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Toxic mechanism of the Mongolian medicine "Hunqile-7" based on metabonomics and the metabolism of intestinal flora

TOXICOLOGY RESEARCH 2023年第1期12卷 49-61页

作者： Wang, Xiye Bao, Leer Jiang, Mingyang Li, Dan Xu, Liang Bai, Meirong Inner Mongolia Minzu Univ Coll Chem & Mat Sci Tongliao 028000 Peoples R China Inner Mongolia Minzu Univ Inner Mongolia Key Lab Chem Nat Prod Chem & Synth Tongliao 028000 Peoples R China Inner Mongolia Autonomous Reg Drug Inspection Ctr Hohhot 010000 Peoples R China Inner Mongolia Minzu Univ Coll Comp Sci & Technol Tongliao 028000 Peoples R China Inner Mongolia Minzu Univ Dev Project Minist Educ Key Lab Mongolian Res Tongliao 028000 Peoples R China Inner Mongolia Minzu Univ Room 208Ligong Bldg Tongliao Inner Mongolia Peoples R China

The traditional Mongolian medicine Hunqile-7 (HQL-7), which is mainly used to relieve pain in clinic, has certain toxicity. Therefore, toxicological investigation of HQL-7 is of great significance to its safety assessment. In this study, the toxic mechanism of HQL-7 was explored based on a combination of metabolomics and intestinal flora metabolism. UHPLC-MS was used to analyze the serum, liver and kidney samples of rats after intragastric administration of HQL-7. The decision tree and K Nearest Neighbor (KNN) model were established based on the bootstrap aggregation (bagging) algorithm to classify the omics data. After samples were extracted from rat feces, the high-throughput sequencing platform was used to analyze the 16s rRNA V3-V4 region of bacteria. The experimental results confirm that the bagging algorithm improved the classification accuracy. The toxic dose, toxic intensity, and toxic target organ of HQL-7 were determined in toxicity tests. Seventeen biomarkers were identified and the metabolism dysregulation of these biomarkers may be responsible for the toxicity of HQL-7 in vivo. Several kinds of bacteria was demonstrated to be closely related to the physiological indices of renal and liver function, indicating liver and kidney damage induced by HQL-7 may be related to the disturbance of these intestinal bacteria. Overall, the toxic mechanism of HQL-7 was revealed in vivo, which not only provides a scientific basis for the safe and rational clinical use of HQL-7, but also opens up a new field of research on big data for Mongolian medicine.

关键词： "Hunqile-7" toxic mechanism metabonomics intestinal flora bagging algorithm UHPLC-MS

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Machine Learning Approach to Identify Promising Mountain Hiking Destinations Using GIS and Remote Sensing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 980-988页

作者： Naimi, Lahbib Ouaddi, Charaf Benaddi, Lamya Bouziane, El Mahi Jakimi, Abdeslam Manaouch, Mohamed Moulay Smail Univ Fac Sci & Tech Errachidia Dept Informat Software Engn & Informat Syst Engn Team Errachidia 52000 Morocco Ibn Tofail Univ Fac Humanities & Social Sci Dept Geog Kenitra 14000 Morocco

the objective of this study is to address the complex task of identifying optimal locations for mountain hiking sites in the Eastern High Atlas region of Morocco, considering topographical factors. The study assesses the effectiveness of a commonly used machine learning classifier (MLC) in mapping potential mountain hiking areas, which is crucial for promoting and enhancing tourism in the area. To begin with, an extensive inventory of 120 mountain hiking sites was conducted, and precise measurements of three topographical parameters were collected at each site. Subsequently, a machine learning algorithm called bagging was employed to develop a predictive model. The model achieved a high performance, with an area under the curve (AUC) value of 0.93. The model effectively identified favorable areas, encompassing around 24% of the study region, which were predominantly located in the western part. These areas were characterized by mountainous terrain, shorter slopes, and higher altitudes. The research findings provide valuable guidance to decision-makers, offering a roadmap to enhance the discovery of mountain hiking sites in the region.

