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Machine learning approach for the detection of vitamin D level: a comparative study

BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 1-19页

作者： Sancar, Nuriye Tabrizi, Sahar S. Near East Univ Dept Math TR-99138 Nicosia Turkiye Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran

BackgroundAfter the World Health Organization declared the COVID-19 pandemic, the role of Vitamin D has become even more critical for people worldwide. The most accurate way to define vitamin D level is 25-hydroxy vitamin D(25-OH-D) blood test. However, this blood test is not always feasible. Most data sets used in health science research usually contain highly correlated features, which is referred to as multicollinearity problem. This problem can lead to misleading results and overfitting problems in the ML training process. Therefore, the proposed study aims to determine a clinically acceptable ML model for the detection of the vitamin D status of the North Cyprus adult participants accurately, without the need to determine 25-OH-D level, taking into account the multicollinearity *** study was conducted with 481 observations who applied voluntarily to Internal Medicine Department at NEU Hospital. The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant's metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the *** to the presence of multicollinearity, the findings showed that the performance of the SVM(RBF) is obviously negatively affected when the test is examined. Moreover, it can be obviously detected that RF is more robust than other models when the variations in the size of training data are examined. This experiment's result showed that the selected RF and ENOR showed better performances than the other two models when the size of training samples was reduced. Since the multicollinearity is more severe in the small samples, it can be concluded that RF and ENOR are not affected by the prese

关键词： Machine learning models Vitamin D Metabolic syndrome Comparative study Multicollinearity classification algorithm

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Data Analysis and Visualization for Electrical Safety Tools and Equipment 5

Data Analysis and Visualization for Electrical Safety Tools ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Xue, Liang Yang, Lei Suzhou City Univ Comp Sci & Artificial Intelligence Coll Suzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed, and imprecise feature selection when processing this type of data. This paper proposes a method for predicting the loss of safety tools based on classification algorithms, which provides data support for procurement by predicting the loss. In addition, a supplier scoring model based on scoring indicators was constructed to explore the potential value of the data. First, the data is characterized by construction, elimination, fusion, and reconstruction, and correlation coefficients are used for feature selection and tested on different prediction models;Secondly, a data classification model based on scoring indicators is established to form a new data dimension, including after-sales service, product quality, scale indicators, and customer feedback;Finally, visualize the data set from the perspective of the overall table and sub-tables, and the changing trends of key evaluation indicators were analyzed. This method can not only provide data for predicting the loss of electric power tools but also solve the problem of supplier evaluation and realize multi-dimensional in-depth mining and comprehensive visualization of data.

关键词： component Machine learning Feature engineering classification algorithm Data analysis Data visualization

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Digital mapping of soil texture classes for efficient land management in the Piedmont plain of Iran

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SOIL USE AND MANAGEMENT 2022年第4期38卷 1705-1735页

作者： Keshavarzi, Ali Sanchez del Arbol, Miguel Angel Kaya, Fuat Gyasi-Agyei, Yeboah Rodrigo-Comino, Jesus Univ Tehran Dept Soil Sci Lab Remote Sensing & GIS POB 4111 Karaj *** Iran Univ Granada Fac Filosofia & Letras Dept Anal Geog Reg & Geog Fis Campus Univ Cartuja Granada Spain Isparta Univ Appl Sci Fac Agr Dept Soil Sci & Plant Nutr TR-32260 Isparta Cunur Turkey Griffith Univ Sch Engn & Built Environm Nathan Qld Australia

Accurate prediction of digital soil maps allows for the evaluation of larger areas with respect to the design of efficient land management plans at the regional scale. Nowadays, there is an increasing demand for high spatial resolution-gridded soil data for crop planning and management because it saves time and costs. One of the most essential soil physical properties affecting water holding capacity, nutrient availability and crop growth is soil texture. It exhibits a high spatial variability, but accurate maps for larger scales are lacking. The aim of this research was to produce gridded maps of soil texture fractions (clay, silt and sand) using regression-based approaches and to establish soil texture classes using classification-based techniques for the semi-arid Piedmont plain of Iran. To this end, a digital elevation model and derived topographic indices, vegetation and soil-based indices generated from 4-year timeseries of remote sensing products of Landsat 8 OLI were used as covariates. The decision tree (linear) and its improved version of random forest (nonlinear) algorithms were used for both the regression and classification analysis. For both algorithms, the topography-based indices and remotely sensed products were the most important predictors for the soil particle fractions. For the estimation of the different textural classes with multiple algorithms, we recorded a moderate overall accuracy rate of 54% and a Kappa coefficient of 17% for the validation datasets. It was observed that the nonlinear classification method of the random forest was more effective, and this was also the case for the regression modelling. In general, the random forest algorithm produced a more useful gridded map to help to design regional management plans based on soil properties.

