Academic Articles & Research Papers

On the Origin of Deep Learning

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...

ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning

COVID-19 has become a global pandemic that has affected not only the health sector but also economic, social, and psychological well-being. Individuals are using social media platforms to communicate their feelings and sentiments about the ...

An Ownership Verification Mechanism Against Encrypted Forwarding Attacks in Data-Driven Social Computing

Data-driven deep learning has accelerated the spread of social computing applications. To develop a reliable social application, service providers need massive data on human behavior and interactions. As the data is highly relevant to users...

A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography

Abstract The inferior alveolar nerve (IAN) is the major sensory nerve innervating the mandibular region, and its automatic segmentation is crucial. It has been indirectly identified on the mandibular canal, which surrounds the IAN, using co...

Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production b...

Enhancing Deep Learning–Based Subabdominal MR Image Segmentation During Rectal Cancer Treatment: Exploiting Multiscale Feature Pyramid Network and Bidirectional Cross-Attention Mechanism

Conclusion: A multiscale feature pyramid network effectively reduces the semantic gap, and the bidirectional cross-attention mechanism facilitates feature alignment between the encoding and decoding stages....

A Dual-Segmentation Framework for the Automatic Detection and Size Estimation of Shrimp

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. ...

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

BackgroundSimultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image recon...

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particu...

Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and...

A Self-Attention Feature Fusion Model for Rice Pest Detection

To address the problem that existing deep learning methods are not sufficiently accurate to detect rice pests with changeable shapes or similar appearances, a self-attention feature fusion model for rice pest detection (SAFFPest) was propos...

Comprehensive framework of machine learning and deep learning architectures with metaheuristic optimization for high-fidelity prediction of nanofluid specific heat capacity

Abstract Accurately predicting the specific heat capacity of nanofluids is critical for optimizing their performance in engineering and industrial applications. This study explores twelve machine learning and deep learning models using conv...

A State-of-the-Art Survey on Deep Learning Theory and Architectures

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some ne...

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

Deep learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval, and more. However, with the progressive improvements in deep learning models, their number of paramet...

A comprehensive review on ensemble deep learning: Opportunities and challenges

In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms t...

Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion

The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. However, traditional methods and some deep learning models have li...

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Abstract Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite pr...

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks

Abstract Background Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural n...

A Brief Survey on Semantic Segmentation with Deep Learning

Understanding tourist engagement with traditional dwellings through multimodal deep learning: a case study of the Gao Family Compound, Xi’an

Abstract This study applies a multimodal deep learning approach to examine tourist engagement with traditional architectural heritage. The Gao Family Compound in Xi’an serves as a representative case. Social-media texts and images from Weib...