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Applied Artificial Intelligence
Scopus
ISSN 0883-9514 · Taylor & Francis Group · 892 artikel di indeks
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Artikel Jurnal
A Hybrid RAG-LLaMA Framework for Scalable and Accurate Interpretation of Legal Texts
Interpreting Indian tax law is challenging due to frequent legislative amendments, complex statutory structures, and the requirement for precise citation. This paper presents TaxFlow, a domain-specific legal assistant that integrates a hybrid Retrieval-Augmented Generation (RAG) framework with the…
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Artikel Jurnal
A Comprehensive Study of Grey Wolf Optimizer Variants for Optimizing Feature Selection in High-Dimensional Data
Metaheuristic algorithms, notably the Grey Wolf Optimizer (GWO), are widely used for wrapper-based feature selection due to their superior ability to balance exploration and exploitation. We conduct a comparative evaluation of the original GWO and eight established variants on ten benchmark…
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Fine-Tuning Monolingual Pre-Trained BERT Models for Sentiment Analysis in Peruvian Slang Contexts
The continuous advancements in natural language processing (NLP) have led to the development of highly effective models such as BERT, RoBERTa, GPT-4, Llama 3, and Gemini. However, adapting these models to specific dialects, especially in languages other than English, remains underexplored…
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Improving LIME Stability via Density-Awareness: Evaluation and Comparison of AKDE-LIME
This paper addresses the critical instability of Local Interpretable Model-agnostic Explanations (LIME). We introduce Adaptive Kernel Density Estimation LIME (AKDE-LIME), a novel approach that enhances local explanation stability by incorporating a density-aware weighting scheme. Unlike LIME’s…
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A Noise-Score-Based Cleaning Framework for Multi-Class Label Noise
In machine learning, the objective of training a classification model is to learn the mapping relationship between features and labels. Label noise data has a severe detrimental effect on model performance, often surpassing the impact of feature noise. Consequently, label noise cleaning techniques…
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Neuroevolution and Neuroswarm with Hooke-Jeeves Local Search Algorithm for Predicting the Construction Effort of Software Projects
Software construction corresponds to one of the software development life cycle (SDLC) activities. It consists of coding, unit testing, integration testing, and debugging activities. The SDLC effort (i.e. the number of person-hours) is an important variable predicted for budgeting the software…
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Extracting Abstraction Dimensions by Identifying Syntax Patterns from Texts
Extracting abstraction dimensions from a large set of texts is a way to efficiently manage, explore, and understand the texts from multiple dimensions. Subject, action, object, and adverbial are categories formed by abstracting various specific representations of syntax elements, which can be used…
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Artikel Jurnal
QBUILD: Quantum-Resistant Blockchain Architecture for Secure and Transparent Construction Supply Chains in the Post-Quantum Era
The control of insecurity, transparency, and real-time tracking of assets are the urgent challenges of the construction supply chain because of the complex multi-stakeholder structure and the emergence of quantum computing threats. This paper applies a four-phase systematic approach to developing a…
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Artikel Jurnal
SBSD-Detector: A Stick-Breaking Smoothed Dirichlet-Based Multimodal Model for Detecting Fake News
The proliferation of artificial-intelligence-generated fake content demands robust detection technologies. Traditional unimodal approaches fail to capture cross-modal dependencies, while existing multimodal methods suffer from non-stochastic latent representations that limit nuanced interaction…
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Chronological Review and Performance Analysis of YOLO-Based Deep Learning Frameworks in Complex Geospatial Environments
The rapid evolution of YOLO (You Only Look Once) models has transformed object detection into a real-time, scalable solution across a variety of domains. This article presents a comprehensive and chronological review of YOLO-based frameworks, emphasizing their adoption in remote sensing…
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Artikel Jurnal
SPINE: Segmentation-guided Processing and Integration of Multimodal Spinal MRI for Natural-Language Enhanced Report Generation
Automated radiology report generation remains a key challenge in clinical AI, requiring alignment between visual, anatomical, and linguistic representations. This study introduces SPINE – a Segmentation-guided Processing and Integration framework for spinal MRI report generation – designed to…
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Artikel Jurnal
Next-Generation Hybrid Machine Learning Model for Dual Property Prediction in Sustainable Concrete
Predicting concrete compressive strength (CS) and fire resistance (FR) is a crucial difficulty due to the high cost and time required for laboratory tests. This study introduces a hybrid machine learning system aimed at predicting these properties, utilizing nine models derived from 408 mix…
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Physics-Informed Recurrent Neural Networks for Deterministic Multi-Step Battery Scheduling in Microgrids
Iterative metaheuristic schedulers have stochastic convergence, which yields unpredictable runtimes and is problematic for real-time microgrid control. This paper proposes a deterministic multi-step scheduling method in which the complete battery control profile over a given forecasting horizon is…
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Artikel Jurnal
p–LSGR: A lightweight post–hoc latent–space resampling framework for imbalanced and complex graph distributions
Graph neural networks (GNNs) are widely used across domains but remain sensitive to class imbalance, class overlap, and complex data distributions, limiting reliability in real-world settings. Existing imbalance-mitigation strategies provide only partial robustness, are often computationally…
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Artikel Jurnal
Selective Consolidation in AI Agent Research: A Co-Citation Analysis of Its Intellectual Structure and Relevance for Human–Computer Interaction
Research on Artificial Intelligence (AI) agents has expanded rapidly across dis-ciplines, generating a diverse but fragmented body of knowledge. Despite the proliferation of domain-specific reviews, a field-level understanding of how AI agent research is intellectually structured and how it evolves…
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A Combined Machine Learning and Weighted Gene Co-expression Network Analysis Strategy for Lung Adenocarcinoma Biomarker Discovery
Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), remains a leading cause of cancer-related deaths worldwide, largely due to difficulties in early detection and variable treatment responses. Identifying molecular biomarkers that predict disease progression…
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Artikel Jurnal
Phenotyping of Risk Factors for Adverse Events: New Methods for Explaining Prognostic Estimates in Cardiology
The implementation of machine learning in clinical practice is limited by decision‑making opacity and the difficulty of interpreting prognostic models. Multivariate logistic regression yields transparent prognostic models that make it possible to describe how the predictors influence the occurrence…
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Artikel Jurnal
Context-Aware Graph Neural Networks (CAGNN) for Multimodal Prediction of Parkinson’s Disease Dementia
Parkinson’s disease dementia is a severe cognitive decline in up to 95% of Parkinson’s disease sufferers within 10–20 years after diagnosis. Diagnosis relies on comprehensive clinical evaluation; however, research indicates that underlying physiological changes to white matter tracts precede…
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Artikel Jurnal
Data-Driven Precision Harvest Planning via Visual Growth Forecasting
While Artificial Intelligence (AI) techniques have been widely applied to problem-solving, their true transformative power lies in enhancing people’s practice. For instance, in greenhouse agriculture, data analytics can enhance crop monitoring and resource planning; computer vision can support…
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AI-based Identity Fraud Detection: A Systematic Review
With the rapid development of digital services, personally identifiable information (PII) is increasingly exposed to identity fraud (IDF). The growing use of artificial intelligence (AI)-enabled deepfake technologies has further amplified this threat, enabling fraudsters to produce highly…