Diagnosis of Malignant Endometrial Lesions from Ultrasound Radiomics Features and Clinical Variables Using Machine Learning Methods by Shanshan Li, Jiali Wang, Li Zhou, Hui Wang, Xiangyu Wang, Jian Hu, Qingxiu Ai

“…Six common machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Tree, and k-Nearest Neighbors, were employed to identify benign and malignant changes in endometrial tissue. …”
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Analisis Efek Augmentasi Dataset dan Fine Tune pada Algoritma Pre-Trained Convolutional Neural Network (CNN) by Theopilus Bayu Sasongko, Haryoko Haryoko, Agit Amrullah

“…The amount of increase in accuracy after random erase or zoom range augmentation that occurs is about 0.03% to 0.1%. …”
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Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations by Jihong Sun, Peng Tian, Zhaowen Li, Xinrui Wang, Haokai Zhang, Jiangquan Chen, Ye Qian

“…Experimental results indicate that the random forest model performs best after individual machine learning modeling, with RMSE, R², and MAPE values of 0.2777, 0.9062, and 17.04%, respectively. …”
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Machine learning methods of satellite image analysis for mapping geologic landforms in Niger: A comparison of the Aïr mountains, Niger River basin and Djado Plateau by Lemenkova Polina

“…Data were processed by scripts using ML algorithms by modules r.random, r.learn.train, r.learn.predict, i.cluster, and i.maxlik. …”
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Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks by Marjan Radi, Behnam Dezfouli, Kamalrulnizam Abu Bakar, Shukor Abd Razak

“…As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. …”
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Application of Improved Deep Learning Method in Intelligent Power System by HuiJie Liu, Yang Liu, ChengWen Xu

“…The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. The results showed the following: in the power load forecast results over the weekend, the random forest and the LSTM algorithm forecast results were relatively close and the RMSEs were 17.3 and 17.1, respectively, while the SVM predicted a larger RMSE error of 27.5. …”
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A Novel Method to Forecast Nitrate Concentration Levels in Irrigation Areas for Sustainable Agriculture by Halil Karahan, Müge Erkan Can

“…The proposed model, although based on an artificial neural network (ANN), also has the potential to be adapted for methods used in machine learning and artificial intelligence, such as Support Vector Machines, Decision Trees, Random Forests, and Ensemble Learning Methods.…”
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Correlation of Knowledge and Public Attitude With Vaccination Covid-19 Injection at Sub-Discrit Tuwelei of Tolitoli Regency by Herawati, Eka Prasetia Hati Baculu

“…Sample selection In this research used proportionate random sampling technique.sample selected in this research used proportionate random sampling technique. …”
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Relationships between Loneliness and Occupational Dysfunction in Community-Dwelling Older Adults: A Cross-Sectional Study by Daiki Nakashima, Keisuke Fujii, Yuta Kubo, Kyosuke Yorozuya

“…Bayesian statistics with the dependent variable as “loneliness” showed that the best model used “occupational dysfunction” as the independent variable and included confounding factors and random effects (WAIC=474.5 and WBIC=213.1). The best model identified an association between occupational dysfunction and loneliness (odds ratio OR=2.363; 95% Bayesian confidence interval CI=1.105–5.259). …”
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A novel wind power forecast diffusion model based on prior knowledge by Li Han, Yingjie Cheng, Shuo Chen, Shiqi Wang, Junjie Wang

“…Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. …”
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Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data by Olaotse Lokwalo Thabeng, Elhadi Adam, Stefania Merlo

“…The guided regularised random forest (GRRF) was used to identify important wavelengths for the discrimination of abovementioned archaeological and nonarchaeological soils. …”
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Behavior of Overcoming graduate degree in nursing Cienfuegos by Vladimir Barco Díaz, Zoraida Caridad Álvarez Figueredo, Yusimy Miranda Pérez, Juan Carlos Alvarado Peruyero, Esteban Jesús Sánchez Martínez

“…<strong><br />Methods</strong>: A retrospective descriptive study of 72 nursing graduates working in Primary Health Care, selected by random combination, first each of the areas of health was considered a stratum and then the professionals were selected by simple random method. …”
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Research on TCN Model Based on SSARF Feature Selection in the Field of Human Behavior Recognition by Wei Zhang, Guibo Yu, Shijie Deng

“…To overcome this problem, this paper investigates a temporal convolutional neural network (TCN) model based on improved sparrow search algorithm random forest (SSARF) feature selection to accurately identify human behavioral traits based on wearable devices. …”
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Reliability Estimation of Inverse Lomax Distribution Using Extreme Ranked Set Sampling by Amer Ibrahim Al-Omari, Amal S. Hassan, Naif Alotaibi, Mansour Shrahili, Heba F. Nagy

“…In this study, the estimation of R=P Y<X is investigated when the stress and strength random variables are independent inverse Lomax distribution. …”
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The role of firm-level factors and regional innovation capabilities for Polish SMEs by Lukasz Arendt, Wojciech Grabowski

“…By deploying a regional random effects approach, we assessed indirectly the effectiveness of innovation policies conducted in Polish NUTS 2 regions within a RIS and S3 framework. …”
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Investigation of The Relationship Between Misconceptions and Answering Behaviors on The Example of The Mathematical Literacy by Emre Kucam, Ergül Demir

“…When answeringbehaviors are analyzed according to the types of misconceptions, it isdetermined that the random marking is the most used in the type ofover-specialization misconceptions and in the case of the other misconceptionstypes, the most random marking answering behaviors are used. …”
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The Influence of mangrove arrangement on wave transmission using smoothed particle hydrodynamics by Trimulyono Andi, Ardi Setiawan Dikky, Samuel, Arswendo Adietya Berlian, Budi Santosa Ari Wibawa, Tuswan

“…Three variations of mangrove trees are used,i.e., uniform 0.3 m, random 0.3 m, and random 0.15 m. The piston wavemaker is based on a previous study that has been validated with experiments. …”
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An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems by Ebru Efeoğlu

“…The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system.…”
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Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures by Bader Huwaimel, Jowaher Alanazi, Muteb Alanazi, Tareq Nafea Alharby, Farhan Alshammari

“…These algorithms include Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB). …”
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A Small Tamper-Resistant Anti-Recycling IC Sensor With a Reused I/O Interface and DC Signalling by Alexandros Dimopoulos, Mihai Sima, Stephen W. Neville

“…Combining this sensor with a random sample-based testing strategy allows for low-cost and time efficient detection of fraudulently recycled batches of ICs. …”
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