Academic Articles & Research Papers

Deep neural network-based prediction of tsunami wave attenuation by mangrove forests

The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsuna...

A Gradient Boosting-Based Machine Learning Model for the Prediction of the Hardness of Thermoplastic Polymers.

Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions

Abstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintain...

Predicting adolescent depression: an interpretable machine learning model.

Machine learning models integrating 2D and 3D intratumoral heterogeneity features from multi-sequence MRI: accurate prediction of perineural invasion in pancreatic ductal adenocarcinoma.

Incorporating natural language processing-derived features from pathology reports into machine learning models to predict differentiated thyroid cancer recurrence.

Geographic authentication of premium tobacco extracts using machine learning models trained on LC-HRMS fingerprints.

A machine learning model for predicting six-month rehospitalization in heart failure patients with multiple comorbidities.

Development, Validation, and Deployment of an Interpretable Machine Learning Model for In-Hospital Mortality Prediction in Critically Ill Patients with COPD and Sepsis: A Multicenter Cohort Study.

Large-scale screening of high-entropy materials for superionic solid electrolytes

Abstract Solid electrolytes have emerged as a promising alternative to conventional liquid electrolytes that have posed safety concerns. However, the relatively low ionic conductivity of solid electrolytes is one of the challenges for their...

Optimal pre-train/fine-tune strategies for accurate material property predictions

Abstract A pathway to overcome limited data availability in materials science is to use the framework of transfer learning, where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (smaller) d...

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivit...

Explainable machine learning in materials science

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explaina...

Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Re...

Quantum Computing in Transportation Engineering: A Survey

Quantum computing introduces new computational methods that address complex optimization and machine learning challenges in transportation engineering. This literature survey reviews fundamental quantum principles, including qubits, superpo...

Modeling antiretroviral therapy adherence among people living with HIV: a cross-sectional study at Pantang Hospital, Ghana

Abstract Background Poor adherence to antiretroviral therapy (ART) continues to undermine HIV treatment outcomes in sub-Saharan Africa, including Ghana. Objective This study aimed to quantify ART treatment adherence among people living with...

Developing an Interferogram-Based Module with Machine Learning for Maintaining Leveling of Glass Substrates

In this research, we propose a method that utilizes machine learning to maintain the parallelism of the resonant cavity in a Fabry–Perot interferometer designed specifically for glass substrates. Based on the optical principle and theory, w...

Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs

Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method w...

Development and validation of an interpretable machine learning model for predicting the risk of coronary heart disease risk in diabetes mellitus patients: a dual-center retrospective study

Abstract Background Diabetes mellitus (DM) combined with coronary heart disease (CHD) significantly increases the risk of cardiovascular events with a greater mortality rate. Therefore, establishing a predictive model can help DM patients r...

Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision ...