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Predicting User Acceptance of Automated Vehicles Based on Basic User Characteristics through Big Data Analysis 3

Predicting User Acceptance of Automated Vehicles Based on Ba...

引用

3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)

作者： Zheng, Min Zhang, Jingyu Yang, Cai Li, Xiaoyu Luo, Ji Huang, Ying Dai, Qi Univ Chinese Acad Sci Dept Psychol Inst Psychol Chinese Acad Sci Beijing Peoples R China Geely Auto Grp Co Ltd Hangzhou Peoples R China Wuhan Lotus Technol Co Ltd Shanghai Peoples R China Changan Ford Automobile Co Ltd Chongqing Peoples R China NIO Co Ltd Shanghai Peoples R China SAIC Gen Motors Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350310801

User acceptance is the key to the adoption of automated vehicles, yet previous models rely heavily on using psychological constructs which are time-consuming to measure to make the prediction. Thus, we establish a simple model to predict user acceptance through big data analysis based on basic user characteristics. According to 456 responses from a survey, the decision tree algorithm was used to build a classification model of users' acceptance of automated vehicles using age, income, gender, highest education, and marital status. The classification model with a maximum depth of four showed a portrait of people who had a high level of acceptance. This portrait was applied to the rest of the participants with 86.75% of the hit rate. We also used this model to select a group of customers to participate in an offline activity to experience real automated vehicles. From comparing the results with another group chosen by random selection, the results showed that in the model-selected group, more customers agreed to participate in the whole activity. We discussed the potential to use the model in customer portrait and company marketing activities.

关键词： big data decision tree algorithm automated vehicle customer portrait user acceptance

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Urdu Text Classification Using decision trees 12

Urdu Text Classification Using Decision Trees

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2015 12th International Conference on High Capacity Optical Networks and Enabling Emerging Technologies (HONET)

作者： Khan, K. Khan, R. Ullah Alkhalifah, Ali Ahmad, N. Univ Brescia Dept Informat Engn I-25121 Brescia Italy Qassim Univ Coll Comp Buraydah Saudi Arabia Univ Engn & Technol Peshawar Dept Comp Syst Engn Peshawar Pakistan

ISBN: (纸本)9781467392686

this article reports the development and experimental analysis of an Urdu Optical Character Recognition (OCR) system. The proposed approach presents the preprocessing, features extraction and classification of Urdu language text. Three different features extraction techniques, the Hu moments, Zernike moments and the Principal Component Analysis (PCA) are used. decision tree algorithm J-48 is used for classification. A medium size database of 441 characters is created consisting of hand written and machine written Urdu language characters. An overall best recognition accuracy of 92.06 % is achieved using the Hu moments.

关键词： classification decision tree algorithm feature extraction Urdu optical character recognition

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A Method for Evaluating the Performance of Unmanned Aerial Navigation Systems Based on Fuzzy decision trees 13th

A Method for Evaluating the Performance of Unmanned Aerial N...

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13th China Satellite Navigation Conference (CSNC) - Digital Economy and Intelligent Navigation

作者： Chen, Changyuan Shi, Chunlin Liu, Teng Li, Haojie Li, Qiyang Ma, Li Informat Engn Univ Zhengzhou Peoples R China Northwestern Polytech Univ Xian Peoples R China Coll Aeronaut Engn Xian Peoples R China Changchun Univ Sci & Technol Changchun Peoples R China

ISBN: (纸本)9789811925887;9789811925870

In the process of application, unmanned aerial vehicles (UAVs) are faced with the threat of a complex electromagnetic environment, causing the airborne navigation system to fail to achieve ideal working conditions, and seriously restricting the overall effectiveness of the UAV system. Therefore, accurate assessment of UAV navigation system performance is an important prerequisite for scientific decision-making on the use of UAVs in complex environments in the future. In system design and experimentation, the more realistic electromagnetic interference environments that UAVs may be exposed to are difficult to build, resulting in insufficient data being collected to verify UAV navigation system performance. However, as real data from UAVs is continuously accumulated in various application scenarios at a later stage, it is necessary to explore a data mining-based method for UAV navigation system performance evaluation. In this study, a decision tree algorithm was used to construct a UAV navigation performance level classification assessment model. UAV performance was analysed through UAV navigation capability and survivability indicators. Finally, simulation experiments were conducted to further develop the construction and testing of the decision tree model. The experimental results showed that the model accuracy is above 90%, validating the feasibility of the method.

关键词： UAV navigation Electromagnetic environment Performance evaluation Data mining decision tree algorithm

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Exploratory analysis of methods for automated classification of clinical diagnoses in Veterinary Medicine 21

Exploratory analysis of methods for automated classification...

