ADULT-ONSET STILL’S DISEASE: ASPECTS OF THE HEMATOLOGY CLINIC by M. M. Agakishiev, I. I. Mulina, A. M. Popova, I. N. Nechunaeva, L. M. Maslova, I. B. Kovynev, T. I. Pospelova

“…A feature of the clinical case was the detection of antibodies against yersenia. …”
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Learning Transferable Convolutional Proxy by SMI-Based Matching Technique by Wei Jin, Nan Jia

“…We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.…”
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A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns by Lauren K. Fink, Brian K. Hurley, Joy J. Geng, Petr Janata

“…Rhythm is a ubiquitous feature of music that induces specific neural modes of processing. …”
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AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring by Venkatraman Manikandan, Suresh Neethirajan

“…Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…Subsequently, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms were adopted to screen key ARGs related to DN. …”
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Non-invasive urinary proteomic biomarkers for prognostic assessment in sepsis by Qingbo Zeng, Nianqing Zhang, Junjie Zeng, Lincui Zhong, Qingwei Lin, Longping He, Xiaomin Song, Jingchun Song

“…KEGG and GO analyses were conducted to investigate the possible functions of these proteins. Feature variables were selected from the differentially expressed proteins using the Least Absolute Shrinkage and Selection Operator (LASSO) and the random forest algorithms and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. …”
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Enhancing Stock Portfolio Selection with Trapezoidal Bipolar Fuzzy VIKOR Technique with Boruta-GA Hybrid Optimization Model: A Multicriteria Decision-Making Approach by Sunil Kumar Sharma

“…The Boruta-GA approach combines the advantages of the Genetic Algorithm (GA) and Boruta Optimization Algorithm (BOA) methods, implementing the comprehensive feature selection capability of Boruta to detect all relevant features and harnessing the strength of GA to bring about improvement within a wide range of datasets. …”
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Dual Gm-APD Polarization Lidar to Acquire the Depth Image of Shallow Semitransparent Media With a Wide Laser Pulse by Lu Xu, Xu Yang, Long Wu, Chenfei Jin, Yijia Zhang

“…An experiment of real shallow water layer with different depth features is also performed to verify the result.…”
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Unregistered Biological Words Recognition by Q-Learning with Transfer Learning by Fei Zhu, Quan Liu, Hui Wang, Xiaoke Zhou, Yuchen Fu

“…As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. …”
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ASM-VoFDehaze: a real-time defogging method of zinc froth image by Wenhui Xiao, Zhaohui Tang, Ce Yang, Wei Liang, Meng-Yen Hsieh

“…First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. …”
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Transient Voltage Unit Protection Based on Fault Information Fusion by Zhenwei Guo, Ruiqiang Zhao, Haojie Li, Yongyan Jiang

“…Transient voltage high-frequency instantaneous amplitudes are obtained and integrated using the Variational Mode Decomposition Hilbert Transform (VMD-HT) to increase the stability of high-frequency fault signal detection. This algorithm solved the original problem of 42.2% cross-data, 41.6% false operations, and 8.9% refusal to operate. …”
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Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy by Chenxiao Li, Jiatong Yu, Sheng Wang, Qinglong Zhao, Qian Song, Yanlei Xu

“…To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). …”
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A method based on clustering fast search for bearing performance degradation assessment by ShengWen Zhou, Li Zhang, Xiaoming Yang, Fan Xu, BaiGang Du, RuiPing Luo, Wenhui Zeng

“…Second, SV1 and SV2 are used as input to the CFS algorithm to identify the cluster centers automatically. …”
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Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors by Fernando Rodriguez, Philipp Krauss, Jonas Kluckert, Franziska Ryser, Lennart Stieglitz, Christian Baumann, Roger Gassert, Lukas Imbach, Oliver Bichsel

“…We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. …”
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Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM by Qunyan Jiang, Juying Dai, Faming Shao, Shengli Song, Fanjie Meng

“…Aiming at SVM parameter optimization problem, an improved chaos firefly optimization algorithm was proposed. Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.…”
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Identification of m6 A-regulated ferroptosis biomarkers for prognosis in laryngeal cancer by Xin Wang, Wen Zhang, Kun Liang, Yujuan Wang, Jin Zhang, Jinping Wang, An Li, Yonggang Yun, Hiu Liu, Yanan Sun

“…Additionally, qRT-PCR and western blot were performed to detect the TFRC, RGS4, and FTH1 expression. Three biomarkers were identified to build a risk model using the univariate Cox and LASSO regression algorithms. …”
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Research on fault localization method of valve area in converter station based on metric learning and knowledge reasoning by WEI Yun, LI Xianwei, ZHANG Yanlong, CAO Hui, XU Dan, QIU Junhong

“…A data dimensionality reduction network based on metric learning is constructed to achieve feature extraction of recorded electrical quantities by maximizing inter class distance and minimizing intra class distance. …”
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Two-Dimensional Correlation Infrared Spectroscopy for Rapid Analysis of the Effect of pH on the Secondary Structure Content of Soybean Protein Isolate by LIU Chang, WU Dandan, WANG Ning, WANG Ruiying, WANG Liqi, LIU Feng, YU Dianyu

“…Mean centralization (MC), multivariate scattering correction (MSC), standard normal variate transformation, and normalization were used for infrared data preprocessing. Feature wavebands were identified based on two-dimensional correlation infrared spectra, and predictive modeling of the secondary structure content of SPI against pH was performed using partial least squares (PLS) and arithmetic optimization algorithm-random forests (AOA-RF). …”
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IoT-driven smart assistive communication system for the hearing impaired with hybrid deep learning models for sign language recognition by Mashael Maashi, Huda G. Iskandar, Mohammed Rizwanullah

“…Finally, the attraction-repulsion optimization algorithm (AROA) adjusts the hyperparameter values of the CNN-BiGRU-A method optimally, resulting in more excellent classification performance. …”
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