检索结果-内蒙古大学图书馆

5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Jain, Richa Devendran, V Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9781665426428

The rapid growth of data science in medicine has been fueled by the digitalization of the medical services, which has resulted in a flood of clinical huge data. The information gathered from this flood of data should be organized in such a way that it can provide better healthcare insights. The efficiency and effectiveness of the medical care systems can be improved by data mining algorithms by allowing them to use data more effectively. data mining can be beneficial to the health-care industry in many perspectives for instance, predictive medicine, customer relationship management, fraud and also abuse discovery, healthcare monitoring, and also keeping track of the efficiency of certain therapies, etc. This paper has presented a comparison of various data mining algorithms and also highlights applications and challenges of data mining in healthcare.

关键词： data mining Healthcare data mining algorithms

来源：评论

学校读者我要写书评

暂无评论

Introducing a framework for modeling of drug electrochemical removal from wastewater based on data mining algorithms, scatter interpolation method, and multi criteria decision analysis (DID)

引用

JOURNAL OF CLEANER PRODUCTION 2020年 266卷 122075-122075页

作者： Farzin, Saeed Chianeh, Farideh Nabizadeh Anaraki, Mahdi Valikhan Mahmoudian, Fatemeh Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Semnan Univ Dept Appl Chem Fac Chem Semnan Iran

The contamination of waters by persistent organic pollutants, especially pharmaceutical contaminants, is one of the concerns all over the world. To date, among the treatment methods, the efficient EAOPs method have shown a high ability to treat this type of pollutant. However, conducting adequate tests for taking into account almost all possible conditions to predict the amount of pollutant removal in different conditions is still a challenge. On the other hand, achieving this aim requires a lot of cost and time. The superiority of data mining based methods over conventional mathematical methods have made these methods a good solution to solve this problem. Hence, in present study a model by employing data mining algorithms includes Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree, least-square support vector machine (LSSVM) and hybrid of LSSVM and firefly optimization algorithm (FFA), scatter interpolation method, and multi-criteria decision making, namely DID, is presented for modeling of drugs removal. For this purpose, four different inputs include current density, electrolyte concentration, pH, and electrolysis time are used for electrochemical removal of Ciprofloxacin (CIP) as a model pollutant. Subsequently, Scatter interpolation method is used for generating enough data for more accurate modeling and more reliable results. In the final part of the survey, the TOPSIS method under six scenario, is employed for ranking of algorithms by considering accuracy and time criteria. In defined scenarios for TOPSIS, six different weights are considered for time criteria as well as the weights of accuracy are considered as equal in each scenario. Also, the sum of scores of each algorithm in all scenarios is used for final decision. The finding results by TOPSIS for original data showed the superiority of LSSVM_FFA. After generating new data, the M5 and the ANFIS have better results in 0.25 time weight. However, by decreasing t

关键词： Drug removal modeling data mining algorithms Scatter interpolation Multi criteria decision analysis Ciprofloxacin

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Samples with an Unknown Matrix Using data mining algorithms

引用

INORGANIC MATERIALS 2017年第14期53卷 1454-1457页

作者： Molchanova, E. I. Korzhova, E. N. Stepanova, T. V. Kuz'min, V. V. Irkutsk State Transport Univ Irkutsk Russia Irkutsk State Univ Irkutsk Russia

In determining a limited number of analytes in samples having a complex chemical composition with an unknown matrix, the combination of data mining algorithms (problems of clustering and regression) is proposed. This makes it possible to compensate for the influence of the components of the host medium on the intensity of the analytical line of an element being determined. The technology developed is tested in the X-ray fluorescence determination of S, Fe, Cu, Zn, and As in float concentrate samples during processing of polymetallic ores and V and Fe in synthetic film samples that are adequate in physicochemical properties to samples of welding fumes deposited on a filter. The error of the results of analysis has decreased by a factor 1.5-5 compared to the use of the Lucas-Tooth classical regression equation. The developed technology considerably increases the rapidity of analysis when it is used with X-ray spectrometers of consecutive action.

