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The efficacy of machine learning algorithm for raw drug authentication in Coscinium fenestratum (Gaertn.) Colebr. employing a DNA barcode database

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PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS 2021年第3期27卷 605-617页

作者： Unnikrishnan, Remya Sumod, M. Jayaraj, R. Sujanapal, P. Dev, Suma Arun Kerala Forest Res Inst Forest Genet & Biotechnol Div Trichur 680653 Kerala India Kerala Forest Res Inst Sustainable Forest Management Div Trichur 680653 Kerala India Kerala Forest Res Inst Forest Ecol & Biodivers Div Trichur 680653 Kerala India Cochin Univ Sci & Technol Kochi Kerala India

Medicinal plants are a valuable resource for traditional as well as modern medicine. Consequently huge demand has exerted a heavy strain on the existing natural resources. Due to over exploitation and unscientific collection most of the commercially traded ayurvedic plants are in the phase of depletion. Adulteration of expensive raw drugs with inferior taxa has become a common practice to meet the annual demand of the ayurvedic industry. Although there are several recommended methods for proper identification varying from the traditional taxonomic to organoleptic and physiochemical, it is difficult to authenticate ayurvedic raw drugs available in extremely dried, powdered or shredded forms. In this regard, the study addresses proper authentication and illicit trade in Coscinium fenestratum (Gaertn.) Colebr. using CBOL recommended standard barcode regions viz. nuclear ribosomal-Internally Transcribed Spacer (nrDNA- ITS), maturase K (matK), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), and psbA-trnH spacer regions. Further, an integrated analytical approach employing Maximum Likelihood phylogenetic tree and machine learning Approach, Waikato Environment for Knowledge Analysis was employed to prove efficacy of the method. The automated species identification technique, Artificial Intelligence uses the ability of computers to build models that can receive the input data and then conduct statistical analyses which significantly reduces the human labour. Concurrently, scientific management, restoration, cultivation and conservation measures should be given utmost priority to reduce the depletion of wild resources as well as to meet the rapidly increasing demand of the herbal industries.

关键词： machine learning algorithm Artificial intelligence DNA barcoding Threatened species Medicinal plants

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Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of machine learning algorithm

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INDIAN JOURNAL OF HEMATOLOGY AND BLOOD TRANSFUSION 2021年第3期37卷 453-457页

作者： Jahan, Aarzoo Singh, Garima Gupta, Ruchika Sarin, Namrata Singh, Sompal Hindu Rao Hosp Dept Pathol Delhi India ICMR Natl Inst Canc Prevent & Res Div Cytopathol Noida India

Antenatal screening for beta thalassemia trait (BTT) followed by counseling of couples is an efficient way of thalassemia control. Since high performance liquid chromatography (HPLC) is costly, other cost-effective screening methods need to be devised for this purpose. The present study was aimed at evaluating the utility of red cell indices and machine learning algorithms including an artificial neural network (ANN) in detection of BTT among antenatal women. This cross-sectional study included all antenatal women undergoing thalassemia screening at a tertiary care hospital. Complete blood count followed by HPLC was performed. Receiver operating characteristic (ROC) curve analysis was performed for obtaining optimal cutoff for each of the indices with determination of test characteristics for detection of BTT. machine learning algorithms including C4.5 and Naive Bayes (NB) classifier and a back-propagation type ANN including the red cell indices was designed and tested. Over a period of 15 months, 3947 patients underwent thalassemia screening. BTT was diagnosed in 5.98% of women on the basis of HPLC. ROC analysis yielded the maximum accuracy of 63.8%, sensitivity and specificity of 66.2% and 63.7%, respectively for Mean corpuscular hemoglobin concentration (MCHC). The C4.5 and NB classifier had accuracy of 88.56%-82.49% respectively while ANN had an overall accuracy of 85.95%, sensitivity of 83.81%, and specificity of 88.10% in detection of BTT. The present study highlights that none of the red cell parameters standalone is useful for screening for BTT. However, ANN with combination of all the red cell indices had an appreciable sensitivity and specificity for this purpose. Further refinements of the neural network can provide an appropriate tool for use in peripheral settings for thalassemia screening.

