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Flexible Job Shop Scheduling Rules Mining Based on Random Forest

Flexible Job Shop Scheduling Rules Mining Based on Random Fo...

引用

作者： Yizhong Wang School of Aerospace Transport and Manufacturing Cranfield University

With the development of the global economy and customization,the manufacturing scheduling problem is increasingly *** job shops(FJSs) have to be more flexible and dynamic to handle these complex and various manufacturing *** at the dynamic scheduling problem of FJS,a method of mining scheduling rules from scheduling related historical data with industrial big data characteristics is *** the mining of scheduling rules,an improved random forest algorithm is proposed,which is suitable for mining scheduling rules from historical data related to large-scale,high-dimensional,and noisy *** results show that the scheduling rules obtained by the mining method have good performance in terms of scheduling performance and computational efficiency.

关键词： Flexible Job Shop scheduling rules mining random forest classification algorithm

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Improved Convolutional Neural Network for Lung Cancer Detection

Improved Convolutional Neural Network for Lung Cancer Detect...

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作者： Firdaous Essaf Yujian Li Seybou Sakho Pius Kwao Gadosey School of Computer Science and Technology Beijing University of Technology School of Artificial Intelligence Guilin University of Electronic Technology

The examination of the lungs is an important part of the annual physical examination. There are hundreds or thousands of cases in the physical examination, and each case contains many lung cross-sectional CT images. These all require professional doctors to screen for cases with pulmonary nodules one by one, which is not only a heavy workload but also a possibility of incorrect screening. Aiming at the above problems, a Convolutional Neural Network(CNN) is introduced to screen out the CT images for pulmonary nodules, and a classification algorithm based on CNN is proposed. The experimental results in the LIDC database show that compared with the widely used lenet-5 network, traditional methods, and other deep learning models, the use of customized convolutional neural networks improves the classification accuracy. The AUC value is 0.821 6, which is also the highest among several classifiers. Compared with other methods, this method can more accurately identify CT images of the lungs and can provide a more objective reference for clinical diagnosis as it can be used for CAD systems.

关键词： Pulmonary nodule Convolutional Neural Network classification algorithm deep learning

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The Application of Negative Selection algorithm in Multi-angle Infrared Vehicle Images Recognition

The Application of Negative Selection Algorithm in Multi-ang...

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International Conference on Telecommunications and Signal Processing

作者： Xi Yu Dongmei Fu Tao Yang Kamil Riha School of Information and Engeering University of Science and Technology Beijing Department of Telecommunications Brno University of Technology

ISBN: (纸本)9781479984992

At present, the objects of the vehicle recognition based on image processing are mostly visible images with rich details and limited camera angles. Few studies have been done on the recognition of multi-angle infrared vehicle images with less detail. However, it has important implications for transportation and especially public security. This paper extracts object contour of the vehicle from infrared vehicle images. After image correction and normalization, the geometric features and moment invariants are extracted. Based on the tolerance process of biological immune cells, this paper proposes a new Multi-classification Artificial Immunity Negative Selection (MAINS) algorithm. This algorithm can recognize vehicle types by the objects' geometric features and moment invariants. It has a stable performance and global search ability, also a high correct recognition rate in infrared vehicle image recognition.

关键词： classification algorithm Infrared Images Negative Selection Vehicle Recognition

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A Hybrid Monitoring Method for Robust Multivariate Process Control

A Hybrid Monitoring Method for Robust Multivariate Process C...

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SCIEI 2015 Singapore Conference

作者： Sung Ho Park Seoung Bum Kim Korea University/ Department of Industrial Management Engineering

Control chart,the most representative statistical process control technique,can be considered to share a common goal with classification algorithms in that both of them aim at estimating the decision boundaries that classify the observations into either in-control or the out-of-control *** main limitation that conventional control chart cannot take advantage of using out-of-control information can be eased by integrating classification algorithms with existing monitoring *** the present study,we proposed a hybrid scheme that combines T2 control chart and binary class classification algorithms by eliciting the nature of class *** and real case studies demonstrated that the proposed method can be effectively used for monitoring multivariate processes when their distributions are nonnormally distributed or unknown.

关键词： Multivariate control chart classification algorithm Hybrid monitoring statistic

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The Study of Automatic Machine Image Monitoring of Floating Waste Bottles

The Study of Automatic Machine Image Monitoring of Floating ...

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2nd International Conference on Simulation and Modeling Methodologies,Technologies and Applications(SMTA 2015)

作者： Yong-xin Qin Rong Hu Zhao-min Ma Bo Hu Yu-qiu Shi College of Electronic & Information Engineering Guangxi University of Science and Technology

Floating bottles is one of the most common pollutant of our water. As automatic monitoring is the basis of prompt and effective waste management, a new method of automatic machine image monitoring is proposed. The original image on the surface of the water is firstly segmented, then the segmented image is processed with 5 morphological operations including dilation, closing, skeleton, skeleton after dilation and skeleton after closing. Then 6 areas characteristics are extracted from the segmented images and the 5 morphological images. Finally, the need for the salvage of the floating bottles is identified with the 6 areas characteristics based on the KNN algorithm. There are 80 images used as training samples and 50 images used as test samples in the experiments. The results of the experiments show that compared with manual identification, the accuracy rates of the training samples and test samples are always 90% higher with different KNN algorithm parameters. This means that the method is effective for the automatic monitoring of floating bottles on the water surfaces and should be helpful for the automatic salvage of waste floating bottles.