关键词： data mining algorithms cluster heterogeneous materials X-ray fluorescence analysis models of calibration functions regression equations least-squares method calibration samples adequacy error

来源：评论

学校读者我要写书评

暂无评论

A Review on Lung Cancer Diagnosis Using data mining algorithms

引用

CURRENT MEDICAL IMAGING 2021年第1期17卷 16-26页

作者： Heydari, Farzad Rafsanjani, Marjan Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman *** Iran

Due to the serious consequences of lung cancer, medical associations use computer-aided diagnostic procedures to diagnose this disease more accurately. Despite the damaging effects of lung cancer on the body, the lifetime of cancer patients can be extended by early diagnosis. data mining techniques are practical in diagnosing lung cancer in its first stages. This paper surveys a number of leading data mining-based cancer diagnosis approaches. Moreover, this review draws a comparison between data mining approaches in terms of selection criteria and presents the advantages and disadvantages of each method.

关键词： Lung cancer machine learning data mining algorithms detection accuracy dignosis MRI

来源：评论

学校读者我要写书评

暂无评论

Enhancing data-driven modeling of fluoride concentration using new data mining algorithms

引用

ENVIRONMENTAL EARTH SCIENCES 2022年第3期81卷 89页

作者： Gupta, Praveen Kumar Maiti, Saumen IIT ISM Dept Appl Geophys Dhanbad 826004 Bihar India

Groundwater is an essential constituent of drinking water in hard rock areas and hence it requires the analysis of contaminant resources. Fluoride contamination with large spatial variation in the part of Sindhudurg district is reported. The present study focuses on the development of data-driven modeling of fluoride concentration using on-site measurement of physicochemical parameters. In this configuration, six machine learning(ML) architectures, namely data mining algorithms were explored including novel algorithms Gaussian process (GP) and long short term memory (LSTM). The results were compared with support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and multi-layer perceptron (MLP) as a benchmark to test the robustness of the modeling process. In total 225 water samples from different dug-wells/bore- wells were obtained from the area (latitude:15.37-16.40 degree, longitude:73.19-74.18 degree) in the period of 2009-2016. Two subsets of data were divided with 80% data in training and 20% in testing. Different 9 physicochemical parameters pH, EC, TDS, Ca2+, Mg2+, Na+, Cl-, HCO3-, SO42- were used in the modeling of fluoride (F-). In this context logarithmic transformation of raw data was employed to improve the correlation between input and target and therefore to enhance the modeling accuracy. Different quantitative and qualitative (visual) measures were taken to establish the prediction power of models. Results revealed that GP outperform all other models in fluoride prediction followed by LSTM, SVM, MLP, RF, and ELM, respectively. Results also revealed that the model's performance depends on model structure and data accuracy.

关键词： Fluoride data transformation data mining algorithms Gaussian process

来源：评论

学校读者我要写书评

暂无评论

Predicting coronary artery disease: a comparison between two data mining algorithms

引用

BMC PUBLIC HEALTH 2019年第1期19卷 1-9页

作者： Ayatollahi, Haleh Gholamhosseini, Leila Salehi, Masoud Iran Univ Med Sci Hlth Management & Econ Res Ctr Tehran Iran Iran Univ Med Sci Sch Hlth Management & Informat Sci Dept Hlth Informat Management Tehran Iran AJA Univ Med Sci Sch Paramed Sci Tehran Iran Iran Univ Med Sci Sch Publ Hlth Dept Biostat Tehran Iran

BackgroundCardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected *** present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n=1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical *** SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test's result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN *** to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value o

关键词： Coronary artery disease (CAD) data mining algorithms Artificial neural network (ANN) Support vector machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

Risk of Hepatotoxicity Associated with the Use of Telithromycin: A Signal Detection Using data mining algorithms

引用

ANNALS OF PHARMACOTHERAPY 2008年第12期42卷 1791-1796页

作者： Chen, Yan Guo, Jeff J. Healy, Daniel P. Lin, Xiaodong Patel, Nick C. Univ Cincinnati James L Winkle Coll Pharm Div Pharm Practice & Adm Sci Acad Hlth Ctr Cincinnati OH 45267 USA Univ Cincinnati McMicken Coll Arts & Sci Dept Math Sci Cincinnati OH 45267 USA Med Coll Georgia Dept Psychiat Augusta GA 30912 USA Univ Georgia Coll Pharm Augusta GA USA