关键词： classification algorithm digital soil mapping physical soil properties prediction random forest regional issues

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Predictive machine learning-based integrated approach for DDoS detection and prevention

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第3期81卷 4185-4211页

作者： Kebede, Solomon Damena Tiwari, Basant Tiwari, Vivek Chandravanshi, Kamlesh Hawassa Univ Inst Technol Dept Informat Technol Hawassa Ethiopia Hawassa Univ Inst Technol Dept Comp Sci Hawassa Ethiopia Dr SP Mukherjee IIIT NR Dept CSE Raipur Madhya Pradesh India LNCT Dept Informat Technol Bhopal India

Distributed Denial of Service attack has been a huge threat to the Internet and may carry extreme losses to systems, companies, and national security. The invader can disseminate Distributed denial of service (DDoS) attacks easily, and it ends up being significantly harder to recognize and forestall DDoS attacks. In recent years, many IT-based companies are attacked by DDoS attacks. In this view, the primary concern of this work is to detect and prevent DDoS attacks. To fulfill the objective, various data mining techniques such that Jrip, J48, and k-NN have been employed for DDoS attacks detection. These algorithms are implemented and thoroughly evaluated individually to validate their performance in this domain. The presented work has been evaluated using the latest dataset CICIDS2017. The dataset characterizes different DDoS attacks viz. brute force SSH, brute force FTP, Heartbleed, infiltration, botnet TCP, UDP, and HTTP with port scan attack. Further, the prevention method takes place in progress to block the malicious nodes participates in any of the said attacks. The proposed DDoS prevention works in a proactive mode to defend all these attack types and gets evaluated concerning various parameters such as Throughput, PDR, End-to-End Delay, and NRL. This study claimed that the proposed technique outperforms with respect to the AODV routing algorithm.

关键词： CICIDS2017 DDoS Attack Machine learning classification algorithm DDoS Detection DDoS Prevention

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KNN Normalized Optimization and Platform Tuning Based on Hadoop

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IEEE ACCESS 2022年 10卷 81406-81433页

作者： Ma, Chen Chi, Yuhong Xijing Univ Sch Comp Sci Xian 710123 Peoples R China

Big data has become part of the life for many people. The data about people's life are being continously collected, analysized and applied as our society progresses into the big data era. Behind the scene, the computer server clusters need to process hundres of millions pieces of data every day. It is very important to choose the right big data processing platform and algorithm to deal with different kinds of datasets. Therefore, in order to be fully familiar with the related work of driving big data processing, it is necessary to master the classification algorithm of data. It aims to help us carry out a classification model or operation analysis of classification function by screening and classifying the current data in data mining. In addition, the given data can be mapped to the specified category area, and the development trend of future data can be predicted through classification models. So this kind of algorithm helps to reduce the difficulty of work operation and improve people's work efficiency. This paper optimizes the classical classification algorithm-KNN, and designs a new normalized algorithm called PEWM_G KNN. From the perspective of distance measurement, we use Pearson correlation coefficient to replace the traditional Euclidean Metric, then we further refine the study for attribute values of datasets and introduce the entropy weight method, combined with Pearson's measure to optimize the distance calculation equation. After the K value is fixed, we added Gaussian Function to carry out the selection of classification. In this study, we compared the effects of every step, and tested datasets with different data types and sizes, in order to test the performance of the algorithm under different scenarios. The datasets we used include Iris, Breast Cancer, Dry Bean and HTRU2 (All datasets are from The University of California, Irvine). Finally, we further analyze the performance of different system configuration parameters on the prediction rate and time

关键词： Prediction algorithms Optimization Big Data Data models Predictive models Training data Correlation KNN classification algorithm HDFS Hadoop-YARN ZooKeeper Hadoop-HA Hadoop MapReduce

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Android Malware classification: Updating Features Through Incremental Learning Approach(UFILA) 24

Android Malware Classification: Updating Features Through In...

