FEATURES OF TRANSFORMATIONS OF RED BLOOD CELLS IN CHILDREN WITH BRONCHIAL ASTHMA by E. N. Suprun, S. V. Suprun, O. E. Guseva, M. A. Loschenko, T. V. Pivkina, E. A. Savitskaya, S. V. Anuriev, V. A. Osmolkina, S. S. Braga

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FEATURES OF PLASMAPHERESIS IN THE TREATMENT OF GRAFT REJECTION AFTER KIDNEY TRANSPLANTATION by A. V. Vatazin, A. B. Zulkarnaev, R. O. Kantarija, M. Krstich, A. P. Faenko

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Child’s attachment to mother as a predictor of psychological well-being in primary school age by E. V. Kuftyak, O. V. Magdenko, Ju. A. Zadorova

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GFDet: Multi-Level Feature Fusion Network for Caries Detection Using Dental Endoscope Images by Nan Gao, Yukai Li, Peng Chen, Jijun Tang, Tianshuang Liu

“…However, automatically identifying dental caries remains challenging due to the uncertainty in size, contrast, low saliency, and high interclass similarity of dental caries. To address these problems, we propose the Global Feature Detector (GFDet) that integrates the proposed Feature Selection Pyramid Network (FSPN) and Adaptive Assignment-Balanced Mechanism (AABM). …”
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Object detection model design for tiny road surface damage by Chenguang Wu, Min Ye, Hongwei Li, Jiale Zhang

“…Firstly, a backbone applied to road surface damage feature extraction is designed to solve the problems of feature loss and insufficient extraction of tiny damage during feature extraction. …”
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Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion by Junming Wang, Yuanfeng Gong, Wupeng Deng, Enshun Lu, Xinyu Hu, Daode Zhang

“…Firstly, the backbone network integrates FasterNet-Block, C2f, and an efficient multi-scale EMA attention mechanism to address attention dispersion problems during feature extraction, delivering real-time object detection across different scales. …”
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A systematic literature review of diabetes prediction using metaheuristic algorithm-based feature selection: Algorithms and challenges method by Sirmayanti, Pulung Hendro PRASTYO, Mahyati, Farhan RAHMAN

“…To address the problems, we can employ metaheuristic algorithm-based feature selection. …”
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SPIFFNet: A Statistical Prediction Interval-Guided Feature Fusion Network for SAR and Optical Image Classification by Yingying Kong, Xin Ma

“…The problem of the feature extraction and fusion classification of optical and SAR data remains challenging due to the differences in optical and synthetic aperture radar (SAR) imaging mechanisms. …”
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Image semantic segmentation with hierarchical feature fusion based on deep neural network by Dawei Yang, Yan Du, Hongli Yao, Liyan Bao

“…The accuracy of image semantic segmentation is damaged. To solve this problem, we present an image semantic segmentation with hierarchical feature fusion based on deep neural network (ISHF). …”
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EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification by Wenna Chen, Junqiang Liu, Xinghua Tan, Jincan Zhang, Ganqin Du, Qizhi Fu, Hongwei Jiang

“…Firstly, the FExM is used to extract features and the multi-scale parallel subnetwork is constructed to fuse shallow and deep features. …”
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YOLO-v4 Small Object Detection Algorithm Fused With L-α by ZHANG Ning, YU Ming, REN Honge, AO Rui, ZHAO Long

“… The detection ability for small object is still need to be improved urgently in spite of the rapidly developing object detection technology based on deep learning at present.Compared with large objects, small object detection tasks hold drawbacks of low resolution and feature loss which leads to that many general algorithms cannot be directly applied to small object detection.The feature pyramid fusion can effectively combine the features of deep and shallow layers to enhance the performance.To solve the problem most models existing ignoring the imbalance of information during the feature fusion between adjacent layers, it is proposed to integrate the idea of fusion factor into the PANet of YOLOv4, use the fusion factor L-αto control the amount of information transmitted from the deep layer to the shallow, so as to effectively improve the efficiency of information fusion and enhance the ability of YOLO-v4 for small objects detection.With the addition of L-αin YOLO- V4 model, the experiment results show that the APtiny50and APsmall50on the TinyPerson are improved by 2.14% and 1.85% respectively, while the AP and APS on the MS COCO are separately increased by 1.4% and 2.7%.It is proved that this improved method is effective for small object detection with the evidence of better result than other small object detection algorithms.…”
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Automatic Registration of Panoramic Images and Point Clouds in Urban Large Scenes Based on Line Features by Panke Zhang, Hao Ma, Liuzhao Wang, Ruofei Zhong, Mengbing Xu, Siyun Chen

“…In order to solve this problem, this paper proposes an automatic and accurate registration method for panoramic images and point clouds of urban large scenes based on line features. …”
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A low illumination target detection method based on a dynamic gradient gain allocation strategy by Zhiqiang Li, Jian Xiang, Jiawen Duan

“…This method optimizes the model through enhancements in multi-scale feature fusion, feature extraction, detection head, and loss function. …”
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Multi-Strategy Enhanced Parrot Optimizer: Global Optimization and Feature Selection by Tian Chen, Yuanyuan Yi

“…Optimization algorithms are pivotal in addressing complex problems across diverse domains, including global optimization and feature selection (FS). …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. …”
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Research on text sentiment classification based on improved feature selection method by Mingxin LIU, Jing CHEN, Qiyuan WANG

“…An improved information gain feature selection method based on sentiment dictionary was proposed.Firstly,aiming at the existing problems of information gain feature selection,such as paying attention to the frequency of feature word and ignoring the balance of corpus,an improved method was proposed.Secondly,considering the influence of sentiment words in text classification,a feature selection method IGSC (information gain combining sentiment classification) based on sentiment dictionary was proposed for text classification.By matching the text emotion words and combining the weight of emotion words,the feature dimension reduction was realized and the problem of text data sparseness affecting classification performance was solved.Finally,according to the proposed feature selection method of travel review data set for experimental verification and analysis,the experimental results show that the improved text sentiment classification feature selection method has been improved in terms of classification accuracy,recall and F value,and classification has better stability.…”
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Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data by A. Gaspar-Cunha, G. Recio, L. Costa, C. Estébanez

“…Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. …”
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An Indoor Scene Classification Method for Service Robot Based on CNN Feature by Shaopeng Liu, Guohui Tian

“…To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a novel feature matching algorithm. …”
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Analysis and Identification of Students with Financial Difficulties: A Behavioural Feature Perspective by Yong Ma, Xingxu Zhang, Xiaoqiang Di, Tao Ren, Huamin Yang, Binbin Cai

“…Based on these features, we proposed the Apriori Balanced Algorithm (ABA) to mine the relationship of poverty level with students’ daily behaviour. …”
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An Improved DGA Feature Clustering-Based Method for Transformer Fault Diagnosis by Yujie Zhang, Jian Feng, Shanyuan Wang

“…In order to solve those problems, this paper uses more features as information sources of power transformer diagnosis based on clustering method. …”
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