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Journal articles on the topic 'Neuro-symbolic networks'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Burattini, Ernesto, Antonio de Francesco, and Massimo de Gregorio. "NSL: A Neuro–Symbolic Language for a Neuro–Symbolic Processor (NSP)." International Journal of Neural Systems 13, no. 02 (2003): 93–101. http://dx.doi.org/10.1142/s0129065703001480.

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A Neuro–Symbolic Language for monotonic and non–monotonic parallel logical inference by means of artificial neural networks (ANNs) is presented. Both the language and its compiler have been designed and implemented in order to translate the neural representation of a given problem into a VHDL software, which in turn can set devices such as FPGA. The result of this operation leads to an electronic circuit that we call NSP (Neuro–Symbolic Processor).

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Fatima, Tuba, and Dr Rehan Muhammad. "The Impact of Neuro-Symbolic AI on Cognitive Linguistics." ACADEMIA International Journal for Social Sciences 4, no. 3 (2025): 455–66. https://doi.org/10.63056/acad.004.03.0386.

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Neuro-Symbolic Artificial Intelligence (AI) is indeed a fascinating domain, merging the structured reasoning of symbolic methods with the learning capabilities of neural networks. Its long-standing history reflects its significance in advancing AI towards achieving more robust and interpretable solutions. Neuro-symbolic AI is such an exciting and transformative field, as it combines the structured reasoning of symbolic AI with the adaptability and learning capabilities of neural networks. Your summary elegantly captures the breadth and depth of this growing discipline. The focus on representat

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Yevdokymov, Serhii. "Neuro-symbolic models for ensuring cybersecurity in critical cyber-physical systems." Computational Problems of Electrical Engineering 14, no. 1 (2024): 42–50. https://doi.org/10.23939/jcpee2024.01.042.

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This paper presents the results of a comprehensive study on the application of the neuro-symbolic approach for detecting and preventing cyber threats in railway systems, a critical component of cyber-physical infrastructures. The increasing complexity and integration of physical systems with digital technologies have made such infrastructures vulnerable to cyberattacks, where breaches can result in severe consequences, including system failures, financial losses, and threats to public safety and the environment. The objective of this study was to assess the effectiveness of the neuro-symbolic

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Anil Kumar. "Neuro Symbolic AI in personalized mental health therapy: Bridging cognitive science and computational psychiatry." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1663–79. https://doi.org/10.30574/wjarr.2023.19.2.1516.

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Personalized mental health therapy has gained increasing attention as advancements in artificial intelligence (AI) enable tailored treatment strategies based on individual cognitive and emotional profiles. Neuro-symbolic AI, a hybrid approach combining symbolic reasoning and neural networks, offers a promising solution for bridging cognitive science and computational psychiatry. Unlike conventional AI models that rely solely on deep learning, neuro-symbolic AI integrates human-interpretable knowledge representations with data-driven learning, enhancing the adaptability and explainability of AI

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Sen, Prithviraj, Breno W. S. R. de Carvalho, Ryan Riegel, and Alexander Gray. "Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8212–19. http://dx.doi.org/10.1609/aaai.v36i8.20795.

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Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiable operators from fuzzy or real-valued logic that are parameter-free thus diminishing their capacity to fit the data, other approaches are only loosely based on logic making it difficult to interpret the learned ``rules". In this paper, we propose learning rules with the recently proposed logical neural networks (LNN). Compared to others, LNNs offer a strong connection to class

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Morel, Gilles. "Neuro-symbolic A.I. for the smart city." Journal of Physics: Conference Series 2042, no. 1 (2021): 012018. http://dx.doi.org/10.1088/1742-6596/2042/1/012018.

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Abstract Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI). However, today’s AI mainly concerns machine learning and artificial neural networks (deep learning), whereas the first forty years of the discipline (the last decades of the 20th century) were essentially focused on a knowledge-based approach, which is still relevant today for some tasks. In this article we advocate a merging of these two AI trends – an approach known as neuro-symbolic AI – for

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Marra, Giuseppe. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22678. http://dx.doi.org/10.1609/aaai.v38i20.30294.

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The integration of learning and reasoning is one of the key challenges in artificial intelligence and machine learning today. The area of Neuro-Symbolic AI (NeSy) tackles this challenge by integrating symbolic reasoning with neural networks. In our recent work, we provided an introduction to NeSy by drawing several parallels to another field that has a rich tradition in integrating learning and reasoning, namely Statistical Relational Artificial Intelligence (StarAI).