关键词： Machine learning mountain hiking AI-based tourism GIS remote sensing tourism bagging algorithm decision-making

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A recommendation attack detection approach integrating CNN with bagging

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COMPUTERS & SECURITY 2024年 146卷

作者： Zhou, Quanqiang Huang, Cheng Qingdao Univ Technol Sch Informat & Control Engn Qingdao Shandong Peoples R China

Due to their open architecture, collaborative filtering recommender systems are susceptible to recommendation attacks, in which attackers inject fake rating data into the system to affect the accuracy of recommendation results. To detect these attacks, numerous detection methods have been designed and proven effective. However, in recent years, deep learning-based recommendation attack models such as GSA-GAN have shown higher concealment, posing new challenges to existing detection methods. Motivated by the need for improved detection, in this paper we propose a new approach called CNN-BAG, which integrates convolutional neural network (CNN) and bagging (BAG) techniques. CNN-BAG can enhance the detection performance by simultaneously leveraging the deep learning capabilities of CNN and the ensemble learning strengths of bagging. Firstly, we construct a deep neural network based on CNN as the base learner to automatically extract and learn features of recommendation attacks. Secondly, we use the bagging algorithm to perform bootstrap sampling on the training data to generate multiple diverse training subsets. The above constructed base learners are then trained on these generated training subsets to produce multiple base classifiers for classifying recommendation attacks. Finally, we combine the base classifiers' outputs using a majority voting method to obtain the final detection results. To assess the performance of CNN-BAG in detecting recommendation attacks, we compared it against several well-established detection methods on the Movielens-10M and Amazon datasets. Our experiments revealed that CNN-BAG is adept at identifying various attack types, including the deep learning-based recommendation attack models.

关键词： Collaborative filtering recommender system Recommendation attacks Convolutional neural network bagging algorithm Attack detection

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Actively Searching for an Effective Neural Network Ensemble

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Connection Science 1996年第3-4期8卷 337-337页

作者： Opitz, D. W. Shavlik, J. W. [a] Computer Science Department University of Minnesota Duluth MN USA [b] Computer Sciences Department University ofWisconsin Madison WI USA

A neural network NN ensemble is a very successful technique where the outputs of a set of separately trained NNs are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called ADDEMUP that uses genetic algorithms to search explicitly for a highly diverse set of accurate trained networks. ADDEMUP works by first creating an initial population, then uses genetic operators to create new networks continually, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that ADDEMUP is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show ADDEMUP is able to incorporate prior knowledge effectively, if available, to improve the quality of its ensemble.

关键词： Genetic algorithms Knowledge-BASED Neural Networks bagging algorithm Regent algorithm Kbann algorithm

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Optimal Power Generation in Energy-Deficient Scenarios Using bagging Ensembles

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IEEE ACCESS 2019年 7卷 155917-155929页

作者： Mehmood, Kashif Ul Hassan, Hafiz Tehzeeb Raza, Ali Altalbe, Ali Farooq, Haroon Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China Univ Lahore Dept Elect Engn Lahore 54000 Pakistan Univ Punjab Dept Elect Engn Lahore 54000 Pakistan King Abdulaziz Univ Dept Informat Technol Jeddah 21589 Saudi Arabia Univ Engn & Technol Rachna Coll Engn & Technol Dept Elect Engn Gujranwala Campus Lahore 54000 Pakistan

This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches;the conventional artificial neural network (ANN) with feed-forward back-propagation model and a bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR.

关键词： Power generation bagging Economics Generators Propagation losses Biological neural networks Artificial neural networks bootstrap aggregation bagging algorithm disjoint partition economic dispatch optimal power generation

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Decision tree based predictive models for breast cancer survivability on imbalanced data

Decision tree based predictive models for breast cancer surv...

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3rd International Conference on Bioinformatics and Biomedical Engineering

作者： Liu Ya-Qin Wang Cheng Zhang Lu Shanghai Jiao Tong Univ Dept Biomed Engn Sch Basic Med Shanghai 200030 Peoples R China

ISBN: (纸本)9781424429011

Based on imbalanced data, the predictive models for 5-year survivability of breast cancer using decision tree are proposed. After data preprocessing from SEER breast cancer datasets, it is obviously that the category of data distribution is imbalanced. Under-sampling is taken to make up the disadvantage of the performance of models caused by the imbalanced data. The performance of the models is evaluated by AUC under ROC curve, accuracy, specificity and sensitivity with 10-fold stratified cross-validation. The performance of models is best while the distribution of data is approximately equal. bagging algorithm is used to build an integration decision tree model for predicting breast cancer survivability.

