RAM-ROM sebagai Pendukung Algoritma Zigzag Scan Menggunakan Metode Pemetaan pada Kompresi Citra Real-Time by Robby Candra

“…Guna mendukung konsep real-time diperlukan 2 komponen Random Access Memory (RAM) yang dapat menyimpan data sehingga tidak terdapat antrian data. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…To tackle the challenge of designing an improved diabetes classification algorithm that is more accurate, random oversampling and hyper‐tuning parameter techniques have been used in this study. …”
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e-GLR Score Predicts Early Graft Loss in Adult Live-Donor Liver Transplantation by Viniyendra Pamecha, MS, MRCS, FRCS, FEBS, Nilesh Sadashiv Patil, MS, MCh, Tharun Gattu, MS, Guresh Kumar, MSc, MPhil, PhD, Bramhadatta Pattnaik, MS, MCh, Nihar Mohapatra, MS, MCh, Gaurav Sindwani, MD, PDCC, Ashok Choudhury, MD, DM

“…A homogeneous group of 387 patients was analyzed: random cohort A (n = 274) for primary analysis and random cohort B (n = 113) for validation. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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Artificial intelligence for body composition assessment focusing on sarcopenia by Sachiyo Onishi, Takamichi Kuwahara, Masahiro Tajika, Tsutomu Tanaka, Keisaku Yamada, Masahito Shimizu, Yasumasa Niwa, Rui Yamaguchi

“…A cohort of 3096 cases undergoing CT imaging up to the third lumbar (L3) level between 2011 and 2021 were included. Random division into preprocessing and sarcopenia cohorts was performed, with further random splits into training and validation cohorts for BMI_AI and Body_AI creation. …”
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Hubungan Komunikasi Dokter–Pasien Terhadap Kepuasan Pasien Berobat Di Poliklinik RSUP DR. M. Djamil Padang by Tiara Wahyuni, Amel Yanis, Erly Erly

“…The design of study was cross-sectional sampling technique that is proportionate stratified random sampling with a total sample of 107 people. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Conclusion Machine learning algorithms, particularly Random Forest, are effective in predicting fall risk among hospitalized patients. …”
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Study of Fluid Flow Characteristics and Mechanical Properties of Aviation Fuel-Welded Pipelines via the Fluid–Solid Coupling Method by Changhong Guo, Mengran Di, Hanwen Gong, Jin Zhang, Shibo Sun, Kehua Ye, Bin Li, Lingxiao Quan

“…Furthermore, the numerical simulation results are compared and verified using modal and random vibration tests. This paper addresses the impact of diverse fluid characteristics on the velocity field, pressure field, and stress in disparate areas, and it also conducts an investigation into the random vibration characteristics of the pipeline. …”
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Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI by Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir, Brij B. Gupta, Akshat Gaurav, Kwok Tai Chui

“…It combines XGBoost and Random Forest with Logistic Regression in an Ensemble Learning Meta-Learning Model to identify ADS-B injection risks and categorise them. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…The top five most important features for classifying child or adolescent health were measures of an adult in the household, selected at random: BMI, obesity diagnosis, being single, seeking care at private healthcare, and having paid TV in the home. …”
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Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam by Hung Nguyen Trong, The Dung Nguyen, Martin Kappas

“…The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.…”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…As a result, Random Forest with 30 features selected by IGCS proved superior to any combination of classifiers and feature selection methods in malware detection, achieving 99.0% accuracy, recall, precision, and F1-Score. …”
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Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms by Susan Thapa, Lori A. Fischbach, Robert Delongchamp, Mohammed F. Faramawi, Mohammed S. Orloff

“…Using the training set, we used the random forest method to rank potential predictors based on their predictive importance. …”
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Predictive modeling of burnout dimensions based on basic socio-economic determinants in health service managers and support personnel in a resource-limited health center by Grey Castro-Tamayo, Mario Hernandez-Tapia, Ivan David Lozada-Martinez, Ivan Portnoy, Jessica Manosalva-Sandoval, Tobías Parodi-Camaño

“…Statistical analyses included correlation tests and predictive models using random forest models to identify significant associations and cast predictions.ResultsA total of 76 participants were included. …”
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Efficacy of virtual reality techniques in cardiopulmonary resuscitation training: protocol for a meta-analysis of randomised controlled trials and trial sequential analysis by Jia Wang, Lu Zhang, Guo Chen, Li Du, Jianqiao Zheng, Xiaoqian Deng

“…Data will be synthesised by either fixed-effects or random-effects models according to the I2 value. Trial sequential analysis and modified Jadad Scale will be used to control the risks of random errors and evaluate the evidence quality. …”
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Research on water and fertilizer irrigation system of tea plantation by Xuetao Jia, Ying Huang, Yanhua Wang, Daozong Sun

“…Packet loss rate values in diamond deployment are lower than those in random deployment. In order to improve the accuracy of tea deficiency detection, data fusion technology was adopted. …”
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Prevalence and pattern of rheumatic valvular heart disease in Africa: Systematic review and meta-analysis, 2015-2023, population based studies. by Seid Mohammed Abdu, Altaseb Beyene Kassaw, Amare Abera Tareke, Gosa Mankelkl, Mekonnen Belete, Mohammed Derso Bihonegn, Ahmed Juhar Temam, Gashaw Abebe, Ebrahim Msaye Assefa

“…The pooled estimate of the prevalence of rheumatic heart disease was computed by a random effects model.<h4>Results</h4>Out of 22 population-based studies analyzed using random-effects, the pooled magnitude of rheumatic heart disease was found to be 18.41/1000 (95% CI: 14.08-22.73/1000). …”
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Library Facilities, Effective Teaching and Learning of Christian Religious Education in Selected Secondary Schools in Ny Akishenyi Sub-County Rukungiri District, Uganda. by Niwatuha, Jackline

“…The researcher used both purposive and simple random sampling. The schools were selected using Purposive sampling to achieve the desired objectives and the students were selected using random sampling to ensure partiality. …”
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Economic Background of Undergraduate Students and their Academic Performance at Kabale University. by Kyarikunda, Miria

“…The target population of the study consisted of students of kabale university mainly from four randomly chosen faculties. The samples were selected using simple random sampling, purposeful sampling and strati ed random sampling. …”
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Assessing the Impact of Inflation on Household Consumption in Kisoro District: A Case Study of Ny Akabande Subcounty by Nzabanita, Alikanjero

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