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Shraddhaben R. Gajjar. "Neuro-symbolic ai for cloud intrusion detection: A hybrid intelligence approach." GSC Advanced Research and Reviews 22, no. 2 (2025): 142–44. https://doi.org/10.30574/gscarr.2025.22.2.0049.

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As cloud computing becomes more prevalent, ensuring robust cybersecurity is an ongoing challenge. Traditional intrusion detection systems (IDS) often struggle to keep pace with emerging cyber threats due to their reliance on static rule-based mechanisms. Neuro-Symbolic AI presents a hybrid intelligence approach that merges deep learning (neural networks) with symbolic reasoning, offering enhanced accuracy, explainability, and adaptability in intrusion detection. This paper investigates the role of Neuro-Symbolic AI in cloud security, detailing its application in threat detection, real-time res

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Letychevskyi, O. O., and Yu H. Tarasich. "Neuro-symbolic approach for the biological systems and processes research." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 271–79. https://doi.org/10.15407/pp2024.02-03.271.

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The current state and prospects for the application of neural network technology and systems built based on neural networks for studying biological systems and processes (in particular, the processes of virus-cell interaction) were considered in the article. In particular, the concept of the neuro-symbolic approach, which combines the use of neural networks and algebraic modelling, was described. The use of an algebraic approach in combination with neural networks allows us to obtain an effective formal proof of biological objects' properties (for example, cells' and viruses' properties) or pr

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Mekras, Nikolaos, Electra Mekra, and Christos Georgiou. "Neuro – symbolic AI for materials modelling and processes design." MATEC Web of Conferences 401 (2024): 14004. http://dx.doi.org/10.1051/matecconf/202440114004.

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This paper presents possibilities of combined use of Artificial Neural Networks (ANNs) with symbolic AI methods for improving the creation and the efficiency of models for materials and manufacturing processes. Besides a short presentation of one of the most basic and common ANNs architecture, which is the Multi-Layer Perceptron (MLP), and the presentation of main symbolic AI methods for knowledge representation and processing, the paper discusses their combined use and possibilities of hybrid neuro–symbolic AI applications for modelling materials and processes. This combined AI modelling appr

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Shadrach C Matthew, Sanjay Siddharthan R, and Elavarasan R. "Adaptive Neuro-Symbolic Systems for Real Time Ethical Decision-Making in Autonomous Agents." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1571–76. https://doi.org/10.47392/irjaem.2025.0254.

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With the rapid emergence of autonomous systems, appropriate robust frameworks that could make ethical decisions in real time are needed. The adaptive neuro-symbolic approach to decision making by autonomous agents is thus presented here, integrating the advantages of symbolic ability like conventional AI with the adaptability imparted through neural networks. This proposed system enables symbolic reasoning by the AI along with learning from data, thus ensuring transparency and adaptability in decisions. This system, with deep learning models integrated with symbolic representations, would have

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Saha, Amrita, Shafiq Joty, and Steven C. H. Hoi. "Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning over Text." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11238–47. http://dx.doi.org/10.1609/aaai.v36i10.21374.

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Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However to achieve this, contemporary Neural Module Networks models obtain strong supervision in form of specialized program annotation from the QA pairs through various heuristic parsing and exhaustive computation of all possible discrete operations on discrete arguments. Consequently they fail to generalize to more open-ended settings witho

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Souici-Meslati, Labiba, and Mokhtar Sellami. "A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (2006): 17–25. http://dx.doi.org/10.20965/jaciii.2006.p0017.

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In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm

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Pasupuleti, Murali Krishna. "Synthetic Cognition: Building Artificial Minds for Adaptive Learning." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 343–54. https://doi.org/10.62311/nesx/rp05ai2.

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Abstract: Synthetic cognition refers to the construction of artificial systems that replicate or simulate cognitive processes such as learning, reasoning, and adaptation. This paper investigates the computational frameworks and architectures for building artificial minds capable of adaptive learning, drawing on concepts from neuroscience, machine learning, and cognitive psychology. By evaluating neuro-symbolic architectures, reinforcement learning with episodic memory, and meta-learning models, Researcher demonstrated how these systems can generalize across tasks and environments. Using regres

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Gaur, Manas, Kalpa Gunaratna, Shreyansh Bhatt, and Amit Sheth. "Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI." IEEE Internet Computing 26, no. 4 (2022): 5–11. http://dx.doi.org/10.1109/mic.2022.3179759.

