FDIA Detection in Power Grid Based on Opposition-Based Whale Optimization Algorithm and Multi-layer Extreme Learning Machine by Lei XI, Yixiao WANG, Miao HE, Chen CHENG, Xilong TIAN

“…The proposed method not only extends the extreme learning machine into a multi-layer neural network to solve the problem of its limited ability of feature expression, but also introduces the whale optimization algorithm to optimize the number of neurons of the multi-layer extreme learning machine, and uses the opposition-based learning strategy to improve its convergence speed and detection accuracy so as to prevent the influence of randomly determining the number of neurons in each hidden layer on the generalization performance and location detection results of the detection method. …”
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Purine metabolism state in patients with type 1 diabetes mellitus by A. O. Cherniaieva, M. R. Mykytiuk, Yu. I. Karachentsev, O. I. Pliekhova, L. Yu. Serhiienko

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DEADLINES OPTIMAL CORRECTION OF PROJECT EXECUTION FOR ANY DEVIATIONS FROM NETWORK SCHEDULE by Yu. I. Buryak, I. B. Ivenin, A. А. Skrynnikov

“…The correction algorithm is based on using dynamic programming method taking into account the problem inherent feature when calculating the target function.…”
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QLAW: An Improved Quantization-Based Local Audio Watermarking Scheme Using Inter-Frame Correlation by Qiutong Li, Zheng Xing, Ju Wang, Guoheng Huang, Xiaochen Yuan

“…Based on the main energy region, the Stable Frequency and Energy Region Extractor is conducted to find the local feature region for embedding. After segmenting the local feature embedding region into several frames, Adjacent Frame Extraction Process (AFEP) is conducted to select the adjacent frame. …”
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LCAMNet: a lightweight model for apple leaf disease classification in natural environments by Yuanyuan Jiao, Honghui Li, Xueliang Fu, Buyu Wang, Buyu Wang, Kaiwen Hu, Shuncheng Zhou, Daoqi Han

“…A multi-scale structure is introduced to enhance lesion feature diversity representation. An improved triplet attention mechanism is utilized to better capture deep lesion features. …”
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Risk Factors for Obesity in Adolescents of Ethnic Groups in Rural Areas of the Republic of Buryatia: a Cross-Sectional Study by Ljubov V. Rychkova, Z. G. Ajurova, A. V. Pogodina, A. S. Kosovtseva

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The Ecological Status of Livestock and Poultry in the Border Regions of Russia and South-East Finland by A. Yu. Bryukhanov, H. Huhta, E. V. Shalavina, E. A. Vorobyeva, N. S. Vasileva

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Analysis of goal, feedback and rewards on sustained attention via machine learning by Nethali Fernando, Nethali Fernando, Matthew Robison, Matthew Robison, Pedro D. Maia, Pedro D. Maia

“…Finally, we quantify changes in accuracy when coarser features (averaged throughout multiple trials) are used. …”
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Mobile malware detection method using improved GhostNetV2 with image enhancement technique by Yao Du, CaiXia Gao, Xi Chen, MengTian Cui, LiLi Xu, AoJi Ning

“…Abstract In recent years, image-based feature extraction and deep learning classification methods are widely used in the field of malware detection, which helps improve the efficiency of automatic malicious feature extraction and enhances the overall performance of detection models. …”
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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means by Bo Li, Guochao Qian, Lijun Tang, Peng Sun, Zhensheng Wu

“…When a fault occurs in the distribution network, the sensor device based on optimal configuration collects fault feature data, combines it with the FCM clustering algorithm to classify nodes according to fault feature similarity, and divides the most significant fault-affected section as the core fault area. …”
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AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification by Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang

“…TS-separable linear self-attention mechanism and convolutional feedforward network achieve feature extraction in a lightweight way by decoupling temporal and spatial features of time series. …”
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Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework by Ziyin Zeng, Qingyong Hu, Zhong Xie, Bijun Li, Jian Zhou, Yongyang Xu

“…We investigate the problem of 3D point clouds semantic segmentation. …”
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A Lightweight Method for Road Defect Detection in UAV Remote Sensing Images with Complex Backgrounds and Cross-Scale Fusion by Wenya Zhang, Xiang Li, Lina Wang, Danfei Zhang, Pengfei Lu, Lei Wang, Chuanxiang Cheng

“…Moreover, the CAA attention mechanism is employed to strengthen the model’s global feature extraction abilities; (2) a cross-scale feature fusion strategy known as GFPN is developed to tackle the problem of diverse target scales in road damage detection; (3) to reduce computational resource consumption, a lightweight detection head called EP-Detect has been specifically designed to decrease the model’s computational complexity and the number of parameters; and (4) the model’s localization capability for road damage targets is enhanced by integrating an optimized regression loss function called WiseIoUv3. …”
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Bayesian curriculum generation in sparse reward reinforcement learning environments by Onur Akgün, N. Kemal Üre

“…Diverging from traditional methodologies, this algorithm utilizes Bayesian networks to dynamically create tasks by altering problem parameters, thereby impacting task difficulty. …”
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FD²-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection by Junwen Zhu, Jinbao Sheng, Qian Cai

“…Furthermore, the Dynamic Inter-Domain Feature Fusion module (DIFF) is introduced, which uses large-kernel deep convolution and Hadamard to enable the adaptive fusion of features from different domains, thus addressing the problem of difficult feature fusion due to domain differences. …”
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Multi-class segmentation of knee MRI based on hybrid attention by Yuhang Xiang, Xinglin Zhang, Tao Meng, Tao Meng, Tao Chen, Tao Chen

“…However, many existing methods are hindered by class imbalance and fail to capture the features of small structures, leading to suboptimal segmentation performance.MethodsThis study applies hybrid attention and multi-scale feature extraction methods to the problem of multi-class segmentation of knee MRI images and innovates the classic U-Net architecture. …”
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An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution by Minghong Duan, Linhao Qu, Zhiwei Yang, Manning Wang, Chenxi Zhang, Zhijian Song

“…The proposed ISTE contains a feature aggregation branch and a texture learning branch. …”
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Learning Class-Aware Local Representations for Few-Shot Remote Sensing Scene Classification by Liu Wang, Li Zhuo, Hui Zhang, Jiafeng Li

“…It generates class-aware support local representations by calibrating the local features with class-level global features to alleviate the problem of ignoring the class-semantic information. …”
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MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements by Jiarong Wang, Luzheng Bi, Aberham Genetu Feleke, Weijie Fei

“…The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. …”
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Mixture of Emotion Dependent Experts: Facial Expressions Recognition in Videos Through Stacked Expert Models by Ali N. Salman, Karen Rosero, Lucas Goncalves, Carlos Busso

“…Recent advancements in <italic>dynamic facial expression recognition</italic> (DFER) have predominantly utilized static features, which are theoretically inferior to dynamic features. …”
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