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Navigating the Ethical Terrain of AI-Generated Text Tools: A Review

Yasir Abdelgadir Mohamed, Abdul Hakim H. M. Mohamed, Akbar Khanan, Mohamed Bashir et al. • IEEE Access (2024)

This review examines the ethical, social, and technical challenges posed by AI-generated text tools, focusing on their rapid advancement and widespread adoption. An exhaustive literature search across many databases, strict inclusion/exclus...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3521945

Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design

Lei Zhang, Yiru Huang, Leiming Yan, Jinghao Ge et al. • Advanced Intelligent Systems (2024)

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine ...

DOAJ Open Access (DOAJ) DOI: 10.1002/aisy.202300678

A systematic literature review on spam content detection and classification

Sanaa Kaddoura, Ganesh Chandrasekaran, Daniela Elena Popescu, Jude Hemanth Duraisamy • PeerJ Computer Science (2022)

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. ...

DOAJ Open Access (DOAJ) DOI: 10.7717/peerj-cs.830

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

Natural Language Processing for Smart Healthcare

Binggui Zhou, Guanghua Yang, Zheng Shi, Shaodan Ma • IEEE Reviews in Biomedical Engineering (2022)

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural ...

OPENALEX Open Access DOI: https://doi.org/10.1109/rbme.2022.3210270

Assessing GPT's capabilities in consumer food survey analysis: A comparative approach for understanding food technophobia and novel protein perceptions

Peihua Ma, Si Chen, Wenfan Su, Jiping Sheng et al. • Journal of Agriculture and Food Research (2025)

This study explores the application of GPT models for automating consumer food survey analysis, focusing on Chinese consumers' acceptance of plant-based foods, cultured meat, insect-based proteins, and microbial proteins. Traditional survey...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jafr.2025.102086

Natural Language Processing for Requirements Engineering

Liping Zhao, Waad Alhoshan, Alessio Ferrari, Keletso J. Letsholo et al. • ACM Computing Surveys (2021)

Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to...

OPENALEX Open Access DOI: https://doi.org/10.1145/3444689

Natural Language Processing in Oncology

Wen-wai Yim, Meliha Yetişgen, William Proctor Harris, Sharon W. Kwan • JAMA Oncology (2016)

IMPORTANCE: Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest im...

OPENALEX DOI: https://doi.org/10.1001/jamaoncol.2016.0213

Natural language processing

Gobinda G Chowdhury • Annual Review of Information Science and Technology (2003)

Previous ARIST chapters (Haas, 1996;Warner, 1987) described a number of theoretical developments that have influenced research in NLP.The most recent theoretical developments can be grouped into four classes: (i) statistical and corpus-base...

OPENALEX Open Access DOI: https://doi.org/10.1002/aris.1440370103