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Shilov, Nikolay, Andrew Ponomarev, and Alexander Smirnov. "The Analysis of Ontology-Based Neuro-Symbolic Intelligence Methods for Collaborative Decision Support." Informatics and Automation 22, no. 3 (2023): 576–615. http://dx.doi.org/10.15622/ia.22.3.4.

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The neural network approach to AI, which has become especially widespread in the last decade, has two significant limitations – training of a neural network, as a rule, requires a very large number of samples (not always available), and the resulting models often are not well interpretable, which can reduce their credibility. The use of symbols as the basis of collaborative processes, on the one hand, and the proliferation of neural network AI, on the other hand, necessitate the synthesis of neural network and symbolic paradigms in relation to the creation of collaborative decision support sys

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Hu, Wen-Chao, Wang-Zhou Dai, Yuan Jiang, and Zhi-Hua Zhou. "Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17333–41. https://doi.org/10.1609/aaai.v39i16.33905.

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Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems often generate outputs inconsistent with domain knowledge and it is challenging to rectify them. Inspired by the human Cognitive Reflection, which promptly detects errors in our intuitive response and revises them by invoking the System 2 reasoning, we propose to improve NeSy systems by introducing Abductive Reflection (ABL-Refl) based on the Abduct

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Showemimo, Paul. "Neuro-Symbolic Architectures for Explainable Multi-Modal Plagiarism Detection in Academic Assessment." International Journal of Advances in Engineering and Management 7, no. 7 (2025): 483–99. https://doi.org/10.35629/5252-0707483499.

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The pervasive integration of Artificial Intelligence (AI) into educational ecosystems has introduced both unprecedented opportunities and complex challenges, particularly concerning academic integrity. While AI-driven tools have become indispensable for identifying traditional forms of plagiarism, the emergence of sophisticated content generation models and multi-modal assignments necessitates a paradigm shift in detection methodologies. This paper proposes a novel MultiModal Neuro-Symbolic Assessment System (MNSAS) architecture specifically designed for explainable plagiarism detection. Our a

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Onchis, Darian, Codruta Istin, and Eduard Hogea. "A Neuro-Symbolic Classifier with Optimized Satisfiability for Monitoring Security Alerts in Network Traffic." Applied Sciences 12, no. 22 (2022): 11502. http://dx.doi.org/10.3390/app122211502.

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We introduce in this paper a neuro-symbolic predictive model based on Logic Tensor Networks, capable of discriminating and at the same time of explaining the bad connections, called alerts or attacks, and the normal connections. The proposed classifier incorporates both the ability of deep neural networks to improve on their own through learning from experience and the interpretability of the results provided by the symbolic artificial intelligence approach. Compared to other existing solutions, we advance in the discovery of potential security breaches from a cognitive perspective. By introdu

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Siva Prasad Sunkara. "Neuro-symbolic AI for enterprise systems: Transforming CRM & ERP through logical and statistical AI integration." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 340–49. https://doi.org/10.30574/wjaets.2025.15.2.0491.

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Neuro-Symbolic AI (NSAI) represents a paradigm shift in enterprise software by integrating deep learning capabilities with symbolic reasoning frameworks to address critical limitations in current AI implementations for Customer Relationship Management and Enterprise Resource Planning systems. This hybrid approach combines the pattern recognition strengths of neural networks with the logical inference capabilities of symbolic AI, creating intelligent systems that offer enhanced explainability, improved reasoning, and more transparent decision-making processes. By bridging statistical machine le

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Dong, Yiqi, Dongxiao He, Xiaobao Wang, et al. "Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8354–62. http://dx.doi.org/10.1609/aaai.v38i8.28677.

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In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical dec

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Yevdokymov, Serhii, and Edgars Elsts. "Modern approaches to the diagnosis of neurological disorders using artificial neural networks." Advances in Cyber-Physical Systems 10, no. 1 (2025): 22–27. https://doi.org/10.23939/acps2025.01.022.

