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Effective Classification and Handling of Incoming data Packets in Mobile Ad Hoc Networks (MANETs) Using Random Forest Ensemble Technique (RF/ET) 3rd

Effective Classification and Handling of Incoming Data Packe...

引用

international conference on data Management, analytics and Innovation (ICDMAI)

作者： Nayyar, Anand Mahapatra, Bandana Duy Tan Univ Grad Sch Da Nang Vietnam Siksha O Anusandhan Univ Comp Sci & Engn Dept Bhubaneswar Orissa India

ISBN: (纸本)9789811393648;9789811393631

Mobile ad hoc network is a network type, where wireless network comprises independently moving nodes that cooperatively contribute towards making a network operate successfully. These nodes are tiny electronic devices operating on battery power and have limited operational resources, such as memory, buffer capacity and processing units. These nodes can act as client, server or router, depending upon the network requirement because of the absence of a centralized server control. MANET faces lots of challenges while maintaining a secured and resilient communication due to its limited resources and operational capacity. One of the serious challenges is handling and classifying tons of incoming data at a single point of time for efficient processing. As tons of data enter MANETs network at a single point of time, it is necessary to deploy a new mechanism not only for classification but also for further processing. The heavy and extensive data become unmanageable on the part of mobile nodes while implementing techniques like data analysis to trace anomaly or extracting the required information from the data pool incoming into MANETs. This unmanageable data calls for classification technique so as to segregate data into specific group that can be further utilized for implementing or formulating a specialized mechanism that tends to solve many other research problems. The objective of this paper is to classify the incoming data into MANETs and reduce the data set using the RF/ET technique. The results showed that the proposed model attained an accuracy level of 86% in handling data packets in MANETs.

关键词： data classification MANETs Random forest/ensemble tree Regression technique Machine learning

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

Smart Group Decision Making on Leadership Style Identificati...

引用

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

Difference of Chinese and western ancient architectural char...

引用

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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Sustainable smart Cities: Optimization of Demand Responsive Transport by Using data Science Tools 2nd

Sustainable Smart Cities: Optimization of Demand Responsive ...

引用

2nd international conference on Advanced intelligent Systems for Sustainable Development, AI2SD 2019

作者： Fares, Naila Lyahyaoui, Abdelouahid Sedqui, Abdelfettah Tangier Morocco Rabat Morocco

ISBN: (纸本)9783030366704

Faced with the widely demonstrated disadvantages of individual transport, demand responsive transport (DRT) is the solution chosen by companies to ensure the transportation of its employees, however, the growth of industrial activities generates great difficulty in managing this model of transport. This research can be applied to very broad areas, in our case we aim to apply it to the city of Tangier. The objective is to minimize the collection routes of transport vehicles. In this article, we propose an optimization approach in data mining, by using clustering, multidimensional positioning (MDS), as well as a wide range of graph theory techniques. It is a vital element, of the strategies developed within the smart cities framework. We test our approach by a random data set of 40 employee pick-up points. An optimal cycle is founded, optimizing the collection. © Springer Nature Switzerland AG 2020.

关键词： smart city

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On the application of large data technology in B2C e-commerce precision marketing mode

On the application of large data technology in B2C e-commerc...

引用

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

作者： Xu, Gang Chen, Qiaolin Hubei Business College Wuhan430079 China

ISBN: (纸本)9789811525674

In recent years, big 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 popularization of online consumption and payment, big data technology is more and more widely used in e-commerce. E-commerce is mainly divided into three types, but this paper mainly analyzes the application of big data technology in B2C e-commerce precision marketing pattern. Firstly, the notion and features of B2C e-commerce as well as the concept and characteristics of precision marketing mode are described. Secondly, this paper further analyzes some challenges faced by B2C e-commerce in the times of large data. Finally, taking China Amazon B2C electronic commerce as an example, this paper briefly analyzes the ratio of B2C e-commerce in China’s online retail B2C market share in the third quarter of 2017, which is 4.1%. This shows that B2C e-commerce is developing rapidly under the background of large data era. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption

Ultra-fast Machine Learning Classifier Execution on IoT Devi...

引用

IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Bharath Sudharsan Pankesh Patel John G. Breslin Muhammad Intizar Ali Confirm SFI Research Centre for Smart Manufacturing Data Science Institute NUI Galway Ireland School of Electronic Engineering Dublin City University Ireland

ISBN: (纸本)9781665447249

With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be flashed and executed on tiny IoT devices, particularly the embedded systems. Edge analytics eliminates expensive device-to-cloud communications, thereby producing intelligent devices that can perform energy-efficient real-time offline analytics. Any increase in the training data results in a linear increase in the size and space complexity of the trained ML models, making them unable to be deployed on IoT devices with limited memory. To alleviate the memory issue, a few studies have focused on optimizing and fine-tuning existing ML algorithms to reduce their complexity and size. However, such optimization is usually dependent on the nature of IoT data being trained. In this paper, we presented an approach that protects model quality without requiring any alteration to the existing ML algorithms. We propose SRAM-optimized implementation and efficient deployment of widely used standard/stable ML-frameworks classifier versions (e.g., from Python scikit-learn). Our initial evaluation results have demonstrated that ours is the most resource-friendly approach, having a very limited memory footprint while executing large and complex ML models on MCU-based IoT devices, and can perform ultra-fast classifications while consuming 0 bytes of SRAM. When we tested our approach by executing it on a variety of MCU-based devices, the majority of models ported and executed produced 1-4x times faster inference results in comparison with the models ported by the sklearn-porter, m2cgen, and emlearn libraries.

