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smart Group Decision Making on Leadership Style Identification Using Bayes Theorem 4th

Smart Group Decision Making on Leadership Style Identificati...

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4th international conference of Reliable Information and Communication Technology (IRICT)

作者： Okfalisa Ambarwati, Frica A. Insani, Fitri Saktioto, Toto Angraini UIN Sultan Syarif Kasim Riau Dept Informat Engn Pekanbaru Indonesia Univ Riau Dept Phys Pekanbaru Indonesia UIN Sultan Syarif Kasim Riau Dept Informat Syst Pekanbaru Indonesia Univ Teknol Malaysia Fac Engn Sch Comp Skudai Malaysia

ISBN: (纸本)9783030335823;9783030335816

The un-synergistic criteria's of the candidate and the organization's vision and mission became the main problem. The subjectivity of certain group tended to bring the frustration hence it provoked the emergence of conflict interests. In order to cover such issues, this paper proposed the blending of an expert system using Bayes theorem and Group Decision Making in identifying the candidate's leadership style. The analysis was conducted in a series of activities through the development of knowledge base, inference engines, weighting, experts' alliance, decision tree diagram, intersection independent and mean sample calculation. As a result, eleven numbers of leadership styles were theoretically identified thus confirmed and weighted by experts. In addition, the correlation between the leadership style and organizational vision and mission was analyzed. The leaders' assessments were perceived by the candidate itself and supported team members. The calculation of estimated values proposed the percentage of candidate leadership style identification. This provided a smart group decision making in recommending organizational decision-makers towards the fit proper elected leader based on the situation. To automate the calculation, prototype assessment software was produced and tested. Black-box and User Acceptance Test found that this application was successfully applied in identifying the leadership style for future leaders.

关键词： Bayes theorem Multiple perspectives Leader election Leader assessment

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Analysis of the relationship between individual traits and marketing effect of e-commerce marketers based on large data analysis

Analysis of the relationship between individual traits and m...

引用

1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Fu, Lei Shenyang Polytechnic College Shenyang China

ISBN: (纸本)9789811525674

In recent years, large data analysis method has been widely used in many fields, such as environmental art design, computer technology application enterprise investment and so on. With the popularity of online consumption and payment, large data technology is more and more widely used in e-commerce. This paper mainly analyzes the relationship between the individual traits of e-commerce marketers and the marketing effect based on large data analysis. First, it simply understands the concept of large data and the traits of large data, and then designs the relationship index and the relationship model according to the relationship between the two. In order to further study the relationship between the individual traits of e-commerce marketers and marketing effect, this paper selects two models: one is the relationship between different individual traits and marketing effect, the other is the relationship between different e-commerce platforms and marketing effect. Finally, it can be concluded that the individual traits of e-commerce marketers have a great impact on the marketing effect, and e-commerce marketers with strong personal charm often get more profits, accounting for 60% of the total marketing results. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Electronic commerce

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intelligent Noise Mapping using smart Phone on Web platform

Intelligent Noise Mapping using Smart Phone on Web platform

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2020 international conference on smart Innovations in Design, Environment, Management, Planning and computing, ICSIDEMPC 2020

作者： Dubey, Rakesh Bharadwaj, Shruti Biswas, Dr. Susham Rajiv Gandhi Institute of Petroleum Technology Amethi Uttar Pradesh India

ISBN: (纸本)9781728159706

Environmental pollution has become very serious due to today's advancement all around the world. Pollutions are of different types which include noise pollution. Noise pollution is considered as one of the most prevalent environmental challenges affecting human health. The rapid growth of population, vehicular density in roads, urbanization, etc., is increasing noise pollution in cities needing techniques to monitor and predict noise or to determine its effects on health. Noise levels of various sources, when integrated with the terrain data, are playing a vital role in determining the extent of noise pollution at various neighboring locations. The technique is tried to be developed over a typical city in India, i.e., Lucknow, with mixed land use pattern is chosen thus far. Further, an important variation in noise levels exists here from a place to another. It also varies with time in this city. It is planned to collect the noise levels of Lucknow from typically very crowded, averagely crowded, and thinly crowded areas. The collected data are also planned to be characterized as peak hour, average hour, and off-peak hour. Noise intensity and frequency data are observed to understand and characterize noise levels with the full spectrum. Once noise data are collected it is planned to be utilized for prediction of noise levels at unknown neighboring locations using terrain data and noise model. As it was understood that the collection of noise data at all source points is difficult, an intelligent technique is planned to be implemented. A smartphone-based technique is thought for it. smartphone with noise App is planned to be utilized to collect the noise data, after the proper calibration of its microphone with Noise Level Meter. A web-based application where people will participate and upload the noise levels with smartphones is planned to serve the purpose. The requirement of terrain data is tried to be managed using a classified web map of google. Predicted noise le

关键词： Noise pollution

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Deep Recurrent Neural Networks for Wi-Fi Based Indoor Trajectory Sensing 22nd

Deep Recurrent Neural Networks for Wi-Fi Based Indoor Trajec...

