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...

Convolutional Neural Network Based Approval Prediction of Enhancement Reports

For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of en...

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.

An urban patrol case urgency classification method based on self-supervised clustering of a graph neural network.

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

A Fast and Generalizable Deep Neural Network for the Detection of Atrial Fibrillation.

Uncovering the spatiotemporal evolution of the service industry based on geo-big-data- a case study on the bath industry in China

Abstract The bath industry has multiple attributes, such as economic, health, and cultural communication. Therefore, exploring this industry's spatial pattern evolution is crucial to forming a healthy and balanced development model. Based o...

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 ...

Modular multi-fidelity simulation framework for advanced sail-driven spacecraft control and dynamics in perturbation-rich regimes

This work presents a high-fidelity Software-in-the-Loop (SIL) simulation framework for the development and validation of control strategies for sail-driven spacecraft operating in Low Earth Orbit (LEO). The framework integrates NVIDIA Omniv...

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...

Field-Aware Click-Through Rate Prediction Model Based on Attention Mechanism

Click-Through Rate(CTR) prediction is one of the most important tools for ad placement.Predicting the CTR of an ad and making recommendations to users can increase ad revenue.Field-aware click-through rate prediction models are superior to ...

Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide ...

BP Neural Network–Based Kalman Filtering Method Under Multiple Cyberattacks

This paper proposes a Kalman-gain-driven neural Kalman filtering (KF) defense framework, termed KFDBP, for secure state estimation in cyber–physical systems (CPSs) under denial-of-service (DoS), spoofing, and replay attacks. Unlike end-to-e...

Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems

Currently, there is growing interest in the modeling of high concentrator photovoltaic modules, due to the importance of achieving an accurate model, to improve the knowledge and understanding of this technology and to promote its expansion...

Artificial neural network modeling of mixed convection viscoelastic hybrid nanofluid across a circular cylinder with radiation effect: Case study

As a result of its use in the manufacturing and construction industries, research on the flow of nanofluid is rather well-known among academics and professionals in related fields. It is helpful for electrical equipment to utilize it for co...

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...