Majorization Problems for Subclasses of Meromorphic Functions Defined by the Generalized <inline-formula><math display="inline"><semantics><mstyle mathvariant="bold"><mi mathvarian... by Ekram E. Ali, Rabha M. El-Ashwah, Teodor Bulboacă, Abeer M. Albalahi

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Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused by <i>Xanthomonas oryzae</i> pv. <i>oryzae</i>, <i>Pantoea ananatis</i> and <i>Enter... by Meng Zhang, Shuqi Tang, Chenjie Lin, Zichao Lin, Liping Zhang, Wei Dong, Nan Zhong

“…<i>oryzae</i>, yet they differ in drug resistance and management strategies. …”
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Models, systems, networks in economics, engineering, nature and society by D.V. Mirosh

“…The materials of this article present the technology of using convolutional neural networks for the diagnosis of inter-turn circuits in three-phase asynchronous motors with a short-circuited rotor, based on the use of a graphical representation of the relations of energy characteristics. …”

Non-intrusive Load Decomposition Model Based on Deep Fusion of Multi-modal Integration by YAO Gang, WANG Yun, WANG Yuanliang, SONG Zihao

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Quantum AI: A Cognitive Machine Learning Technique based on Nurturing Food Security Sustainability Predictive Analysis for Life Science - Bioengineering in Healthcare by Senthil G.A., Monica K.M., Prabha R., Prinslin L., Elavarasi R.

“…The system starts with taking photos of food using a Convolutional Neural Network (CNN) is processed by it has a classification accuracy of 91.87%. …”
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Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression by Heba M. Afify, Kamel K. Mohammed, Aboul Ella Hassanien

“…This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). …”
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BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation by Lei Zhang, Changchun Li, Xifang Wu, Hengmao Xiang, Yinghua Jiao, Huabin Chai

“…This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). …”
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Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks by Mahmut Türk, Heybet Kılıç, Cem Haydaroglu

“…In order to reduce these losses, we propose an artificial intelligence-based approach that utilizes deep learning architectures in the detection of different types of leakage (voltage leakage, current leakage and voltage-current leakage). …”
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Electrocardiographic sex index: a continuous representation of sex by Ibrahim Karabayir, Turgay Celik, Luke Patterson, Liam Butler, David Herrington, Oguz Akbilgic

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PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection by Haomeng Guo, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng, Jianhua Yang

“…In order to improve the ability of the algorithm to extract weak texture features, the SPDDEConv (Space to Depth and Difference Enhance Convolution) module was introduced in this study, which reduced the loss of information in the down-sampling process through space-to-depth transformation and enhanced the edge information of weak texture defects through difference convolution. …”
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BCDnet: Parallel heterogeneous eight-class classification model of breast pathology. by Qingfang He, Guang Cheng, Huimin Ju

“…Two convolutional bases (VGG16 convolutional base and Resnet50 convolutional base) obtain breast tissue image features from different fields of view. …”
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An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN by Jie Cao, Zhidong He, Jinhua Wang, Ping Yu

“…MSK has five convolutional kernels with different sizes, and those kernels are used to extract features with varying resolutions in the original signal. …”
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Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

“…With the popularity of convolutional neural networks in image processing, ground-based cloud image recognition algorithms based on convolutional neural network have become a research focus. …”
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Data-driven cultural background fusion for environmental art image classification: Technical support of the dual Kernel squeeze and excitation network. by Chenchen Liu, Haoyue Guo

“…The DKSE module adopts various techniques such as dilated convolution, L2 regularization, Dropout, etc. in the multi-layer convolution process. …”
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MFA-net: Object detection for complex X-ray cargo and baggage security imagery. by Thanaporn Viriyasaranon, Seung-Hoon Chae, Jang-Hwan Choi

“…Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. …”
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AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder by Yunseung Lee, Pilsung Kang

“…Encoder-decoder structures have been widely used in the field of anomaly detection because they can easily learn normal patterns in an unsupervised learning environment and calculate a score to identify abnormalities through a reconstruction error indicating the difference between input and reconstructed images. Therefore, current image anomaly detection methods have commonly used convolutional encoder-decoders to extract normal information through the local features of images. …”
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Depression detection based on dual path DCGAN data generation and classification-regression network by LU Jingxue, LI Hongyan, ZHENG Ruichao, QIN Ruizhen

“…For MFCC features, the Teager energy operator is fused into MFCC features to form MFCC-TEO features, which can further highlight the difference of energy distribution. In addition, the dual path deep convolutional generation adversarial network proposed in this paper is used to enhance the two-dimensional feature maps of each depression level to expand the dataset, increase the diversity of feature maps, and improve the robustness and generalization of the model. …”
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A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG by Guanghui Song, Jiajian Zhang, Dandan Mao, Genlang Chen, Chaoyi Pang

“…A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. …”
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A Deep Paraphrase Identification Model Interacting Semantics with Syntax by Leilei Kong, Zhongyuan Han, Yong Han, Haoliang Qi

“…Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. …”
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