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Artificial Intelligence Models and Cognitive Computing: Innovations from Algorithms to...
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Artificial Intelligence Models and Cognitive Computing: Innovations from Algorithms to Intelligent Systems

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289). This special issue belongs to the section "Cognitive System".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2733

Editors

Prof. Dr. Xin Su

E-Mail Website
Guest Editor
College of Information Science and Engineering, Hohai University, Changzhou, China
Interests: artificial intelligence; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhiquan Bai

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong University, Qingdao, China
Interests: big data/artificial intelligence-based wireless communication; smart communication

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and cognitive computing are evolving rapidly with the emergence of large-scale foundation models that integrate perception, reasoning, and decision-making. This Special Issue focuses on advancing theoretical foundations, model architectures, and intelligent system design for next-generation cognitive AI. Its scope includes large AI models, multimodal learning, neuro-symbolic reasoning, reinforcement learning, generative models, and cognitive architectures that enable adaptive, explainable, and human-centered intelligence.

The purpose of this Special Issue is to bridge the current gap between data-driven AI models and cognitive theory. It aims to supplement and extend existing research, which often treats algorithmic optimization and cognitive mechanisms as separate domains, by integrating insights from cognitive science, neuroscience, and machine learning to create interpretable and scalable AI systems. Furthermore, it seeks to highlight cross-domain applications of cognitive AI in fields such as healthcare, robotics, and smart cities, fostering interdisciplinary perspectives that push beyond traditional AI paradigms.

Through this initiative, we hope to inspire research that connects human-like cognition with artificial intelligence, contributing to a more transparent, generalizable, and trustworthy AI ecosystem.

Prof. Dr. Xin Su
Prof. Dr. Zhiquan Bai
Guest Editors

Manuscript Submission Information

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • cognitive computing
  • large models/foundation models
  • multimodal learning
  • neuro-symbolic reasoning
  • reinforcement learning
  • generative artificial intelligence (Generative AI)
  • edge and cloud intelligence
  • explainable artificial intelligence (XAI)
  • trustworthy and human-centered AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

