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Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Heather Marriott, Renata Kabiljo, Guy P Hunt, Ahmad Al Khleifat et al. • Acta Neuropathologica Communications (2023)

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease lands...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40478-023-01686-8

AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness

Anantaya Timtong, Atthaphon Ariyarit, Wanwanut Boongsood, Prasert Aengchuan et al. • Results in Engineering (2024)

This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . Traditionally, assessing fracture toughness relies heavily on destructive...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.rineng.2024.103289

The Effects of Large Language Models on the Generation of Multiple-choice Questions in Medical Education: A Scoping Review

Baleegh Elsir Ahmed Mohammed, Tanzeel Elsir Ahmed Mohammed, Anthony Paul Breitbach • Sudan Journal of Medical Sciences (2026)

Background: Constructing high-quality multiple-choice questions (MCQs) is critical yet resource-intensive in medical education. Large language models (LLMs), such as ChatGPT, have become promising tools for generating MCQs in medical educat...

DOAJ Open Access (DOAJ) DOI: 10.18502/sjms.v21i2.19868

A physics-informed spatiotemporal graph neural network for photovoltaic power forecasting and micro-anomaly localization

Mortatha Faleh Majed, Ali S. Abed Al Sailawi, Hassan A. Hameed Al-Hamzawi • Next Energy (2026)

Accurate power forecasting and precise fault diagnosis are critical for the reliable operation of large-scale photovoltaic systems. However, existing data-driven models often fail to capture complex spatiotemporal dependencies and lack phys...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.nxener.2026.101127

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

Artificial Intelligence with Uncertainty

Deyi Li, Yi Du • CRC Press eBooks (2017)

This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural la...

OPENALEX DOI: https://doi.org/10.1201/9781315366951

Current Signature-Based Bearing Fault Severity Classification Using a Robust Multilevel Cascaded Framework

Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh, Ali Emadi • IEEE Access (2025)

Bearing faults are a critical concern in electrical machines, particularly permanent magnet synchronous motors (PMSMs), commonly used in electric vehicles. Early and accurate classification of bearing fault severity is essential for predict...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3589750

A review of optimization strategies for deep and machine learning in diabetic macular edema

A. M. Mutawa, Khalid Sabti, Bibin Shalini Sundaram Thankaleela, Seemant Raizada • Frontiers in Artificial Intelligence (2026)

Diabetic macular edema (DME) is a primary contributor to visual impairment in diabetic patients, necessitating precise and prompt analysis for optimal treatment. Recent breakthroughs in deep learning (DL) and machine learning (ML) have yiel...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1684752

An Instructional Optimization Method Based on Bidirectional Transformer and Reinforcement Learning

Ran Zhang, Xiaoping Wu, Xude Zhang, Li Xu • IEEE Access (2025)

With the rapid development of information technology, personalized education has become a key direction for improving the quality of online learning and optimizing individualized learning paths. However, accurately recommending appropriate ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3576878

A bibliometric analysis of industry 4.0 digital manufacturing and smart technologies for sustainable development

Ferhat Yilmaz, Zeynep Ozguner • Discover Sustainability (2026)

Abstract This study provides a broad bibliometric analysis of Industry 4.0 as well as technology-based production systems within the appropriate scientific literature to target the description of the dynamic structure, intellectual foundati...

DOAJ Open Access (DOAJ) DOI: 10.1007/s43621-026-03275-w

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

Designing novel linagliptin analogs through combined generative artificial intelligence, molecular docking, molecular dynamics simulation, and binding energy estimation for targeting fibroblast activation protein

Mingsong Shi, Mingsong Shi, Zhi Yang, Yuhan Yang et al. • Frontiers in Chemistry (2026)

Fibroblast activation protein (FAP) is a highly specific biomarker overexpressed in cancer-associated fibroblasts, making it a promising diagnostic target. Developing novel high-affinity molecular probes for FAP-targeted diagnostics remains...

DOAJ Open Access (DOAJ) DOI: 10.3389/fchem.2026.1848970

<bold>CroSSHeteroFL</bold>: Cross-Stratified Sampling Composition-Fitting to Federated Learning for Heterogeneous Clients

Vo Phuc Tinh, Hoang Hai Son, Nguyen Hoang Nam, Duc Ngoc Minh Dang et al. • IEEE Access (2024)

In the large-scale deployment of federated learning (FL) systems, the heterogeneity of clients, such as mobile phones and Internet of Things (IoT) devices with different configurations, constitutes a significant problem regarding fairness, ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3475737

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

Large language model bias auditing for periodontal diagnosis using an ambiguity-probe methodology: a pilot study

Teerachate Nantakeeratipat • Frontiers in Digital Health (2026)

BackgroundLarge Language Models (LLMs) in healthcare holds immense promise yet carries the risk of perpetuating social biases. While artificial intelligence (AI) fairness is a growing concern, a gap exists in understanding how these models ...

DOAJ Open Access (DOAJ) DOI: 10.3389/fdgth.2025.1687820

Impact of AI-quantified fluid dynamics on visual outcomes over 5 years in patients with treatment-naïve nAMD from the FRB! registry

Philipp Fuchs, Ursula Schmidt-Erfurth, Leonard M. Coulibaly, Hrvoje Bogunovic et al. • Scientific Reports (2025)

Abstract To investigate the impact of retinal fluid dynamics on visual outcomes in patients with treatment-naïve neovascular age-related macular degeneration (nAMD) treated in the real world over 5 years using approved AI-based fluid monito...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-17417-z