Academic Articles & Research Papers

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...

Machine-learning-guided directed evolution for protein engineering

Causes selection and risk level prediction of coal mine gas explosion accident

In order to accurately predict the risk level of coal mine gas explosion accidents, based on the feature vector in line with the actual situation, the particle swarm optimization probabilistic neural network (RWPSO-PNN) is improved to reali...

Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations

Port congestion and prolonged ship waiting times pose challenges for global trade and increase operational costs and inefficiencies. In this study, a novel machine learning-based predictive approach was proposed to improve port operations b...

Editorial for the Special Issue “Machine Learning in Computer Vision and Image Sensing: Theory and Applications”

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [...]...

Bayesian machine learning-based method for prediction of slope failure time

The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomen...

Quantum algorithms for matrix geometric means

Abstract Matrix geometric means between two positive definite matrices can be defined from distinct perspectives—as solutions to certain nonlinear systems of equations, as points along geodesics in Riemannian geometry, and as solutions to c...

Human behavior analysis on political retweets using machine learning algorithms

The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popula...

Ultra-broadband and polarization-insensitive metasurface absorber with behavior prediction using machine learning

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurfa...

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applicatio...

RDDA: Rectified Discriminatory Delta-Adjust

This paper introduces Rectified Discriminatory Delta-Adjust (RDDA), a novel methodology that enhances interpolation-based predictive modeling through adaptive sensitivity parameters. Building upon the foundational Delta-Adjust algorithm, RD...

A nested-block scalable framework for efficient and equitable urban infrastructure management.

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challengin...

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges fo...

Exploring Traffic Patterns Through Network Programmability: Introducing SDNFLow, a Comprehensive OpenFlow-Based Statistics Dataset for Attack Detection

In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-...

Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement...

Face mask mandates alter major determinants of adherence to protective health behaviours in Australia

Face mask wearing is a protective health behaviour that helps mitigate the spread of infectious diseases such as influenza and COVID-19. Understanding predictors of face mask wearing can help refine public health messaging and policy in fut...

Precision water quality indices forecasting through an optimized hybrid SMW-LSSVM-R model enhanced by SATLDE and uncertainty analysis

Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices usin...

Optimization of Operational Parameters for Bio‑Oil Production from Municipal Solid Waste via Microwave Pyrolysis

The efficiency of bio-oil production from municipal solid waste was investigated using microwave-assisted pyrolysis. The effects of temperature, pyrolysis time, and microwave power on bio-oil yield were evaluated using Response Surface Meth...

Decentralized Distributed Sequential Neural Networks Inference on Low-Power Microcontrollers in Wireless Sensor Networks: A Predictive Maintenance Case Study

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challeng...