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Application of supervised machine learning algorithms for the evaluation of utricular function on patients with Meniere's disease: utilizing subjective visual vertical and ocular-vestibular-evoked myogenic potentials

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ACTA OTO-LARYNGOLOGICA 2023年第4期143卷 262-273页

作者： Bragg, Phillip G. Norton, Benjamin M. Petrak, Michelle R. Weiss, Allyson D. Kandl, Lindsay M. Corrigan, Megan L. Bahner, Cammy L. Matsuoka, Akihiro J. Northwestern Med Dept Otolaryngol Head & Neck Surg Chicago IL USA Northwestern Univ Feinberg Sch Med Dept Otolaryngol Head & Neck Surg Chicago IL USA Interacoust AS Assens Denmark Interacoust US Eden Prairie MN USA Hugh Knowles Hearing Ctr Evanston IL USA Univ Calif San Diego Dept Otolaryngol Head & Neck Surg San Diego CA USA 9444 Med Ctr Dr MC7895 La Jolla CA 92037 USA

Background: Research on the otolith organs remains ***: This study seeks to further elucidate utricular function in patients with Meniere's disease (MD) in three ways: (1) We aimed to disambiguate the role of the Subjective Visual Vertical (SVV) and Ocular Vestibular Evoked Myogenic Potential (o-VEMP) tests regarding which utricular subsystem each is measuring. (2) We sought to characterize the acute and chronic state of MD by identifying differences in the relationship of SVV and o-VEMP results across patients with acute and chronic MD. (3) We attempted to find a machine-learning algorithm that could predict acute versus chronic MD using SVV and ***: A prospective study with ninety ***: (1) SVV and o-VEMP tests were found to have a moderate linear relationship in patients with acute MD, suggesting each test measures a different utricular subsystem. (2) Regression analyses statistically differed across the two patient populations, suggesting that SVV results were normalized in chronic MD patients. (3) Logistic regression and Naive Bayes algorithms were found to predict acute and chronic MD ***: A better understanding of what diagnostic tests measure will lead to a better classification system for MD and more targeted treatment options in the future.

关键词： Vestibular-evoked myogenic potentials vestibular diseases vestibular function tests saccule and utricle bone conduction otolith dysfunction subjective visual vertical Meniere's disease machine learning supervised machine learning

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Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

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SMART AND SUSTAINABLE BUILT ENVIRONMENT 2020年第4期9卷 361-374页

作者： Eslamirad, Nasim Malekpour Kolbadinejad, Soheil Mahdavinejad, Mohammadjavad Mehranrad, Mohammad Tarbiat Modares Univ Dept Architectural Technol Tehran Iran Univ Marlik Dept Architecture Nowshahr Iran Islamic Azad Univ Dept Architecture Cent Tehran Branch Tehran Iran

Purpose This research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method - by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones - to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results. Design/methodology/approach The tools of the study are the way of processing by SML, predicting the future based on the past. machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort - predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output. Findings The process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases - all over their life spans. Research li

关键词： Urban thermal comfort Green Sidewalks Hyper parameters learning algorithm supervised machine learning

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Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using supervised machine learning

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METHODS OF INFORMATION IN MEDICINE 2021年第1/02期60卷 49-61页

作者： Oda, Takuma Chiu, Shih-Wei Yamaguchi, Takuhiro Tohoku Univ Grad Sch Med Div Biostat 1-1Seiryo Cho Sendai Miyagi 9808574 Japan

Objective This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data normalization;feature extraction by distributed representation of dataset names, variable names, and variable labels;and supervised machine learning. Materials and Methods Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation: Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms-namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms-to identify the one that could generate the best prediction model. Results The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format. Conclusion By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format;this process is more efficient than the conventional fully manual process.

关键词： data conversion clinical trial supervised machine learning database

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Estrous detection by continuous measurements of vaginal temperature and conductivity with supervised machine learning in cattle

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THERIOGENOLOGY 2019年 123卷 90-99页

作者： Higaki, Shogo Miura, Ryotaro Suda, Tomoko Andersson, L. Mattias Okada, Hironao Zhang, Yi Itoh, Toshihiro Miwakeichi, Fumikazu Yoshioka, Koji Natl Agr & Food Res Org Natl Inst Anim Hlth Tsukuba Ibaraki 3050856 Japan Nippon Vet & Life Sci Univ Dept Vet Sci Musashino Tokyo 1808602 Japan Natl Inst Adv Ind Sci & Technol Ubiquitous MEMS & Micro Engn Res Ctr Tsukuba Ibaraki 3058564 Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 2778563 Japan Inst Stat Math Dept Stat Modeling Tachikawa Tokyo 1900014 Japan

