Origin identification of millet by combining spectral detection and a group attention feature calculation and classification network

Impact of converting graphs into spanning trees on node and graph classification in Graph Neural Network

MVFusion-TSC: A multi-view fusion image-based network for time series classification

Convolution-based adaptive ResUNet3 + with attention-based ensemble convolution networks for COVID-19 segmentation and classification

Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operation is higher in computational complexity and usually requires more energy consumption with longer inference time than other operations, which hinders the deployment of convolutional neural networks on mobile devices. In many resource-limited edge devices, complicated operations can be calculated via lookup tables to reduce computational cost. Motivated by this, in this paper, we introduce a generic and efficient lookup operation which can be used as a basic operation for the construction of neural networks. Instead of calculating the multiplication of weights and activation values, simple yet efficient lookup operations are adopted to compute their responses. To enable end-to-end optimization of the lookup operation, we construct the lookup tables in a differentiable manner and propose several training strategies to promote their convergence. By replacing computationally expensive multiplication operations with our lookup operations, we develop lookup networks for the image classification, image super-resolution, and point cloud classification tasks. It is demonstrated that our lookup networks can benefit from the lookup operations to achieve higher efficiency in terms of energy consumption and inference speed while maintaining competitive performance to vanilla convolutional networks. Extensive experiments show that our lookup networks produce state-of-the-art performance on different tasks (both classification and regression tasks) and different data types (both images and point clouds).

Global-affinity constraint with distillation-enhanced network for dual incomplete multi-view multi-label classification

Lightweight adaptive spatiotemporal information fusion network for medical time series classification

Sparse low-rank retargeted stochastic configuration networks for multiclass classification

SynTaskNet: A synergistic multi-task network for joint segmentation and classification of small anatomical structures in ultrasound imaging

A deep learning model with inception vision transformer and harmonic fusion network for solar panel fault detection and classification

Development of a Deep Learning Model Inspired by Transformer Networks for Multi-class Skin Lesion Classification

Hybrid quantum convolutional neural network for multi-channel image classification

Purpose Roofing is highly susceptible to environmental damage from elements like wind, snow and rain. Regular inspection and maintenance are essential to extend a roof’s lifespan. This study aims to develop an automated system that detects and classifies roofing damage types and their severity using image-based analysis, helping asset managers prioritize repairs and allocate maintenance resources more effectively. Design/methodology/approach This study uses Convolutional Neural Networks (CNNs) for image-based damage detection and classification. Over 3,000 images of roofing segments (1.5 × 1.12 m) from institutional buildings were used for training and testing. The model first identifies damage type – no damage, vegetation or ponding – then classifies vegetation damage severity into low, moderate or severe. Findings The developed CNN model achieved over 94% accuracy in both damage type and severity classification. The results demonstrate the model’s effectiveness in analyzing roofing defects. Research limitations/implications Future enhancements include expanding the system to detect additional defect types like cracks and flashing defects, offering a scalable solution for systematic roof condition assessment and maintenance planning. Originality/value Unlike traditional manual inspections, this approach uses computer vision techniques to offer a scalable, data-driven framework that identifies damage types and quantifies severity levels. This makes roofing inspections more efficient, consistent and safer.

Switcher-HNet: A switchable hierarchical network for tree species classification from forest stand to individual tree tasks

A novel multi-scale context aggregation and feature pooling network for Mpox classification

CRPointFeatureNet: A cross-resolution point cloud feature network for part machining feature classification

Efficient ensemble learning with multi-scale fusion based deep neural network for skin lesion classification

MAMF-GCN model for anxious and non-anxious depression classification and neuroimaging marker recognition.

BBANet: Bilateral biological auditory-inspired neural network for heart sound classification

Saliency-Guided Feature Mining Network for Multi-Scale Fine-Grained Scene Classification in Remote Sensing Image Archives