BACKGROUND: With the exception of case reports, limited data are available regarding the risk of hepatotoxicity associated with the use of telithromycin. OBJECTIVE: To detect the safety signal regarding the reporting of hepatotoxicity associated with the use of telithromycin using 4 commonly employed data mining algorithms (DMAs). METHODS: Based on the Adverse Events Reporting System (AERS) database of the Food and Drug Administration, 4 DMAs, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the information component (IC), and the Gamma Poisson Shrinker (GPS), were applied to examine the association between the reporting of hepatotoxicity and the use of telithromycin. The study period was from the first quarter of 2004 to the second quarter of 2006. The reporting of hepatotoxicity was identified using the preferred terms indexed in the Medical Dictionary for Regulatory Activities. The drug name was used to identify reports regarding the use of telithromycin. RESULTS: A total of 226 reports describing hepatotoxocity associated with the use of telithromycin were recorded in the AERS. A safety problem of telithromycin associated with increased reporting of hepatotoxicity was clearly detected by 4 algorithms as early as 2005, signaling the problem in the first quarter by the ROR and the IC, in the second quarter by the PRR, and in the fourth quarter by the GPS. CONCLUSIONS: A safety signal was indicated by the 4 DMAs suggesting an association between the reporting of hepatotoxicity and the use of telithromycin. Given the wide use of telithromycin and serious consequences of hepatotoxicity, clinicians should be cautious when selecting telithromycin for treatment of an infection. In addition, further observational studies are required to evaluate the utility of signal detection systems for early recognition of serious, life-threatening, low-frequency drug-induced adverse events.

关键词： data mining algorithms spontaneous reporting system telithromycin

来源：评论

学校读者我要写书评

暂无评论

A Performance Comparison of data mining algorithms Based Intrusion Detection System for Smart Grid

A Performance Comparison of Data Mining Algorithms Based Int...

引用

IEEE International Conference on Electro Information Technology (EIT)

作者： El Mrabet, Zakaria El Ghazi, Hassan Kaabouch, Naima Univ North Dakota Sch Elect Engn & Comp Sci Grand Forks ND 58201 USA Natl Inst Posts & Telecommun Rabat Rabat Morocco

ISBN: (纸本)9781728109275

Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of green technology to meet the environmental requirements. Unfortunately, information technologies have inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. A performance evaluation of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with a higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy.

关键词： Smart Grid IDS data mining algorithms probability of detection probability of false alarm Random Forest Naive Bayes

来源：评论

学校读者我要写书评

暂无评论

Parallelization of data mining algorithms for Multicore Processors 4

Parallelization of Data Mining Algorithms for Multicore Proc...

引用

4th Mediterranean Conference on Embedded Computing (MECO

作者： Kholod, Ivan Kuprianov, Mikhail Shorov, Andrey St Petersburg Electrotech Univ LETI Fac Comp Sci & Technol St Petersburg Russia

ISBN: (纸本)9781479989997

The article describes a approach of parallel data mining algorithms to be executed on multicore processors of various architecture. The suggested method presents an algorithm as a consequence of pure functions with unified interfaces. For parallel execution additional functions are introduced to share data and models between the parallel threads. Besides such functions allow to obtain various parallel algorithm structures and implement various strategies of execution for different environment conditions. Application of the described method is illustrated through algorithm Naive Bayes.

关键词： data mining parallel data mining data mining algorithms distributed data mining parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

The Formal Model of data mining algorithms for Parallelize algorithms 19

The Formal Model of Data Mining Algorithms for Parallelize A...

引用

19th International Multiconference on Advanced Computer Systems

作者： Kholod, Ivan Karshiyev, Zaynidin Shorov, Andrey St Petersburg Electrotech Univ LETI Ul Prof Popova 5 St Petersburg Russia

ISBN: (纸本)9783319151472;9783319151465

The present paper describes the formal model of data mining algorithms. These models consider each data mining algorithm as a sequence of operations. This allows us to determine ways for parallel execution of data mining algorithms. The software implementation of the formal model is executed on the Java language. A few data mining algorithms were developed on the basis of the suggested formal modal. The algorithm k-means is described in the paper as the example.

关键词： data mining Parallel data mining data mining algorithms Parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：