29 pages, 16650 KB  
Article
Cognitive Detection at Big-Data Scale: A CNN-LSTM-DQN Framework with Prioritized Experience Replay for Cross-Attack-Family Generalization and Multi-Seed Initialization Sensitivity Analysis
by Rushendra, Kalamullah Ramli, Prima Dewi Purnamasari, Teddy Surya Gunawan and Muhammad Salman
Big Data Cogn. Comput. 2026, 10(7), 239; https://doi.org/10.3390/bdcc10070239 - 16 Jul 2026
Viewed by 443
Abstract
Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay [...] Read more.
Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay (PER) evaluated on a 5,000,000-flow naturalistic sample of the TON_IoT Processed_Network dataset (4,000,000 training/1,000,000 temporally held-out test flows; 94.5% attack ratio) under a strict temporal split. The cognitive agent optimizes detection decisions using an Alerts per Million Flows (ARMF)-aware reward function that encodes both alert-fatigue cost and missed-attack penalty. We conduct a cross-attack-family generalization study: the methodology—architecture template, reward design, and hyperparameter calibration—is inherited from a framework previously validated on CSE-CIC-IDS2018, re-instantiated and retrained on the structurally different TON_IoT environment, and compared against the previously published benchmark. Initialization sensitivity is characterized across five independent random seeds using paired Wilcoxon signed-rank and t-tests. Across the five seeds, the proposed X2 model attains recall 0.833 ± 0.306 and F1 0.874 ± 0.241 (mean ± sample SD), versus the supervised X1 baseline at 0.858 ± 0.178 and 0.912 ± 0.116; the best-performing seed (42) achieves 97.52% accuracy, 98.02% attack recall, 99.46% precision, and 98.73% F1-score on 1,000,000 held-out XSS flows—an attack family entirely absent from training—with temporal stability variances of 4.63 × 10−7 (recall) and 1.38 × 10−7 (F1). The X2 advantage observed among the four stable seeds is not statistically demonstrated at n = 5 (statistical power ≈ 5.1%); the initialization-sensitivity finding itself, including one degenerate alert-suppression seed, is reported as a primary contribution. A formal, exactly additive ARMF decomposition distinguishes the detected-attack (structural) component (99.46%) from the model-induced false-positive component (0.54%), and we report a multi-seed, ARMF-aware cognitive IDS evaluation on naturalistic TON_IoT traffic under an unseen-attack-family test condition that, to the best of our knowledge, has not been reported in the surveyed RL-based NIDS literature. Full article
(This article belongs to the Special Issue Artificial Intelligence Models and Cognitive Computing: Innovations from Algorithms to Intelligent Systems)
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47 pages, 2250 KB  
Article
DIKWP+BUG Architecture for Purpose-Aware Cognitive Computing
by Zhendong Guo and Yucong Duan
Big Data Cogn. Comput. 2026, 10(6), 196; https://doi.org/10.3390/bdcc10060196 - 21 Jun 2026
Viewed by 382
Abstract
Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a [...] Read more.
Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from 4.5k to 7.8k logged events, reduced perception–action latency from 340ms to 120ms, reduced CPU utilization from 95% to 68%, lowered smart-city congestion duration by 30%, improved emergency response time by approximately 40%, achieved 0 collisions versus approximately 2/10 baseline IoV runs, and improved medical-triage accuracy from 85% to 92%. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article
(This article belongs to the Special Issue Artificial Intelligence Models and Cognitive Computing: Innovations from Algorithms to Intelligent Systems)
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28 pages, 7349 KB  
Article
Symbolic Disentangled Representations for Images
by Alexandr V. Korchemnyi, Alexey K. Kovalev and Aleksandr I. Panov
Big Data Cogn. Comput. 2026, 10(6), 168; https://doi.org/10.3390/bdcc10060168 - 22 May 2026
Viewed by 282
Abstract
The idea of disentangled representations is to reduce the data to a set of generative factors that produce it. Typically, such representations are vectors in latent space, where each coordinate corresponds to one of the generative factors. The object can then be modified [...] Read more.
The idea of disentangled representations is to reduce the data to a set of generative factors that produce it. Typically, such representations are vectors in latent space, where each coordinate corresponds to one of the generative factors. The object can then be modified by changing the value of a particular coordinate, but it is necessary to determine which coordinate corresponds to the desired generative factor—a difficult task if the vector representation has a high dimension. In this article, we propose ArSyD (Architecture for Symbolic Disentanglement), which represents each generative factor as a vector of the same dimension as the resulting representation. In ArSyD, the object representation is obtained as a superposition of the generative factor vector representations. We call such a representation a symbolic disentangled representation. We use the principles of Hyperdimensional Computing (also known as Vector Symbolic Architectures), where symbols are represented as hypervectors, allowing vector operations on them. Disentanglement is achieved by construction, no additional assumptions about the underlying distributions are made during training, and the model is only trained to reconstruct images in a weakly supervised manner. We study ArSyD on the dSprites and CLEVR datasets and provide a comprehensive analysis of the learned symbolic disentangled representations. ArSyD outperforms BetaVAE and FactorVAE baselines on CLEVR1 paired, achieving an FID of 93.72 compared to 129.68 and 115.61, respectively. It also achieves the best IOU value on dSprites paired, at 98.37, compared to 96.43 and 97.11 for the other baselines. We also propose new disentanglement metrics that allow comparison of methods using latent representations of different dimensions. ArSyD allows us to edit the object properties in a controlled and interpretable way, and the dimensionality of the object property representation coincides with the dimensionality of the object representation itself. Full article
(This article belongs to the Special Issue Artificial Intelligence Models and Cognitive Computing: Innovations from Algorithms to Intelligent Systems)
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39 pages, 12238 KB  
Article
Fusing Dynamic Bayesian Network for Explainable Decision with Optimal Control for Occupancy Guidance in Autonomous Air Combat
by Mingzhe Zhou, Guanglei Meng, Biao Wang and Tiankuo Meng
Big Data Cogn. Comput. 2026, 10(2), 44; https://doi.org/10.3390/bdcc10020044 - 29 Jan 2026
Viewed by 939
Abstract
In this paper, an explainable decision-making and guidance integration method is developed based on dynamic Bayesian network and the optimized control method. The proposed method can be applied for the autonomous decision-making and guidance in the game of attacking and defending of unmanned [...] Read more.
In this paper, an explainable decision-making and guidance integration method is developed based on dynamic Bayesian network and the optimized control method. The proposed method can be applied for the autonomous decision-making and guidance in the game of attacking and defending of unmanned combat aerial vehicles in close air combat. Firstly, the target maneuver recognition and target trajectory prediction are carried out according to the target information detected by the sensor. Then, a dynamic Bayesian network model for close combat decision is established by combining space occupancy situation and equipment performance information with target maneuver identification results. The decision model realizes the intelligent selection of the optimization index function of the maneuver. The optimal control constrained gradient method is adopted to realize the optimal calculation of the unmanned combat aerial vehicle occupancy guidance quantity by considering the constraint of unmanned combat aerial vehicle flight performance. The simulation results of several typical close air combat show that the proposed method can realize rationalized autonomous decision-making and space occupancy guidance of unmanned combat aerial vehicles, overcome the solidification of mobile action mode by traditional methods, and has better real-time performance and optimization performance. Full article
(This article belongs to the Special Issue Artificial Intelligence Models and Cognitive Computing: Innovations from Algorithms to Intelligent Systems)
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