Cyberattack Detection Systems in Industrial Internet of Things (IIoT) Networks in Big Data Environments by Abdullah Orman

“…Using the WUSTL-IIoT-2021 dataset, multiple classification models—including decision tree, random forest, multilayer perceptron (MLP), convolutional neural networks (CNNs), and hybrid deep learning architectures—were systematically evaluated based on key performance metrics, including accuracy, precision, recall, and F1 score. …”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…Abstract Electromagnetic (EM) simulation is widespread in microwave engineering. EM tools ensure evaluation reliability but incur significant expenses. …”
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BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting by Liyun Su, Dan Li, Dongyang Qiu

“…Subsequently, BLS is introduced to enhance the feature representation of the data, while the gating structures within the long short-term memory (LSTM) network are replaced by variational quantum circuits (VQCs) to form QLSTM, thereby further improving prediction accuracy. …”
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An explainable AI-based blood cell classification using optimized convolutional neural network by Oahidul Islam, Md Assaduzzaman, Md Zahid Hasan

“…This study presents an enhanced convolutional neural network (CNN) for detecting blood cells with the help of various image pre-processing techniques. …”
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RMIS-Net: a fast medical image segmentation network based on multilayer perceptron by Binbin Zhang, Guoliang Xu, Yiying Xing, Nanjie Li, Deguang Li

“…Medical image segmentation, a pivotal component in diagnostic workflows and therapeutic decision-making, plays a critical role in clinical applications ranging from pathological diagnosis to surgical navigation and treatment evaluation. To address the persistent challenges of computational complexity and efficiency limitations in existing methods, we propose RMIS-Net—an innovative lightweight segmentation network with three core components: a convolutional layer for preliminary feature extraction, a shift-based fully connected layer for parameter-efficient spatial modeling, and a tokenized multilayer perceptron for global context capture. …”
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Prediction of Equipment Failure Rates in Power Distribution Networks based on Machine-learning Method by Rita Jamasheva, Noor Hanoon Haroon, Ahmed Read Al-Tameemi, Israa Alhani, Ali Murad Khudadad, Bahira Abdulrazzaq Mohammed, Ali H. O. Al Mansor, Mustafa Asaad Hussein

“…In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive Neuro-Fuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. …”
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An evolution model for urban rail transit hyper networks based on allometric growth relationship by Zehua Zhang, Ruining Wei, Shumin Feng, Lei Xu, Fan Yang, Hao Liu, Yiqiang Jiang

“…Empirical validation using subway network data from Beijing, Shanghai, and Guangzhou (222–378 stations) was conducted via Python simulations, with model efficacy evaluated through Kolmogorov-Smirnov (K-S) tests and multi-index comparisons. …”
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A Spatiotemporal Fusion Network for Remote Sensing Based on Global Context Attention Mechanism by Weisheng Li, Yusha Liu, Yidong Peng, Fengyan Wu

“…This network comprises an extracting feature network and a difference network. …”
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Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions by Min-Woo Nam, Hyeon-Ju Lee, Seok-Jun Buu

“…Evaluations on a real-world Bitcoin transaction network demonstrate TD-GCN’s superior scam detection performance. …”
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Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network by Xiaorong Li, Lijun Zhang, Dongchen Li, Dan Guo

“…A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross-entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. …”
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Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images by Feifeng Liu, Weihu Wang

“…Through the aesthetic evaluation experiment, the overall test accuracy is improved by 0.58% and the average accuracy of the integration of color harmony and composition features is 75.17%, which is higher than that of a single feature. …”
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta

“…Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. …”
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A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding by Juntao Xue, Feiyue Ren, Xinlin Sun, Miaomiao Yin, Jialing Wu, Chao Ma, Zhongke Gao

“…The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. …”
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Artificial Neural Networks for Residual Capacity Estimation of Cycle-Aged Cylindric LFP Batteries by Pasquale Franzese, Diego Iannuzzi, Roberta Merolla, Mattia Ribera, Ivan Spina

“…This paper introduces a data-driven methodology for accurately estimating the residual capacity (RC) of lithium iron phosphate (LFP) batteries through a tailored artificial neural network (ANN) architecture. The proposed model integrates a long short-term memory (LSTM) layer with a fully connected layer, leveraging their combined strengths to achieve precise RC predictions. …”
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A capsule network framework for flood mapping integrating remote sensing fusion techniques by Pouya Ahmadi, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade, Nazila Kardan Halvaie, Ebrahim Ghaderpour

“…In this methodology, optical and radar remote sensing data are fused, utilizing a deep learning capsule network. Thanks to the multi-dimensional/multi-scale kernels and feature extraction in vector form within the suggested capsule network, notable outcomes are achieved. …”
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Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network by Anparasy Sivaanpu, Kumaradevan Punithakumar, Rui Zheng, Michelle Noga, Dean Ta, Edmond H. M. Lou, Lawrence H. Le

“…An improved swin block is also introduced into the network to improve robustness by extracting more significant features. …”
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A VNS-NPGA approach to multi-objective optimization of hub-and-spoke logistics network by Changxi Ma, Chuwei Shi, Yun Yang, Yongpeng Zhao, Zhuye Xu, Bo Du

“…By considering the structure of the multi-level hub-and-spoke logistics network and the features of the connectivity between the hub and spoke points, the multi-objective optimization model is constructed with two objectives of minimizing the total network operation costs and the total network service time. …”
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A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines by MD Azam Khan, Arifur Rahman, Farhad Uddin Mahmud, Kanchon Kumar Bishnu, Hadiur Rahman Nabil, M. F. Mridha, Md. Jakir Hossen

“…This article proposes a Physics-Guided Bayesian Neural Network (PINN-BNN) model that integrates physics-informed learning with Bayesian inference to improve fault detection in wind turbines. …”
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Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…Hence, this work employs the artificial neural network (ANN) to develop a model that accurately predicts HPR in PEMWE setups. …”
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