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The article explores the application of neuro-symbolic approaches utilizing artificial neural networks for diagnosing neurological disorders among individuals with autism spectrum conditions. It demonstrates how these networks can identify and enhance distinctive strengths, such as advanced pattern recognition and systematic reasoning, facilitating their integration into professional environments. The study emphasizes the importance of inclusive employment initiatives supported by modern diagnostic tools, while addressing ethical considerations, including data privacy and non-discrimination. I

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Achler, Tsvi. "Neural networks that perform recognition using generative error may help fill the “Neuro-Symbolic Gap”." Biologically Inspired Cognitive Architectures 3 (January 2013): 6–12. http://dx.doi.org/10.1016/j.bica.2012.10.001.

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Alwakeel, Mohammed. "Neuro-Driven Agent-Based Security for Quantum-Safe 6G Networks." Mathematics 13, no. 13 (2025): 2074. https://doi.org/10.3390/math13132074.

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Around the same time that 6G networks will be launched, advances in quantum computing could challenge existing cryptographic security. This study provides a new approach for designing a quantum-safe 6G security architecture powered by neurons. The framework uses connected cognitive agents that apply neuro-symbolic learning to respond quickly to any quantum-based security threats that may appear in network slices. Experiments carried out using simulations across various network setups with different threats verify that the presented method improves the detection rate of quantum attacks by 37.8%

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Barnaby, Celeste, Qiaochu Chen, Roopsha Samanta, and Işıl Dillig. "ImageEye: Batch Image Processing using Program Synthesis." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 686–711. http://dx.doi.org/10.1145/3591248.

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This paper presents a new synthesis-based approach for batch image processing. Unlike existing tools that can only apply global edits to the entire image, our method can apply fine-grained edits to individual objects within the image. For example, our method can selectively blur or crop specific objects that have a certain property. To facilitate such fine-grained image editing tasks, we propose a neuro-symbolic domain-specific language (DSL) that combines pre-trained neural networks for image classification with other language constructs that enable symbolic reasoning. Our method can automati

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Anil Kumar Pakina and Mangesh Pujari. "Neuro- Symbolic Compliance Architectures: Real-Time Detection of Evolving Financial Crimes Using Hybrid AI." International Journal Science and Technology 3, no. 3 (2024): 56–66. https://doi.org/10.56127/ijst.v4i1.1961.

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This paper proposes NeuroSym-AML, a new neuro-symbolic AI framework explicitly designed for the real-time detection of evolving financial crimes with a special focus on cross-border transactions. By combining Graph Neural Networks (GNNs) with interpretable rule-based reasoning, our system dynamically adapts to emerging money laundering patterns while ensuring strict compliance with FATF/OFAC regulations. In contrast to static rule-based systems, NeuroSym-AML shows better performance-an 83.6% detection accuracy to identify financial criminals, which demonstrated a 31% higher uplift compared wit

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Custode, Leonardo Lucio, Federico Mento, Sajjad Afrakhteh, et al. "Neuro-symbolic interpretable AI for automatic COVID-19 patient-stratification based on standardised lung ultrasound data." Journal of the Acoustical Society of America 151, no. 4 (2022): A112—A113. http://dx.doi.org/10.1121/10.0010820.

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In the current pandemic, being able to efficiently stratify patients depending on their probability to develop a severe form of COVID-19 can improve the outcome of treatments and optimize the use of the available resources. To this end, recent studies proposed to use deep-networks to perform automatic stratification of COVID-19 patients based on lung ultrasound (LUS) data. In this work, we present a novel neuro-symbolic approach able to provide video-level predictions by aggregating results from frame-level analysis made by deep-networks. Specifically, a decision tree was trained, which provid

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Jankovics, Vince, Michael Garcia Ortiz, and Eduardo Alonso. "HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15803–4. http://dx.doi.org/10.1609/aaai.v35i18.17898.

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This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that our approach outperforms the state-of-the-art

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Carrow, Stephen, Kyle Erwin, Olga Vilenskaia, et al. "Neural Reasoning Networks: Efficient Interpretable Neural Networks with Automatic Textual Explanations." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15669–77. https://doi.org/10.1609/aaai.v39i15.33720.