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25th International Database Applications and Engineering Symposium (IDAES)

作者： Tamburis, Oscar Masciari, Elio Fatone, Gerardo Univ Naples Federico II Dept Vet Med & Anim Prod Naples Italy Univ Naples Federico II Dept Comp Sci & Elect Engn Naples Italy

ISBN: (纸本)9781450389914

The present work describes the analysis conducted on the diagnoses made during the general physical examinations in the decade 2010-2020, starting from the DB of the EMR previously implemented in the University Veterinary Teaching Hospital at Federico II University of Naples. A decision tree algorithm was implemented to work out a predictive model for an effective recognition of neoplastic diseases and zoonoses for cats and dogs from Campania Region. The results achievable by data mining techniques for what concerns computer aided disease diagnosis and exploration of risk factors and their relations to diseases, show the increasing importance of Veterinary Informatics within the wider field of Biomedical and Health Informatics, and in particular its capacity to point out the existing connections between humans, animals, and surrounding environment, according to the One (Digital) Health perspective specifics.

关键词： Electronic Medical Record Veterinary Informatics decision tree algorithm Data Mining One Digital Health

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Fault Diagnosis of Power Transformers Based on Comprehensive Machine Learning of Dissolved Gas Analysis 20

Fault Diagnosis of Power Transformers Based on Comprehensive...

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20th IEEE International Conference on Dielectric Liquids (ICDL)

作者： Guo, Chenxi Dong, Ming Wu, Zhanyu Xi An Jiao Tong Univ State Key Lab Elect Insulat Power Equipment Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728117188

Currently, the commonly used fault diagnosis methods of power transformers are often difficult to deal with the ambiguity problems encountered in the troubleshooting process. Even with some artificial intelligent techniques already experimented, the results have not been not systematic compared and are far from real application. Therefore, this paper tries to make a thorough use of machine learning tools towards result data from dissolved gas analysis. This paper establishes a machine learning model based on dissolved gas analysis for internal fault diagnosis of power transformers, and makes a comparison between multiple machine learning methods. Firstly, an overall neural network model of transformer diagnosis based on the dissolved gas analysis is formed. Next, the performance of optimized BP neural network, probabilistic neural network (PNN), and decision tree algorithm is compared from the aspects of speed and accuracy. Furthermore, the case-based reasoning method based on the Euclidean distance and normalized energy intensity algorithm is employed to get the closest matching similar case to realize the transformer defect prediction and assistant decision-making. With actual examples verified, the case-based reasoning method can help detect the most likely abnormal causes of faulty transformers through providing the most similar matching case.

关键词： transformer fault diagnosis BP neural network probabilistic neural network decision tree algorithm case-based reasoning

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Learning classifier systems for user context learning

Learning classifier systems for user context learning

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IEEE Congress on Evolutionary Computation

作者： Shankar, A Louis, S Univ Nevada Dept Comp Sci & Engn ECSL Reno NV 89557 USA

ISBN: (纸本)0780393635

Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application program which is designed to learn a mapping from user-related contextual features to reminder actions. In this paper, we consider the feasibility of using a genetics-based machine learning technique, XCS, for the purpose of learning this mapping from a set of context features to reminder actions as a predictive data-mining task. We compare XCS's performance with a decision tree algorithm on this learning task and show that XCS outperforms the decision tree learner.

关键词： data mining genetic algorithms learning (artificial intelligence) personal computing user interfaces Sycophant XCS decision tree algorithm learning classifier systems learning user preferences machine learning predictive data mining task user context lea

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Recruitment Prediction using Machine Learning

Recruitment Prediction using Machine Learning

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5th International Conference on Computing, Communication and Security (ICCCS)

作者： Reddy, Jagan Mohan D. Regella, Sirisha Seelam, Srinivasa Reddy Lakireddy Bali Reddy Coll Engn Dept CSE Krishna Andhra Pradesh India

ISBN: (纸本)9781728191805

Recruitment follows some major steps like attracting, selecting, appointing appropriate candidates for that position. There is ever-increasing focus of a functional recruitment. Every company invest lot of money and time for recruiting people to fill some specific positions and wasting resources for searching potential candidates. The total investment becomes loss if the selected candidates do not join in the company after completing this entire process. The main aim of this research is to predict joining efficient candidates on before resume selection and total process is to be done in an efficient way with minimal cost and minimal time. This prediction based on some relevant attributes of quantitative and qualitative aspects like age, gender, work experience, current salary and salary hikes etc., to predict the hiring candidates by applying various statistical measures on feature selection and using various machine learning algorithms to build the model. These results will help to predict the candidates who are joining in the end.