关键词： Red cell indices Beta thalassemia trait machine learning algorithm

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The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm

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BMC INFECTIOUS DISEASES 2023年第1期23卷 881-881页

作者： Chen, Lijiao Yuan, Lingke Sun, Tingting Liu, Ruiqing Huang, Qing Deng, Shaoli Army Med Univ Daping Hosp Dept Lab Med Chongqing 400042 Peoples R China Carnegie Mellon Univ Sci Computat Finance Pittsburgh PA USA Chongqing Med & Pharmaceut Coll Coll Med Technol Chongqing Peoples R China

BackgroundTuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis *** total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model's predictive performance for dentifying active and latent tuberculosis *** verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing *** machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.

关键词： Leukocyte VCS (volume, conductivity, light scatter) parameters machine learning algorithm Active tuberculosis infection Latent tuberculosis infection Distinguish

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Green extraction and optimization of bioactive compounds from Solanum torvum Swartz. using ultrasound-aided solvent extraction method through RSM, ANFIS and machine learning algorithm

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SUSTAINABLE CHEMISTRY AND PHARMACY 2023年 36卷

作者： Petchimuthu, Priya Sumanth, Gorla Buchayyagari Kunjiappan, Selvaraj Kannan, Suthendran Pandian, Sureshbabu Ram Kumar Sundar, Krishnan Kalasalingam Acad Res & Educ Dept Biotechnol Krishnankoil 626126 Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil 626126 Tamil Nadu India Kalasalingam Acad Res & Educ Dept Informat Technol Krishnankoil 626126 Tamil Nadu India

Biologically important phytocompounds based on total polyphenolic, flavonoid contents and their free radical scavenging potential were completely harvested from Solanum torvum Sw. through ultrasound-aided solvent extraction (UASE) method. The face-centered central composite design (FCCD) in response surface methodology (RSM) was applied to optimize the effective extraction parameters, and adaptive neuro-fuzzy inference system (ANFIS), as well as machine learning (ML) algorithm models were used to validate the optimized extraction parameters for the highest yield of bioactive phytocompounds. The effect of five extraction parameters, namely methanol concentration (X1: 55-65% in v/v with water), ultrasound-intensity (X2: 50-60 W cm-2), temperature (X3: 30-45 degrees C), time (X4: 15-25 min) and particle size (X5: 0.5-1.5 mm) at five levels (very low (-2.37), low (-1), medium (0), high (+1) and very high (+2.37)) were studied for optimized yields of total polyphenolic (TP;(y1)), total flavonoid contents (TF;(y2)) and their free radical scavenging abilities (%DPPH*sc (y3), %ABTS*sc (y4), and %H2O2*sc (y5)). The optimal condition was attained at X1 = 65%, X2 = 60 W cm-2, X3 = 45 degrees C, X4 = 15 min, and X5 = 0.5 mm;under this condition, the highest yields of y1 = 830.677 mg gallic acid equivalent (GAE)/g, y2 = 535.38 mg rutin equivalents (RE)/g, y3 = 75.22%, y4 = 84.12%, and y5 = 85.02% was obtained. Most of the experimental values were well matched with the predicted responses of the RSM model. Further, ANFIS and random forest machine learning algorithm were utilized to compare and validate the optimized extraction parameters. Also, GC-MS and LC-MS were carried out to investigate the presence of various bioactive phytocompounds in the optimized extract.