关键词： Floating bottle machine vision K Nearest Neighbour(KNN) classification algorithm

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Research on Wireless Network Consumer Satisfaction Prediction Method 21

Research on Wireless Network Consumer Satisfaction Predictio...

引用

Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jian Xu Xi Wang Wireless Network Optimization Center China Mobile Communications GroupFujian Co.Ltd. China

ISBN: (纸本)9781450384087

With 5G network globalization, consumers have higher requirements for telecom operators' services. It is necessary to predict consumer satisfaction for analyzing consumer requirements. Based on the understanding of telecommunications services, the wireless network consumer satisfaction prediction is divided into three sub-predictive models: network quality, promotional activities, and tariff packages. At the same time, a hybrid sampling algorithm based on support vector machine (HS-SVM) which is used to classify the consumer satisfaction imbalance dataset is proposed to predict the consumer satisfaction of these three sub-predictive models, and the consumer's overall satisfaction is obtained by merging the results of the three sub-predictive models. The validity of the model is verified by wireless network consumer satisfaction dataset compared with the popular five separate classification algorithms and SMOTE combined with the five classification algorithms. The experimental results show that the F-value and G-mean of the proposed algorithm are improved. The proposed method has better classification performance and stronger robustness in the prediction of wireless network consumer satisfaction.

关键词： classification algorithm Support vector machine (SVM) Synthetic minority over- sampling technique (SMOTE) Wireless network consumer satisfaction imbalanced data

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Prediction Model based on Internet News Buzzword Data

Prediction Model based on Internet News Buzzword Data

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3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Xuan Lei School of Control and computer Engineering North China Electric Power University

The partial classification algorithm is mainly used to predict the popularity of network news and to explore the best model to predict the popularity of network news, so as to help network news service providers predict the popularity of news before publication. The popularity of network news is predicted according to the data analysis process: first, UCI data sets are pre-processed;secondly, feature selection is conducted for the data sets by using recursive feature elimination algorithm;then modelling and analysis is carried out, and finally through the confusion matrix, risk map and ROC(Receiver Operating Characteristic) chart performance evaluation, the performance of the model is compared and analyzed. Through comparison, it is found that random forest is the best prediction model.

关键词： network news feature selection classification algorithm model evaluation

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Wearable Pressure Sensor Suit for Real-Time Detection of Incorrect Exercise Techniques

Wearable Pressure Sensor Suit for Real-Time Detection of Inc...

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IEEE Sensors

作者： Ivin Kuriakose Shirley Chauhan Anis Fatema Aftab M. Hussain FeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd IndiFeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd IndiFeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd IndiFeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd Indi

ISBN: (数字)9781665484640

ISBN: (纸本)9781665484657

In recent years, the popularity of weight training has increased significantly. However, incorrect technique or physical form while performing an exercise can not only slow down progress but also cause serious injuries. Deadlift is a famous weight training exercise, which if done incorrectly, can lead to chronic back pain. In this paper, we present a wearable pressure sensor system that checks the posture of the user while performing deadlift. The suit employs a set of flexible pressure sensors, made using velostat (piezoresistive) material, to get the amount of bending for specific muscles. We have developed an algorithm to process the data in real-time so that feedback about incorrect posture can be provided to the user immediately. The algorithm has been configured using data from a single subject, however, it provides accurate classification for multiple subjects in the same weight class, thus eliminating the need for subject-wise configuration. The algorithm classifies a repetition (rep) of the exercise into a good rep or bad rep with an overall accuracy of 95.5%, across three subjects. With some enhancements, the system can also be configured to classify reps of other exercises.

关键词： classification algorithm Deadlift Flexible system Pressure sensor Wearable suit

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Analysis on the Application of Data Mining Technology in Policing Monitoring System

Analysis on the Application of Data Mining Technology in Pol...

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2014 2nd International Conference on Economic,Business Management and Education Innovation(EBMEI 2014)

作者： Xu Jia Yun Bai School of Science HeBei University of Technology School of Economics and Management HeBei University of Technology

With the occurrence of violence in Kun Ming railway stations, policing monitoring system in security issues has become the focus of attention. The article analyzes the application of association rules and classification algorithm in the policing monitoring system, instructing feasibility and convenience of this idea from the perspective of data mining technology.

关键词： Data Mining Policing Monitoring System Association Rules classification algorithm

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Embedding Na?ve Bayes algorithm Data Model in Predicting Student Graduation

Embedding Na?ve Bayes Algorithm Data Model in Predicting Stu...

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作者： Ace C.Lagman Joseph Q.Calleja Ma.Corazon G.Fernando Joseph G.Gonzales John Benedict Legaspi John Heland Jasper C.Ortega Ronel F.Ramos Maria Vicky S.Solomo Regina C.Santos FEU Institute of Technology

In the Philippines,according to Philippine Authority of Statistics,there is an imbalance between the student enrollment and student *** half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on *** study aims to utilize how Na?ve Bayes algorithm-a data classification algorithm that is based on probabilistic analysis-can be used in educational data mining specifically in student *** study is focused on the application of the Na?ve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time,so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： Na?ve Bayes machine learning algorithm data mining student graduation prediction Big Data Analytics classification algorithm

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