MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

“…The proposed hybrid model refines predictions from traditional tracking methods using machine learning to enhance performance. …”
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Integrative Analysis of Neutrophil-Associated Genes Reveals Prognostic Significance and Immune Microenvironment Modulation in Cervical Cancer by Ting Hu, Haijing Wu, Xinghan Cheng, Haoyue Gao, Min Yang

“…High-risk patients exhibited an immunosuppressive tumor microenvironment, elevated TIDE scores, and lower predicted responsiveness to immunotherapy. SEMA6B was significantly downregulated in the tumour group but may be reactivated during metastasis. …”
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Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method by Jiawei Zhou, Junhao Wen, Liwen Yao, Zidong Yang, Lijun Xu, Lijian Yao

“…The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. …”
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Host Circulating Immunometabolism-Associated Biomarkers for Early Diagnosis of Active Tuberculosis: Multi-Omics Screening with Experimental Validation by Yang Z, Dong Y, Shang Y, Li H, Ren W, Li S, Pang Y

“…Four metabolism-related hub genes (GCH1, GK, MTHFD2, and SLC7A6) were identified using machine learning algorithms for the diagnosis of active TB with high accuracy and verified using external datasets. …”
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Spatial-Spectral Linear Extrapolation for Cross-Scene Hyperspectral Image Classification by Lianlei Lin, Hanqing Zhao, Sheng Gao, Junkai Wang, Zongwei Zhang

“…Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. …”
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Treatment of infections in young infants in low- and middle-income countries: a systematic review and meta-analysis of frontline health worker diagnosis and antibiotic access. by Anne C C Lee, Aruna Chandran, Hadley K Herbert, Naoko Kozuki, Perry Markell, Rashed Shah, Harry Campbell, Igor Rudan, Abdullah H Baqui

“…For study question 1, meta-analysis showed that clinical sign-based algorithms predicted bacterial infection in young infants with high sensitivity (87%, 95% CI 82%-91%) and lower specificity (62%, 95% CI 48%-75%) (six studies, n = 14,254). …”
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Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. …”
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Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records by Ioannis Koutroulis, Tom Velez, Tony Wang, Seife Yohannes, Jessica E. Galarraga, Joseph A. Morales, Robert J. Freishtat, James M. Chamberlain

“…Using feature sets used in related clustering studies, LCA and K‐means algorithms were used to derive 4 distinct phenotypic pediatric sepsis segmentations. …”
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Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4 by Baohua Zhu, Ziyang Mo, Yi Bao, Xinxin Gan, Linhui Wang

“…Ten machine learning algorithms and their 101 combinations were used to analyze the prognostic significance of acetylation-related differentially expressed genes (DEGs) and to construct a prognostic risk model. …”
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Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis by Yaling Wang, Peichun HSU, Haiyan Hu, Feng Lin, Xiaokang Wei

“…The intersecting genes were identified from weighted gene co-expression network analysis (WGCNA), DEGs (osteosarcoma vs. normal) and DE-AAMRGs (high- vs. low-score). An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. …”
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Wide-angle deep ultraviolet antireflective multilayers via discrete-to-continuous optimization by Kim Jae-Hyun, Kim Dong In, Lee Sun Sook, An Ki-Seok, Yim Soonmin, Lee Eungkyu, Kim Sun-Kyung

“…To date, various optimization algorithms have been used to design non-intuitive photonic structures with unconventional optical performance. …”
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Multi-omics analysis of the effects of pla2g4a on the prognosis of various cancers and its experimental validation in breast cancer cell lines by Yao Qian, Quan Yuan, Hao Yu, Rongjie Ye, Ming Niu, Feng Liu

“…The function of PLA2G4A in BC was validated through experiments, and its drug response and molecular docking were predicted using various databases and software tools. …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). …”
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Integrating microplastic research in sustainable agriculture: Challenges and future directions for food production by Marcelo Illanes, María-Trinidad Toro, Mauricio Schoebitz, Nelson Zapata, Diego A. Moreno, María Dolores López-Belchí

“…Furthermore, machine learning algorithms can be employed to analyze complex datasets, enhancing our ability to predict the impacts of MPs on plant health and crop performance under different environmental conditions. …”
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MORPHOLOGICAL VARIABILITY OF LEAVES OF ACER NEGUNDO L. POPULATIONS IN THE ALTITUDINAL GRADIENT OF THE NORTH-WEST CAUCASUS (REPUBLIC OF ADYGEA) by Evgenia M. Ednich, Irina V. Chernyavskaya, Tatyana N. Tolstikova, Mariet N. Khagur, Marat V. Aliev

“…Understanding the adaptive strategies of invasive species, including Аcer negundo L. based on morphological mechanisms of adaptation in the altitude gradient is relevant for predicting of A. negundo behavior in the floodplain forests of Adygea and will be a basis for further study of A. negundo invasiveness in the North-West Caucasus. …”
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Using artificial intelligence and promoter-level transcriptome analysis to identify a biomarker as a possible prognostic predictor of cardiac complications in male patients with Fa... by Hiroshi Kobayashi, Norio Nakata, Sayoko Izuka, Kenichi Hongo, Masako Nishikawa

“…Cardiac complications, such as cardiomyopathy, cardiac muscle fibrosis, and severe arrhythmia, are the most common mortality causes in patients with Fabry disease. To predict cardiac complications of Fabry disease, we extracted RNA from the venous blood of patients for cap analysis of gene expression (CAGE), performed likelihood ratio tests for each RNA expression dataset obtained from individuals with and without cardiac complications, and analyzed the correlation between cardiac functional factors observed using magnetic resonance imaging data extracted using artificial intelligence algorithms and RNA expression. …”
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Experimental Research on an Active Sting Damper in a Low Speed Acoustic Wind Tunnel by Jinjin Chen, Xing Shen, Fanfan Tu, Ehtesham Mustafa Qureshi

“…For sting mode only, 95% reduction of displacement response under exciter stimulation and 98% energy elimination of sting mode frequency have been achieved.…”
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Enhanced Moisture Retrieval Near Boundary Layer From Satellite Sounder Data Through Atmospheric‐Surface Radiance Separation by Ronglian Zhou, Di Di, Jun Li, Zhenglong Li

“…A modified one‐dimensional variational algorithm for surface‐free radiances is also developed. …”
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Research on obstacle avoidance for aerial work vehicle booms with light arrays based on an improved gradient projection method by Yi HUANG, Lei DENG, Kangyi DENG, Qin SHU, Guangan REN, Zhihong ZHOU

“…At the same time, the end position error reduction algorithm is added to reduce the end velocity and improve the trajectory tracking accuracy. …”
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Assessing carbon footprint and performance of the Kalman filter track fitter in the CBM experiment by Grigory Kozlov, Grigory Kozlov, Ivan Kisel, Ivan Kisel, Ivan Kisel, Ivan Kisel

“…We evaluate the performance and energy efficiency of the Kalman Filter-based fitting algorithm on both CPUs and GPUs. The GPU implementation demonstrates up to a threefold improvement in energy efficiency, resulting in a proportional reduction in power consumption and associated CO2 emissions during data processing. …”
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