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11th international conference on intelligent Networking and Collaborative Systems (INCoS) / 22nd international conference on Network-Based Information Systems (NBiS)

作者： Li, Hao Ng, Joseph K. Ke, Junxing Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9783030290290;9783030290283

Wi-Fi based sensing becomes more and more popular in ubiquitous computing with prevalence of Wi-Fi devices. In this paper, we propose a new task, indoor trajectory sensing, based on Received Signal Strength Indicator (RSSI) of Wi-Fi. Traditional distance measure based methods, like Dynamic Time Warping (DTW) based Nearest-Neighbor (1NN) method, have poor performance in indoor trajectory sensing due to that RSSI of Wi-Fi in indoor environment is fluctuating, partially missing, time-varying and device-dependent. Recently, Recurrent Neural Networks (RNN) and its variants have strong abilities in learning the temporal dependency of sequence data since it can extract more meaningful features. Consequently, it is necessary to design an RNN model for the indoor trajectory sensing problem with relatively small size data. We adopt a passive way to collect Wi-Fi signals from the smart phone to ensure more data collected and generate multiple time series for each trajectory. For the recurrent neural network training, RNN and its variants are applied into our sequence data to find more meaningful patterns especially for different environment and devices. Series of real-world experiments have been conducted in our test bed and the results show that the deep based approach can achieve better performance than traditional methods with challenging environment and device factors.

关键词： Ubiquitous computing Wi-Fi sensing Indoor trajectory Recurrent neural networks

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Difference of Chinese and western ancient architectural characteristics based on big data analysis

Difference of Chinese and western ancient architectural char...

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1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Zhao, Yuehang Yin, Lijie Wang, Qi Jilin Engineering Normal University ChangchunJilin130052 China

ISBN: (纸本)9789811525674

Big data as a new information environment at present, there are few research and Application on architectural characteristics. In this context, this paper aims to analyze and mine the differences between Chinese and Western ancient buildings, find out the intrinsic relationship between Chinese and Western ancient buildings, design mining algorithms to analyze and classify the differences of ancient building features, and realize the information mining based on the large data of ancient building features. a certain amount of building data analysis is used as the driving force to carry out the experiment. The attribute classification of Chinese and Western ancient buildings is realized by using the information entropy splitting criterion in data analysis and data mining, and a decision tree is established to provide computer decision support for the difference of the characteristics of ancient buildings. The experimental results show that the classification algorithm proposed in this paper achieves more than 91% of the correct rate of classification for the characteristics of ancient buildings in China and the West, basically realizes the classification and recognition of the differences between the characteristics of ancient buildings in China and the West, and provides support for the design evaluation of the different architectural characteristics of ancient buildings in China and the West. © Springer Nature Singapore Pte Ltd. 2020.

关键词： data mining

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Discussion of innovated training management based on big data

Discussion of innovated training management based on big dat...

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1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Wang, Hui Ma, He Department of Information and Business Administration Dalian Neusoft University of Information DalianLiaoning China

ISBN: (纸本)9789811525674

The challenges of being a training manager or a trainer are always including information of training needs and statistics reflecting training effectiveness. And these problems are always difficult to solve because they need collect and analyze valuable information from amount of data. The technology of big data seems to be a perfect breakthrough. Based on big data technology, we are able to provide the staff with customized courses and more convenient ways of learning. At the same time, we collect information of learning activities and transform it into evaluation indicator of training effectiveness and improve training service. Therefore it is an inevitable trending of innovative training management. At the same time, with the help of e-HR platform, the sharing of knowledge and information within the company become more convenient. Staffs will be more comfortable to share, communicate and obtain what they truly need in the learning community. It is a better way to construct a learning organization. This article brings out the method of training management based on big data and designs a model of training management system. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Neural network analysis of plc traffic in smart city street lighting network 13th

Neural network analysis of plc traffic in smart city street ...