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Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features influencing the prediction is necessary to ensure fairness, safety, and legal compliance. In this paper we consider one class of such tasks, tabular dataset classification, and propose a novel neuro-symbolic architecture, Neural Reasoning Networks (NRN), that is scalable and generates logically sound textual explanations for its predictions. NRNs are connected layers of logical neurons t

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Akhter, Shamima, Joynul Arefin, Md Shahadat Hossen, et al. "Neuro-Symbolic AI for IoT-Driven Smart Cities: A Next-Generation Framework for Urban Intelligence." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 36–55. https://doi.org/10.32996/jcsts.2025.7.2.4.

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The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) is revolutionizing urban landscapes by enhancing operational efficiency, resource management, and sustainability in smart cities. IoT enables real-time data acquisition through distributed sensor networks, while AI processes this data to facilitate intelligent decision-making across critical urban domains, including transportation, energy management, environmental monitoring, public safety, and healthcare. Despite its potential, this convergence presents critical challenges such as data heterogeneity, security vul

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Skryagin, Arseny, Daniel Ochs, Devendra Singh Dhami, and Kristian Kersting. "Scalable Neural-Probabilistic Answer Set Programming." Journal of Artificial Intelligence Research 78 (November 16, 2023): 579–617. http://dx.doi.org/10.1613/jair.1.15027.

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The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural networks (DNNs). However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this en

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Arrotta, Luca, Gabriele Civitarese, and Claudio Bettini. "Semantic Loss." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 4 (2023): 1–29. http://dx.doi.org/10.1145/3631407.

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Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information into HAR deep learning classifiers. However, existing NeSy methods for context-aware HAR require computationally expensive symbolic reasoners during classification, making them less suitable for deployment on resource-constrained devices (e.g., mobile devices). Additionally, NeS

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Grilli, Eleonora, Alessandro Daniele, Maarten Bassier, Fabio Remondino, and Luciano Serafini. "Knowledge Enhanced Neural Networks for Point Cloud Semantic Segmentation." Remote Sensing 15, no. 10 (2023): 2590. http://dx.doi.org/10.3390/rs15102590.

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Deep learning approaches have sparked much interest in the AI community during the last decade, becoming state-of-the-art in domains such as pattern recognition, computer vision, and data analysis. However, these methods are highly demanding in terms of training data, which is often a major issue in the geospatial and remote sensing fields. One possible solution to this problem comes from the Neuro-Symbolic Integration field (NeSy), where multiple methods have been defined to incorporate background knowledge into the neural network’s learning pipeline. One such method is KENN (Knowledge Enhanc

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Amitabha Mandal. "Hybrid Deep Learning Models in Image Classification: Integrating CNNs with Attention, Capsule Networks, and Graph Neural Networks." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 888–96. https://doi.org/10.52783/jisem.v10i42s.8212.

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Image classification has been transformed by convolutional neural networks (CNNs), yet single-architecture solutions are increasingly reaching performance plateaus on complex, fine-grained, and cross-domain tasks. A new research frontier therefore explores hybrid deep learning models that fuse complementary architectural paradigms—attention mechanisms, Capsule Networks, recurrent/transformer layers, and graph neural networks (GNNs)—with CNN backbones to capture richer spatial hierarchies, relational cues, and long-range dependencies. This review synthesizes 2020-2025 literature on such hybrids

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Schultz Kisby, Caleb, Saúl A. Blanco, and Lawrence S. Moss. "What Do Hebbian Learners Learn? Reduction Axioms for Iterated Hebbian Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14894–901. http://dx.doi.org/10.1609/aaai.v38i13.29409.

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This paper is a contribution to neural network semantics, a foundational framework for neuro-symbolic AI. The key insight of this theory is that logical operators can be mapped to operators on neural network states. In this paper, we do this for a neural network learning operator. We map a dynamic operator [φ] to iterated Hebbian learning, a simple learning policy that updates a neural network by repeatedly applying Hebb's learning rule until the net reaches a fixed-point. Our main result is that we can "translate away" [φ]-formulas via reduction axioms. This means that completeness for the lo

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Nezhadettehad, Alireza, Arkady Zaslavsky, Abdur Rakib, and Seng W. Loke. "Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks." Sensors 25, no. 11 (2025): 3463. https://doi.org/10.3390/s25113463.