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24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Sawadogo, Zakaria Mendy, Gervais Dembelle, Jean Marie Ouya, Samuel Gaston Berger Univ Lab LANI St Louis Senegal Univ Cheikh Anta Diop Dakar Lab LITA Dakar Senegal

ISBN: (纸本)9791188428090

exponential growth of the use of connected objects, in particular of smartphones, is the consequence of the digitization of services. All types of applications, from the least critical to the most critical are available on mobile devices through mobile applications. The daily penetration of mobile applications in widely used devices brings certain threats. We find in the software repositories malwares and good application at the same time, which is a major cybersecurity problem. To resolve this problem, machine learning approaches have been proposed in the literature for the detection of malware in general and Android malicious applications in particular. Obfuscation techniques are used by developers to hide malicious applications that implies the need to update Android malware detection models. But many approaches in the literature are more focus on data than features. Hence our contribution is an incremental learning approach capable of detecting Android malware. We propose through UFILA approach an updating of features for the detection and classification of android malware by adding new features. We evaluated 13 classification algorithms and chose four most efficient algorithms to implement our approach. The results obtained by our approach surpass several malware detection approaches in the literature. The values of the metrics obtained respectively by the Accuracy, the precision, the recall and an F1-Score are 99%, 99%,98.6 %98%.

关键词： Android malware classification classification algorithm incremental learning Android malware detection machine learning cyber-security

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Capped l1-norm regularized least squares classification with label noise

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4051-4063页

作者： Yang, Zhi Gan, Haitao Li, Xuan Wu, Cong Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Hubei Peoples R China

Since label noise can hurt the performance of supervised learning (SL), how to train a good classifier to deal with label noise is an emerging and meaningful topic in machine learning field. Although many related methods have been proposed and achieved promising performance, they have the following drawbacks: (1) They can lead to data waste and even performance degradation if the mislabeled instances are removed;and (2) the negative effect of the extremely mislabeled instances cannot be completely eliminated. To address these problems, we propose a novel method based on the capped l(1) norm and a graph-based regularizer to deal with label noise. In the proposed algorithm, we utilize the capped l(1) norm instead of the l(1) norm. The used norm can inherit the advantage of the l(1) norm, which is robust to label noise to some extent. Moreover, the capped l(1) norm can adaptively find extremely mislabeled instances and eliminate the corresponding negative influence. Additionally, the proposed algorithm makes full use of the mislabeled instances under the graph-based framework. It can avoid wasting collected instance information. The solution of our algorithm can be achieved through an iterative optimization approach. We report the experimental results on several UCI datasets that include both binary and multi-class problems. The results verified the effectiveness of the proposed algorithm in comparison to existing state-of-the-art classification methods.

关键词： Artificial intelligence classification algorithm graph-based learning label noise l(1) norm

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Multivariate control charts that combine the Hotelling T² and classification algorithms

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2019年第6期70卷 889-897页

作者： Park, Sung Ho Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea

Multivariate control charts, including Hotelling's T-2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling's T-2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.

关键词： Quality control statistical process control multivariate control chart classification algorithm

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Defining user spectra to classify Ethereum users based on their behavior

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JOURNAL OF BIG DATA 2022年第1期9卷 1-39页

作者： Bonifazi, Gianluca Corradini, Enrico Ursino, Domenico Virgili, Luca Polytech Univ Marche Dept Informat Engn Ancona Italy

Purpose In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albei

关键词： Blockchain classification algorithm Ethereum User spectrum Multivariate Time Series Eros distance Extended Frobenius Norm Etherscan

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Machine Learning Framework for Multi-Level classification of Company Revenue

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IEEE ACCESS 2021年 9卷 96739-96750页

作者： Choi, Jung-Gu Ko, Inhwan Kim, Jeongjae Jeon, Yeseul Han, Sanghoon Yonsei Univ Yonsei Grad Program Cognit Sci Seoul 03722 South Korea Yonsei Univ Dept Psychol Seoul 03722 South Korea Yonsei Univ Dept Appl Stat Seoul 03722 South Korea

The planning and execution of a business strategy are important aspects of the strategic human resource management of a company. In previous studies, machine learning algorithms were used to determine the main factors correlating employees with company performance. In this study, we introduced a method based on machine-learning algorithms for the classification of company revenue. Both annual and integrated datasets were examined to evaluate the classification performance of the framework under both binary and multiclass conditions. The performance of the proposed method was validated using six evaluation metrics: accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve. As the experimental results indicate, the XGBoost classifier displayed the best classification performance among the three algorithms (XGBoost classifier, stochastic gradient descent classifier, and logistic regression) used in this study. Moreover, we confirmed the important features of the trained XGBoost model in accordance with variables focusing on human resource management studies. These results demonstrate that the proposed framework has strength in terms of both classification and practical implementation. This study provides novel insights into the relationship between employees and the revenue levels of their employer.

关键词： Companies Machine learning classification algorithms Machine learning algorithms Feature extraction Human resource management Data models Machine learning company revenue level classification algorithm human resource management

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