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Showing 20 of 1,495 academic articles

Explainable hybrid deep learning framework for air quality index prediction in Indian cities

Priyal Singhal, Praneet Saurabh, Mayank Sharma, Jyoti Badge et al. • Discover Artificial Intelligence (2026)

Abstract Air pollution constitutes one of the most pressing environmental and public health crises of the twenty-first century, disproportionately affecting rapidly urbanising nations such as India. Accurate prediction of the Air Quality In...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-02131-0

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

Shima Javanmardi, Ali Mohammad Latif, Mohammad Taghi Sadeghi, Mehrdad Jahanbanifard et al. • Sensors (2022)

In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have b...

DOAJ Open Access (DOAJ) DOI: 10.3390/s22218376

Ensembles machine learning for short duration rainfall intensity forecasting

Susilowati, Aprizal, Dyah Indriana Kusumastuti • Discover Artificial Intelligence (2026)

Abstract This study introduces an ensemble machine learning (EML) framework to enhance short-duration rainfall-intensity prediction under limited-data conditions. By integrating multiple base learners within a stacking architecture, the pro...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01772-5

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Ivan S. Blekanov, Nikita Tarasov, Svetlana S. Bodrunova • Future Internet (2022)

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the mo...

DOAJ Open Access (DOAJ) DOI: 10.3390/fi14030069

Challenges in Natural Language Processing

• Cambridge University Press eBooks (1993)

Although natural language processing has come far, the technology has not achieved a major impact on society. Is this because of some fundamental limitation that cannot be overcome? Or because there has not been enough time to refine and ap...

OPENALEX DOI: https://doi.org/10.1017/cbo9780511659478

Fraud detection with natural language processing

Petros Boulieris, John Pavlopoulos, Alexandros Xenos, Vasilis Vassalos • Machine Learning (2023)

Abstract Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ...

OPENALEX cc-by DOI: https://doi.org/10.1007/s10994-023-06354-5

A fault location method for secondary virtual circuits in smart substations based on the integration of GAE and GCN

Yuxi Wang, Yue Wang, Sha Han • Discover Artificial Intelligence (2026)

Abstract The secondary virtual circuits in smart substations enable digital and networked information exchange between devices. However, their extensive logical connections, complex structure, and implicit interdependencies pose significant...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-02248-2

USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION

Oleksandr Tymoshchuk • Інформаційні технології і засоби навчання (2025)

The expansion of nuclear technologies in various industries, combined with the constant threat of radiation-related incidents, highlights the urgent need for effective radiation education. This study is devoted to an empirical investigation...

DOAJ Open Access (DOAJ) DOI: 10.33407/itlt.v105i1.5905

Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey

Pratikkumar Chauhan, Munindra Lunagaria, Deepak Kumar Verma, Krunal Vaghela et al. • IEEE Access (2024)

Classification of brain tumour is critical in medical image analysis and diagnosis as it aids in determining the affinity of cases to select treatment paths. This feature underscores the importance of accurate classification of brain tumour...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3460380

A Model-Driven Approach Employing DSL and Generation Templates to Accelerate the Development of Conversational Agents for Smart Tourism

Charaf Ouaddi, Lamya Benaddi, Adnane Souha, Hamza Abdelmalek et al. • IEEE Access (2025)

Conversational agents are intelligent, ubiquitous software applications widely used across various sectors, such as commerce, tourism, and more. Their key benefits include automating tasks, improving customer service, and ensuring service a...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3574641

Prioritized replay dueling double DQN for intelligent task scheduling in IoT Fog Cloud computing

Naima Jawhar, Adnane Talha, Anas Bouayad • Discover Artificial Intelligence (2026)

Abstract Task scheduling remains a challenge in IoT-Fog-Cloud systems due to dynamic workloads and heterogeneous resources. Ineffective scheduling strategies increase latency, energy consumption, and Service Level Agreement (SLA) violations...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-02614-0

Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review

Maria Frasca, Davide La Torre, Gabriella Pravettoni, Ilaria Cutica • Discover Artificial Intelligence (2024)

Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-024-00114-7

A new image similarity metric for a perceptual and transparent geometric and chromatic assessment

Antonio Di Marino, Vincenzo Bevilacqua, Emanuel Di Nardo, Angelo Ciaramella et al. • Discover Artificial Intelligence (2026)

Abstract Full-reference image quality assessment metrics such as PSNR and SSIM misalign with human perceptual judgments on texture and shape distortions, a limitation documented across multiple benchmarks. Deep perceptual metrics such as LP...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-02424-4

An Analysis of the Training Data Impact for Domain-Adapted Tokenizer Performances—The Case of Serbian Legal Domain Adaptation

Miloš Bogdanović, Milena Frtunić Gligorijević, Jelena Kocić, Leonid Stoimenov • Applied Sciences (2025)

Various areas of natural language processing (NLP) have greatly benefited from the development of large language models in recent years. This research addresses the challenge of developing efficient tokenizers for transformer-based domain-s...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15137491