检索结果-内蒙古大学图书馆

Relevance Study of Data Mining for the Identification of Negatively Influenced Factors in Sick Groups

Procedia Computer Science 2015年 47卷 101-108页

作者： A.S. Aneeshkumar C. Jothi Venkateswaran Research Scholar PG and Research Department of Computer Science Presidency College Chennai 600 005 India Dean Department of Computer Science & Applications Presidency College Chennai 600 005 India

The application of medical data mining is emphasis with temporal data in this piece of writing. The application of computational methods in medical field is well known and which started in previous decades, but still now also lot of researches take place in artificial intelligence, knowledge discovery and mining. Correlating computational intelligence with medical intelligence to predict negatively influenced epidemiological factors in liver disorder patients.

关键词： Neural Networks back propagation algorithm Analysis of Variance.

来源：评论

学校读者我要写书评

暂无评论

Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

引用

FUEL 2023年第1期337卷

作者： Liang, Bin Liu, Jiang You, Junyu Jia, Jin Pan, Yi Jeong, Hoonyoung Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China Chongqing Univ Sci & Technol Chongqing 401331 Peoples R China Res Inst Petr Explorat & Dev Beijing 100086 Peoples R China Seoul Natl Univ Res Inst Energy & Resources Inst Engn Res Dept Energy Syst Engn Seoul 08826 South Korea

Accurate prediction of hydrocarbon production is crucial for the oil and gas industry. However, the strong heterogeneity of underground formation, the inconsistency in oil-gas-water distribution, and the complex flow mechanisms make hydrocarbon production forecasting (HPF) difficult, which leads to a high level of uncertainty in the prediction results. The explosion of machine learning (ML) methodologies that are capable of analyzing big data shed new light on HPF using production data. In this article, an in-depth review is provided regarding HPF using ML methodologies. Firstly, the merits and drawbacks of traditional HPF methods are analyzed and summarized. Then, the applications of ML algorithms in HPF are reviewed in detail, especially concentrating on artificial neural network, support vector machine, and ensemble learning. For each algorithm, the basic theory and its variants are first introduced, and its applications in HPF are comprehensively demonstrated subsequently. Finally, this article presents the challenge and prospects of machine-learning-based HPF. Sophisticated ML proxy models can be con-structed and employed to deal with an extended type of input data such that improving the efficacy of data utilization. On the other hand, deep learning models designed to handle time-series data can gain more attention. Modeling approaches for multivariate time-series hydrocarbon production data using deep neural networks with similar functionality to LSTM may lead to more accurate and computationally efficient production forecasting.

关键词： AI Artificial intelligence ANN Artificial neural network SVM Support vector machine DCA Decline curve analysis MFNN Multilayer feed-forward neural networks MLP Multilayer perceptron BP back propagation algorithm DNN Deep neural network LSTM Long short-term memory GAN Generative adversarial network CNN Convolutional neural network FCNN Fully connected neural network RNN Recurrent neural network BPANN back propagation artificial neural network NN Neural network cDC-GAN Conditional deep convolutional generative neural network CONV-1D 1D convolutional neural network GA Genetic algorithm DE Differential evolution SA Simulated annealing GSA Gravitational search algorithm PSO Particle swarm optimization MPA Marine predator algorithm ICA Imperialist competitive algorithm LM Levenberg Marquardt BRA Bayesian regularization algorithm MNN Multiple-neural-network HONN Higher-order neural network HOSO Higher-order synaptic operations MLMVN Multilayer neural network with multi-valued neurons DLSTM Deep long-short term memory EEMD Ensemble empirical mode decomposition FNN Feed-forward neural network BFGS Quasi-newton algorithm ORF Oil recovery factor GOR Gas oil ratio ADAM Adaptive moment estimation method WOPR well oil production rate SGD stochastic gradient descent SVR Support vector regression LSSVM Least squares support vector machine RSM Response surface method LR Linear regression RF Random forest AdaBoost Adaptive boosting GBDT Gradient boosting decision tree GBM Gradient-boosting machine MORF Multi-objective random forest GBT Gradient boosting tree PCA Principal component analysis RREE Reverse recursive feature elimination

来源：评论

学校读者我要写书评

暂无评论

Hand Drawn Optical Circuit Recognition

引用

Procedia Computer Science 2016年 84卷 41-48页

作者： Mahdi Rabbani Reza Khoshkangini H.S. Nagendraswamy Mauro Conti Department of Computer Science University of Mysore India Department of Computer Science University of Padova Italy

Electrical diagram is foundation of studies in electrical science. A circuit diagram convey many information about the system. Behind any device there are plenty of electrical ingredients which perform their specific tasks, today all the electrical software tools failed to effectively convert the information automatically from a circuit image diagram to digital form. Hence electrical engineers should manually enter all information into computers, and this process takes time and bring errors with high probability. Moreover, when the diagram is hand drawn, the problem is more complicated for any electrical analysis. Thus, in this paper we propose a new method using Artificial Neural Network (ANN) to make a machine that can directly read the electrical symbols from a hand drawn circuit image. The recognition process involves two steps: first step is feature extraction using shape based features, and the second one is a classification procedure using ANN through a back propagation algorithm. The ANN was trained and tested with different hand drawn electrical images. The results show that our proposal is viable and brings good performances.

