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Frontiers in Artificial Intelligence
Scopus
ISSN 2624-8212 · Frontiers Media S.A. · 2.684 artikel di indeks
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Artikel Jurnal
Improved attention-based PCNN with GhostNet for epilepsy seizure detection using EEG and fMRI modalities: extractive pattern and histogram feature set
IntroductionDetecting epileptic seizures remains a major challenge in clinical neurology due to the complex, heterogeneous, and non-stationary characteristics of electroencephalogram (EEG) signals. Although recent machine learning (ML) and deep learning (DL) approaches have improved detection…
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KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework
Background and motivationThe Michaelis constant Km is one of the key kinetic parameters in the quantification of enzyme-substrate affinity within the context of the Michaelis–Menten theory. While Km values are traditionally subjected to labor-intensive governance via in vitro assays, the brisk…
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LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification
The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation…
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Artificial intelligence usage, breakthrough innovation, and innovation performance in high-tech enterprises: the nonlinear moderating role of Not-Invented-Here Syndrome
The integration of artificial intelligence (AI) with frontier technologies such as large language models and quantum computing has significantly enhanced enterprises’ potential for breakthrough innovation, becoming a critical driver of innovation performance. However, the internal mechanisms and…
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Augmenting large language models with psychologically grounded models of causal reasoning for planning under uncertainty
Large Language Models (LLMs) have come a long way in their ability to solve a wide range of problems. Yet, LLM decision-making still relies primarily on pattern recognition, which may limit its ability to make sound decisions under uncertainty. In contrast, human reasoning often makes use of…
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HealthProcessAI: a technical framework and proof-of-concept for LLM-enhanced healthcare process mining
BackgroundProcess mining has emerged as a powerful analytical technique for understanding complex healthcare workflows. However, its application faces significant barriers, including technical complexity, a lack of standardized approaches, and limited access to practical training resources. To…
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A systematic review and future directions for AI-driven detection of fraud patterns in SACCO transactions
Fraud in Savings and Credit Cooperative Organizations (SACCOs) remains a major challenge that undermines financial inclusion and sustainability in developing countries. This study conducted a systematic literature review to examine both traditional and emerging fraud patterns and evaluate fraud…
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Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection
IntroductionThis study presents a modified version of Particle Swarm Optimization (PSO) using an all-optical computational update mechanism. The primary innovation and objective of this collaboration aimed to leverage the inherent properties of coherent optical systems, including specialized…
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AI and digital justice in EU labor law. A comparative study on predictive tools and judicial transformation
This paper examines how artificial intelligence (AI) and digital tools are reshaping European labor law litigation, particularly in redundancy disputes. Conducted within the I-Tools To Design And Enhance Access To Justice (IDEA) project, it draws on a comparative survey across six Member…
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Large language model-driven time-series forecasting of financial network indicators
IntroductionFinancial markets operate as dynamic networks in which institutional cross-holdings shape the diffusion of information and the propagation of risk. Forecasting the evolution of stock information networks is critical for anticipating herding behavior and safeguarding systemic stability…
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Traditional machine learning in biomedical image analysis: before you go too deep
Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, computational efficiency, and robustness on smaller datasets. This review provides a comprehensive…
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Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis
Background and objectivesThe aim of this study is to evaluate the performance of DL algorithms in diagnosing early gastric cancer (EGC) using white light endoscopic images.MethodsA systematic literature search was conducted in PubMed, Embase, Cochrane Library, and Web of Science up to July 25…
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Artikel Jurnal
Artificial intelligence for biodiversity and tourism governance: predictive insights from multilayer perceptron models in Amazonia
Tourism in biodiversity-rich regions was among the sectors most severely disrupted by the COVID-19 pandemic, which amplified existing socioeconomic vulnerabilities and placed cultural and natural heritage conservation at risk. In the Peruvian Amazon, Bagua Province illustrates this challenge, where…
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Real-time grading method of tunnel surrounding rock based on image recognition
To enable rapid, accurate grading of tunnel surrounding rock during construction, we propose a real-time grading method that integrates image processing with lightweight deep learning. We developed an automated pipeline that combines image-processing techniques and machine-learning algorithms to…
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Small language models applied in text summarization task of health-related news to improve public health audit: an experimental case study
ContextFraud and corruption are among the main crimes affecting public institutions, with the healthcare sector being particularly vulnerable due to its structural complexity, the coexistence of public and private providers, the large number of actors involved, the globalized nature of supply…
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Predicting and identifying correlates of inequalities in breast cancer screening uptake using national level data from India
BackgroundDespite national screening initiatives, coverage of breast cancer screening is low, and late-stage diagnosis remains a major contributor to mortality among Indian women. Accurate, precise, and actionable prediction of socioeconomic and structural inequities in screening uptake is critical…
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CBAM-DenseNet with multi-feature quality filtering: advancing accuracy in small-sample iris recognition
In the context of the information age, traditional password and key-based authentication mechanisms are no longer sufficient to meet the growing demands for information security. Iris recognition technology has garnered attention due to its high security and uniqueness. Current iris recognition…
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Marketing-AutoM3L: domain-aware automated machine learning for financial customer analytics
Financial customer analytics requires specialized machine learning pipelines that incorporate domain-specific understanding of customer behavior. Existing automated ML approaches often lack the capacity to effectively construct marketing-relevant features and that manual construction of predictive…
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The strategic trajectory of artificial intelligence in Qatar’s healthcare sector: a model for UN Sustainable Development Goal 9
This narrative review is first of its kind to strategically position AI and healthcare in the Qatari state under the broad framework of QNV-2030. The review highlights Qatar’s forward-looking digital infrastructure upon which its AI initiatives are being built. The paper lists several innovative…
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Bayesian RAG: uncertainty-aware retrieval for reliable financial question answering
Large language models excel at generating plausible responses but often produce factually incorrect answers in high-stakes financial analysis, leading to regulatory violations and financial losses, a critical challenge for deploying AI systems in production. Traditional Retrieval-Augmented…
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