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...

Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults.

Leveraging human-trained neural networks for cross-species chromatin regulation annotations.

Predicting bacterial vaginosis incidence using artificial neural networks.

Explainable spatiotemporal Graph Neural Networks: Multi-City PM2.5 forecasting in Bangladesh with geographic graph topology validation.

Optimization Method for Classifier Output Repeatability Based on Siamese Networks

In industrial surface Quality Control (QC) scenarios, deep classification neural networks are widely used to classify product images for qualified judgment or quality grading. However, surface QC equipment equipped with deep classification ...

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 Novel Construction Method of Bayesian Neural Networks Based on Multi-type Engineering Knowledge

Abstract In the field of engineering, the utilization of surrogate models to replace computationally intensive simulation software has become a widely adopted approach. However, when addressing complex engineering problems, the costs of sim...

Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation

Predictive maintenance is the latest frontier in the management and maintenance of many industrial assets, including aeroengines. Made possible by last decades advances in monitoring equipment and machine learning algorithms, it permits ind...

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...

Testing the Physical Parameter Constraining Power of HCN and HNC with Neural Networks

We quantify the utility of HCN and HNC to characterize gas conditions in the nearby starburst galaxy NGC 253. We use measurements from the Atacama Large Millimeter/Submillimeter Array (ALMA) Large Program ALCHEMI: the ALMA Comprehensive Hig...

EVALUATION OF ARTIFICIAL NEURAL NETWORK APPLICATIONS IN TRANSPORTATION ENGINEERING

The increased interest in artificial neural networks (ANNs) seen in government and private research as well as business and industry has included relatively little activity in transportation engineering. The position that ANNs, as a branch ...

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...

Computing with Bees: Attacking Complex Transportation Engineering Problems

The Bee System (an artificial bee swarm) is introduced in this paper. The proposed approach is applied to the Traveling Salesman Problem. The obtained results are very promising. The potential applications of the developed Bee System in the...

Encyclopedic Dictionary of Applied Geophysics

The fourth edition of SEG's best seller is a valuable, comprehensive reference that is a must for every geophysicist, geologist, explorationist, engineer, energy adviser, economist, editor, and student involved in the field. Hundreds of ter...

Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder

Artificial neural networks are a powerful tool for managing data that are difficult to process and interpret. This article presents the design and implementation of backpropagated multilayer artificial neural networks, structured with a vec...

A Graph Neural Networks-Based Learning Framework With Hyperbolic Embedding for Personalized Tag Recommendation

Learning high-quality representations of users, items, and tags from historical interactive data is crucial for personalized tag recommendation (PTR) systems. Currently, most PTR models are committed to learning representations from first-o...

[Mathematical models of hysteresis]

The research described in this proposal is currently being supported by the US Department of Energy under the contract Mathematical Models of Hysteresis''. Thus, before discussing the proposed research in detail, it is worthwhile to describ...

Deep learning prediction of pathological complete response in breast cancer using Mamba architecture

Abstract Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectur...

Research in medical informatics

Medical informatics is concerned with the application of computers in the medical and biological sciences and has been considered a field of research in its own right for more than twenty years. In Malaysia, however, there are very few publ...