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A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography

Changhee Yun, Bomi Eom, Sungjun Park, Chanho Kim et al. • Sensors (2023)

In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delica...

DOAJ Open Access (DOAJ) DOI: 10.3390/s23052864

Application of artificial intelligence to the electrocardiogram

Zachi Itzhak Attia, David Harmon, Elijah Raphael Behr, Paul A. Friedman • European Heart Journal (2021)

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG,...

OPENALEX Open Access DOI: https://doi.org/10.1093/eurheartj/ehab649 PMC: 8500024

Risk of Ischemic Heart Disease in Patients With Postpancreatectomy Diabetes and Pancreatic Cancer: A Population‐Based Study

Daegwang Yoo, Minsun Kang, Jaehun Jung • Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease (2023)

Background Postpancreatectomy diabetes can be caused by resection of functioning pancreatic tissue and is associated with postoperative pancreatic islet cell mass loss and subsequent endocrine dysfunction. Diabetes is a well‐known risk fact...

DOAJ Open Access (DOAJ) DOI: 10.1161/JAHA.123.031321

A satellite-derived, ground-measurement-independent monthly PM2.5 mass concentration dataset over China during 2000 - 2015

Ying Zhang, Zhengqiang Li, Yuanyuan Wei, Zongren Peng • Big Earth Data (2021)

Following the accelerated development of urbanization and industrialization, atmospheric particulate matter has become a significant threat to public health globally. Environmental health studies usually use the mass concentration of fine p...

DOAJ Open Access (DOAJ) DOI: 10.1080/20964471.2021.1918908

A is for Artificial Intelligence

Randi Williams, Hae Won Park, Cynthia Breazeal • CHI Conference on Human Factors in Computing Systems (2019)

We developed a novel early childhood artificial intelligence (AI) platform, PopBots, where preschool children train and interact with social robots to learn three AI concepts: knowledge-based systems, supervised machine learning, and genera...

OPENALEX Open Access DOI: https://doi.org/10.1145/3290605.3300677

A review of artificial intelligence

E. S. Brunette, R.L.C. Flemmer, Claire L. Flemmer • International Conference on Autonomous Robots and Agents (ICARA) (2009)

This paper reviews the field of artificial intelligence focusing on embodied artificial intelligence. It also considers models of artificial consciousness, agent-based artificial intelligence and the philosophical commentary on artificial i...

OPENALEX DOI: https://doi.org/10.1109/icara.2000.4804025

Artificial intelligence in Internet of things

Ashish Kumar Ghosh, Debasrita Chakraborty, Anwesha Law • CAAI Transactions on Intelligence Technology (2018)

Functioning of the Internet is persistently transforming from the Internet of computers (IoC) to the ‘Internet of things (IoT)’. Furthermore, massively interconnected systems, also known as cyber‐physical systems (CPSs), are emerging from t...

OPENALEX cc-by-nd DOI: https://doi.org/10.1049/trit.2018.1008

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Shuiguang Deng, Hailiang Zhao, Weijia Jia Fang, Jianwei Yin et al. • IEEE Internet of Things Journal (2020)

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, edge computing, is surging in popularity. Meanwhile, the artificial intelligence (AI) applications...

OPENALEX Open Access DOI: https://doi.org/10.1109/jiot.2020.2984887

Raman Spectroscopy Pre-Trained Encoder: A Self-Supervised Learning Approach for Data-Efficient Domain-Independent Spectroscopy Analysis

Abhiraam Eranti, Yogesh Tewari, Rafael Palacios, Amar Gupta • IEEE Access (2026)

Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2026.3672109

Association between myosteatosis and impaired glucose metabolism: A deep learning whole‐body magnetic resonance imaging population phenotyping approach

Matthias Jung, Hanna Rieder, Marco Reisert, Susanne Rospleszcz et al. • Journal of Cachexia, Sarcopenia and Muscle (2024)

Abstract Background There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with th...

DOAJ Open Access (DOAJ) DOI: 10.1002/jcsm.13527

Artificial Intelligence

Mark Kac, Gian‐Carlo Rota, Jacob T. Schwartz • Birkhäuser Boston eBooks (1992)

Scientists Doing Research On Artificial Intelligence are still far from their ultimate goal — a computer-based analog of the human brain. Nevertheless, their efforts have regularly produced results useful in applied computer science. For ex...

OPENALEX DOI: https://doi.org/10.1007/978-0-8176-4775-9_16

Cost-effective, open-source, automated apparatus for testing transitive inference in mice

Silvia Margarian, Yihan Chen, Jumana Waheed, Parham Rezaeimanesh et al. • Scientific Reports (2026)

Abstract Transitive inference is a form of reasoning that relies on prior knowledge and benefits survival in diverse species. While initially designed for humans, the transitive inference task has been adapted for mice, allowing for integra...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-43430-x

Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence

Stone, Peter, Rodney A. Brooks, Erik Brynjolfsson, Ryan Calo et al. • arXiv (Cornell University) (2022)

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.2211.06318