检索结果-内蒙古大学图书馆

Congress on Evolutionary Computation

作者： Mohammed Khalaf Abir Jaafar Hussain Dhiya Al-Jumeily Thar Baker Robert Keight Paulo Lisboa Paul Fergus Ala S. Al Kafri University of Anbar Ministry of Higher Education and Scientific Research Bagdad Iraq Faculty of Engineering and Technology Liverpool John Moores University Liverpool L3 3AF UK

In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide-scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models.

关键词： Floods machine learning algorithms Classification algorithms Training machine learning Support vector machines Predictive models

来源：评论

学校读者我要写书评

暂无评论

Breast cancer relapse prognosis by classic and modern structures of machine learning algorithms

Breast cancer relapse prognosis by classic and modern struct...

引用

Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)

作者： Noushin Jafarpisheh Nahid Nafisi Mohammad Teshnehlab Department of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Medical Science Iran University Tehran Iran

According to medical reports, cancers are big problems in the world society. In this paper we are supposed to predict breast cancer recurrence by multi-layer perceptron with two different outputs, a deep neural network as a feature extraction and multi-layer perceptron as a classifier, rough neural network with two different outputs, and finally, support vector machine. Then, we compare the results achieved by each method. It can be understood that rough neural network with two outputs leads to the highest accuracy and the lowest variance among other structures.

关键词： Breast cancer Support vector machines Biological neural networks Prognostics and health management machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Using machine learning algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting

Using Machine Learning Algorithms in Medication for Cardiac ...

引用

International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Hsiao-Ko Chang Cheng-Tse Wu Ji-Han Liu Jyh-Shing Roger Jang Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan R.O.C

Target-In this paper, we focus on using medicine for patients who have cardiac arrest then must have to do Cardiopulmonary Resuscitation (CPR). We want to know the medicine influence in predicting state of an illness deterioration. Therefore, we proposes a Medication for Cardiac Arrest Early Warning System (MCAEWS). It's not only assist physicians to early diagnose of an illness and immediately warning, but also increase sensitivity, decrease false positive rate and mortality rate. The most important role is greatly improve medical quality. Methods-In this study, the data is from the emergency department of National Taiwan University Hospital (NTUH). It is from January 2014 to December 2015. The patients who stayed in the emergency detention area for more than six hours during this two years. The patients were included in the retrospective cohort study. To comparative measures for the machine learning models, we used such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area under the Precision-Recall Curve (AUPRC). Results-The data were analyzed for CPR and non-CPR groups respectively. Furthermore, we evaluated sensitivity and specificity. The Random Forest Algorithm (AUC: 0.98; AUP: 0.23) compare with others such as Logistic Regression Algorithm (AUC: 0.94; AUP: 0.13), Decision Tree (AUC: 0.97; AUP: 0.05), and Extreme Random Tree (AUC: 0.91; AUP: 0.08), it was significantly high performance. Conclusion-Increasing the drug factors in vital signs, that it effectively improved the accuracy of predicting cardiac arrest. The results of this study, it's help for emergency clinical Physicians and hospital quality management will validly solve clinical medical resource allocation issues and improve medical quality through decision support systems.

关键词： Drugs Medical diagnostic imaging Cardiac arrest Hospitals machine learning algorithms machine learning

来源：评论

学校读者我要写书评

暂无评论

Measuring the effectiveness of Government Schemes using machine learning algorithms

Measuring the effectiveness of Government Schemes using Mach...

引用

International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Amruta M Deshpande Vidyadheesh J Pandurangi Dept. of CSE KLS Gogte Institute of Technology Belagavi India

ISBN: (数字)9781538677063

ISBN: (纸本)9781538677070

Communication is being an important aspect of life for exchanging the information. Digital communication has got the real boom in the recent past. Internet has made the biggest impact on the digital communication. Social media have contributed in a big way for the communication such as facebook, gmail, Twitter, yahoo, linkedin etc. The data generated through these social media is really huge and unstructured. The processing and analyzing of this huge data is very much essential. The knowledge extraction from the analysis of this huge data could be helpful in decision making process in various domains. The paper focuses on analyzing the twitter data about various government schemes such as "swatch Bharat Abhiyan", "digital India" and "demonetization" using Naïve Bayes and Maximum entropy algorithms. At the end the effectiveness of these algorithms is compared based on their performance. Also the popularity of these schemes is analyzed using people's opinion.

关键词： Classification algorithms machine learning algorithms Feature extraction Entropy machine learning Government Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Analogizing of Evolutionary and machine learning algorithms for Prognosis of Breast Cancer

Analogizing of Evolutionary and Machine Learning Algorithms ...

引用

International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Anubha Sethi School of Information and Communication Technology GGSIP University Dwarka New Delhi India

ISBN: (纸本)9781538646939

Breast cancer has proven to be a serious disease caused in women according to medical science. This study focuses on prediction of breast cancer in three different datasets, namely: Wisconsin breast cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Prognosis Breast Cancer (WPBC) datasets. The comparative study has been done between evolutionary algorithms and machine learning algorithms. Evolutionary algorithms include Particle Swam Optimization (CPSO) and Genetic Algorithm for Neural Network (GANN) whereas machine learning algorithms include KNN and C4.5 for predicting the breast cancer. The results are obtained after performing the experiment on different algorithms on the basis of their accuracy and standard deviation which may help people in medical science for better prediction of their disease and hence enabling appropriate treatment.

