An Efficient and Accurate 2W-PE Method for Solving Spatial Field Strength Based on CVNN and PINN by An-Qi Li, Cheng-You Yin, Qian-Qian Zhang, Han Liu

“…A two-way parabolic equation (2W-PE) method based on complex-valued neural networks (CVNNs) and a physics-informed neural network (PINN) is proposed to predict the spatial field in the environment of dielectric obstacles with high efficiency and accuracy. …”
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On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSA by Ahmad Kamal Hassan, Adnan Affandi

“…This paper deals with analytical modelling of microstrip patch antenna (MSA) by means of artificial neural network (ANN) using least mean square (LMS) and recursive least square (RLS) algorithms. …”
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A Global Perspective on Lunar Granular Flows by V. T. Bickel, S. Loew, J. Aaron, N. Goedhart

“…Here, we build and deploy a convolutional neural network and map 28,101 flow features between 60°N and S by scanning through ∼150,000 Lunar Reconnaissance Orbiter images. …”
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Dilated SE-DenseNet for brain tumor MRI classification by Yuannong Mao, Jiwook Kim, Lena Podina, Mohammad Kohandel

“…Abstract In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks’ attention mechanisms. …”
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A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images by Varun Srivastava, Ravindra Kumar Purwar

“…The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. …”
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Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia. by Dyah Aryani Perwitasari, Imaniar Noor Faridah, Haafizah Dania, Didik Setiawan, Triantoro Safaria

“…There were significant differences in ALT and AST between good and poor adherence groups, especially in the female patients. The Neural Network and Random Forests were the most suitable models to predict tuberculosis patients' adherence with good Area Under The Curve (AUC).…”
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Image Sequence Fusion and Denoising Based on 3D Shearlet Transform by Liang Xu, Junping Du, Zhenhong Zhang

“…The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. …”
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Rancang Bangun Purwarupa Pemilah Sampah Pintar Berbasis Deep Learning by Kahlil Muchtar, Nyak Twoman Anshari, Chairuman Chairuman, Khalid Alhabibie, Khairul Munadi

“…Deep Learning method applied here is using Convolutional Neural Network (CNN). The algorithm is like human nerves and is one of supervised learning. …”
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Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI) by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional neural network (CNN) dengan transfer learning adalah tiga algorimta klasifikasi populer yang dibandingkan dalam penelitian ini. …”
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Clinical Treatment and Nursing Intervention Study of Clipping Treatment of Cerebral Aneurysm under the Health Model of Data Analysis by Yuyou Huang, Liping Huang

“…The model combines the characteristics of cerebral aneurysms for targeted analysis, and then through the understanding of the clipping treatment of cerebral aneurysms, this paper combines the deep learning in the neural network to train the treatment plan under the data analysis health model. …”
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Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images by Lucas T. Fromm, Laurence C. Smith, Ethan D. Kyzivat

“…Maximum Likelihood (MLC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classification algorithms are tested on individual, monthly- and multi-seasonal composite images. …”
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Geographical patterns of intraspecific genetic diversity reflect the adaptive potential of the coral Pocillopora damicornis species complex. by M Carr, C Kratochwill, T Daly-Engel, T Crombie, R van Woesik

“…A deep-learning, multi-layer neural-network model showed that geographical location played a major role in intraspecific diversity, with mean sea-surface temperature and oceanic regions being the most influential predictor variables differentiating diversity. …”
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Research on Fault Diagnosis Method Based on Improved CNN by Hu Hao, Feng Fuzhou, Zhu Junzhen, Zhou Xun, Jiang Pengcheng, Jiang Feng, Xue Jun, Li Yazhi, Sun Guanghui

“…To solve these problems, a fault diagnosis method based on an improved convolutional neural network (CNN) is proposed. Based on the traditional CNN model, a variety of convolution stride modes were added to extract features of different scales of signals and expand the feature dimension. …”
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A Scalable Blockchain Framework for ELA Assessment by Chunxia Tian

“…We propose a proficiency evaluation model based on a Discrete Hopfield Neural Network (DHNN). Firstly, the hierarchical analysis method is used to construct the evaluation index system of students’ English ability, and then the ability classification indexes are divided into 5 levels.The network achieves the classification of students’ English proficiency through the associative memory of the classification criteria, and the classification results are compared with those of the BPNN model. …”
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Advancing buffet onset prediction: a deep learning approach with enhanced interpretability for aerodynamic engineering by Jing Wang, Wei Liu, Hairun Xie, Miao Zhang

“…In this study, utilizing a comprehensive database of supercritical airfoils generated through numerical simulations, a convolutional neural network (CNN) model is firstly developed to perform buffet classification based on the flow fields. …”
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Design of an Anti-Windup PID Algorithm for Differential Torque Steering Systems by Dengzhi Peng, Bin Huang, Hao Huang

“…To investigate the differential steering process, the 2 DOF (degree of freedom) dual-track reference models with linear and nonlinear tire models are established, and based on the steering process analysis and yaw rate gain calculation, a BP-NN (backpropagation neural network) model is initiated to maintain the accuracy of the calculated yaw rate gain. …”
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Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction by Neeti Sangwan, Vishal Bhatnagar

“…These latent features train the fused Convolutional Neural Network (CNN) with LSTM to predict the popularity of unseen videos on the trained deep learning network. …”
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Low-latency hierarchical routing of reconfigurable neuromorphic systems by Samalika Perera, Ying Xu, André van Schaik, Runchun Wang

“…A reconfigurable hardware accelerator implementation for spiking neural network (SNN) simulation using field-programmable gate arrays (FPGAs) is promising and attractive research because massive parallelism results in better execution speed. …”
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A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty by Chuanfeng Li, Hao Wu, Zhile Yang, Yongji Wang, Zeyu Sun

“…In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN) is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. …”
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Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning by Chunjie Li, Pan Liu, Zhenlong Xie, Zhibin Li, Huan Huan

“…Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. …”
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