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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USE OF METHODOLOGICAL TASKS IN PROFESSIONAL TRAINING OF FUTURE TEACHERS OF INFORMATICS by Nataliia S. Pavlova

“…The methodological task integrates the main elements of methodological activity concerning planning and designing of educational process and its management by the teacher, and also predicts educational and cognitive actions of pupils. …”
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Develoment of The Computer Simulation of Oscillation in Physics Learning by Y Sumardi, A F Amalia, U N Prabowo

“…The use of simulations within teaching process had a positive impact on students’ being able to predict and explain the phenomena. It also designed to provide possibility to explore physics phenomenon which cannot be realized in school conditions.  …”
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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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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

“…The LASSO + RSF combination model performed best, and the model could accurately predict patient prognosis. The survival of patients in the high-risk group was significantly worse than that in the low-risk group. …”
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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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Sustainable Innovation: Harnessing AI and Living Intelligence to Transform Higher Education by Hesham Mohamed Allam, Benjamin Gyamfi, Ban AlOmar

“…AI-driven solutions can help optimize energy use, predict maintenance needs, and reduce waste, all contributing to a smaller environmental footprint. …”
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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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Efficient Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Joint Optimization Approach by Moein Valitabar, Mohammad Fathi, Keivan Navaie

“…Performance evaluation results demonstrate the effectiveness of these algorithms, achieving significant reductions in total energy consumption while maximizing the efficiency of communication and computational resources. …”
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Molecular characterization and prognostic modeling associated with M2-like tumor-associated macrophages in breast cancer: revealing the immunosuppressive role of DLG3 by Ziqiang Wang, Jing Zhang, Huili Chen, Xinyu Zhang, Kai Zhang, Feiyue Zhang, Yiluo Xie, Hongyu Ma, Linfeng Pan, Qiang Zhang, Min Lu, Hongtao Wang, Chaoqun Lian

“…Risk scores can effectively predict the prognosis and immunotherapy response of BC patients, in which DLG3 is a key gene that may be involved in shaping the immunosuppressive microenvironment of breast cancer, and down-regulation of DLG3 can inhibit M2 polarization of macrophages.DiscussionWe constructed and comprehensively solved a model of M2-like TAM-related molecular subtypes and prognosis, which helps stratify and customize treatment regimens for BC patients. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …”
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Evaluation of the shielding initiative in Wales (EVITE Immunity): protocol for a quasiexperimental study by Stephen Jolles, Ashley Akbari, Andrew Carson-Stevens, Helen Snooks, Alan Watkins, Adrian Edwards, Ann John, Alison Porter, Victoria Williams, Bridie Angela Evans, Ronan Lyons, Bernadette Sewell, Mark Rhys Kingston, Tony Whiffen, Jane Lyons, Rowena Bailey, Catherine A Thornton, Lesley Bethell, Samantha Bufton, Lucy Dixon

“…Introduction Shielding aimed to protect those predicted to be at highest risk from COVID-19 and was uniquely implemented in the UK during the COVID-19 pandemic. …”
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Causes of embryo implantation failure: A systematic review and metaanalysis of procedures to increase embryo implantation potential by Francesco M. Bulletti, Romualdo Sciorio, Alessandro Conforti, Roberto De Luca, Carlo Bulletti, Antonio Palagiano, Marco Berrettini, Giulia Scaravelli, Roger A. Pierson

“…Subsequent studies ought to concentrate on modulating endometrial responses immunologically and developing algorithms to improve the precision of predicting implantation success; as well as the timing of endometrial receptivity and the occurrence of dormant embryo phenomena also warrants further investigation.…”
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Investigating Transcriptional Age Acceleration in Inflammatory Skin Diseases by Richie Jeremian, Melissa Galati, Rayyan Fotovati, Kaiyang Li, Carolyn Jack, David O. Croitoru, Stephan Caucheteux, Philippe Lefrançois, Vincent Piguet

“…Five of 7 transcriptional clocks demonstrated moderate-to-strong accuracy in predicting age across groups (patients with atopic dermatitis: ρ = 0.40–0.86, those with hidradenitis suppurativa: ρ = 0.46–0.74, those with plaque psoriasis: ρ = 0.50–0.80, healthy subjects: ρ = 0.32–0.60; P < .05). …”
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Pre-treatment tumour PET metrics and clinical outcomes of anal cancer in patients living with and without HIV by Michael Pennock, N. Patrik Brodin, Christian Velten, Megi Gjini, Nitin Ohri, Chandan Guha, Shalom Kalnicki, Wolfgang A. Tome, Madhur K. Garg, Rafi Kabarriti

“…Pre-treatment PET metrics were validated as significantly predicting outcomes for the entire cohort and HIV-negative patients, not PLWH. …”
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