关键词： Optical circuit recognition electrical circuit moments of image artificial neural network back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Automatic text classification using BPLion-neural network and semantic word processing

引用

The Journal of Photographic Science 2018年第2期66卷 69-83页

ABSTRACTABSTRACTText mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the back propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.

关键词： Text classification back propagation algorithm Lion optimization algorithm semantic words hyponymy

来源：评论

学校读者我要写书评

暂无评论

Prediction of cutting force of AISI 304 stainless steel during laser-assisted turning process using ANFIS

引用

Materials Today: Proceedings 2021年 38卷 2366-2371页

作者： C. Naresh P. Subhash Chandra Bose C. Suryaprakash Rao N. Selvaraj Department of Mechanical Engineering National Institute of Technology Warangal 506004 India National Institute of Technology Andhrapradesh 560017 India

In this article, the adaptive neuro based fuzzy inference system (ANFIS) model has evolved to predict the cutting force of AISI 304 stainless steel during Laser-Assisted Turning (LAT) process. LAT experiments are accomplished with TiAlN carbide insert at different range of cutting speeds (25, 50 and 75 m/min), feed rates (0.025, 0.05 and 0.075 mm/rev) depth of cuts (0.5, 0.75, 1 mm) and laser powers (0, 150, 300 and 450 W) under laser-assisted dry cutting conditions. This neural network combines the fuzzy inference system with a backpropagation algorithm of the neural network. Gauss membership function (gaussmf) was used during the training process of ANFIS for the prediction of cutting force during LAT process. The comparison of ANFIS predicted values with experimental data signifies that the application of gaussmf in proposed system achieved satisfactory accuracy. Our result proves that ANFIS model with gaussmf is an excelling predictive tool for the prophecy of cutting forces during laser-assisted turning process with minimum average test error.

关键词： Cutting force Adaptive neuro-fuzzy inference system Laser-assisted turning process AISI 304 stainless steel back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Analysis of CT images for detection of Colorectal Cancers using Hybrid Artificial Neural Networks and Fire Fly algorithm

引用

Procedia Computer Science 2020年 171卷 1517-1526页

作者： Sujatha. K Chandrashaker reddy. B Ponmagal. R.S Su-Qun Cao Professor EEE Dept. Dr.MGR Educational and Research Institute Chennai Tamil Nadu India Asst. Prof. ECE Dept. NallaNarasimha Reddy Educational Societies Group of Institutions Hyderabad India Professor Dept. of CSE SRM Institute of Science and Technology Tamil Nadu India Professor Faculty of Electronic Information Engineering Huaiyin Institute of Technology China

The longest part of the large intestine or colon is called as rectum. The uncontrolled division of cells in the colon forming a polyp at initial stage is called as colorectal cancer. Initially the polyp formed may be benign and later it may turn out to be malignant. The advancements in the field of medicine has made the screening techniques to be an effective tool in identification of the colon cancer. Early detection of the colon cancer may lead to complete cure of the disease normally, but it is very difficult to diagnose at the start. It is because, it does not show any symptoms at initial stages. Colonoscopy, a type of computed tomography (CT) is usually recommended for the patients to detect the colon cancer and it is usually a painful test conduct to identify the disease. To relieve the patients from the suffering, image processing algorithms like Weighted Adaptive Scalable Invariant Transform (WASIT) is used for extraction of features like location, orientation and scale are used as inputs to train the Artificial Neural Network (ANN) using back propagation algorithm (BPA). The optimal set of weights are obtained by adjusting the weights for BPA hybrid with Genetic algorithm (GA) and Fire Fly algorithm (FFA). The Likelihood Ratio (LR+) is found to optimal for the BPA tuned with FFA and is inferred to be in the range of 1.5 to 4 which varies with a deviation of ±0.4% from the nominal value.

