Morphometric Assessment of Occipital Condyles and Foramen Magnum Reveals Enhanced Sexual Dimorphism Detection via 3D Imaging: A Systematic Review and Meta-Analysis Utilizing Classification and Regression Trees by Christos Tsiouris, George Triantafyllou, Nektaria Karangeli, George G. Botis, Panagiotis Papadopoulos-Manolarakis, Theodosis Kalamatianos, George Tsakotos, Maria Piagkou

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AI based diagnostics product design for osteosarcoma cells microscopy imaging of bone cancer patients using CA-MobileNet V3 by Qian Liu, Xing She, Qian Xia

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Big Data Analysis of Lung Cancer Dataset Using Classification by Tannady Hendy, Fernandes Andry Johanes, Susanto William, Tannady Tan Henny, Bin Rakiman Umol Syamsyul

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Electronic Health Record classification and analysis using NLP Techniques by Himavamshi K., Tejaswini D., Sethi Gaurav, Anusuya Devi V.S, Pavani P., Hariharan Shanmugasundaram

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Development of a classification scheme for main food processing processes by Kurbanova Madina, Dodayev Qo’chqor, Kurbanov Zhamshed, Dissanayake Kasun, Maksumova Dilrabo, Rifky Mohamed

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Enhanced Medical Image Classification Using LSA and PCA in CNN by Suhaifa S Thasneem, Mukthar Hussain S Faizal, R Karthikeyan, Yousuf T Sheik, Begum B Rasina, Uveise S A Mohammed

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Automated classification of trees and evaluation of cone yield using drones by Alekhina Anna, Pastukhova Albina, Shcherba Yulia, Rodin Ilya

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Technology and understanding of post-fracturing coring in three-dimensional development zone of Fuling shale gas, Sichuan Basin, SW China by Huanquan SUN, Zhiyong LU, Li LIU, Jichao FANG, Aiwei ZHENG, Jiqing LI, Yuqiang ZHANG, Jialin XIAO

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A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network by Constantinos M. Mylonakis, Zaharias D. Zaharis

“…We approach the DoA estimation challenge as a binary classification task, employing a novel grid in the output layer and a deep convolutional neural network (DCNN) as the classifier. …”
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High-accuracy classification of invasive weed seeds with highly similar morphologies: Utilizing hierarchical bilinear pooling for fine-grained image classification by Lianghai Yang, Jing Yan, Xinyue Cao, Huiru Li, Binjie Ge, JiaXin He, Zhechen Qi, Xiaoling Yan

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Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification by Yu Liu, Caihong Mu, Shanjiao Jiang, Yi Liu

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Application of Big Data Technology to Assessments of Female Ovarian Reserve Dysfunction by Xia Ji'An, Ma YunFei, Wu YiYun, Zhao YouLin, Ni HaoRang, Liu XinYan

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Research on Chinese patent classification based on structured features by Ran Li, Wangke Yu, Shuhua Wang

“…Abstract The three dimensions of Function, Structure, and Purpose are fundamental to patent classification and play a decisive role in improving the accuracy of patent information categorization. …”
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TECHNICAL ASPECTS REGARDING THE CLASSIFICATION OF PIG CARCASSES IN ROMANIA by Monica Esperance GĂUREANU, Mirela CĂRĂTUȘ STANCIU, Dan Ioan COCÎRCĂ, Livia VIDU, Iulian VLAD

“…The three authorized methods of classification are: Zwei Punkte (ZP) - a manual method using the ruler used by small slaughterhouses that sacrificed less than 200 pig on the average weekly in the previous year; semiautomatic methods using the optical probe used in large slaughterhouses: Optigrade Pro (OGP) and Fat-o-Meat'er (FOM). …”
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Improving long‐tail classification via decoupling and regularisation by Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Wenjian Luo, Peiyi Han, Qing Liao, Guandong Xu

“…To tackle these challenges, the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components: classifier norm (i.e. the magnitude of the classifier vector), feature norm (i.e. the magnitude of the feature vector), and cosine similarity between the classifier vector and feature vector. …”
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Detection and Classification of Emotional State Based on Speech Signal by Hiba Younis, Mrewan Mustafa, Rahma Raad

“…In this research, an algorithm was proposed to automatically classify the mood of the speaker by referring to his speech. Three moods were adopted in this study, namely joy, sadness and anger in order to distinguish between them.…”
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The use of low-density EEG for the classification of PPA and MCI by Panteleimon Chriskos, Panteleimon Chriskos, Kyriaki Neophytou, Christos A. Frantzidis, Christos A. Frantzidis, Jessica Gallegos, Alexandros Afthinos, Chiadi U. Onyike, Argye Hillis, Panagiotis D. Bamidis, Kyrana Tsapkini, Kyrana Tsapkini

“…Utilizing the Relative Wavelet Entropy method, we derived (i) functional connectivity, (ii) graph theory metrics and extracted (iii) various energy rhythms. Features from all three sources were used for classification. …”
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CBSNet: An Effective Method for Potato Leaf Disease Classification by Yongdong Chen, Wenfu Liu

“…As potato is an important crop, potato disease detection and classification are of key significance in guaranteeing food security and enhancing agricultural production efficiency. …”
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Classification of renal cell carcinoma based on immunogenomic profiling by Yanfei Chen, Sian Chen, Jun Zou, Jiehui Zhong, Xuejin Zhu, Qingbiao Chen, Bin Wang, Weide Zhong

“…By analyzing the molecular and clinical data of RCC obtained from The Cancer Genome Atlas database, we classified RCC hierarchically based on the scores of 29 immune-associated gene sets, which were generated by single-sample gene-set enrichment analysis. The three RCC subtypes were Cluster1 (C1), Cluster2 (C2) and Cluster3 (C3), and they had distinct prognoses. …”
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Time-series visual representations for sleep stages classification. by Rebeca Padovani Ederli, Didier A Vega-Oliveros, Aurea Soriano-Vargas, Anderson Rocha, Zanoni Dias

“…Additionally, image patching and ensemble methods were applied to enhance classification performance. The results demonstrated that Gramian Angular Field, combined with patching and ensembles, achieved superior performance, exceeding 82% balanced accuracy for two-stage classification and 62% for three-stage classification. …”
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