With the advancement of educational informatization, vast amounts of Chinese text are generated across online platforms and digital textbooks. Effectively classifying such text is essential for intelligent education systems. This study conducts a sys...
Background: Large language models (LLMs) have seen a significant boost recently in the field of natural language processing (NLP) due to their capabilities in analyzing words. These autoregressive models prove robust in classification tasks where tex...
To enhance flight safety and operational efficiency, extracting key actions, flight parameters, and status information from civil aviation flight crew instructions generated during pre-flight and in-flight procedures is crucial. However, such texts a...
Bug severity classification is an important task in software maintenance because it supports bug triage and resource allocation. However, newly created or evolving projects often lack sufficient labeled data, making cross-project severity prediction...
This study examines how sentiment analysis of environmental, social, and governance (ESG) news affects the financial performance of companies in innovative sectors such as mobility, technology, and renewable energy. Using approximately 9828 general E...
Natural language-processing tasks have been improved greatly by large language models (LLMs). However, numerous parameters make their execution computationally expensive and difficult on resource-constrained devices. For this problem, as well as main...
Online hotel reviews provide a major source of information for understanding guest satisfaction, yet rating prediction remains difficult because review text is often short, highly skewed toward positive scores, and affected by inconsistent or repetit...
The rapid proliferation of fake news poses significant societal and psychological risks, driving an urgent need for automated detection systems. While conventional unimodal methods fail to capture complex cross-modal relationships, existing multimoda...
We study language-guided target localization in synthetic aperture radar (SAR) imagery for deployment on resource-constrained platforms. Existing vision-language models either rely on heavy backbones unsuitable for edge devices or are designed for na...
Transformers have emerged as the central backbone architecture for modern generative AI. However, most ML applications targeting low-power, low-cost SoCs (TinyML apps) do not employ Transformers as these models are thought to be challenging to quanti...
Phishing emails remain a significant concern and a growing cybersecurity threat in online communication. They often bypass traditional filters due to their increasing sophistication. This study presents a comparative evaluation of machine learning (M...
With the widespread deployment of IoT devices, deploying efficient network traffic classification models on resource-constrained edge nodes is critical for real-time boundary security. However, traditional lightweight models primarily rely on macro-l...
In general, it is difficult to obtain a huge, labeled dataset for deep learning-based phoneme recognition in singing voices. Studying singing voices also offers inherent challenges, compared to speech, because of the distinct variations in pitch, dur...
In this article, we present a model for analyzing the co-occurrence count data derived from practical fields such as user–item or item–item data from online shopping platforms and co-occurring word–word pairs in sequences of texts....
Large Language Models (LLMs), like GPT and BERT, have significantly advanced Natural Language Processing (NLP), enabling high performance on complex tasks. However, their size and computational needs make LLMs unsuitable for deployment on resource-co...
Background/Objectives: Medical vision–language learning integrates radiographic images and textual information for chest X-ray (CXR) classification. However, in long-tailed multi-label CXR classification, modality imbalance can cause models to...
The growing volume of unstructured clinical and genetic text calls for automated processing methods that can meaningfully support clinical decision-making. Most existing work, however, treats the analysis of patient clinical descriptions and the inte...