BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…To optimize feature space, five separate machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), Extreme Learning Machine (ELM), AdaBoost, and XGBoost, were applied as the base learners. …”
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Knowledge, Attitude, and Associated Factors towards Colostrum Feeding among Antenatal Care Attendant Mothers in Gununo Health Centre, Wolaita Zone, Ethiopia 2019: Cross-Sectional S... by Addisu Yeshambel Wassie, Natnael Atnafu Gebeyehu, Kelemu Abebe Gelaw

“…Data was collected by using structured interviewer questionnaires and the subjects were selected through systematic random sampling. Data template was prepared by Epi data-manger version 4.2 and SPSS version 23 was used for analysis. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…Among the 11 machine learning models, the random forest classifier performed best on multiple evaluation metrics in predicting survival at 1, 2, and 3 years. …”
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Exploring the Functional Brain Activity of Motor Control of Abdominal Muscle Associated with Voluntary Voiding Based on Functional Near-Infrared Spectroscopy by SHI Yinshuang, WANG Menghuan, PAN Yingying, LIN Feng

“…Compared to the non-urination state, stronger activation of the left dorsolateral prefrontal cortex (BA46) was present in the strong urination state (<italic>P</italic>&lt;0.05).ConclusionThe primary somatosensory cortex, primary motor cortex, and pre-motor/supplementary motor area are jointly involved in the regulation of voluntary urination-associated random abdominal muscle contractions. And further activation of the prefrontal cortex can be induced during abdominal muscle contractions in the strong urination state, suggesting that the prefrontal cortex plays a positive role in facilitating abdominal muscle movements and initiating urination following the perception of urinary awareness.…”
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Clinical Features and Risk Factors of Active Tuberculosis in Patients with Behçet’s Disease by Yaxu Liu, Lifan Zhang, Ziyue Zhou, Luxi Sun, Baotong Zhou, Xiaoqing Liu, Wenjie Zheng

“…The control group was selected by random number sampling from the remaining BD patients, including those with latent tuberculosis infection, previous tuberculosis, or without tuberculosis. …”
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RAGE and its ligands in breast cancer progression and metastasis by Madalina Coser, Bogdan Mihai Neamtu, Bogdan Mihai Neamtu, Bogdan Pop, Bogdan Pop, Calin Remus Cipaian, Calin Remus Cipaian, Maria Crisan, Maria Crisan

“…We conducted a meta-analysis to assess the relationship between the RAGE rs1800624 polymorphism and breast cancer risk using fixed-effect or random-effect models to calculate odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs).ResultsRAGE upon activation by its ligands enhances downstream signaling pathways, contributing to breast cancer cells migration, growth, angiogenesis, metastasis, and drug resistance. …”
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Digital sleep phenotype and wrist actigraphy in individuals at clinical high risk for psychosis and people with schizophrenia spectrum disorders: a systematic review and meta-analy... by Andrea Cipriani, Edoardo Giuseppe Ostinelli, John Torous, Paolo Brambilla, Philip McGuire, Dominic Oliver, Rosario Aronica, Charlotte Austin

“…Primary outcome was total sleep time (TST), with secondary outcomes including time in bed (TIB), sleep latency, sleep efficiency, wake after sleep onset, nighttime awakenings and self-reported sleep quality. Random-effects pairwise meta-analyses were used to summarise the effects of each outcome.Results Nineteen studies were included, with 18 contributing to the meta-analyses (202 CHR-P, 584 SSD, 582 HC). …”
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Special Education Combined with Sensory Integration Training for Children with Autism Spectrum Disorder by Ya SHAN, Min ZHU

“…Objective:To investigate the clinical effect of special education combined with sensory integration training on children with autism spectrum disorder (ASD).Methods:A total of 100 ASD children in the department of rehabilitation medicine, Children's Hospital affiliated Nanjing Medical University were randomly assigned to the special sensation group and the single special group by random number table method, with 50 cases in each group. …”
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Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study by Amirreza Sadeghinasab, Jafar Fatahiasl, Marziyeh Tahmasbi, Sasan Razmjoo, Mohammad Yousefipour

“…Applied machine learning models included support vector machine (SVM), random forest (RF), K‐nearest neighbors (KNN), AdaBoost, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), Naïve Bayes (NB) and logistic regression (LR). …”
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Coğrafya Öğretiminde Coğrafi Sorgulama Becerisinin Öğrencilerin Tutum Ve Başarıları İle Öğrenmenin Kalıcılığı Üzerine Etkisi by Recep Bozyiğit, Dilek Akça