关键词： Data Mining Machine Learning decision tree algorithm K-Nearest Neighbor Random Forest Gaussian Naive Bayes

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A Hybrid Model for Predicting the End-Point Phosphorus Content of Electric Arc Furnace

A Hybrid Model for Predicting the End-Point Phosphorus Conte...

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Symposium on Materials Processing Fundamentals at Annual Meeting of The-Minerals-Metals-Materials-Society (TMS)

作者： Chen, Chao Wang, Nan Chen, Min Northeastern Univ Sch Met Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9783030652531;9783030652524

A hybrid model based on the combination of k-means method, BP neural network and decision tree algorithm is proposed for predicting the end-point phosphorus content of electric arc furnace. The industrial data from electric arc furnace is filtered firstly by the box-plot method, and the processed data of end-point phosphorus content is classified into three clusters by k-means analysis method. Then, three BP neural networks with different parameters for each cluster are established to deal with the data overlapping problem and increase the model accuracy. In order to obtain the optimum prediction result, a new method combined with the posterior knowledge of dephosphorization ratio and the decision tree algorithm is employed. With this method, the results predicted, respectively, by the three different BP neural networks are merged according to the merging rule set established by decision tree algorithm and the identified result is taken as the final end-point phosphorus content. In comparison with the traditional BP neural network and deep layer neural network, the hybrid model increases the prediction accuracy of end-point P content to 97.8% with +/- 0.006% error range, and meanwhile for the error ranges of +/- 0.005% and +/- 0.004%, the prediction accuracy is 94.2% and 83.0%, respectively.

关键词： End-point phosphorus content BP neural network model K-means method Data overlapping decision tree algorithm

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An adaptive Technique based on Separability Index to classify PALSAR data 2

An adaptive Technique based on Separability Index to classif...

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2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall)

作者： Thakur, Vartika Ghildiyal, Pragati G. N. Prakash, Rishi Graph Era Univ Dept Elect & Commun Dehra Dun UK India

ISBN: (纸本)9781509034802

In this paper, we have developed an adaptive technique based on separability index for classification of PALSAR data. First of all this paper investigate the effect of sample size on accuracy assessment. Further, we have utilized minimum distance to mean classifier and studied the sample size varying from 20 to 500. A comprehensive analysis was made for different polarized images, i.e., HH, HV, VV and their combinations i.e., HH-HV, HV-VV, HH-VV, and HH-HV-VV. Further, to increase the classification accuracy, separability index, which tells about the idea of class separation in different polarization was analyzed. A decision tree based on separability index was developed and adaptive statistical approach based on mean and standard deviation was used to classify PALSAR data. Good overall accuracy and class wise accuracy was observed with this adaptive technique.

关键词： Polarimetric Synthetic Aperture Radar Supervised classification Minimum distance to mean Separability Index decision tree algorithm Genetic algorithm

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Prescriptive Faculty Performance Analysis: A Case at the Onset of Covid-19 Pandemic

Prescriptive Faculty Performance Analysis: A Case at the Ons...

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IEEE Region 10 Conference (TENCON)

作者： Benosa, Brenda Caguiat, Ma Rowena Raguin De La Salle Univ Coll Comp Studies Manila Philippines

ISBN: (纸本)9781665495325

The challenges of internationalization, the diversion to outcome-based education, and the emergence of the COVID-19 pandemic triggered a growing demand for quality educators. Hence, educational institutions shall ensure continuous evaluation of faculty performance and use its data as a tool to capacitate learning providers and enhance instruction in the classroom. Using the identified performance indicators, this study aims to elicit insights from the dataset extracted from the Faculty Performance Evaluation System (FPES) of the Camarines Sur Polytechnic Colleges (CSPC) to understand how the students perceived their respective instructors' performance levels prior to and at the onset of the COVID-19 pandemic. Generated patterns were uncovered using descriptive analysis based on the students' ratings. Meanwhile, the students' comments, suggestions, and recommendations were analyzed using Sentiment Analysis through TextBlob. The same dataset was further examined to recommend a prescribed action using a supervised learning method (decision tree algorithm). With 98% model accuracy, faculty performance testing dataset were provided with prescribed actions with the following rules: Outstanding & Very Satisfactory Ratings = Re-Hire/No Action Needed;Satisfactory = Mentorship;Unsatisfactory & Poor = Re-Training & Re-Evaluation. The study discovered a decline in the faculty performance evaluation results at the onset of the COVID-19 pandemic. However, the students' sentiments were considerate to the faculty's endeavor as most of its polarity scores fell under "positive." Recommendations to strengthen and boost faculty performance were incorporated based on the findings of the prescriptive analysis.

关键词： Data Mining decision tree algorithm Sentiment Analysis Faculty Performance COVID-19 Pandemic Education

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