关键词： Colorectal cancer Weighted Adaptive Scalable Invariant Transform Artificial neural networks back propagation algorithm Genetic algorithm Fire Fly algorithm Likelihood Ratio

来源：评论

学校读者我要写书评

暂无评论

Regulation of GMA Welding Process Features Using Neural Adaptive Control

引用

IFAC Proceedings Volumes 1997年第1期30卷 39-45页

作者： S.G. Tzafestas G.G. Rigatos E.J. Kyriannakis Intelligent Robotics and Automation Laboratory National Technical University of Athens 15773 Zografou Campus Athens Greece

Neural adaptive control (NAC) is naturally a very good candidate for the regulation of the geometrical and thermal characteristics of the welding process which is subject to strong variations and disturbances. Here two different versions of neural adaptive control are tested. The applied control scheme is the Feedback Error Lerning Architecture (FELA). In its flrst version a Multilayered Perceptron (MLP) Neurocontroller is used while its second version is based on a Radial Basis Function (RBF) Neurocontroller . Extensive numerical results are provided along with a discussion of the relative merits and limitations of the above techniques.

关键词： neural adaptive control back propagation algorithm radial basis function network

来源：评论

学校读者我要写书评

暂无评论

Soft Sensor for Flame Temperature Measurement and IoT based Monitoring in Power Plants

引用

Materials Today: Proceedings 2018年第4期5卷 10755-10762页

作者： K. Sujatha Nallamilli.P.G Bhavani Su-Qun Cao K.S. Ram Kumar Mechanical & Material Engineering Faculty /I yr ECE Dept. of EEE/ECECenter for Electronics Automation & Industrial Research (CEAIR) Dr.MGR Educational & Research Institute/ Huaiyin Institute of Technology China J.N.N Institute of Engineering Chennai India

Investigation of temperature measurement from the flame colour in thermal and gas turbine power plants is of enormous significance in the realm of vision machine technology. The primary objective for this work relies on detection, recognition and understanding of colour image processing for flame colour analysis. In this effort, soft computing methods using Artificial Neural Network (ANN) model with back propagation algorithm (BPA) and Ant Colony Optimisation (ACO) are used for this purpose. The central theme of this work uses the fact that the colour of the flame images is dependent on the temperature. The initial move is to describe a facet quantity for each flame image together with 10 facet rudiments, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid about X and Y, orientation and the two discriminant vectors correspondingly. The superiority of the images used is improved using Curvelet transform. The conception of flame detection and classification is conceded to compute the temperature from its colour. The specimen incorporates 51 flame images, a portion of which is used for trail and testing the ANN and ACO model. Ultimately, the whole specimen flame images are recognized and classified based on the temperatures corresponding to the core of the fire ball. The results are being validated by comparing with the conventional Euclidean classifier. Demonstrations establish an effective and indigenous system for flame temperature measurement. The elucidation states that the Internet of Things (IoT) with the proposed intelligent temperature sensor is connected to the embedded computing system to monitor the fluctuation in flame temperature with respect to colour changes in order to ensure complete combustion. This scheme utilizes wearable electronics technology which constantly monitors and controls the improvement of productivity in p

关键词： Soft Sensor back propagation algorithm Ant Colony Optimization Euclidean classifier Flame Temperature Feature extraction Curvelet transform Fisher’s Linear Discriminant Analysis Arduino UNO and Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Application of Artificial Intelligence to Predict the Performance and Exhaust Emissions of Diesel Engine using Rapeseed Oil Methyl Ester

引用

Procedia Engineering 2012年 38卷 853-860页

作者： S. Arumugam G. Sriram P.R. Shankara Subramanian Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Mechanical Engineering Kanchipuram -631561 Tamilnadu India

Artificial neural network (ANN) in artificial intelligence is an implementation of an algorithm inspired by research into the brain. This paper deals with artificial neural network modeling of a diesel engine to predict the engine performance and exhaust emission characteristics. Experiments were conducted on a single cylinder four stroke diesel engine fuelled with diesel as well as various percentages of blends of rapeseed oil methyl ester with diesel at different loads to acquire data for training and testing the proposed ANN. To train the network, biodiesel blend percentage, engine load, specific fuel consumption and exhaust gas temperature were used as the input variables where as the engine performance together with engine exhaust emissions were used as the output variables. Online back-propagation algorithm was used to train the network. ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficients with very low root mean square errors.

关键词： Artificial Neural Network back propagation algorithm Compression ignition engine Rapeseed oil methyl ester

来源：评论

学校读者我要写书评

暂无评论

Artificial neural network approach for solving fuzzy fractional order initial value problems under gH-differentiability

引用

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2021年第0期

作者： Ezadi, Somayeh Allahviranloo, Tofigh Islamic Azad Univ Sci & Res Branch Dept Math Tehran Iran Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkey

This paper aims to solve the celebrated fuzzy fractional differential equations (FFDEs) using an artificial neural network (ANN) technique. To accomplish the aforementioned aim, the error back propagation algorithm and a multilayer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveal a close match that confirms both accuracy and correctness of the proposed method.

关键词： artificial neural network (ANN) back propagation algorithm fuzzy fractional differential equations (FFDEs) unsupervised learning

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：