This study aimed to evaluate the effectiveness of estrous detection technique based on continuous measurements of vaginal temperature (VT) and conductivity (VC) with supervised machine learning in cattle. The VT and VC of 17 cows in tie-stalls were measured using our developed wearable vaginal sensor from Day 11 (Day 0 = ovulation day) to Day 11 of the subsequent estrous cycle at 15-min interval. After the maximum VT and VC were extracted hourly, their changes were expressed as residual VT (rVT=actual VT - mean VT for the same time on the previous 3 days) and as VC ratio (VCr = actual VC/mean VC for the same time on Day 11-13), respectively, and were used for analysis. Trans-rectal ultrasonography was performed to monitor ovarian structure changes. The plasma concentrations of reproductive hormones (progesterone: P-4, estradiol-17 beta: E-2, and LH) were measured in the experimental period. Standing estrus was confirmed by testing with herd mates at 3-h interval. The rVT decreased transiently, which coincided with decreasing P4 a few days before estrus, and a sharp increase was associated with LH surge during estrus. The VCr increased as estrus approached, corresponding with decreasing P-4 and increasing E2 and LH. After noise reduction, features, possible to follow-up estrus-associated changes in rVT and VCr, were extracted and used for developing estrous detection models;9 models were developed with 3 feature sets (features extracted from rVT alone, VCr alone, and combination of rVT and VCr) and 3 machine learning algorithms (decision tree: DT, support vector machine: SVM, and artificial neural network: ANN). Cross-validation showed that models using the features from the combination of rVT and VCr showed better performance in terms of sensitivity and precision than those using features from VCr alone, and precision than those of using features from rVT alone. Within the models using the features from the combination of rVT and VCr, sensitivity and precision of th

关键词： Cattle Estrous detection supervised machine learning Vaginal conductivity Vaginal temperature Wearable sensor

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Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2024年第6期13卷 1175-1190页

作者： Le, Khac Binh Duong, Minh Thai Cao, Dao Nam Le, Van Vang Vinh Univ Technol Educ Hung Dung Ward 117 Nguyen Viet Xuan St Vinh Vietnam Ho Chi Minh City Univ Transport Inst Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Transport Inst Maritime Ho Chi Minh City Vietnam

In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel. This work investigates the modeling of injecting strategies for a waste-derived biogas-powered dual-fuel engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, and support vector machines (SVM). Mean Squared Error (MSE), R-squared (R2), and Mean Absolute Percentage Error (MAPE) were among the criteria used in evaluations of the models. Random Forest has shown better performance for Brake Thermal Efficiency (BTE) with a test R2 of 0.9938 and a low test MAPE of 3.0741%. Random Forest once more exceeded other models with a test R2 of 0.9715 and a test MAPE of 4.2242% in estimating Brake Specific Energy Consumption (BSEC). With a test R2 of 0.9821 and a test MAPE of 2.5801% Random Forest emerged as the most accurate model according to carbon dioxide (CO2) emission modeling. Analogous results for the carbon monoxide (CO) prediction model based on Random Forest obtained a test R2 of 0.8339 with a test MAPE of 3.6099%. Random Forest outperformed Linear Regression with a test R2 of 0.9756% and a test MAPE of 7.2056% in the case of nitrogen oxide (NOx) emissions. Random Forest showed the most constant performance overall criteria. This paper emphasizes how well machine learning models especially Random Forest can prognosticate the performance of biogas dual-fuel engines.

关键词： Biogas Alternative fuel supervised machine learning Lasso regression Random Forest Taylor diagram

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Using natural history to guide supervised machine learning for cryptic species delimitation with genetic data

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FRONTIERS IN ZOOLOGY 2022年第1期19卷 1-15页

作者： Derkarabetian, Shahan Starrett, James Hedin, Marshal Harvard Univ Dept Organism & Evolutionary Biol Museum Comparat Zool 26 Oxford St Cambridge MA 02138 USA Univ Calif Davis Dept Entomol & Nematol Briggs Hall Davis CA 95616 USA San Diego State Univ Dept Biol 5500 Campanile Dr San Diego CA 92182 USA

