Machine and Deep Learning Models for Stress Detection Using Multimodal Physiological Data by Eman Abdelfattah, Shreehar Joshi, Shreekar Tiwari

“…Traditional machine learning algorithms, including Random Forest, Extra Trees, and XGB Classifiers, on the other hand, each achieved an F1 score of 99% when trained and tested on the data for the same set of subjects. …”
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Deep Reinforcement Learning-Based Task Partitioning Ratio Decision Mechanism in High-Speed Rail Environments with Mobile Edge Computing Server by Seolwon Koo, Yujin Lim

“…When it is 50, there is no significant difference from <i>random</i> and about 2% improvement compared to <i>no_partition</i>. …”
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DETERMINANTS OF CORPORATE TAX AGGRESSIVENESS IN NIGERIAN DEPOSIT MONEY BANKS by Bilikisu Roseline Yusuf, Samson Adewale Adediran

“…Diɑgnostic tests were equɑlly cɑrried out such ɑs ShɑpiroWilk Test for normɑlity, Vɑriɑnce Inflɑtion Fɑctor (VIF) for multicollineɑrity test, Breusch-Pɑgɑn / Cook-Weisberg test for heteroskedɑsticity, Breusch ɑnd Pɑgɑn Lɑgrɑngiɑn multiplier test for rɑndom effects, ɑnd Hɑusmɑn Specificɑtion Test to determine ɑppropriɑte estimɑtion technique between fixed ɑnd rɑndom effect model. …”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…These include ‘i.cluster’ and ‘i.maxlik’ for non-supervised classification used as training dataset of random pixel seeds, ‘r.random’, ‘r.learn.train’, ‘r.learn.predict’ and ‘r.category’ for ML part of image processing. …”
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Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model by Trabelsi Ali, Rezgui Mohamed-Ali, Amdouni Marwan, Dokkar Atef, Jmal Hamdi

“…For dynamic processes, the method operates in three main steps, (i) data preparation by transforming the outputs to maximization functions, (ii) estimate of the composed variation (random and non-random error components), (iii) and, composition of the process uncertainty array for each output across the signal levels. …”
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Implementation of bagging in time series forecasting by Ia. V. Gramovich, D. Yu. Musatov, D. A. Petrusevich

“…This study examines the application of bagging to the random component of a time series formed after removing the trend and seasonal part. …”
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ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized by Vinicius Lins Costa Ok Melo, Pedro Emmanuel Alvarenga Americano do Brasil, PhD

“…A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression. …”
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Methodological Approaches to Comparative Trend Analyses: The Case of Adolescent Toothbrushing by Torbjørn Torsheim, Frank J. Elgar, Alina Cosma, Alina Cosma, Caroline Residori, Oddrun Samdal, Christina Schnohr, Christina Schnohr

“…The fixed effect and the random effect approach converged on a positive but flattening overall trend, with a statistically significant country variation in trends.ConclusionOnly the fixed effect approach and the random effects approach provided clear answers to the research question. …”
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Hidden Markov model based P2P flow identification technique by Bo XU, Ming CHEN, Xiang-lin WEI

“…To identify various P2P flows accurately in real-time,a hidden Markov model(HMM)based P2P flow identification technique was proposed.This approach made use of packet size,inter-arrival time and arrival order to construct flow identification model,in which discrete random variable was used to depict the characteristics of HMM state.A framework called HMM-FIA was proposed,which could identify various P2P flows simultaneously.Meanwhile,the algorithm for selecting the number of HMM state was designed.In a controllable experimental circumstance in the campus network,HMM-FIA was utilized to identify P2P flows and was compared with other identification methods.The results show that discrete random variable can decrease the model constructing time and improve the time-cost and accuracy in identifying unknown flows,HMM-FIA can correctly identify the packet flows produced by various P2P protocols and it can be adaptive to different network circumstance.…”
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STATISTICAL SIMULATION OF RELIABILITY OF NETWORKS WITH EXPONENTIALLY DISTRIBUTED UNIT LIFETIMES by ROTARU, Maria

“…The novelty of the distribution consists in the fact that the number of subnets is random, governed by the Poisson and Logarithmic distributions, the lifetimes of the units in each subnet being independent, identically, exponentially distributed random variables, the number of units in each subnet is the same constant integer number. …”
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Forecasting the Remaining Duration of an Ongoing Solar Flare by Jeffrey W. Reep, Will T. Barnes

