Innovation and Digitalization of Russian Economy: Challenges and Restrictions by V. S. Gritsenko

“…According to the author, these problems have system nature and caused not by occasional circumstances but by deeper reasons. …”
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An Overview of the Application of Harmony Search for Chemical Engineering Optimization by R. M. Fonseca-Pérez, O. Del-Mazo-Alvarado, A. Meza-de-Luna, A. Bonilla-Petriciolet, Z. W. Geem

“…A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. …”
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The Role of Intertheoretical Connections in the System of Sciences (by the Example of Applying the Information Approach to the Theory of Evolution) by A. Yu. Storozhuk

“…Reformulation of scientific problems in another language allows us to explain the features of a number of phenomena and better understand their mechanisms. …”
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Data normalization in machine learning by V. V. Starovoitov, Yu. I. Golub

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Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network by Runfang Hao, Yunpeng Bai, Kun Yang, Yongqiang Cheng, Shengjun Chang

“…Abstract Due to the uncertainty existing in the actual industrial environment, the rolling bearing compound fault features present coupling and complexity, which brings challenges to the compound fault feature extraction. …”
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Nearest-Better Network-Assisted Fitness Landscape Analysis of Contaminant Source Identification in Water Distribution Network by Yiya Diao, Changhe Li, Sanyou Zeng, Shengxiang Yang

“…Contaminant Source Identification in Water Distribution Network (CSWIDN) is critical for ensuring public health, and optimization algorithms are commonly used to solve this complex problem. However, these algorithms are highly sensitive to the problem’s landscape features, which has limited their effectiveness in practice. …”
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用同伦迭代法进行平面Stephenson Ⅲ型六杆机构的函数发生器综合 by 李立, 陈永

“…Two step homotopy path tracking scheme is adopted in this method according to the feature of the mathmatic model for this problem which is a buge and highly dificient nonlinear polynomial system,and thus meaningful solutions for this problem with maximum precision positions are obained for the first time.…”
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Design of Chili Field Navigation System Based on Multi-Sensor and Optimized TEB Algorithm by Weikang Han, Qihang Gu, Huaning Gu, Rui Xia, Yuan Gao, Zhenbao Zhou, Kangya Luo, Xipeng Fang, Yali Zhang

“…The system’s positioning component integrates pose data from the GNSS and the IMU inertial navigation system, and corrects positioning errors caused by the clutter of LiDAR environmental feature points. To solve the problem of local optimization and excessive collision handling in the TEB algorithm during the path planning phase, the weight parameters are optimized based on environmental characteristics, thereby reducing errors in optimal path determination. …”
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A double broad learning approach based on variational modal decomposition for Lithium-Ion battery prognostics by Xiaojia Wang, Xinyue Guo, Sheng Xu, Xibin Zhao

“…First, we use VMD to perform adaptive decomposition of the degraded data to form intrinsic mode function (IMF) components and residual components to solve the data noise problem. Second, these two modal data of the feature extraction and modal fusion are inputted into the trained DBL model. …”
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Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration by Xinxin Cui, Yuee Zhou, Caihong Wei, Guodong Suo, Fengqing Jin, Jianlan Yang

“…However, there are two main limitations in existing research: one is that it over-focuses on the fusion of multi-layer deformation sub-fields on the decoding path, while ignoring the impact of feature encoders on network performance; the other is the lack of specialized design for the characteristics of feature maps at different scales. …”
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Reducing overfitting in vehicle recognition by decorrelated sparse representation regularisation by Wanyu Wei, Xinsha Fu, Siqi Ma, Yaqiao Zhu, Ning Lu

“…Abstract Most state‐of‐the‐art vehicle recognition methods benefit from the excellent feature extraction capabilities of convolutional neural networks (CNNs), which allow the models to perform well on the intra‐dataset. …”
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Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction by Zexi Yang, Qi Yu, Yapeng Zhan, Jiying Liu

“…This network is able to efficiently extract atomic and bonding feature information, while effectively learning the importance weights of all neighbouring nodes. …”
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Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification by Jiannan Chen, Chenju Yang, Rao Wei, Changchun Hua, Dianrui Mu, Fuchun Sun

“…Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. …”
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A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization by Dongjie Zhong, Cancan Yi, Han Xiao, Houzhuang Zhang, Anding Wu

“…Moreover, the concerned features are submerged in strong background noise. …”
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Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification by Rong Liu, Zhilin Li, Jiaqi Yang, Jian Sun, Quanwei Liu

“…To explore the potential of large kernel convolution, we propose a hyperspectral image (HSI) classification algorithm in this article that utilizes a large kernel convolution module combined with multiscale coattention and an adaptive geometric feature (AGF) classifier, named Hyper-LKCNet. By integrating this feature enhancement module, our method effectively adjusts the contributions of various spectral and spatial features, ensuring that the network captures critical but easily overlooked information across both dimensions and improving the performance to classify HSI. …”
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Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study by Marina Galanina, Anna Rekiel, Anna BaCzyk, Bozena Kostek

“…Depression is a significant mental health problem and presents a challenge for the machine learning field in the detection of this illness. …”
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Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions by Ke-Lin Du, Bingchun Jiang, Jiabin Lu, Jingyu Hua, M. N. S. Swamy

“…Based on statistical learning theory and the maximum margin principle, SVM attempts to determine an optimal hyperplane by addressing a quadratic programming (QP) problem. Using Vapnik–Chervonenkis dimension theory, SVM maximizes generalization performance by finding the widest classification margin within the feature space. …”
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A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification by Mohammed Yousif, Belal Al-Khateeb, Begonya Garcia-Zapirain

“…In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. …”
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Research on Underwater Small Target Detection Technology Based on Single-Stage USSTD-YOLOv8n by Weiguo Yi, Jinwei Yang, Lingwei Yan

“…First, CARAFE is adopted as anew up-sampling method to achieve more correct feature reconstruction under low underwater visibility. …”
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Enhanced diagnosis of planetary gear train faults based on bispectrum and attention mechanism deep convolutional generative adversarial networks by Dalian Yang, Yang Zhang, Renjie Li, Hui Long, Changzheng Huang

“…Abstract Aiming at the problem of inaccurate fault diagnosis caused by limited fault samples and challenging feature extraction in helicopter planetary gear trains, this study proposes a fault diagnosis method based on Bispectrum and Attention Mechanism Deep Convolutional Generative Adversarial Networks (BAMDCGAN). …”
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