This paper presents a neuro-symbolic approach for constructing distributed knowledge graphs to facilitate cooperation through communication among spatially proximate agents. We develop a graph autoencoder (GAE) that learns rich representations from h...
As the applications of robots expand across a wide variety of areas, high-level task planning considering human–robot interactions is emerging as a critical issue. Various elements that facilitate flexible responses to humans in an ever-changin...
This paper introduces a neuro-symbolic approach for relational exploration in cultural heritage knowledge graphs, exploiting Large Language Models (LLMs) for explanation generation and a mathematically grounded model to quantify the interestingness o...
The human ability to imagine alternative realities has long supported reasoning, communication, and creativity through storytelling. By constructing hypothetical scenarios, people can anticipate outcomes, solve problems, and generate new knowledge. T...
Digital twins have revolutionized manufacturing and maintenance, allowing us to interact with virtual yet realistic representations of the physical world in simulations to identify potential problems or opportunities for improvement. However, traditi...
This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with va...
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 classifi...
MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain...
The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poo...
Posture recognition is critical in modern educational and office environments for preventing musculoskeletal disorders and maintaining cognitive performance. Existing methods based on human keypoint detection typically rely on convolutional neural ne...
In the context of autonomous driving, predicting pedestrian behavior is a critical component for enhancing road safety. Currently, the focus of such predictions extends beyond accuracy and reliability, placing increasing emphasis on the explainabilit...
This study introduces an explainable neuro-symbolic and large language model (LLM)-driven framework for intelligent interpretation of corneal topography and precision surgical decision support. In a prospective cohort of 20 eyes, comprehensive IOLMas...
While object detection and recognition have been extensively adopted by many applications in decision-making, new algorithms and methodologies have emerged to enhance the automatic identification of target objects. In particular, the rise of deep lea...
Post-Traumatic Stress Disorder (PTSD) poses complex clinical challenges due to its emotional volatility, contextual sensitivity, and need for personalized care. Conventional AI systems often fall short in therapeutic contexts due to lack of explainab...
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 fr...
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 wid...
With the rapid growth of electric vehicles, the efficient and safe recycling of high-energy battery packs, particularly the removal of structural bolts, has become a critical challenge. This study presents a NeuroSymbolic robotic system for battery d...
Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation...
Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adapta...
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...
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...
As intelligent systems become increasingly embedded in industrial ecosystems, the demand for transparency, reliability, and interpretability has intensified. This study investigates how explainable artificial intelligence (XAI) contributes to enhanci...
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...
With the increasing complexity of cyber threats and the inefficiency of traditional vulnerability management, artificial intelligence has been increasingly integrated into cybersecurity. This review provides a comprehensive evaluation of AI-powered s...
English argumentative writing is a cornerstone of academic and professional communication, yet it remains a significant challenge for second-language (L2) learners. While Large Language Models (LLMs) show promise as components in automated feedback s...
Background: Chest X-ray (CXR) is widely used for the assessment of thoracic diseases, yet automated multi-label interpretation remains challenging due to subtle visual patterns, overlapping anatomical structures, and frequent co-occurrence of abnorma...