Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning by Qinglei Zhang, Laifeng Tang, Jiyun Qin, Jianguo Duan, Ying Zhou

“…This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. …”
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A cross-stage features fusion network for building extraction from remote sensing images by Xiaolong Zuo, Zhenfeng Shao, Jiaming Wang, Xiao Huang, Yu Wang

“…The deep learning-based building extraction methods produce different feature maps at different stages of the network, which contain different information features. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…The student network adopts a Temporal Convolutional Network–LSTM (TCN–LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. …”
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Method to generate cyber deception traffic based on adversarial sample by Yongjin HU, Yuanbo GUO, Jun MA, Han ZHANG, Xiuqing MAO

“…In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.…”
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Prediction of Intraday Electricity Supply Curves by Guillermo Vivó, Andrés M. Alonso

“…This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. …”
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Deep learning approach to bacterial colony classification. by Bartosz Zieliński, Anna Plichta, Krzysztof Misztal, Przemysław Spurek, Monika Brzychczy-Włoch, Dorota Ochońska

“…DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.…”
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Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods by Jinzi Liu

“…The dataset is trained with dynamic production data under different permeability, interlayer dip angle, and injection pressure. …”
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Fast QTMT partition decision based on deep learning by Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN

“…Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.…”
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Radar signal recognition exploiting information geometry and support vector machine by Yuqing Cheng, Muran Guo, Limin Guo

“…Specifically, the time‐frequency images of different LPI radar signals are obtained via the Choi‐Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time‐frequency features. …”
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Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System by Ahmet Bozdag, Muhammed Yildirim, Mucahit Karaduman, Hursit Burak Mutlu, Gulsah Karaduman, Aziz Aksoy

“…<b>Results:</b> The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. …”
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A Comprehensive Evaluation of Machine Learning and Deep Learning Models for Churn Prediction by Nabil M. AbdelAziz, Mostafa Bekheet, Ahmad Salah, Nissreen El-Saber, Wafaa T. AbdelMoneim

“…Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). …”
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An Edge Recognition Method for Insulator State Based on Multi-dimension Feature Fusion by Dongmei HUANG, Yueqi WANG, Anduo HU, Jinzhong SUN, Shuai SHI, Yuan SUN, Lingfeng FANG

“…And a deep learning network integrating multi-dimension feature extraction is designed, which, by using the ResNet101 as the main feature extraction network, uses the Inception module to build the data pooling layer, and embeds the compression incentive module and convolution attention module to extract features from different dimensions. …”
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Automatic Morpheme Segmentation for Russian: Can an Algorithm Re-place Experts? by Дмитрий Алексеевич Морозов, Тимур Александрович Гарипов, Ольга Николаевна Ляшевская, Светлана Олеговна Савчук, Борис Леонидович Иомдин, Анна Валерьевна Глазкова

“… Introduction: Numerous algorithms have been proposed for the task of automatic morpheme segmentation of Russian words. Due to the differences in task formulation and datasets utilized, comparing the quality of these algorithms is challenging. …”
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Local Auxiliary Spatial&#x2013;Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification by Qiusheng Chen, Zhuoqun Fang, Zhaokui Li, Qian Du, Shizhuo Deng, Tong Jia, Dongyue Chen

“…However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial global (SaG) and spectral global (SeG) information shared by both the source and target domains. …”
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Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes by Yufeng Xu, Yan Fu

“…The method proposed in this article combines the spatial pyramid pooling of the backbone with an efficient partial self-attention, enabling the network to effectively capture long-range dependencies and establish global correlations between features, obtaining feature information at different scales. At the same time, the GSEAM module and GSCConv were introduced into the neck network to compensate for the loss caused by differences in lighting levels by combining depth-wise separable convolution and residual connections, enabling it to extract effective features from visual data with poor illumination levels. …”
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Recognition of Suspension Liquid Based on Speckle Patterns Using Deep Learning by Jinhua Yan, Ming Jin, Zhousu Xu, Lei Chen, Ziheng Zhu, Hang Zhang

“…Further recognition from three different food suspensions with unknown concentration was achieved with high accuracy of 99&#x0025;. …”
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Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

“…This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. …”
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Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China by Zhuo Chen, Tao Liu

“…A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background.…”
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Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8 by Yuelong He, Yunfeng Peng, Chuyong Wei, Yuda Zheng, Changcai Yang, Tengyue Zou

“…The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. …”
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