Parallel deep forest algorithm based on Spark and three-way interactive information by Yimin MAO, Zhan ZHOU, Zhigang CHEN

“…To address issues such as excessive redundancy and irrelevant features, long class vectors, slow model convergence, and low efficiency of parallel training in parallel deep forests, a parallel deep forest algorithm based on Spark and three-way interactive information was proposed.Firstly, a feature selection based on feature interaction (FSFI) strategy was proposed to filter the original features and eliminate irrelevant and redundant features.Secondly, a multi-granularity vector elimination (MGVE) strategy was proposed, which fused similar class vectors and shortened the class vector length.Subsequently, the cascade forest feature enhancement (CFFE) strategy was proposed to improve the utilization of information and accelerate the convergence speed of the model.Finally, a multi-level load balancing (MLB) strategy was proposed, combined with the Spark framework, to improve the parallelization efficiency through adaptive sub-forest division and heterogeneous skew data partitioning.Experimental results demonstrate that the proposed algorithm significantly improves the model classification effect and reduces the parallelization training time.…”
Get full text

INITIAL PROFESSIONAL TRAINING OF WORKERS' AND PEASANTS' MILITIA OFFICERS OF THE RSFSR PROVINCES IN THE 1920S (BASED ON THE MATERIALS OF THE FAR EASTERN REGION) by Tatyana A. Ornatskaya

“…All this determined the protracted nature of the inclusion of Far Eastern educational institutions of initial vocational training of police officers in the all-Union system.…”
Get full text

RESTORATION-BASED EDUCATION OR PRACTICAL TRAINING IN ECOLOGICAL RESTORATION?: APPROACH FROM AN EXPERIENCE IN A DESERTIFIED SOUTH AMERICAN REGION by Daniel R. Pérez, Julieta Farina

“…Contents related to historical environmental problems, and marked differences in priority approaches along time in four groups of residents were outstanding features of the educational process. Restoration-Based Education (RBE) is presented as a theoretical framework and base to remark differences with practical training in ecological restoration. …”
Get full text

Effectiveness of a Web-Based Virtual Simulation to Train Nursing Students in Suicide Risk Assessment: Randomized Controlled Investigation by Paul Roux, Yujiro Okuya, Cristina Morel, Mariane Soulès, Hugo Bottemanne, Eric Brunet-Gouet, Solène Frileux, Christine Passerieux, Nadia Younes, Jean Claude Martin

“…Virtual simulation (VS) based training can be particularly effective because it allows interaction with patients without the risk of causing harm. …”
Get full text

Postoperative functional training program for vascularised Iliac flap donor site in jaw defect reconstruction based on the Delphi method by Li Li, Qian He, Na Zhou, Zhaoxia Zhang, Xiaoming Lv, Jie Zhang

“…Through three rounds of anonymous consultations, and by integrating literature evidence with postoperative mobility assessments, we developed a phased, individualised progressive functional training (PFT) protocol featuring dynamic evaluation, coordinated activation of abdominal and hip muscle groups, and safe exercise strategies during head and neck immobilisation, while overcoming conventional hip rehabilitation limitations (e.g., restrictions on flexion < 90°, and bans on squatting or cross-legged sitting). …”
Get full text

1D-Concatenate based channel estimation DNN model optimization method by Min LU, Zehao QIN, Zhihui CHEN, Min ZHANG, Guangxue YUE

“…In order to improve the channel estimation accuracy of DNN model in wireless communication, a DNN model optimization method based on 1D-Concatenate was proposed.In this method, Concatenate performs one-dimensional data transformation, the DNN model was introduced by hopping connection, the gradient disappearance problem was suppressed, and 1D-Concatenate was used to restore the data features lost during network training to improve the accuracy of DNN channel estimation.In order to verify the effectiveness of the optimization method, a typical DNN-based wireless communication channel estimation model was selected for comparative simulation experiments.Experimental results show that the estimated gain of the existing DNN model can be increased by 77.10% by the proposed optimization method, and the channel gain can be increased by up to 3 dB under high signal-to-noise ratio.This optimization method can effectively improve the channel estimation accuracy of DNN model in wireless communication, especially the improvement effect is significant under high signal-to-noise ratio.…”
Get full text

Feedback-Based Validation Learning by Chafik Boulealam, Hajar Filali, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi

“…Unlike conventional methods that utilize validation datasets for performance evaluation post-training, FBVL integrates these datasets into the training process. …”
Get full text

Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging by Wang-Sheng Chen, Fang-Xiong Fu, Qin-Lei Cai, Fei Wang, Xue-Hua Wang, Lan Hong, Li Su

“…The combined radiomic model achieved an AUC of 0.814 (95% CI: 0.767–0.862) in the training set and 0.808 (95% CI: 0.736–0.859) in the testing set, outperforming models based on intratumoral or peritumoral features alone. …”
Get full text

Cross-dataset micro-expression identification based on facial ROIs contribution quantification by Yu Kun, Guo Chunfeng

“…In this work, we propose a novel cross-dataset micro-expression identification method based on facial regions of interest contribution quantification, where the training samples are from the source dataset and the test samples are from the target dataset. …”
Get full text

Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

“…Furthermore, we provide patch size-considered incremental learning based on QP distance that combines patch size and QP distance for network training. …”
Get full text

sEMG-Based Control of Prosthesis Hand Using LSTM Classification and SMC by Amanual Tesfaye Takele, Dereje Shiferaw Negash

Get full text

The Imbalanced Target Classification Method Based on Mixed Learning of Virtual and Real Data by Fengyu Yang, Peng Wang, Wutao Qin, Zhangze Liao

“…We proposes a category imbalance classification model based on mixed feature enhancement between virtual and real domains to address the class imbalance problem in maritime target classification applications. …”
Get full text

DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.MethodsBased on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. …”
Get full text

Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…A multi-modal attention-based feature fusion network was used to fuse the extracted features in order to guarantee the integration of key multi-modal features. …”
Get full text

Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm by LIN Kaifeng, LI Yiming, ZHANG Bo, YANG Changyu, ZHU Zeting, YANG Zhenda

Get full text

Estimation of Sediment Grain Size Distribution Using Optical Image-Based Spatial Feature Representation Learning with Data Augmentation by Jongwon Choi, Sulki Kim, Jaejoong Jin, Jinhoon Kim, Sungyeol Chang, Inho Kim

“…The model achieves sufficient network capacity by stacking two-dimensional convolution-based encoder blocks to learn the spatial features that relate sediment images to grain size distribution. …”
Get full text

A multi-modal feature combination mechanism for identification of harmonic load in distribution networks based on artificial intelligence models by Renzeng Yang, Shuang Peng, Gang Yao

“…The most suitable intrinsic mode sequences are selected as input features for sequential neural networks training. Finally, a multi-modal feature tensor combination mechanism that integrates reshaped vector layers into the sequential neural networks architecture is introduced, enabling adaptive extraction of spatial–temporal characteristics and significantly improving the accuracy of harmonic load identification without prior knowledge of their spectral features.…”
Get full text

Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer by Liangrui Pan, Qingchun Liang, Wenwu Zeng, Yijun Peng, Zhenyu Zhao, Yiyi Liang, Jiadi Luo, Xiang Wang, Shaoliang Peng

Get full text

An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…To overcome these challenges, this paper presents a model for detecting small objects, AAPW-YOLO, based on adaptive convolution and reconstructed feature fusion. …”
Get full text

An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information by Huixiang Liu, Xin Zhao, Qiong Liu, Wenbai Chen

“…We apply feature map separation-based SPDConv for downsampling, providing PAN-FPN with rich, fine-grained shallow-layer features. …”
Get full text