Global Dense Vector Representations for Words or Items Using Shared Parameter Alternating Tweedie Model by Taejoon Kim, Haiyan Wang

“…We present the Shared Parameter Alternating Tweedie (SA-Tweedie) model and an algorithm to estimate the parameters. We introduce a learning rate adjustment used along with the Fisher scoring method in the inner loop to help the algorithm stay on track with optimizing direction. …”
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An Optimized Capacitor Voltage Balancing Control Strategy of Modular Multilevel Converter Based on Comparison of Variable Reference Values by Fengyang GAO, Guodong QIANG, Yunbo GAO, Guoheng ZHANG

“…The number of current input modules is calculated by the nearest level modulation, then the reference value is adjusted according to the number of input modules, and based on this reference value, the sub-module capacitor voltage sequences are divided into two groups. At the same time, the random scramble algorithm is used to sort the elements in the group. …”
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Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data by Bastian Engelmann, Anna-Maria Schmitt, Moritz Heusinger, Vladyslav Borysenko, Niklas Niedner, Jan Schmitt

“…The best results for the F1 score were achieved with the Random Forest, the CatBoost, and the Extra Trees algorithm (2-phases: 99.4–99.7%, 6-phases: 85.2–85.9%, 23-phases: 77.7–79.4%). …”
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Trustworthy and efficient project scheduling in IIoT based on smart contracts and edge computing by Peng Liu, Xinglong Wu, Yanjun Peng, Hangguan Shan, Saïd Mahmoudi, Bong Jun Choi, Haksrun Lao

“…Subsequently, the solution integrates combinatorial auction with random sampling (CA-RS) into smart contracts. Alongside security analysis, simulations are conducted using real data sets. …”
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Computer analysis shows differences between mitochondrial miRNAs and other miRNAs by P. S. Vorozheykin, I. I. Titov

“…To identify the most pronounced characteristics of mitochondrial miRNAs that distinguish them from other miRNAs, we classified mitomiR sequences using the Random Forest algorithm. The analysis revealed, for the first time, a significant difference between mitomiRs and other microRNAs by the following criteria (in descending order of importance in the classification): mitomiRs are evolutionarily older (have a lower phylostratigraphic age index, PAI); have more targets and disease associations, including mitochondrial ones (twosided Fisher’s exact test, average p-values 1.82×10–89/1.13×10–96 for all mRNA/diseases and 6.01×10–22/1.09×10–9 for mitochondria, respectively); and are in the class of “circulating” miRNAs (average pvalue 1.20×10–56). …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). …”
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Adaptive and Asymmetrical Blending for Bi-Directional Inter Prediction of Various Visual Contents in Video Coding by Minhun Lee, Donggyu Sim

“…Experimental results demonstrate that the algorithm achieves a Bjøntegaard Delta bitrate (BD-rate) savings of 0.86% and 0.50% for low-delay B and random-access configurations, respectively, compared to VVC. …”
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Identifying cell types from spatially referenced single-cell expression datasets. by Jean-Baptiste Pettit, Raju Tomer, Kaia Achim, Sylvia Richardson, Lamiae Azizi, John Marioni

“…To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. …”
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Chaotic Mountain Gazelle Optimizer Improved by Multiple Oppositional-Based Learning Variants for Theoretical Thermal Design Optimization of Heat Exchangers Using Nanofluids by Oguz Emrah Turgut, Mustafa Asker, Hayrullah Bilgeran Yesiloz, Hadi Genceli, Mohammad AL-Rawi

“…A two-step improvement procedure is implemented to enhance the overall search efficiency of the original algorithm. The first step concerns substituting uniformly random numbers with chaotic numbers to refine the solution quality to better standards. …”
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Untying the knot: Unraveling genetic mechanisms behind black knot disease resistance in Prunus salicina (Japanese plum) by Chloe Shum, Mohsen Najafabadi, Maxime deRonne, Davoud Torkamaneh, Walid El Kayal, Jayasankar Subramanian

“…Population stratification identified four subpopulations, and the Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm was used for this analysis. …”
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Validation of eight endotypes of lupus based on whole-blood RNA profiles by Peter E Lipsky, Prathyusha Bachali, Amrie C Grammer, Erika Hubbard

“…Objective We previously described a classification system of persons with SLE based on whole blood RNA profiles and a random forest (RF) algorithm to predict individual patient endotypes. …”
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An efficient medical data encryption scheme using selective shuffling and inter-intra pixel diffusion IoT-enabled secure E-healthcare framework by Dhivya Ravichandran, W. Sylvia Lilly Jebarani, Hemalatha Mahalingam, Padmapriya Velupillai Meikandan, Padmapriya Pravinkumar, Rengarajan Amirtharajan

“…The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. …”
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Exosomal microRNA signatures in youth at clinical high risk for bipolar disorder by Xinyu Meng, Shengmin Zhang, Yingzhen Xu, Zaohui Ma, Shuzhe Zhou, Yantao Ma, Hong Ma, Xin Yu, Lili Guan

“…Exosomal small RNA sequencing was undertaken in the plasma sample of the participants. …”
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MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer by Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu

“…Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. …”
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Exploring the comorbidity mechanisms of ITGB2 in rheumatoid arthritis and membranous nephropathy through integrated bioinformatics analysis by Wenlong Cao, Yuqin Wang, Jing Xiong

“…Topology analysis and the random forest algorithm were applied to identify hub genes. …”
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The Key Role and Mechanism of Oxidative Stress in Hypertrophic Cardiomyopathy: A Systematic Exploration Based on Multi-Omics Analysis and Experimental Validation by Sijie Zhang, Tianzhi Li, Shiyi Sun, Yujiao Jiang, Yuxin Sun, Yan Meng

“…Four oxidative stress-related characteristic genes (<i>DUSP1</i>, <i>CCND1</i>, <i>STAT3</i>, and <i>THBS1</i>) were identified using LASSO regression, SVM-RFE, and Random Forest algorithms. Their functional significance was validated by immune infiltration analysis, drug prediction using the cMAP database, and molecular docking. …”
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The SPN Network for Digital Audio Data Based on Elliptic Curve Over a Finite Field by Ijaz Khalid, Tariq Shah, Khalid Ali Almarhabi, Dawood Shah, Muhammad Asif, M. Usman Ashraf

“…For the diffusion property, the scheme generates pseudo-random number sequences used for block permutation and achieves the property of diffusion. …”
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Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation by Sixuan Wu, Yuanbin Tang, Qihong Pan, Yaqin Zheng, Yeru Tan, Junfan Pan, Yuehua Li

“…In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. …”
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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Then, the relation matrix is constructed by applying random matrix theory. After that, the reward principle is defined for computing the Q value of Q-learning based on weight ranking, relation matrix, and the number of errors, according to which a convolutional neural network model is trained on datasets until the sequences of metric pairs are generated for all datasets acting as the revised feature set. …”
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A longitudinal investigation of gut microbiota dynamics in laying hens from birth to egg-laying stages by Seojin Choi, Eun Bae Kim

“…DNA was extracted from the samples and the V4 region of the 16S rRNA gene was sequenced using an Illumina MiSeq platform. The Random Forest algorithm was applied to identify microbial predictors and explore their relationships with age. …”
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