“…As the duration of a solar flare is not related to the size of that flare, it is not directly clear how long those blackouts can persist. Using a random forest regression model trained on data taken from X‐ray light curves, we have developed a direct forecasting method that predicts how long the event will remain above background levels. …”
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Experimental Study of the Composition and Structure of Granular Media in the Shear Bands Based on the HHC-Granular Model by Guang-jin Wang, Xiang-yun Kong, Chun-he Yang

“…The researchers cannot control the composition and structure of coarse grained soil in the indoor experiment because the granular particles of different size have the characteristics of random distribution and no sorting. Therefore, on the basis of the laboratory tests with the coarse grained soil, the HHC-Granular model, which could simulate the no sorting and random distribution of different size particles in the coarse-grained soil, was developed by use of cellular automata method. …”
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Controlled Sink Mobility Algorithms for Wireless Sensor Networks by Metin Koç, Ibrahim Korpeoglu

“…We did extensive simulation experiments to evaluate the performance of the components of our mobility scheme and to compare our solution with static case and random movement strategy. The results show that our algorithms are effective in improving network lifetime and provide significantly better lifetime compared to static sink case and random movement strategy.…”
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Sampling method for IDS in high bandwidth network by NING Zhuo1, GONG Jian1, GU Wen-jie1

“…A novel sampling method,IDSampling,was developed to solve the performance unbalance problem that IDS could not scale well in G+bit/s link,which was adaptive with the consumption of the memory bottleneck.With the help of the heuristic messages,such as the entropy of the single-packet flow and the flow length,IDSampling applied the simple sampling strategy based on the entropy of the single-packet flow when the large-scale anomaly occurred,or another complicated one instructed by the feedback of the rear detection results by default.In both cases IDSampling tried to guaran-tee the equal security with detection cost as low as it could.The results of experiment show that ①IDSampling keeps IDS effective by cutting off its load significantly when it is overloaded,at the same time it can guarantee the detection accuracy of the large-scale attack;②Comparing with the other two overwhelming sampling methods,the random packet sampling and the random flow sampling,the number of attack packets sampled by IDSampling is higher than that of the former two,the number outweighs the former two one order of magnitude especially in the large-scale anomaly case.…”
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Reliability Modeling for Multistate System with Preventive Maintenance under Customer Demand by Jinlei Qin, Zheng Li

“…According to the regular degradation and random failure at each state, based on the Markov random process, the proposed MSS with preventive maintenance can be modeled to satisfy the customer demand in a specific state. …”
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Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning by Yang Li, Fubin Zhang, Demin Xu, Jiguo Dai

“…This paper presents a new version of rapidly exploring random trees (RRT), that is, liveness-based RRT (Li-RRT), to address autonomous underwater vehicles (AUVs) motion problem. …”
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Rich Spatiotemporal Dynamics of a Vegetation Model with Noise and Periodic Forcing by Xia-Xia Zhao, Jian-Zhong Wang

“…The growth of vegetation is undeniably subject to random fluctuations arising from environmental variability and internal effects due to periodic forcing. …”
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An Improved Genetic Algorithm for Developing Deterministic OTP Key Generator by Ashish Jain, Narendra S. Chaudhari

“…In this paper, two main characteristics (speed and randomness) of the GRKG method are significantly improved by presenting the IGRKG method (improved genetic-based random key generator method). …”
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Channel interleaver design and performance analysis for ultrasonic through-metal communication by Linsen XU, Wei YANG, Hongxian TIAN

“…Aiming at the problem that the UTM channel can produce successive error bits, a multiple input channel interleaver for UTM communication was proposed.By taking advantages of the grouping idea in multiple input interleaver and the periodic oscillation characteristic of the power gain in the UTM channel, encoded bits were first divided into several groups so that error probabilities of bits in each group varied smoothly.And then, the shift operation and the rectangular interleaver were applied to encoded bits in each group.This not only ensured that interleaved bits in the same group were dispersed into different oscillation cycles, which avoided that the successive bits suffer the high error probability, but also maintained the original bit position sequences, which reduced the influence of UEP.Approximate BER performances for the random interleaver and the proposed interleaver were analyzed and verified by simulation.Simulation results indicate that the proposed interleaver increases the frequency diversity of the UTM communication system and has a lower BER than the random interleaver at high SNR.…”
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Screening for Stereopsis of Children Using an Autostereoscopic Smartphone by Yanhui Yang, Huang Wu

“…Lang stereotest I, Lang stereotest II, Pass Test 3, Dinosaur Stereoacuity Test, and Random Dot Stereo Acuity Test, respectively; Wilcoxon signed-rank test, P value all >0.05). …”
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