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Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely applied, they lack mechanisms to quantify uncertainty, limiting their robustness in real-world deployments. This paper proposes a Bayesian Neural Network (BNN)-based framework for parking occupancy prediction that explicitly models both epistemic and aleatoric uncertainty. Although BNNs have shown promise in other domains, they remain underutil

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Ren, Zhiyuan, Shijie Zhou, Dong Liu, and Qihe Liu. "Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing." Applied Sciences 15, no. 14 (2025): 8092. https://doi.org/10.3390/app15148092.

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Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid

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Hu, Yiwen, and Markus J. Buehler. "Deep language models for interpretative and predictive materials science." APL Machine Learning 1, no. 1 (2023): 010901. http://dx.doi.org/10.1063/5.0134317.

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Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied. While conventional feedforward neural networks are typically limited to performing tasks related to static patterns in data, recursive models can both work iteratively based on a changing input and discover complex dynamical relationships in the data. Deep language models can model flexible modalities of data and are capable of learn

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An, Jiyao, Jin Zhao, Qingqin Liu, Xinjiao Qian, and Jiali Chen. "Self-Constructed Deep Fuzzy Neural Network for Traffic Flow Prediction." Electronics 12, no. 8 (2023): 1885. http://dx.doi.org/10.3390/electronics12081885.

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Traffic flow prediction is a critical component of intelligent transportation systems, especially in the prevention of traffic congestion in urban areas. While significant efforts have been devoted to enhancing the accuracy of traffic prediction, the interpretability of traffic prediction also needs to be considered to enhance persuasiveness, particularly in the era of deep-learning-based traffic cognition. Although some studies have explored interpretable neural networks from the feature and result levels, model-level explanation, which explains the reasoning process of traffic prediction thr

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Sirisha, Udandarao, Bhukya Krishna, and Chappa Ramesh. "Design of an Improved Model for Personalized Adaptive E-Learning Using Context-Aware Federated Learning and Hierarchical Semantic Graph Analysis." EPJ Web of Conferences 328 (2025): 01072. https://doi.org/10.1051/epjconf/202532801072.

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The improvement of e-learning platforms has raised a challenge of building an adaptive, personalized, and privacy-preserving scalable system, which can service hugely varied types of learners. The challenges faced by most traditionally adaptive systems include limited personalization, context unawareness, low explanation, and reliance on shallow metrics of engagement. Most existing federated learning models left out the real-life context of the learners while semantic understanding modules do less in multi-level cognitive states capturing. The current reinforcement learning techniques in educa

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Ekechi, Chijioke C. "Explainable AI Models for Autonomous UAV Decision Making in Complex Terrains: A Comparative Analysis." International Journal of Future Engineering Innovations 2, no. 4 (2025): 29–36. https://doi.org/10.54660/ijfei.2025.2.4.29-36.

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This paper investigated the integration of Explainable Artificial Intelligence (XAI) models into Unmanned Aerial Vehicle (UAV) systems to enhance decision making in complex and dynamic terrains. The motivation stemmed from the growing reliance on autonomous UAVs in mission critical operations where transparency, trust, and accountability are essential. The study presented a structured overview of various XAI techniques ranging from inherently interpretable models such as decision trees and rule based systems to post hoc methods like LIME, SHAP, and Grad CAM, as well as hybrid approaches includ

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Biryukov, D. N., and A. S. Dudkin. "Explainability and interpretability are important aspects in ensuring the security of decisions made by intelligent systems (review article)." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 25, no. 3 (2025): 373–86. https://doi.org/10.17586/2226-1494-2025-25-3-373-386.

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The issues of trust in decisions made (formed) by intelligent systems are becoming more and more relevant. A systematic review of Explicable Artificial Intelligence (XAI) methods and tools aimed at bridging the gap between the complexity of neural networks and the need for interpretability of results for end users is presented. A theoretical analysis of the differences between explainability and interpretability in the context of artificial intelligence as well as their role in ensuring the security of decisions made by intelligent systems is carried out. It is shown that explainability implie

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Onisawa, Takehisa. "Special Issue on Selected Papers in SCIS & ISIS 2004 – No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (2005): 225. http://dx.doi.org/10.20965/jaciii.2005.p0225.

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The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to selec

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Rosemarie, Velik. "BRAIN Journal - Quo vadis, Intelligent Machine?" BRAIN - Broad Research in Artificial Intelligence and Neuroscience 1, no. 4 (2010): 13–22. https://doi.org/10.5281/zenodo.1037355.