关键词： Solanum torvum Swartz Response surface methodology ANFIS machine learning algorithm FCCD Ultrasound-aided solvent extraction

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Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

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Journal of Southeast University(English Edition) 2022年第2期38卷 126-136页

作者： Pan Minghui Liao Wenhe Xing Yan Tang Wencheng School of Mechanical Engineering Nanjing University of Science and Technology Nanjing 210094 China Digital Forming Technology and Equipment National-Local United Engineering Laboratory Nanjing University of Science and Technology Nanjing 210094 China School of Mechanical Engineering Southeast University Nanjing 211189 China

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna *** effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly *** machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping *** with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,*** results indicate that FNN generated the best predicted welding *** under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of *** finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

关键词： parallel T-shaped thin-walled parts welding assembly property finite element analysis mapping relationship machine learning algorithm

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Identification of key genes for IgA nephropathy based on machine learning algorithm and correlation analysis of immune cells

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TRANSPLANT IMMUNOLOGY 2023年第1期78卷 101824-101824页

作者： Chen, Suzhi Li, Yongzhang Wang, Guangjian Song, Lei Tan, Jinchuan Yang, Fengwen Hebei Prov Hosp Chinese Med Dept Nephrol 1 389 Zhongshan East Rd Shijiazhuang 050011 Hebei Peoples R China Hebei Prov Hosp Chinese Med Dept Res Ctr 389 Zhongshan East Rd Shijiazhuang 050011 Hebei Peoples R China Hebei Prov Hosp Chinese Med Dept Androl 389 Zhongshan East Rd Shijiazhuang 050011 Hebei Peoples R China Tianjin Univ Tradit Chinese Med Tianjin Peoples R China Hebei Technol Innovat Ctr TCM Spleen & Kidney Dis Cangzhou Peoples R China

Introduction: The pathogenesis and progression mechanism of Immunoglobulin A nephropathy (IgAN) is not fully understood. There is a lack of panoramic analysis of IgAN immune cell infiltration and algorithms that are more efficient and accurate for screening key pathogenic genes. Methods: RNA sequencing (RNA-seq) data sets on IgAN were downloaded from the Gene Expression Omnibus (GEO) database, including GSE93798, GSE35489, and GSE115857. The RNA-seq data set of kidney tissue as control samples were downloaded from the Genotype-Tissue Expression (GTEx) database. Three machine learning algorithms-weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-were used to identify the key pathogenic gene sets of the IgAN disease. The ssGSEA method was applied to calculate the immune cell infiltration (ICI) of IgAN samples, whereas the Spearman test was used for correlation analysis. The receiver operator characteristic curve (ROC) was used to evaluate the diagnostic efficacy of key genes. The correlation between the key genes and ICI was analyzed using the Spearman test. Results: A total of 177 genes were screened out as differentially expressed genes (DEGs) for IgAN, including 135 up-regulated genes and 42 down-regulated genes. The DEGs were significantly enriched in the inflammatory-or immune-related pathways (gene sets). Activating transcription factor 3 (AFT3), C-X-C Motif Chemokine Ligand 6 (CXCL6), and v-fos FBJ murine osteosarcoma viral oncogene homolog B (FOSB) were identified using WGCNA, support vector machine, and LASSO algorithms. These three genes revealed good diagnostic efficacy in the training and test cohorts. The CXCL6 expression positively correlated with activated B cells and memory B cells. Conclusion: ATF3, FOSB, and CXCL6 genes were identified as potential biomarkers of IgAN. These three genes exhibited good diagnostic efficacy for IgAN. We described the landscape of immun

关键词： Immunoglobulin a nephropathy machine learning algorithm ATF3 CXLC6 FOSB

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Multi Strategy machineenglish Translation System based on machine learning algorithm 22

Multi Strategy Machineenglish Translation System based on Ma...

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Proceedings of the 2022 5th International Conference on machine learning and machine Intelligence

作者： Shuang Jiang Jilin Animation Institute China

ISBN: (纸本)9781450397551

The advent of the big data era has brought unprecedented data to machine translation, which is particularly important for data-driven models and algorithms. The degree of public quotation of source text is becoming more and more difficult to understand, but the overall translation performance is improving. On this basis, the multi Strategy machineenglish translation (MSMT) system based on MLA is designed in this paper. Firstly, the MSMT system and machineenglish translation process are analyzed, and the Interactive MSMT system is discussed. Then the neural network translation model of machine learning is proposed. In order to improve the performance of the MSMT system based on MLA proposed in this paper, The results show that in the case of full-text retrieval, the time of English translation is greatly increased, and these time are basically spent in finding and matching these sentences. The sentences that cannot be translated due to the failure of feature index can be translated by the Multi Strategy machine English translation system based on MLA proposed in this paper, which verifies the rationality of the translation system proposed in this paper.

