Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding by Wasim Khan, Mohammad Ishrat, Ahmad Neyaz Khan, Mohammad Arif, Anwar Ahamed Shaikh, Mousa Mohammed Khubrani, Shadab Alam, Mohammed Shuaib, Rajan John

“…SCCA incorporates sparsity by making the model choose fewer variables, which adds another level of complexity. …”
Get full text

Indirect Measurement of Tensile Strength of Materials by Grey Prediction Models GMC(1,n) and GM(1,n) by Tzu-Li Tien

“…However, for the first-order grey prediction model with n variables, specifically the traditional GM(1,n) model, modelling values are derived using a rough approximation method. …”
Get full text

A Novel Electrical Load Forecasting Model for Extreme Weather Events Based on Improved Gated Spiking Neural P Systems and Frequency Enhanced Channel Attention Mechanism by Yuanshuo Guo, Jun Wang, Yan Zhong, Tao Wang, Zeyuan Sui

“…Then inspired by the interaction mechanism of impulses between biological neuronal cells, FAGSNP is able to consider the load variability and effectively predict load trends. In addition, to address load prediction challenges posed by extreme weather and promote the sustainable development of power systems, the proposed model integrates many models to solve this problem. …”
Get full text

AdaptiveSwin-CNN: Adaptive Swin-CNN Framework with Self-Attention Fusion for Robust Multi-Class Retinal Disease Diagnosis by Imran Qureshi

“…Utilizing the adaptive baseline augmentation and dataset-driven preprocessing of input images, the AdaptiveSwin-CNN model resolves the problem of the variability of fundus images in the dataset. AdaptiveSwin-CNN achieved a mean accuracy of 98.89%, sensitivity of 95.2%, specificity of 96.7%, and F1-score of 97.2% on RFMiD and ODIR benchmarks, outperforming other solutions. …”
Get full text

Insurance claims estimation and fraud detection with optimized deep learning techniques by P. Anand Kumar, S. Sountharrajan

“…Unlike traditional statistical methods, which often struggle with the intricate nature of insurance claims data, deep learning models performs well in handling diverse variables and factors influencing claim outcomes. To this extent, it explores the deep learning models like VGG 16 & 19, ResNet 50, and a custom 12 & 15-layer Convolutional Neural Network for accurate estimation of insurance claims and detection of fraud. …”
Get full text

Soil moisture retrieval and spatiotemporal variation analysis based on deep learning by Zihan Zhang, Jinjie Wang, Jianli Ding, Jinming Zhang, Liya Shi, Wen Ma

“…The Boruta algorithm and correlation analysis were applied to select key variables. Nine deep learning models, including three basic architectures (Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Transformer) and six hybrid structures (CNN-LSTM, LSTM-CNN, CNN-with-LSTM, CNN-Transformer, GAN-LSTM, Transformer-LSTM), were systematically compared to evaluate the impact of neural network structure on model performance. …”
Get full text

Real-Time Defect Detection for Fast-Moving Fabrics on Circular Knitting Machine Under Various Illumination Conditions by Yan-Qin Ni, Pei-Kai Huang, Ching-Han Yang, Chin-Chun Chang, Wei-Jen Wang, Deron Liang

“…First, to tackle the challenges of real-time detection, limited training data, and varying illumination conditions, we develop a lightweight semantic segmentation model, LBUnet, which leverages local binary (LB) convolution to effectively handle variable lighting conditions. …”
Get full text

Deep learning method for cucumber disease detection in complex environments for new agricultural productivity by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Xin Liu

“…The model effectively handles symptom variability and complex detection scenarios, outperforming mainstream detection algorithms in accuracy, speed, and compactness, making it ideal for embedded agricultural applications.…”
Get full text

LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…Remote sensing landscape image classification is essential for environmental monitoring, land management, and ecological assessment, but presents critical challenges due to complex spatial distributions and high intra-class variability inherent in landscape scenes. Traditional deep convolutional neural networks, such as VGG16, though effective, are computationally intensive and unsuitable for deployment on resource-constrained platforms commonly used in landscape monitoring applications. …”
Get full text

Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions by Javed Mallick, Saeed Alqadhi, Majed Alsubih, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…In this study, the entropy weight-based DWQI averaged 77.90 with a high standard deviation (std) of 39.08, reflecting considerable variability. The automated CNN models demonstrated robust performance in predicting water quality indices, with high accuracy (R2 = 0.959 in training and 0.945 in testing) for sodium percentage (Na%). …”
Get full text

Extension of the First-Order Recursive Filters Method to Non-Linear Second-Kind Volterra Integral Equations by Rodolphe Heyd

“…A new numerical method for solving Volterra non-linear convolution integral equations (NLCVIEs) of the second kind is presented in this work. …”
Get full text

Defect Detection and Classification on Wind Turbine Blades Using Deep Learning with Fuzzy Voting by Reed Pratt, Clark Allen, Mohammad A. S. Masoum, Abdennour Seibi

“…To improve defect detection performance, a multi-variable fuzzy (MVF) voting system is proposed. This method demonstrated superior accuracy compared to the individual models. …”
Get full text

An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection by Ziaullah Khan, Aakanksha Dayal, Hee-Cheol Kim

“…However, the complexity and variability of epileptic patterns make traditional visual analysis subjective, time-consuming, and impractical for continuous monitoring. …”
Get full text

Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused by <i>Xanthomonas oryzae</i> pv. <i>oryzae</i>, <i>Pantoea ananatis</i> and <i>Enter... by Meng Zhang, Shuqi Tang, Chenjie Lin, Zichao Lin, Liping Zhang, Wei Dong, Nan Zhong

“…The results indicated that the 1DCNN model, after feature selection using uninformative variable elimination (UVE), achieved an accuracy of 86.11% and an F1 score of 0.8625 on the five-class dataset. …”
Get full text

BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation by Lei Zhang, Changchun Li, Xifang Wu, Hengmao Xiang, Yinghua Jiao, Huabin Chai

“…IntroductionIn the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. …”
Get full text

Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…The correlation between the mining footage and water inflow of the mining face was selected as the characteristic variable for the time series prediction of mine water inflow. …”
Get full text

Optimizing linear/non-linear Volterra-type integro-differential equations with Runge–Kutta 2 and 4 for time efficiency by Martin Ndi Azese

“…Additionally, a complex VTIDE is constructed featuring nonlinearities both within and outside the convolutions, as well as a derivative-of-dependent-variable integrant. …”
Get full text

A topological analysis of p(x)-harmonic functionals in one-dimensional nonlocal elliptic equations by Goodrich Christopher S.

“….$$ In addition, we consider a broader class of problems, of which the model case in a special case, by writing the argument of M as a finite convolution. As part of the analysis, a simple but fundamental lemma in introduced that allows the estimation of u′(x)p(x) ${\left\vert {u}^{\prime }(x)\right\vert }^{p(x)}$ in terms of constant exponents; this is the key to circumventing the variable exponent. …”
Get full text

DLI: A Deep Learning-Based Granger Causality Inference by Wei Peng

“…And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.…”
Get full text

Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method by Yujie Yuan, Yantao Wang, Chun Sing Lai

“…The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. …”
Get full text