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ABSTRACT Artificial Intelligence (AI) is a branch of computer science concerned with making computers behave like humans. At least this was the original idea. However, it turned out that this was no easy task to solve. This article aims to give a comprehensible review on the last 60 years of artificial intelligence taking a philosophical viewpoint. It is outlined what happened so far in AI, what is currently going on in this research area, and what can be expected in the future. The goal is to mediate an understanding for the developments and changes in thinking in the course of time about how

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Shehab, Abdulhabib Alzaeemi, Sathasivam Saratha, and Velavan Muraly. "Hybrid Genetic Algorithm Model in Neuro Symbolic Integration." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2144–49. https://doi.org/10.35940/ijeat.D8761.049420.

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The development of artificial neural network and logic programming plays an important part in neural network studies. Genetic Algorithm (GA) is one of the escorted randomly searching technicality that uses evolutional concepts of the natural election as a stimulus to solve the computational problems. The essential purposes behind the studies of the evolutional system is for developing adaptive search techniques which are robust. In this paper, GA is merged with agent based modeling (ABM) by using specified proceedings to optimise the states of neurons and energy function in the Hopfield neural

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Li, Qi, Yuekai Liu, Shilin Sun, Zhaoye Qin, and Fulei Chu. "Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI." Journal of Manufacturing Systems 77 (December 2024): 652–61. http://dx.doi.org/10.1016/j.jmsy.2024.10.007.

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Ansari, Daniel, Ian M. Lyons, Lucia van Eimeren, and Fei Xu. "Linking Visual Attention and Number Processing in the Brain: The Role of the Temporo-parietal Junction in Small and Large Symbolic and Nonsymbolic Number Comparison." Journal of Cognitive Neuroscience 19, no. 11 (2007): 1845–53. http://dx.doi.org/10.1162/jocn.2007.19.11.1845.

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There exists a long-standing debate regarding whether small and large numerosities engage different networks of processing. The ability to rapidly enumerate small (1–4) numerosities is referred to as “subitizing” and is thought to be qualitatively different from large numerosity processing. Functional neuro-imaging studies have attempted to dissociate neural correlates of small and large number processing by contrasting subitizing with counting of numerosities just outside the subitizing range. In the present study, we used functional magnetic resonance imaging (fMRI) to contrast the processin

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Filatova, A.A. "What should we do with neuroscience? from the epistemology of suspicion to an epistemology of care." Sociology of Power, no. 2 (June 7, 2020): 18–47. https://doi.org/10.22394/2074-0492-2020-2-18-47.

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Today, neuroscience is undoubtedly at the focus of close public attention and interest. It is associated with the greatest hopes, but also arouses the innermost fears. Neuroscience has become a challenge not only for practical fields such as medicine or pharmacology but for all of the human sciences. Representatives of leading trends in social sciences and humanities have entered the discussion about the possible benefits and threats related to the rapid growth of knowledge in neuroscience. The neuro-turn has become a conceptual framework in which the neuro-centric style of thinking sets the r

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Sarker, Md Kamruzzaman, Lu Zhou, Aaron Eberhart, and Pascal Hitzler. "Neuro-symbolic artificial intelligence." AI Communications, September 16, 2021, 1–13. http://dx.doi.org/10.3233/aic-210084.

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Neuro-Symbolic Artificial Intelligence – the combination of symbolic methods with methods that are based on artificial neural networks – has a long-standing history. In this article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.

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Hitzler, Pascal, Aaron Eberhart, Monireh Ebrahimi, Md Kamruzzaman Sarker, and Lu Zhou. "Neuro-Symbolic Approaches in Artificial Intelligence." National Science Review, March 4, 2022. http://dx.doi.org/10.1093/nsr/nwac035.

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Abstract Neuro-Symbolic Artificial Intelligence refers to a field of research and applications that combines machine learning methods based on artificial neural networks, such as deep learning, with symbolic approaches to computing and Artificial Intelligence (AI), as can be found for example in the AI subfield of Knowledge Representation and Reasoning. Neuro-Symbolic AI has a long history, however it remained a rather niche topic until recently, when landmark advances in machine learning – prompted by deep learning – caused a significant rise in interest and research activity in combining neu

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