The pursuit of Artificial General Intelligence (AGI) demands AI systems that not only perceive but also reason in a human-like manner. While symbolic systems pioneered early breakthroughs in logic-based reasoning, such as MYCIN and DENDRAL, they suff...
Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due...
This work represents the natural continuation of the development of the cognitive architecture developed and named Sophimatics, organically integrating the spatio-temporal processing mechanisms of the Super Time Cognitive Neural Network (STCNN) with...
Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension...
This paper reports the Sophimatic Multi-Agent System (MAS), the first integrated operational implementation of the six-phase Sophimatics framework: a three-tier containerised prototype (React/FastAPI/MySQL, ~25,000 lines of code) with an agent catalo...
Automotive SPICE® (ASPICE) conformance activities require systematic discovery and evaluation of project documentation against standardised process indicators, consuming an estimated 40–60% of assessment preparation time with high extraneou...
The complexity of internal control in commercial banks continues to increase, and relevant reports exhibit notable lag and template issues. In response to the demand to transform unstructured disclosures into actionable insights, this study proposes...
Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a sp...
Human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdisciplinary field of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic...
Artificial intelligence architecture is no longer adequately described by model size alone. Dense Transformers remain the reference architecture for language and multimodal reasoning, but production systems increasingly combine conditional computatio...
Dropout prediction in Massive Open Online Courses (MOOCs) has been extensively studied, yet existing systems share three fundamental limitations: Accurate models are black boxes, post-hoc explanations approximate rather than faithfully represent mode...
Computer vision education is increasingly important in modern technology curricula; yet, it often lacks a systematic approach integrating both theoretical concepts and practical applications. This study proposes a staged framework for computer vision...
6 November 2026
Social media messages are short and noisy: the same tokens can denote a mention, a follow, or a reply depending on the surrounding user graph. Pre-trained language models frequently propose relations that a formal domain ontology forbids, whereas gra...
Large Language Models (LLMs) have demonstrated extraordinary capabilities in natural language generation; however, their real-world deployment is frequently hindered by the generation of factually incorrect or biased content, along with an inherent d...
Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fideli...
Deploying deep vision models on edge hardware requires understanding how architecture and training hyperparameters jointly determine accuracy and inference latency, yet these relationships remain poorly characterized in a systematic, data-driven mann...
Financial document understanding remains a critical challenge for Large Language Models, primarily due to the complex interplay between narrative text and structured numerical tables. Existing Retrieval-Augmented Generation (RAG) systems often treat...
As artificial intelligence (AI) systems continue to outperform humans in an increasing range of specialised tasks, a fundamental question emerges at the intersection of philosophy, cognitive science, and engineering: should we aim to build AIs that t...
Background/Objectives: Large language models (LLMs) have shown promising results in medical decision support; however, their effectiveness in managing acute cholecystitis and other gallbladder diseases remains insufficiently examined. This study eval...
Neurosymbolic AI promises to address the widening gap between logic and predictive inference in AI systems. It is the reaction to the dominance of deep learning neural networks and the lack of reasoning. Neurosymbolic models add human readable reason...
Symbolic execution is a key program analysis technique for ensuring the quality, security and safety of embedded, cyber–physical and electronic systems, but it is limited by path explosion and costly constraint solving. This study systematicall...
Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the InnoCube 3U n...
The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theor...
Neurosymbolic AI is an emerging paradigm that combines neural network learning capabilities with the structured reasoning capacity of symbolic systems. Although machine learning has achieved cutting-edge outcomes in diverse fields, including healthca...
This research introduces an artificial intelligence-based strategy for improving symbol recognition within the field of library science, concentrating on the creation and application of sophisticated technological solutions. Consistent with the objec...
The rapid expansion of AI applications in various domains demands models that balance predictive power with human interpretability, a requirement that has catalyzed the development of hybrid algorithms combining high accuracy with human-readable outp...
With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual be...
Symbolic execution of embedded systems faces path explosion, Satisfiability Modulo Theories (SMT) solver bottlenecks, interrupt nondeterminism, and environment modeling complexity. Recent artificial intelligence (AI)-guided approaches using reinforce...
Artificial intelligence (AI) is changing the way we study transport systems, yet its contribution to sustainable transportation planning remains uneven. While AI-based methods have shown strong performance in operational tasks such as traffic predict...
This paper addresses the opportunities and risks of integrating Large Language Models (LLMs) into structural engineering. Exclusive reliance on LLMs is inadequate in this field, because their probabilistic nature can lead to hallucinations and inaccu...
Artificial intelligence (AI) is revolutionizing multiple-input-multiple-output (MIMO) technology, making it a promising contender for the coming sixth-generation (6G) and beyond-fifth-generation (B5G) networks. However, the detection process in MIMO...
For many years, symbolic paradigms have dominated AI, but the impressive accomplishments of neural approaches have made them a new dominant paradigm. However, each of these paradigms has distinct advantages. This talk presents a hybrid paradigm that...
Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical models. This review provides a holistic foundation by detailing the ne...
Background/Objectives: The clinical adoption of AI in radiology requires models that balance high accuracy with interpretable, anatomically plausible reasoning. This study presents an integrated diagnostic framework that addresses this need by unifyi...
An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetric...
This paper presents the development of a 32-degree-of-freedom (DoF) humanoid robotic system designed for autonomous piano performance. The system integrates a vision-based music sheet reader with a YOLOv8 neural network for real-time detection and cl...
Kolmogorov–Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional Artificial Neural Networks (ANNs), offering superior accuracy and interpretability, which are two critical requirements in healthcare applications....
The cement industry, responsible for 26% of industrial CO2 emissions and 8% of global emissions, is under growing pressure to reduce its environmental footprint while maintaining profitability. In this context, optimizing grinding processes is essent...
Sub-symbolic Machine Learning (ML) techniques, and specifically Neural Network-based ones, recently took over the research landscape, thanks to their efficiency and impressive effectiveness. On the other hand, the recent debate on ethics and AI and t...
With the rapid advancement of artificial intelligence (AI) technology, training deep neural networks has become a core computational task that consumes significant energy in data centers. Researchers often employ various methods to estimate the energ...
Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative...
With artificial intelligence (AI) rapidly increasing in popularity and presence in everyday life, new applications utilizing AI are being explored across virtually all domains, from banking and healthcare to cybersecurity to generative AI for images,...
Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a...
Radiographic imaging remains a cornerstone of diagnostic practice. However, accurate interpretation faces challenges from subtle visual signatures, anatomical variability, and inter-observer inconsistency. Conventional deep learning approaches, such...
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...
This paper investigates an artificial intelligence (AI)-enhanced visible light communication (VLC) system for body area networks (BANs) based on a hybrid modulation framework that jointly employs quadrature amplitude modulation (QAM) and pulse-positi...
Can we turn AI black boxes into code? Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interp...
Unmanned Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage duri...
Diffuse optical imaging (DOI) uses scattered light to non-invasively sense and image highly diffuse media, including biological tissues such as the breast and brain. Despite its clinical potential, widespread adoption remains limited because physical...
Assistive braille technology has existed for many years with the purpose of aiding the blind in performing common tasks such as reading, writing, and communicating with others. Such technologies are aimed towards helping those who are visually impair...
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