Random k conditional nearest neighbor for high-dimensional data by Jiaxuan Lu, Hyukjun Gweon

“…The proposed approach aggregates multiple kCNN classifiers, each constructed from a randomly sampled feature subset. …”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…While ANN and CNN demonstrate strong capacity in identifying complex fraud patterns, their communication efficiency and overfitting challenges are significant. …”
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Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network by Ozgur Alaca, Ali Riza Ekti, Jhi-Young Joo, Nils Stenvig

“…The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. …”
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DeepGlioSeg: advanced glioma MRI data segmentation with integrated local-global representation architecture by Ruipeng Li, Yuehui Liao, Yueqi Huang, Xiaofei Ma, Guohua Zhao, Yanbin Wang, Chen Song

“…The model includes two primary components. First, a CTPC (CNN-Transformer Parallel Combination) module leverages parallel branches of CNN and Transformer networks to fuse local and global features of glioma images, enhancing feature representation. …”
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Fine-scale forest classification with multi-temporal sentinel-1/2 imagery using a temporal convolutional neural network by Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang, Juan Gu

“…The model was compared with CNN, random forest, XGBoost, and long short-term memory to validate its advantages. …”
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Improving Road Semantic Segmentation Using Generative Adversarial Network by Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri

“…However, most CNN approaches cannot obtain high precision segmentation maps with rich details when processing high-resolution remote sensing imagery. …”
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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection by Rong Pang, Yan Yang, Aiguo Huang, Yan Liu, Peng Zhang, Guangwu Tang

“…Although the Faster Region-based Convolutional Neural Network (Faster R-CNN) model has obvious advantages in defect recognition, it still cannot overcome challenging problems, such as time-consuming, small targets, irregular shapes, and strong noise interference in bridge defect detection. …”
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Violence Detection From Industrial Surveillance Videos Using Deep Learning by Hamza Khan, Xiaohong Yuan, Letu Qingge, Kaushik Roy

“…The lightweight convolutional neural network (CNN) model initially identifies individuals in the video stream to minimize the processing of irrelevant frames. …”
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Automatic summarization of cooking videos using transfer learning and transformer-based models by P. M. Alen Sadique, R. V. Aswiga

“…Initially, Focus is given for frame summary generation which employs a combination of two convolutional neural networks and a GPT-based model. A pre-trained CNN model called Inception-V3 is fine-tuned with food image dataset for dish recognition and another custom-made CNN is built with ingredient images for ingredient recognition. …”
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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

“…Dari hasil penelitian yang dilakukan menggunakan 5 cross validation, CNN dengan arsitektur Xception memiliki nilai F1 Score tertinggi yaitu 99,57% dengan waktu training rata-rata 1.387 detik. …”
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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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A Deep Neural Network-Based Fault Detection Scheme for Aircraft IMU Sensors by Yiming Zhang, Hang Zhao, Jinyi Ma, Yunmei Zhao, Yiqun Dong, Jianliang Ai

“…This scheme adopts a deep neural network with a CNN-LSTM-fusion architecture (CNN: convolution neural network; LSTM: long short-term memory). …”
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Efficient Image Super-Resolution with Multi-Branch Mixer Transformer by Long Zhang, Yi Wan

“… Deep learning methods have demonstrated significant advancements in single image super-resolution (SISR), with Transformer-based models frequently outperforming CNN-based counterparts in performance. However, due to the self-attention mechanism in Transformers, achieving lightweight models remains challenging compared to CNN-based approaches. …”
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The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas by Suboh Alkhushayni, Du’a Al-zaleq, Luwis Andradi, Patrick Flynn

“…We also created more traditional data models, including support vector classification, K-nearest neighbor, Naïve Bayes, random forest, and gradient boosting algorithms, and compared them to the CNN-based models we had created. Results had indicated that CNN-based algorithms significantly outperformed other data models we had created. …”
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Research on credit risk of listed companies: a hybrid model based on TCN and DilateFormer by Chuanhe Shen, Junzhe Wu

“…In this paper, we apply the concept of combining Transformer and CNN to the financial field, building on the traditional CNN-Transformer model’s capacity to effectively process local features, perform parallel processing, and handle long-distance dependencies. …”
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Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network by Yongchao Ye, Lingjie Lao, Diqun Yan, Rangding Wang

“…In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitch-shifted voice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. …”
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SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks by M. Suriya, M. G. Sumithra

“…The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters.…”
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ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES by Arif Arifoğlu, Tuğrul Kandemir

“…In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. …”
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Capsule neural network and adapted golden search optimizer based forest fire and smoke detection by Luling Liu, Li Chen, Mehdi Asadi

“…This study introduces an innovative methodology for detecting forest fires and smoke using an enhanced capsule neural network (CNN) together with an adapted golden search optimizer (AGSO). …”
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Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups by Yuanhui Fang, Yanmei Cui, Xianzhi Ao

“…The results show that CNN has a productive performance in identification of the magnetic types in solar active regions (ARs). …”
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