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13th international conference on Computational Intelligence in Security for Information Systems, CISIS 2020

作者： Andrysiak, Tomasz Saganowski, Lukasz Institute of Telecommunications and Computer Science UTP University of Science and Technology in Bydgoszcz Al. Prof. S. Kaliskiego 7 Bydgoszcz85-796 Poland

ISBN: (纸本)9783030578046

In the article, we present a system allowing to detect different kinds of anomalies/attacks in street lighting critical infrastructure realized by means of Power Line Communication. There is proposed a two-phase method of anomaly detection. Firstly, all the outliers are detected and eliminated from the examined network traffic parameters with the use of the Cook’s distance. data prepared this way is used in step number two to create models based on multi-layer perceptron neural network and autoregressive neural network which describe variability of the examined street lighting network parameters. In the following stage, resemblance between the expected network traffic and its real variability are analysed in order to identify abnormal behaviour which could demonstrate an attempt of an anomaly or attack. Additionally, we propose a recurrent learning procedure for the exploited neural networks in case there occur significant fluctuations in the real Power Line Communication traffic. The experimental results confirm that the presented solutions are both efficient and flexible. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Time series analysis

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Scalable and Hybrid Ensemble-Based Causality Discovery

Scalable and Hybrid Ensemble-Based Causality Discovery

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IEEE international conference on smart data Services (SMDS)

作者： Guo, Pei Ofonedu, Achuna Wang, Jianwu Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA Catholic Univ Amer Dept Elect Engn & Comp Sci Washington DC 20064 USA

ISBN: (纸本)9781728187778

Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-driven causality discovery methods, e.g., Granger causality, PCMCI and Dynamic Bayesian Network, have been proposed. Many of these causality discovery approaches mine time series data and generate a directed causality graph where each graph edge denotes a cause-effect relationship between the two connected graph nodes. Our benchmarking of different causality discovery approaches with real-world climate data shows these approaches often generate quite different causality results with the same input dataset due to their internal learning mechanism differences. Meanwhile, there are ever-increasing available data in virtually every discipline, which makes it more and more difficult to use existing causality discovery algorithms to produce causality results within reasonable time. To address these two challenges, this paper utilizes data partitioning and ensemble techniques, and proposes a two-phase hybrid causality ensemble framework. The framework first conducts phase 1 data ensemble for partitioned data and then conducts phase 2 algorithm ensemble from data ensemble results. To achieve scalability, we further parallelize the ensemble approaches via the Spark big data analytics engine. Our experiments show that our proposed approaches achieve good accuracy through ensemble and high scalability through data-parallelization in distributed computing environments.

关键词： Causality discovery Ensemble learning data parallelism Granger causality Dynamic Bayesian Network

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Operating Performance Assessment of smart Meters Based on Bayesian Networks and Convex Evidence Theory 4th

Operating Performance Assessment of Smart Meters Based on Ba...

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4th international conference on intelligent, Interactive Systems and Applications, IISA2019

作者： Yu, Haibo Li, Helong Zheng, Zehao Zhu, Yungang China Electric Power Research Institute Beijing China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9783030343866

smart electricity meters have been widely installed in recent years. In order to automatically assess the operating performance of smart meters, in this paper we propose a method based on Bayesian network. Bayesian network is adopted to represent the casual relationship among attributes and operating performance of smart meter. Multiple Bayesian networks are trained from data with genetic algorithm and bagging sampling, then a subset of Bayesian networks are selected for assessment. The evidence theory is used to fuse the multiple results from Bayesian networks and generate final assessment results for smart meters. The experimental results show the effectiveness and efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Bayesian networks

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Development Analysis of Government E-Government System Under the Background of Cloud computing

Development Analysis of Government E-Government System Under...

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international conference on Cyber Security Intelligence and analytics, CSIA 2020

作者： Shen, Kaiqiang Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030433055

With the rapid development of Internet and computer technology, cloud computing, as a computing method under big data, is becoming a research hotspot at home and abroad. Cloud computing is carried out on the basis of the Internet platform, using the virtualization technology that has been relatively mature at present. It can manage and dispatch a large number of resources uniformly through the network, so as to provide better services for users. Especially in the rapid development of mobile communication technology, cloud computing has been widely used in the field of government administration. For government administrative work, some of the original systems mainly use SMS, MMS or some simple client to work, slightly more complex work is difficult to complete. In this case, the original simple system cannot have big data interaction under the background of modern office, nor can it bring good interaction perception, so the existing government office system needs to be upgraded. The e-government system based on cloud computing is a good attempt. © Springer Nature Switzerland AG 2020.

关键词： Cloud computing

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