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Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...

Abstract Vision Transformers have demonstrated strong performance across diverse visual tasks; however, their high computational cost due to long token sequences remains a critical challenge. Sequence reduction via pooli...

BackgroundSystemic sclerosis (SSc) is characterized by a complex and heterogeneous interplay between vascular dysfunction, immune activation, and progressive fibrosis. Despite advances in molecular characterization, ther...

Immersive walkthroughs are increasingly used to document, interpret, and communicate heritage sites to remote audiences. This study presents a framework for designing and implementing a cross-platform WebVR walkthrough o...

Abstract Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accel...

Abstract This research proposes an innovative Arabic financial forecasting model that integrates linguistic relation extraction with advanced deep learning and machine learning techniques. The framework combines Arabic O...

Abstract As reported by the World Health Organization (WHO), Skin Cancer (SC) is a growing medical concern with millions of cases diagnosed each year. Deep Learning methods show promising results in early detection and c...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

Dairy farms increasingly deploy sensor networks that generate high-frequency behavioral and spatial data, yet translating these multimodal streams into actionable welfare insights remains a key challenge. This study pres...

Abstract Glaucoma remains one of the primary causes of irreversible blindness, characterized by gradual damage to the optic nerve, which often goes undetected until advanced stages. Accurate and early diagnosis depends h...

To address issues such as missed detections and false positives in safety equipment wear detection tasks caused by complex backgrounds, severe occlusions, and small target sizes, as well as frequent ID switching and low ...

Abstract Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems have significantly advanced data modelling capabilities and improved opportunities for extracting knowledge from vast and heterogeneo...

The field of artificial intelligence (AI), machine learning and intelligent automation has become pervasive in our modern digital world. It extends from business and public services to environmental management and even i...

In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-...

In recent years, large language models and agent systems based on them have been considered a promising tool for the digital transformation of public administration. However, the practical effectiveness of such systems i...

Infrared dim and small target detection is crucial in fields, such as military reconnaissance and remote sensing. However, due to the extremely small target size, lack of texture, and low signal-to-noise ratio, the direc...

Current watermarking approaches for LLM-generated text typically embed watermarks during the generation process, using a secret key controlled by the LLM. Although this enables verification of LLM-generated content, it m...

A this article presents a design studio model that integrates architectural pedagogy with urban resilience by focusing on the theme of “healing” within a historical context. Conducted in Sarıkaya, an ancient Roman t...

Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Recti...

We propose a technique for producing `visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activat...

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrar...

Materias / palabras clave: contextual architecture, urban planning, regionalism in design, sustainable architecture, heritage and adaptive reuse, social participation in design; Technology: Building construction: Architectural engineering. Structu...