Academic Articles & Research Papers

Artificial intelligence in plant breeding

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing nume...

From Design to Bedside: Brain-Targeted Engineered Nanoparticles and Predictive Modeling for Neurodegenerative Diseases Therapeutics.

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

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...

An automated deep learning framework for brain tumor classification using MRI imagery

Abstract The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety of sizes, shapes, and visual characteristics, requiring individualized treatment s...

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

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...

Rethinking evidence-informed policy and practice in the age of generative artificial intelligence

The rise of generative artificial intelligence tools such as ChatGPT is transforming how knowledge is produced, accessed and used across educational systems. While these technologies offer new opportunities for efficiency and scalability, t...

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

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...

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

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...

A review of prediction methods for moving target trajectories

With the rapid emergence of mobile terminal equipment in intelligent transportation system, the deep understanding and accurate prediction of moving target trajectories are capable of reducing the traffic accident probability, and promoting...

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

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...

Creativity and Artificial Intelligence

Creativity and Artificial Intelligence: A Conceptual Blending Approach takes readers into a computationally plausible model of creativity. Inspired by a thorough analysis of work on creativity from the areas of philosophy, psychology, cogni...

Artificial Intelligence with Uncertainty

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...

Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.

Translational modelling of human microglia from cultures to chimeras.

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

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...

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

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...

An Instructional Optimization Method Based on Bidirectional Transformer and Reinforcement Learning

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 ...

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

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...

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

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...

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties ...