A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy by Gregorius Airlangga, Alan Liu

“…This study introduces an advanced hybrid model combining a gradient boosting machine (GBM) and neural network (NN) to address these complexities. Unlike traditional approaches, this hybrid leverages a GBM to handle structured data features and an NN to extract deeper nonlinear relationships. …”
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Intelligent Modeling; Single (Multi-layer perceptron) and Hybrid (Neuro-Fuzzy Network) Method in Forest Degradation (Case Study: Sari County) by somayeh mehrabadi

“…The mean squared error (MSE) was used to evaluate the performance of models, which was 0.0535, 0.0704, and 0.0908 for the perceptron neural network in the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, respectively. …”
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Default Risk Prediction of Enterprises Based on Convolutional Neural Network in the Age of Big Data: Analysis from the Viewpoint of Different Balance Ratios by Zhe Li, Zhenhao Jiang, Xianyou Pan

“…Second, we propose a comprehensive metric model based on multimachine learning algorithms (CMM-MLA) to select the best-derived dataset with the optimal balance ratio and feature combination. Finally, the convolutional neural network (CNN) is trained on the selected derived dataset to evaluate the performance of our approach in terms of type-II error, accuracy, G-mean, and AUC. …”
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Enhanced residual attention-based subject-specific network (ErAS-Net): facial expression-based pain classification with multiple attention mechanisms by Mahdi Morsali, Aboozar Ghaffari

“…This research aims to solve this issue by presenting ErAS-Net, an Enhanced Residual Attention-Based Subject-Specific Network that employs various attention mechanisms. …”
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A Local Dwarf Galaxy Search Using Machine Learning by Huanian Zhang, Guangping Ye, Rongyu Wu, Dennis Zaritsky

“…We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate features, compare different machine learning algorithms, and find that a seven-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of 95% and a recall of 76%. …”
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Real-Time Cyber-Physical Digital Twin for Low Earth Orbit Satellite Constellation Network Enhanced Wide-Area Power Grid by Tianshi Cheng, Tong Duan, Venkata Dinavahi

“…This marks the initial effort to develop a digital twin for real-time co-simulation of large-scale power systems and LEO satellite constellation networks. Evaluations on a wide-area synthetic ac–dc system with multiple satellite network types confirm the efficiency and precision of our approach, underscoring its potential in bridging LEO satellite networks with power system applications.…”
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Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs) by Robert A. Sowah, Kwaku Apeadu, Francis Gatsi, Kwame O. Ampadu, Baffour S. Mensah

“…Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. …”
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DisasterAdaptiveNet: A robust network for multi-hazard building damage detection from very-high-resolution satellite imagery by Sebastian Hafner, Sebastian Gerard, Josephine Sullivan, Yifang Ban

“…Notably, several studies have proposed new network architectures and demonstrated their improved performance on xBD. …”
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Cyberbullying Detection, Prevention, and Analysis on Social Media via Trustable LSTM-Autoencoder Networks over Synthetic Data: The TLA-NET Approach by Alfredo Cuzzocrea, Mst Shapna Akter, Hossain Shahriar, Pablo García Bringas

“…Due to the fact that online social networks continue to daily expand, the proliferation of hate speech is also growing. …”
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APGCN-CF: A Spatio-Temporal-Aware Graph Convolutional Network Framework for Minor Agricultural Product Recommendation in Rural E-Commerce by Wenjin Hou, Ke Zhu, Pingzeng Liu, Honghua Jiang, Yan Zhang, Xinran Yu

“…Comprehensive experimental evaluations conducted on real-world minor agricultural product datasets, featuring scallions and garlic as representative cases, demonstrate that APGCN-CF consistently outperforms existing state-of-the-art baseline methods across multiple performance metrics, including precision, recall, and F1-score. …”
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Continuous wave mud pulse data transmission method based on continuous gradation frequency keying modulation and Convolution neural network demodulation by Yingzhong Zhu, Zhenhua Xia, Yue Yang, Cihao Zhu, Guoqing Cai, Shuang Hu, Junhao Wang

“…This method employs Continuous Gradation Frequency Keying (CGFK) modulation combined with Convolution Neural Network (CNN) demodulation for continuous mud pulse data transmission. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

“…We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. …”
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Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination by Huiying Liu, Jiarong Wang, Weijun Zhong, Haitao Nie, Xiaotong Deng, Jiaqi Sun, Ming Zhu, Ming Wei

“…Thirdly, we propose a residual network Res-LK-SLR for IRS classification based on large kernels (LKs, providing long-term dependence) and shapelet-level representations (SLRs, where the hidden layer features are aligned with the learned high-level representations to obtain the optimal segmentation and generate shapelet-level representations). …”
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Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large‐Scale Structures of the Solar Corona by Tingyu Wang, Bingxian Luo, Jingjing Wang, Xianzhi Ao, Liqin Shi, Qiuzhen Zhong, Siqing Liu

“…To forecast SIR‐driven storms over longer horizons (3 days), a neural network model is constructed by incorporating coronal magnetic field physical parameters and the coronal hole features extracted from solar images (Pch). …”
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Rénovation méthodologique dans l’historiographie littéraire en belgique francophone au xxie siècle by Ándré Bénit

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Study on the Anti-demyelination Mechanism of Bu-Shen-Yi-Sui Capsule in the Central Nervous System Based on Network Pharmacology and Experimental Verification by Zheng Zha, Yi-Jiang Liu, Si-Si Liu, Nan Zhang, Jun-Ling Li, Fang Qi, Liang-Yun Jin, Bing Xue, Tao Yang, Yong-Ping Fan, Hui Zhao, Lei Wang

“…The protein-protein interaction (PPI) network was constructed using bioinformatics methods. …”
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A spatio-temporal fusion-based approach for multi-dimensional classification of Parkinson’s disease progression using multi-modal dataset by Vinay Kukreja, Vandana Ahuja, Modafar Ati, Hariharan Shanmugasundaram, Murugaperumal Krishnamoorthy, Rishabh Sharma, Abhishek Bhattacherjee

“…Methods: By employing the fusing approach for structural medical resonance imaging (MRI) and dopamine transporter scan (DaTSCAN) along with clinical biomarker data to examine spatial and temporal relationships a fused feature representation was developed. PPMI dataset was employed for training and evaluation of the multi-task classification model with Cohen’s Kappa, cumulative link model (CLM), the area under the precision-recall curve (AUC-PR), and Brier score prominent evaluation metrics for analyzing the robustness of the model. …”
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SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement by Rohan Pattnaik, Jeyhan S. Kartaltepe, Clive Binu

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Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms by Li Wei, Meng Ding, Shuai Li

“…Building on the foundational framework of <i>Monodepth2</i>, we introduce an intermediate module between the encoder and decoder of the depth estimation network to facilitate multiscale fusion of feature maps. …”
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