Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge by Mahyar Jahani-nasab, Mohamad Ali Bijarchi

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Automated sleep staging from single-channel electroencephalogram using hybrid neural network with manual features and attention by Qingyun Wan, Yuyang Bo, Ying Zhang, Mufeng Li, Xiaoqiu Wang, Chuang Chen, Lanying Liu, Wenzhong Wu

“…However, prior studies often overlook expert-derived manual features, relying solely on deep neural networks for automatic feature extraction. …”
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Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

“…These concatenated features were evaluated using various machine learning classifiers and neural networks. …”
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Multi-Level Feature Fusion Attention Generative Adversarial Network for Retinal Optical Coherence Tomography Image Denoising by Yiming Qian, Yichao Meng

“…<b>Methods</b>: We propose MFFA-GAN, a generative adversarial network integrating multilevel feature fusion and an efficient local attention (ELA) mechanism. …”
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CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features by Wenxuan Ge, Xubing Yang, Rui Jiang, Wei Shao, Li Zhang

“…In the encoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extracting local and global features simultaneously. …”
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X-FASNet: cross-scale feature-aware with self-attention network for cognitive decline assessment in Alzheimer's disease by Wenhui Chen, Shunwu Xu, Yiran Peng, Yiran Peng, Hong Zhang, Jian Zhang, Huaihao Zheng, Hao Yan, Zhaowen Chen, Zhaowen Chen

“…Current multi-scale neural networks have limited cross-scale feature integration capabilities, which constrain their effectiveness in identifying early neurodegenerative markers. …”
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Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction by Feiyu Teng, Ling Wu, Shukuan Liu

“…Simultaneously, this paper proposes a hybrid backbone for feature extraction. A hybrid backbone combines the respective advantages of the Resnet and Transformer backbone networks to balance local features and global features in feature extraction. …”
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A comparative study of convolutional neural networks and traditional feature extraction techniques for adulteration detection in ground beef by Leila Bahmani, Saied Minaei, Alireza Mahdavian, Ahmad Banakar, Mahmoud Soltani Firouz

“…Convolutional Neural Network (CNN) was also used to extract features and classify images. …”
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Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets by Megha Sunil Borse, Murali Prasad R, Tummala Ranga Babu

“…The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor. …”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Prediction of crystalline structure evolution during solidification of aluminum at different cooling rates using a hybrid neural network model by Rafi B. Dastagir, Shorup Chanda, Farsia K. Chowdhury, Shahereen Chowdhury, K. Arafat Rahman

“…By combining the temporal pattern descriptors of LSTMs with the feature extraction potential of convolutional neural networks (CNN), the hybrid Conv1D-LSTM model achieves higher accuracy in predicting crystal structural evolution curves, in contrast to the performance of standalone LSTM and CNN models. …”
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DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.MethodsBased on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. …”
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Evolutionary model of heterogeneous clustering wireless sensor networks based on local world theory by Xiu-wen FU, Wen-feng LI

“…Current research on the scale-free evolutionary model of wireless sensor networks (WSN) treated each network as a homogenous one and does not take into account the evolutionary characteristics of the network in realistic scenarios,thus leading to significant differences between homogenous networks and realistic ones.Therefore,based on local-world theory,a heterogonous evolution model of WSN in relation to cluster-structure,energy-sensitivity and dynamic behavior of WSN (e.g.…”
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Evolutionary model of heterogeneous clustering wireless sensor networks based on local world theory by Xiu-wen FU, Wen-feng LI

“…Current research on the scale-free evolutionary model of wireless sensor networks (WSN) treated each network as a homogenous one and does not take into account the evolutionary characteristics of the network in realistic scenarios,thus leading to significant differences between homogenous networks and realistic ones.Therefore,based on local-world theory,a heterogonous evolution model of WSN in relation to cluster-structure,energy-sensitivity and dynamic behavior of WSN (e.g.…”
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Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques by Zhen Chen, Weiguang Zhai, Qian Cheng

“…Therefore, this study evaluates the potential of multi-source feature fusion and convolutional neural networks (CNN) in estimating maize LAI. …”
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CFNN for Identifying Poisonous Plants by Israa Mohammed Hassoon, Shaymaa Akram Hantoosh

“…Combination of shape features and statistical features are extracted from leaf then fed to cascade-forward neural network which used TRAINLM function for training. 500 samples of leaf images are used, 250 samples are poisonous, the remaining 250 samples are non-poisonous.300 samples used in training, 200 samples for testing. …”
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Multi-Level Intertemporal Attention-Guided Network for Change Detection in Remote Sensing Images by Shuo Liu, Qinyu Zhang, Yuhang Zhang, Xiaochen Niu, Wuxia Zhang, Fei Xie

“…To address this issue, we proposed a Multi-level Intertemporal Attention-guided Network (MIANet) for CD. Firstly, an Intertemporal Fusion Attention Unit (IFAU) is proposed to facilitate early feature interaction, which helps eliminate irrelevant changes. …”
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Computing the Deep Semantics of Visual Communications by Trpimir Jeronim Ježić, Marko Maričević, Ivana Pavlović, Miroslav Mikota

Subjects: “…convolutional neural networks…”
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A Comparative Analysis of QoS, GoS, and QoE Metrics in Network Performance Evaluation by V. A. Gasimov, L. R. Zeynalli-Huseynzade, G. A. Zahidova

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