关键词： English Translation machine learning algorithm Multi Strategy machine Translation System Design

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QAM-DMT of Hybridly Integrated EAM-DFBLD Chip-on-Board With Adaptive machine learning algorithm for 149.6-Gbit/s BtB and 138-Gbit/s 10-km-SMF Transmission

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2022年第4期28卷 1页

作者： Wu, Wei-Li Cheng, Chih-Hsien Lin, Huang-Shen Lin, Yu-Hong Su, Borching Lin, Gong-Ru Natl Taiwan Univ Grad Inst Photon & Optoelect Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Univ Tokyo Res Ctr Adv Sci & Technol Tokyo 1530041 Japan

By using the generalized frequency division multiplexing (GFDM) and the machine learning algorithm for self-feedback amplitude/phase pre-distortion, the electro-absorption modulator integrated distributed-feedback semiconductor laser diode (EAM-DFBLD) is encoded with improved transmission quality and capacity. Both back-to-back (BtB) and 10-km single-mode fiber (SMF) transmissions are preliminarily demonstrated for inter-data-center applications. Optimizing the EAM-DFBLD transmitter at 1306.8 nm with a bias current of 100 mA and a bias voltage of -1.5 V can provide a 34.5-GHz bandwidth for 16-QAM discrete multitone (DMT) and GFDM formats. The parametric comparison reveals that the 16-QAM GFDM with K = 16 can improve its performance from BtB to 10-km SMF transmission with decoding BER slightly increasing from 1.44 x 10(-3) to 2.82 x 10(-3). The EAM-DFBLD carried GFDM data also provides a better power penalty by -0.25 dB. This enables higher raw data rates of 149.6 Gbit/s under BtB case and 138 Gbit/s in 10-km SMF transmission link below the forward error correction (FEC) criterion. The GFDM format carried by the EAM-DFBLD transmitter thus becomes the more suitable candidate than the DMT to implement ultra-high-speed inter-data-center and data access link applications.

关键词： Bandwidth Modulation Optical transmitters Symbols Quadrature amplitude modulation Adaptive optics Time-frequency analysis machine learning algorithm electro-absorption modulator distributed feedback laser diode quadrature amplitude modulation discrete multitone generalized frequency division multiplexing inter-data-center

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Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and machine learning algorithm

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REMOTE SENSING 2022年第21期14卷 5305-5305页

作者： Zhao, Yubo Yu, Tao Hu, Bingliang Zhang, Zhoufeng Liu, Yuyang Liu, Xiao Liu, Hong Liu, Jiacheng Wang, Xueji Song, Shuyao Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China Chinese Acad Sci Key Lab Spectral Imaging Technol Xian 710119 Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing 100049 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R-2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.

关键词： water quality monitoring near-surface remote sensing machine learning algorithm ensemble learning model

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Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy

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Frontiers of Nursing 2021年第3期8卷 209-221页

作者： Li-Li Wei Yue-Shuai Pan Yan Zhang Kai Chen Hao-Yu Wang Jing-Yuan Wang Department of Nursing The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China Intensive Care Unit The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China Research Institute for Mathematics and Interdisciplinary Sciences Qingdao University of Science and TechnologyQingdaoShandong 266061China Department of Critical Care and Respiratory Medicine The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China

Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early ***:This study identified indicators related to GDM through a literature review and expert *** women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively *** on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was ***:We obtained 4806 analyzable data from 1625 pregnant *** these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set *** of F1 and F2 was used for training the random forest *** overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 *** explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression *** trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of *** addition,there are cer tain requirements for the propor tions of negative and posi

关键词： early prediction gestational diabetes mellitus machine learning algorithm random forest regression

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