关键词： Performance evaluation Pervasive computing conferences Computational modeling Random access memory Training data Machine learning

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Hourly Electricity Load Forecasting in smart Grid Using Deep Learning Techniques 13th

Hourly Electricity Load Forecasting in Smart Grid Using Deep...

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13th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Khan, Abdul Basit Majeed Javaid, Nadeem Nazeer, Orooj Zahid, Maheen Akbar, Mariam Khan, Majid Hameed Abasyn Univ Islamabad Campus Islamabad 44000 Pakistan COMSATS Univ Islamabad Islamabad 44000 Pakistan Grp 3 Technol Ltd Aldridge England

ISBN: (纸本)9783030222635;9783030222628

In this paper, a Deep Learning (DL) technique is introduced to forecast the electricity load accurately. We are facing energy shortage in today's world. So, it is the need of the hour that proper scenario should be introduced to overcome this issue. For this purpose, moving towards smart Grids (SG) from Traditional Grids (TG) is required. Electricity load is a factor which plays a major role in forecasting. For this purpose, we proposed a model which is based on selection, extraction and classification of historical data. Grey Correlation based Random Forest (RF) and Mutual Information (MI) is performed for feature selection, Kernel Principle Component Analysis (KPCA) is used for feature extraction and enhanced Convolutional Neural Network (CNN) is used for classification. Our proposed scheme is then compared with other benchmark schemes. Simulation results proved the efficiency and accuracy of the proposed model for hourly load forecasting of one day, one week and one month.

关键词： Deep learning smart grid Random forest Mutual Information

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Intelligence in Station-of-Things 4th

Intelligence in Station-of-Things

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4th international conference on Internet of Things and Connected Technologies (ICIoTCT)

作者： Saha, Soumya Bhave, Tejashree Nene, Manisha Def Inst Adv Technol Dept Appl Phys Pune 411025 Maharashtra India Def Inst Adv Technol Dept Comp Sci & Engn Pune 411025 Maharashtra India

ISBN: (纸本)9783030398750;9783030398743

The study in this paper proposes a notion to represent a Station containing a Thing of interest termed as Station-of-Things. The proliferation in communication, computation and sensors technology has enabled to build intelligent station-of-things environments. A station-of-things is pertinent to many scenarios related to occupancy of chairs in a classroom, seats in a theatre or auditorium, cars in parking-lot, books in a bookshelf, etc. The use of occupancy of station and size by a thing leads to extract the information useful to derive intelligence for the station-of-things. In this paper, the proposed communication model, network architecture and experimental results demonstrate the merit of the work which lays a foundation to estimate and evaluate the occupancy along with the data-types or parameters suitable to enrich machine learning models to derive artificial intelligence.

关键词： smart parking system intelligent parking system Internet-of-Things Infrared sensors Ultrasound sensors Artificial intelligence Station-of-Things

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Hybrid Solution for Privacy-Preserving data Mining on the Cloud computing 4th

Hybrid Solution for Privacy-Preserving Data Mining on the Cl...

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

作者： Osman, Huda Maarof, Mohd Aizaini Siraj, Maheyzah Md Univ Teknol Malaysia UTM Fac Engn Sch Comp Johor Baharu Malaysia Univ Kassala Fac Comp Sci & Informat Technol Kassala Sudan

ISBN: (纸本)9783030335823;9783030335816

The continuous evolution of the cloud has attracted many enterprises to outsource their data to the cloud to accomplish data mining tasks and other services. So that data owners can enjoy the benefits of the cloud without fear from violations of data privacy, PPDM approaches came to protect the privacy of data while preserving the usefulness of data. This article reviews the most popular models of PPDM over the cloud along with their strengths and the weaknesses to conclude the research gap. Some of the current PPDM models still vulnerable to various types of attacks like the K-anonymity model whereby surface from background knowledge attack, homogeneity attack, similarity attack and probabilistic inference attack, others of them consume great computational complexity, for example, the methods which depended on cryptography. The hybrid solution was proposed to protect the data privacy and overcome the current problems of PPDM include identify and attributes disclosures whereby preservation the privacy of data before to outsourced on the cloud is the main focus. K-Anonymization is combined with homomorphic encryption to rid from their limitations and take their advantage together to enhance data privacy-preserving and maintain the data utility of outsourced data.

关键词： Privacy-Preserving data mining outsourcing Cloud data privacy

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An Efficient Low Delay Task Scheduling Algorithm Based on Ant Colony System in Heterogeneous Environments 22

An Efficient Low Delay Task Scheduling Algorithm Based on An...

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22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on smart City and 6th IEEE international conference on data Science and Systems, HPCC-smartCity-DSS 2020

作者： Yang, Pei Liu, Jing Chen, Cen Ding, Yucheng Deng, Chunhua Zeng, Zeng Wuhan University of Science and Technology College of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Institute for Infocomm Research Singapore Hunan *** Interactive Entertainment Media Co Ltd Changsha China

ISBN: (纸本)9781728176499

Low delay is an important issue in a variety of applications on heterogeneous computing platforms. A great number of algorithms including heuristics as well as metaheuristics have been designed to minimize the completion time (i.e., makespan) of applications and achieved good results. However, the demand of high performance never stops. In this paper, we propose a new and efficient Ant-Colony based Low Delay Scheduling (ACLDS) algorithm to minimize the makespan of applications which can form directed acyclic graphs and are executed on a heterogeneous system. Simulated results show that our proposed algorithm is highly efficient in reducing makespan in comparison with state-of-The-Art scheduling algorithms. For instance, the reduction ratio can be up to 15.2174% for a Fast Fourier Transform (FFT) benchmark. © 2020 IEEE.

关键词： Scheduling algorithms

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