The diversity of biological and ecological characteristics of organisms, and the underlying genetic patterns and processes of speciation, makes the development of universally applicable genetic species delimitation methods challenging. Many approaches, like those incorporating the multispecies coalescent, sometimes delimit populations and overestimate species numbers. This issue is exacerbated in taxa with inherently high population structure due to low dispersal ability, and in cryptic species resulting from nonecological speciation. These taxa present a conundrum when delimiting species: analyses rely heavily, if not entirely, on genetic data which over split species, while other lines of evidence lump. We showcase this conundrum in the harvester Theromaster brunneus, a low dispersal taxon with a wide geographic distribution and high potential for cryptic species. Integrating morphology, mitochondrial, and sub-genomic (double-digest RADSeq and ultraconserved elements) data, we find high discordance across analyses and data types in the number of inferred species, with further evidence that multispecies coalescent approaches over split. We demonstrate the power of a supervised machine learning approach in effectively delimiting cryptic species by creating a "custom" training data set derived from a well-studied lineage with similar biological characteristics as Theromaster. This novel approach uses known taxa with particular biological characteristics to inform unknown taxa with similar characteristics, using modern computational tools ideally suited for species delimitation. The approach also considers the natural history of organisms to make more biologically informed species delimitation decisions, and in principle is broadly applicable for taxa across the tree of life.

关键词： Integrative taxonomy Multispecies coalescent RADSeq Short-range endemism Southern Appalachians supervised machine learning Ultraconserved elements

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Spatial-temporal distribution of labeled set bias remote sensing estimation: An implication for supervised machine learning in water quality monitoring

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 131卷

作者： Zhou, Yadong Li, Wen Cao, Xiaoyu He, Boayin Feng, Qi Yang, Fan Liu, Hui Kutser, Tiit Xu, Min Xiao, Fei Geng, Xueer Yu, Kai Du, Yun Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Key Lab Environm & Disaster Monitoring & Evaluat H Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Peoples R China Mississippi State Univ Dept Forestry Mississippi State MS 39762 USA Univ Tartu Estonian Marine Inst EE-12618 Tallinn Estonia Troy Univ Dept Geospatial Informat Troy AL 36082 USA

supervised machine learning (SML) has become a crucial tool for estimating water quality parameters (WQPs) from satellite images. Its effectiveness relies heavily on synchronised in-situ datasets covering diverse water bodies. However, collecting such datasets is time-consuming, resulting in temporal gaps between sampling and imaging. In addition, the in situ dataset may exhibit an imbalance. These imperfections could introduce uncertainties to SML-derived models, compromising the accuracy of the WQP estimates. Using in situ data collected automatically every four hours, the estimation of both optically active parameters (OAPs) and non-optically active parameters (nOAPs) in the Middle Reaches of the Yangtze River (MRYR) serves as an example to illustrate the importance of this challenge in freshwater remote sensing. Additionally, the investigation was extended to estimate OAPs and nOAPs in lakes of Wuhan through manual sampling measurements, thereby bridging theoretical insights with real-world applications. Employing four ML algorithms, the SML-based models for each WQP were calibrated using in situ datasets with different spatio-temporal distributions. The results demonstrated that precision decreased with increasing time gaps, whereas most nOAPs (COD, TP, TN, pH, and DO) showed greater robustness to the time gap than the OAPs (turbidity, Secchi depth, Chl-a, and algae density). The mean absolute percentage errors (MAPEs) of these nOAPs were as follows: for all models, pH MAPEs < 6 % and DO MAPEs < 30 %;for models using datasets with time gaps of 0 - +/- 4 days, MAPEs < 50 % for COD, TP, and TN. All models for NH3-N estimation were invalid for both the MRYR water bodies and real-world applications in Wuhan. The model accuracy decreased slightly as the sample size decreased sharply with the constraint of the minimum gap (within +/- 1 days). Furthermore, the SML model based onimbalanced labels produced smooth estimates, resulting in under- or over-estimation in diff

关键词： In situ -remote sensing matchup Time gap Data imbalance supervised machine learning Water quality parameter

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Research on internet traffic classification techniques using supervised machine learning

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High Technology Letters 2009年第4期15卷 369-377页

作者：李君 Zhang Shunyi Wang Pan Li Cuilian Information Networking Institute Naniing University of Posts and Telecommunications Nanjing 210003 P.R. China Department of Telecommunication Engineering Zhejimag Wanli University Ningbo 315100 P.R. China

Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emerged applications （e. g. Peer-to-Peer） using dynamic port numbers, masquerading techniques and encryption to avoid detection. This paper presents a machine learning （ML） based traffic classifica- tion scheme, which offers solutions to a variety of network activities and provides a platform of performance evaluation for the classifiers. The impact of dataset size, feature selection, number of application types and ML algorithm selection on classification performance is analyzed and demonstrated by the following experiments：（1） The genetic algorithm based feature selection can dramatically reduce the cost without diminishing classification accuracy. （2） The chosen ML algorithms can achieve high classification accuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexity and accuracy are both taken into account. （3） Larger dataset and fewer application types would result in better classification accuracy. Finally, early detection with only several initial packets is proposed for real-time network activity and it is proved to be feasible according to the preliminary results.