“…Sınıf seviyesi öğrencilerinden random yöntemiyle seçilen 30 öğrenciden oluşan bir deney grubu ve yine 30 öğrenciden oluşan bir kontrol grubu üzerinde gerçekleştirilmiştir. …”
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Goiter and Its Associated Factors among Adolescent High School Girls at Tach Armachiho District, Northwest Ethiopia: An Institution-Based Cross-Sectional Study by Belaynew Mesfin, Bisrat Misganaw, Melkamu Tamir Hunegnaw, Esmael Ali Muhammad

“…A total of 620 high school adolescent girls were selected using the simple random sampling technique, and structured questionnaires having observational check-list were used for data collection. …”
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Contradictions of students’ motivation for participation in the activities of voluntary organisations by A. V. Starshinova

“…The sample (N = 207) was formed using stratified random sampling. The quantitative research strategy included such tools as drawing up a portrait of a volunteer based on projective questions, using the method of unfinished sentences, the method of direct ranking of motives. …”
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Rapid Increase in Ownership and Use of Long-Lasting Insecticidal Nets and Decrease in Prevalence of Malaria in Three Regional States of Ethiopia (2006-2007) by Estifanos Biru Shargie, Jeremiah Ngondi, Patricia M. Graves, Asefaw Getachew, Jimee Hwang, Teshome Gebre, Aryc W. Mosher, Pietro Ceccato, Tekola Endeshaw, Daddi Jima, Zerihun Tadesse, Eskindir Tenaw, Richard Reithinger, Paul M. Emerson, Frank O. Richards, Tedros Adhanom Ghebreyesus

“…Each survey used multistage cluster random sampling with 25 households per cluster. Household net ownership tripled from 19.6% in 2006 to 68.4% in 2007, with mean LLIN per household increasing from 0.3 to 1.2. …”
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A New Grazing–Vegetation Tradeoff and Coordination Indicator: The Grazing Intensity and Vegetation Cover Harmonization Index (GVCI) by Qinyi Huang, Jianjun Chen, Xinhong Li, Hucheng Li, Zizhen Chen, Yanping Lan, Ming Ling, Haotian You, Xiaowen Han

“…The GVCI was used to quantify the “Harmonization and Conflict” status between GI and the VE in 39 Prefecture-Level Cities (PLCs) of the Qinghai-Tibet Plateau (QTP) and to evaluate the sustainable development level of grazing in different regions. In addition, the Random Forest (RF) model was used to simulate the GVCI development trend of various PLCs from 2015 to 2040. …”
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Pengaruh Ciri Temporal, Spasial, dan Frekuensi pada Klasifikasi Motor Imagery by Afin Muhammad Nurtsani, Muhammad Adib Syamlan, Agung Wahyu Setiawan

“…Keywords: motor imagery, temporal, spatial, frequency, random forest …”
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Evaluating digital literacy of health professionals in Ethiopian health sectors: A systematic review and meta-analysis. by Alex Ayenew Chereka, Agmasie Damtew Walle, Sisay Yitayih Kassie, Adamu Ambachew Shibabaw, Fikadu Wake Butta, Addisalem Workie Demsash, Mekonnen Kenate Hunde, Abiy Tassew Dubale, Teshome Bekana, Gemeda Wakgari Kitil, Milkias Dugassa Emanu, Mathias Nega Tadesse

“…The pooled effect size of each trial is evaluated using a random effect model meta-analysis, which provides a 95% confidence interval.…”
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Prevalence of COVID-19 Vaccine Hesitancy Among Healthcare Workers in Nigeria: A Systematic Review and Meta-Analysis by Taagbara Jolly Abaate, Dabota Yvonne Buowari, Utchay A. Agiri, Tamunodiepiriye Inimgba, Vivian Ifeoma Ogbonna, Chizaram Onyeaghala, Glory Ovunda Worgu, Abiye Somiari, Emmanuella I. Ezebuiro, Ibe Arthur Onuah

“…A single-arm meta-analysis was performed using a random-effects model.ResultsOf the 206 articles, 22 publications with 20,724 participants were included. …”
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Post-COVID pain and quality of life in COVID-19 patients: protocol for a meta-analysis and systematic review by Yongxing Xu, Jianwen Gu, Mengrong Miao, Yitian Yang, Pule Li, Mengqi Jia, Zhaoyu Wen, Mengmeng Yu, Jiaqiang Zhang

“…DerSimonian and Laird random-effects models will be used to calculate the pooled relative risks. …”
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The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context by M. Staudinger, M. Kauzlaric, A. Mas, G. Evin, B. Hingray, D. Viviroli

“…After routing the simulated runoff, we analyzed the important patterns and drivers of extreme flooding at the outlet of the Aare River basin using a random forest. The different return period classes had distinct key predictors and showed specific spatial patterns of antecedent conditions in the sub-catchments, leading to different degrees of extreme flooding. …”
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Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe by Devara P. Adiningrat, Andrew Skidmore, Michael Schlund, Tiejun Wang, Haidi Abdullah, Marco Heurich

“…To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. …”
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