INTEGRATED OPTIMIZATION MODEL OF MANUFACTURING AND TRANSPORTATION PROCESSES by R. S. Rogulin, D. E. Pleshanov, P. V. Nechaev

“…The target setting that integrates all three of the above problems into one, applies to the case when a manufacturing facility declares itself bankrupt and tries to manufacture products from the remains of raw materials for further sale and delivery of the goods produced meeting road system features, maximizing the profit and minimizing carrying costs. …”
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DISCRIMINANT ANALYSIS AS A METHOD OF DETERMINATION OF GESTATIONAL PERIOD IN DRINKING WOMEN AND HEALTH CONDITION OF THEIR NEWBORNS by N. V. Protopopova, L. l. Kolesnikova, A. Y. Maryanyan, I. M. Mikhalevich, N. A. Kurashova, N. V. Koroleva

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Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier by Wanlin Zhao, Zili Wang, Jian Ma, Lianfeng Li

“…An actual hydraulic-pump experiment demonstrates the procedure with a complete feature extraction and accurate mode classification.…”
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Classification of α wave motor imagery based on SVM and PCA by Cai Jing, Liu Guangda, Wang Yaoyao, Gong Xiaoyu

“…A feature screening method based on alpha wave and principal component analysis was proposed to solve the problem that the weakly correlated feature quantity would affect the classification accuracy in EEG motor imagery classification. …”
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An Innovative Small-Target Detection Approach Against Information Attenuation: Fusing Enhanced Programmable Gradient Information and a Novel Mamba Module by Yang Liu, Yatu Ji, Qingdaoerji Ren, Bao Shi, Na Liu, Min Lu, Nier Wu

“…Serious information loss often occurs when the input data undergoes the layer-by-layer feature extraction process through deep neural networks. …”
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Face Recognition using Artificial Intelligent Techniques by Laheeb Ibrahim, Ibrahim Saleh

“…This research deals with the problem of face recognition. A novel approach is presented for both face feature extraction and recognition, first, we introduce Principal Component Analysis (PCA) for face feature extraction, Generalized Regression Artificial Neural network for face recognition. …”
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Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM by Zhu Shuo, Bai Ruilin, Ji Feng

“…The feature vectors of the time and time frequency domain are extracted from the vibration signal of bearing and then the PCA algorithm is used to reduce the dimension of the feature vectors. …”
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Blotch Detection in Archive Films Based on Visual Saliency Map by Yildiz Aydin, Bekir Dizdaroğlu

“…Degradations frequently occur in archive films that symbolize the historical and cultural heritage of a nation. In this study, the problem of detection blotches commonly encountered in archive films is handled. …”
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Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM by Zhao Xiaohui, Tan Qi, Hu Sheng, Yang Wenbin, Huan Kaixuan, Zhang Zhijie

“…The characteristics of non-smoothness and uncertainty of gearbox fault vibration signal lead to the low accuracy of gearbox fault diagnosis. To address this problem, a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm optimization (PSO) kernel extreme learning machine (KELM) is proposed. …”
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Research on fault diagnosis of amorphous alloy transformers by using vibration signals and a PSO-optimized full-process WPT-SVM model by Daosheng Liu, Wentao Yang, Longsheng Liu, Zhe Zhao

“…The PSO algorithm is used to optimize the number of WPT decomposition layers, wavelet basis functions, SVM kernel parameters g and penalty parameters c to enhance the accuracy of feature extraction and classification. Additionally, principal component analysis (PCA) reduces the dimensionality of the redundant frequency band energy after WPT feature extraction, minimizing data redundancy. …”
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Fault Extraction of Wind Turbine Rolling Bearings Using FDEO and the Improved ACYCBD by Gong Yongli, Peng Dikang, Feng Tao, Liu Yibing

“…Finally, for the purpose of addressing the problem such as multiple vibration components existing in a vibration signal collected from wind turbines, which may mask the vibration component of interest, the improved ACYCBD is then used to extract the fault feature. …”
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Passive forensic based on spatio-temporal localization of video object removal tampering by Linqiang CHEN, Quanxin YANG, Lifeng YUAN, Ye YAO, Zhen ZHANG, Guohua WU

“…To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.…”
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High efficiency classification of thyroid cytopathological images based on knowledge distillation and vision transformer by Jiazhe Zhang, Haolin Zhang, Peng Jiang, Qin Huang, Guangya Zhu, Jingjing Chen, Yingling Cheng, Shu Ran, Fusong Jiang

“…To address this practical problem, this paper proposes a knowledge distillation method called Multi-Dimensional Knowledge Distillation, which involves feature-based distillation and response-based distillation.We employ a 12-layer Vision Transformer as the teacher model. …”
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LMFUNet: A Lightweight Multi-fusion UNet Based on Spiking Neural Systems for Skin Lesion Segmentation by Ningkang Hu, Bing Li, Hong Peng, Zhicai Liu, Jun Wang

“…LMFUNet uses an Efficient Multi-scale Feature Extraction block (EMFE) in deep stages, which uses grouping of features by convolution with different dilation rates to reduce model complexity and effectively capture multi-scale features. …”
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Bilingual hate speech detection on social media: Amharic and Afaan Oromo by Teshome Mulugeta Ababu, Michael Melese Woldeyohannis, Emuye Bawoke Getaneh

“…The FastText feature extraction overcomes the problem of out of vocabulary (OOV). …”
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Remote Sensing Image Detection Method Combining Dynamic Convolution and Attention Mechanism by Yunfei Zhang, Ming Chen, Cong Chen

“…This paper proposes an improved YOLOv8 algorithm, incorporating adaptive feature extraction and multi-scale fusion modules to enhance feature representation, effectively capturing the details of small objects. …”
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A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…The goal of this framework is to learn a feature extractor. It can hide the private information that the attack model tries to infer from the intermediate anonymity features, while maximally retaining the interactive behavior features contained in the original traffic for botnet detection. …”
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ACGNet: An Alternating Conjugate Gradient Optimization-Based Neural Network for SAR Image Despeckling by Xin Mao, Ying Liu, Chenghao Qiu, Cong Lin

“…Synthetic aperture radar (SAR) images are characterized by unique speckle noise, and maintaining image details while effectively reducing this noise has always been a challenging problem. Current deep-learning-based denoising methods mainly rely on global noise models or local feature learning, but these methods often fail to achieve a balance between noise suppression and detail preservation. …”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. …”
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Fault Diagnosis Research on Bearingof Motor Based on LMD And Support Vector Machine by YIN Zhao-jie, XU Tong-le, ZHENG Dian-kun

“…Secondly，the new kernel function is used to improve the SVM to complete the adaptive training，and the“one to many”method is used to solve the large sample data and multi classification problem. Finally，the energy feature vector is used as the training sample and test sample of SVM，and the fault information of motor bearing is trained and predicted. …”
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