关键词： supervised machine learning traffic classification feature selection genetic algorithm (GA)

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Systematic analysis of supervised machine learning as an effective approach to predicate β-lactam resistance phenotype in Streptococcus pneumoniae

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BRIEFINGS IN BIOINFORMATICS 2020年第4期21卷 1347-1355页

作者： Zhang, Chaodong Ju, Yingjiao Tang, Na Li, Yun Zhang, Gang Song, Yuqin Fang, Hailing Yang, Liang Feng, Jie Nanyang Technol Univ Interdisciplinary Grad Sch Singapore Singapore Chinese Acad Sci Inst Microbiol State Key Lab Microbial Resources Beijing 100101 Peoples R China Peking Univ Inst Clin Pharmacol Hosp 1 Beijing Peoples R China Southern Univ Sci & Technol Sch Med Shenzhen 518055 Guangdong Peoples R China

Streptococcus pneumoniae is the most common human respiratory pathogen, and beta-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. beta-lactam resistance is steadily increasing in pneumococci and is mainly associated with the alteration in penicillin-binding proteins (PBPs) that reduce binding affinity of antibiotics to PBPs. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. In this study, we developed a systematic strategy for applying supervised machine learning to predict S. pneumoniae antimicrobial susceptibility to beta-lactam antibiotics. We combined published PBP sequences with minimum inhibitory concentration (MIC) values as labelled data and the sequences from NCBI database without MIC values as unlabelled data to develop an approach, using only a fragment from pbp2x (750 bp) and a fragment from pbp2b (750 bp) to predicate the cefuroxime and amoxicillin resistance. We further validated the performance of the supervised learning model by constructing mutants containing the randomly selected pbps and testing more clinical strains isolated from Chinese hospital. In addition, we established the association between resistance phenotypes and serotypes and sequence type of S. pneumoniae using our approach, which facilitate the understanding of the worldwide epidemiology of S. pneumonia.

关键词： Streptococcus pneumonia beta-lactam resistance penicillin-binding protein genotypic antimicrobial susceptibility testing supervised machine learning

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supervised machine learning to estimate instabilities in chaotic systems: Estimation of local Lyapunov exponents

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QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2023年第753期149卷 1236-1262页

作者： Ayers, Daniel Lau, Jack Amezcua, Javier Carrassi, Alberto Ojha, Varun Univ Reading Dept Meteorol Reading England Natl Ctr Earth Observat Reading England Univ Reading Dept Comp Sci Reading England Tecnol Monterrey Sch Sci & Engn Mexico City Mexico Univ Bologna Dept Phys & Astron Augusto Righi Bologna Italy Newcastle Univ Sch Comp Newcastle Upon Tyne England

In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations than in others. Real-time knowledge about the error growth could enable strategies to adjust the modelling and forecasting infrastructure on the fly to increase accuracy and/or reduce computation time. For example, one could change the ensemble size, the distribution and type of target observations, and so forth. Local Lyapunov exponents are known indicators of the rate at which very small prediction errors grow over a finite time interval. However, their computation is very expensive: it requires maintaining and evolving a tangent linear model, orthogonalisation algorithms and storing large matrices. In this feasibility study, we investigate the accuracy of supervised machine learning in estimating the current local Lyapunov exponents, from input of current and recent time steps of the system trajectory, as an alternative to the classical method. Thus machine learning is not used here to emulate a physical model or some of its components, but "nonintrusively" as a complementary tool. We test four popular supervised learning algorithms: regression trees, multilayer perceptrons, convolutional neural networks, and long short-term memory networks. Experiments are conducted on two low-dimensional chaotic systems of ordinary differential equations, the Rossler and Lorenz 63 models. We find that on average the machine learning algorithms predict the stable local Lyapunov exponent accurately, the unstable exponent reasonably accurately, and the neutral exponent only somewhat accurately. We show that greater prediction accuracy is associated with local homogeneity of the local Lyapunov exponents on the system attractor. Importantly, the situations in which (forecast) errors grow fastest are not necessarily the same as those in which it is more difficult to predict local Lyapunov exponents with machine learning.

关键词： chaos local Lyapunov exponents numerical modelling supervised machine learning

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