal and Joint Distributional Learning for Tabular Data Synthesis via Mixture Cramer–Wold Distance

by Seunghwan An, Sungchul Hong and Jong-June Jeon

Appl. Sci. 2026, 16(6), 2928; https://doi.org/10.3390/app16062928 - 18 Mar 2026

Viewed by 239

Abstract

In recent times, slicing methods have yielded a successful outcome in generative models for image, sound, and text data, primarily focusing on joint distributional learning. However, we have identified a critical limitation of the slicing approach for tabular data: it struggles to capture marginal distributional patterns, which are significant for effective tabular data synthesis. To tackle this problem, we introduce a new measure of discrepancy, the mixture Cramer–Wold distance. This measure enables us to capture both marginal and joint distributional patterns simultaneously, striking a balance between the two aspects, and we provide theoretical foundations for its application. Leveraging the power of the mixture Cramer–Wold distance, we present CWDAE (Cramer–Wold Distributional AutoEncoder), a generative model that demonstrates notable performance in generating synthetic tabular data. Furthermore, our model offers the flexibility to adjust the level of data privacy to meet specific needs easily. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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