关键词： Breast cancer machine learning algorithms Evolutionary computation Classification algorithms Standards Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Geographical Area Mapping and Classification Utilizing Multispectral Satellite Imagery Processing Based on machine learning algorithms Classifying Land based on its use for different purposes

Geographical Area Mapping and Classification Utilizing Multi...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Apurva Saksena Anushka Ringshia Arnnava Sharma Aparna Halbe Sardar Patel Institute of Technology Andheri Mumbai

ISBN: (纸本)9781538609668

Geographically, a city is characterized as a patchwork of intensive land-uses. Land-use is the rational and judicious approach of allocating available land resources for different activities (such as settlements, arable fields, pastures, and managed woods) within a city. It is a way of utilizing the land, including the allocation, planning, and management of its resources. The use of a particular patch of land and its physical character are linked. However, research that establishes this link is lacking despite the proliferation of geospatial data. Linking a city's physical form with its function is the goal of this paper.

关键词： Remote sensing Vegetation mapping Satellites Land surface Earth Conferences machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A fault sensitivity analysis for anomaly detection in water distribution systems using machine learning algorithms

A fault sensitivity analysis for anomaly detection in water ...

引用

IEEE International Conference on Intelligent Computer Communication and Processing

作者： Alexandru Predescu Mariana Mocanu Ciprian Lupu Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest

The introduction of machine learning in large scale utility networks extends the room for improvement in the quality of service and maintenance costs. The ever expanding network of smart meters allows for a more accurate estimation of the state of the water distribution systems, at the same time requiring modern data processing solutions. By fusion with the more traditional approach in this field of research it is possible to enhance the existing capabilities for network analysis and to extend the algorithms to the level of cognitive abilities that form a basis for more efficient decision support system. In this paper we extend the fault sensitivity analysis for water distribution systems with the insights provided by state-of-the-art machine learning algorithms for data clustering and anomaly detection.

关键词： Anomaly detection Clustering algorithms machine learning algorithms Smart meters Maintenance engineering Correlation

来源：评论

学校读者我要写书评

暂无评论

Prediction of H1B Visa Using machine learning algorithms

Prediction of H1B Visa Using Machine Learning Algorithms

引用

International Conference on Advanced Computation and Telecommunication (ICACAT)

作者： Debabrata Swain Kushankur Chakraborty Anay Dombe Ashitosh Ashture Nandakishor Valakunde MCA Department IT Vishwakarma Institute of Technology Pune Computer Engineering Department

ISBN: (纸本)9781538653685

The H1 B visa is the most demanded visa world-wide. The H1 B visa applications are very heavily varied across many fields i.e. job, job title, year of petition, accountable wages, city of work etc. The purpose of this research is to estimate the likelihood of visa approval on the basis of metadata provided. We shall consider all aspects by which the petition may be approved or otherwise, strictly working on the data provided in the application. The designed classifier in the after mentioned report serves a dual purpose of H1B applicants and hopeful employers to measure the probability of getting certified prior to and after applying the petition.

关键词： Logistics machine learning algorithms Remuneration Random forests Companies Urban areas Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2018年 150卷 74-87页

作者： Shine, P. Murphy, M. D. Upton, J. Scully, T. Cork Inst Technol Dept Proc Energy & Transport Engn Cork Ireland Cork Inst Technol Dept Comp Cork Ireland Teagasc Moorepk Fermoy Anim & Grassland Res & Innovat Ctr Moorepk West Cork Ireland

This study analysed the performance of a range of machine learning algorithms when applied to the prediction of electricity and on-farm direct water consumption on Irish dairy farms. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, commercial Irish dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed for their predictive power of monthly electricity and water consumption, respectively. These variables were related to milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions. A CART decision tree algorithm, a random forest ensemble algorithm, an artificial neural network and a support vector machine algorithm were used to predict both water and electricity consumption. The methodology employed backward sequential variable selection to exclude variables, which added little predictive power. It also applied hyper-parameter tuning with nested cross-validation for calculating the prediction accuracy for each model on unseen data (data not utilised for model development). Electricity consumption was predicted to within 12% (relative prediction error (RPE)) using a support vector machine, while the random forest predicted water consumption to within 38%. Overall, the developed machine-learning models improved the RPE of electricity and water consumption by 54% and 23%, respectively, when compared to results previously obtained using a multiple linear regression approach. Further analysis found that during the January, February, November and December period, the support vector machine overpredicted electricity consumption by 4% (mean percentage error (MPE)) and water consumption by 21% (MPE), on average. However, overprediction was greatly reduced during the March - October period with overprediction of electricity consumption reduced to 1% while the overprediction of water consump

关键词： Error of prediction dairy farms dairy cattle remote monitoring system electricity consumption Water utilization machine learning algorithms Electricity Support Vector Network

来源：评论

学校读者我要写书评

暂无评论

Comparison of machine learning algorithms and Fruit Classification using Orange Data Mining Tool

Comparison of Machine Learning Algorithms and Fruit Classifi...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Devashree Vaishnav B. Rama Rao University of Mumbai

ISBN: (数字)9781538649855

ISBN: (纸本)9781538649862

With the hype about Artificial Intelligence, machine learning has become a trending topic these days. Lots of tools are available for data visualization, yet most of the model training is dependent on scripting languages. Orange Data Mining tool provides the flexibility with data pre-processing, visualization and model training and test in a single software. The proposed paper applies machine learning algorithms to a fruit image dataset and perform a comparative study of the algorithms to determine which algorithm has the highest Classification Accuracy and Precision score. Decision making is based on the images used to train the algorithm to learn the specific features derived from those trained images. Cross validation is performed in order to evaluate the results for Classification Accuracy. The difference between actual and predicted values is evaluated to determine the number of instances predicted correctly.

关键词： Classification algorithms Prediction algorithms Logistics Forestry Decision trees Data models machine learning 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：