Academic Articles & Research Papers

Deep learning for natural language processing: advantages and challenges

Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...

Computer vision in smart agriculture and precision farming: Techniques and applications

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence (AI) technologies. Thi...

FCM-Encoder: a novel interpretable framework with vision-language reasoning for coronary artery disease and solitary pulmonary nodule classification.

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improvi...

Neural Networks and Natural Intelligence

Stephen Grossberg and his colleagues at Boston University's Center for Adaptive Systems are producing some of the most exciting research in the neural network approach to making computers "think." Packed with real-time computer simulations ...

Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video)

Abstract Background Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tu...

Deep learning for cardiovascular medicine: a practical primer

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via...

Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization

Decision making algorithms are used in a multitude of different applications. Conventional approaches for designing decision algorithms employ principled and simplified modelling, based on which one can determine decisions via tractable opt...

Fairness in Deep Learning: A Computational Perspective

Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. We provide a review covering recent progresses to ta...

Deep learning for power quality

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in ter...

A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images

Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imba...

Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers.

Machine Learning in Chemical Engineering: A Perspective

Abstract The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML)...

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

Abstract Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet menta...

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...

Interpretable machine learning

Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that ...

A Review on Fairness in Machine Learning

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college ad...

lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses

Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and acc...

Resistance to Medical Artificial Intelligence

Abstract Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate a...

Neurosurgery and artificial intelligence

Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. Th...