关键词： imbalanced data decision tree predictive breast cancer survivability 10-fold stratified cross-validation bagging algorithm

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Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior

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Journal of Systems and Information Technology 2017年第1-2期19卷 65-93页

作者： Khodabandehlou, Samira Zivari Rahman, Mahmoud Master of Information Technology Engineering Tehran Iran Allameh Tabataba’i University Tehran Iran

Purpose: This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach: The six stages are as follows: first, collection of customer behavioral data and preparation of the data;second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis;third, selection of training and testing data and reviewing their proportion;fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning;fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables;and sixth, providing appropriate strategies based on the proposed model. Findings: According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models. Research limitations/implications: The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries;therefore, generalizing the results to other business centers should be used with caution. Practical implications: Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Bu

关键词： bagging algorithm Boosting algorithm Churn prediction RFM model Supervised machine learning techniques

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A BEHAVIOR CLUSTER BASED AVAILABILITY PREDICTION APPROACH FOR NODES IN DISTRIBUTION NETWORKS

A BEHAVIOR CLUSTER BASED AVAILABILITY PREDICTION APPROACH FO...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： You, Jiali Xue, Jiao Wang, Jinlin Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing Peoples R China

ISBN: (纸本)9781479903566

To predict the availability state of a node in a distribution network, its history trace is usually used. Sometimes, some usage behavior patterns cannot be captured precisely from the insufficient trace, which may lead to unreliable predictors. In this paper, to alleviate the data sparseness problem, the nodes with the similar behaviors are clustered, and all history information in a same cluster is seen as another information source for any node in it. For each node, an N-gram model is used to train the predictor by the combination of the new source and the node's own trace. In addition, because it is hard to capture the trace of all nodes in large scale networks, such as P2P networks, a bagging based prediction algorithm is proposed, which can be applied in the distribution environment and relieve the effect of the noisy data. In our experiments, three datasets are evaluated. Results show that the prediction performance of our cluster based N-gram predictor is better than the results of several other predictors. And the bagging based prediction algorithm presents its validity in the distribution environment.

关键词： Availability prediction distribution network N-gram cluster K-means bagging algorithm

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An Abnormal Telephone Identification Model Based on Ensemble algorithm 3

An Abnormal Telephone Identification Model Based on Ensemble...

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3rd International Workshop on Pattern Recognition

作者： Yuan, Yahan Ji, Ke Sun, Runyuan Ma, Kun Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China

ISBN: (数字)9781510622692

ISBN: (纸本)9781510622692

Due to the rapid development of the communications industry and the popularization of telephones, more and more personal information leaks and telephone fraud cases have occurred in the life. For the problem of fraudulent calls, there are deficiencies for operators to solve these problems. Inspired by the ensemble algorithm, it was found that the bagging algorithm can solve the classification problem of unbalanced data. This paper proposes an abnormal phone recognition model based on bagging algorithm. In particular, we used PCA dimension reduction in processing data to better mine the effective features of the sample, Multiple training sets are constructed by bootstrap sampling, and the ensemble of multiple training set-trained learners can solve the classification problem of unbalanced abnormal telephone data. Experiments show that the accuracy of prediction results of the abnormal phone recognition model based on the integrated algorithm is better than the prediction results of the single decision tree model, and the problem of unbalanced samples was solved and a relatively ideal prediction effect was achieved.

关键词： ensemble learning bagging algorithm abnormal phone PCA dimension reduction

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Assessing the complex interplay of airborne pollutants and lung cancer prevalence via the improved decision tree-based vine copula modeling

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2024年第3期10卷 4549-4565页

作者： Ahdika, Atina Werdyani, Sista Muhaimin, Muhaimin Univ Islam Indonesia Dept Stat Yogyakarta 55584 Indonesia Univ Islam Indonesia Dept Pharm Yogyakarta 55584 Indonesia Univ Islam Indonesia Dept Chem Yogyakarta 55584 Indonesia

Lung cancer stands as a prevalent respiratory ailment worldwide, with its incidence intricately linked to air pollution. Investigating this relationship is pivotal for implementing effective preventive strategies. Traditionally, research has relied on a simplistic causal model, assuming uniform relationships between air pollutants and lung cancer occurrence. However, this assumption falls short of reality. This study aims to enhance the predictive accuracy of d-vine copula modeling which elucidate the complex relationship between various air pollutants-such as PM2.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2.5}$$\end{document}, PM1.0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{1.0}$$\end{document}, CO, SO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}, and SO4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_4$$\end{document}-and the occurrence of lung cancer in both genders comprehensively, by employing a decision tree-based algorithm, specifically using bagging machine learning algorithm. This method allows for the simultaneous exploration of relationships among the important variables with diverse natures and structures. The results reveal distinctive characteristics and structures in the association between air pollu

关键词： Air pollutant bagging algorithm Decision tree model Dependence Lung cancer Vine copula

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