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Deep ConvLSTM-Inception Network for Traffic Prediction in smart Cities 22

Deep ConvLSTM-Inception Network for Traffic Prediction in Sm...

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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： Huang, Penggui Huang, Bi Zhao, Fan Zhang, Yanfeng Chen, Mingong Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9781728176499

Accurate and real-Time traffic prediction is essential for smart cities. However, due to the diversity of traffic point of interest in the city, the spatial correlation of urban traffic is extremely complicated. The use of ordinary spatial correlation feature extraction methods cannot cope with this complex spatial correlation. In addition, traffic flow changes also have temporal correlation. The traditional time series feature extraction method can't extract the temporal correlation of traffic data well. Therefore, we propose a ConvLSTM-Inception Network(CL-IncNet) model for traffic prediction. The CL-IncNet uses the Inception module to extract the multi-granularity spatial correlation and uses the Inception module's stack to extract the indirectly affected spatial correlation. In terms of extracting time-related features, we chose ConvLSTM structure. Compared with LSTM and GRU, this structure has fewer parameters and can guarantee the spatial relative relation of output information. The experimental results on the NYC-Taxi dataset show that our method outperforms four well-known baseline results. © 2020 IEEE.

关键词： smart city

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Statistical Metric-Theoretic Approach to Activity Recognition Based on Accelerometer data 5th

Statistical Metric-Theoretic Approach to Activity Recognitio...

引用

5th international conference on Advanced intelligent Systems and Informatics (AISI)

作者： Gomaa, Walid E JUST Cyber Phys Syst Lab Alexandria Egypt Alexandria Univ Fac Engn Alexandria Egypt

ISBN: (纸本)9783030311292;9783030311285

Providing accurate information on people's actions, activities, and behaviors is one of the key tasks in ubiquitous computing and it has a wide range of applications including healthcare, well being, smart homes, gaming, sports, etc. In the domain of Human Activity Recognition, the primary goal is to determine the action a user is performing based on data collected through some sensor modality. Common modalities adopted to this end include visual and Inertial Measurement Units (IMUs), with the latter taking precedence in recent times due to their unobtrusiveness, low cost and mobility. In this work we consider the accelerometer signals streamed through a wearable IMU unit and use this data to recognize the user's activity. We develop a novel approach based on representing the coming signal as a probability distribution function and then use some distance metric to infer the dissimilarity between probability distributions corresponding to different accelerometer signals in order to infer the correct activity. Experiments are performed on 14 activities of daily living with results showing promising potential for this technique.

关键词： Human activity recognition Probability distribution function Distance metric Inertial Measurement Unit Accelerometer

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Using Resampling Techniques with Heterogeneous Stacking Ensemble for Mobile App Stores Reviews analytics 5th

Using Resampling Techniques with Heterogeneous Stacking Ense...

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5th international conference on Advanced intelligent Systems and Informatics (AISI)

作者： Gomaa, Ahmed El-Shorbagy, Sara El-Gammal, Wael Magdy, Mohamed Abdelmoez, Walid Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Alexandria Egypt

ISBN: (纸本)9783030311292;9783030311285

Over the past few years, a boom in the popularity of mobile devices and mobile apps has appeared. More than 205 billion apps were downloaded in 2018. Developers directly distribute mobile apps to end users via a centralized platform like the " App Store" for iOS or the "Play Store" for Android. The Mobile app developers get continuous feedback from users' reviews added to these stores. Tools like CLAP or AR-MINER were used to crawl reviews from the stores and try to classify them into lots of classifications like (Bug, required feature, usability issue, performance issue,) to facilitate the categorization of issues or features addition to the developer. Some machine learning techniques is used to get the most accurate data classification to help the developer to classify the reported reviews on the stores. This paper presents a machine learning model that uses the Resampling techniques for handling imbalanced classes in addition to ensemble learning and stacking. The model outperforms those tools and enhances the results applied on Mobile App Stores Reviews analytics. In addition to that the paper provides experiments applied on different kinds of datasets and showed improvements in accuracy from 85% (previous model) to 90% (our model) and ROC from 96% to 98% especially on the Reviews dataset.

关键词： Mobile app reviews Machine learning Ensemble SMOTE Software engineering Classification Stacking Heterogeneous

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Ontology-based Automatic Semantic Annotation Method for IoT data Resources 13

Ontology-based Automatic Semantic Annotation Method for IoT ...

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2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on smart data, smartdata 2020

作者： Zhang, Miao Han, Lijing Yuan, Lingyun Chen, Nan School of Information Science and Technology Yunnan Normal University Kunming China

ISBN: (纸本)9781728176475

The Internet of Things (IoT) system contains tens of thousands of front-end sensing devices with different functions and various patterns of manifestation. Therefore, the raw IoT data acquired by those devices was born with the feature of multi-source and heterogeneous, which leads to the difficulties and challenges for collaborative interaction and data fusion between IoT resources. Therefore, how to shield the isolation and uncertainty of IoT data, realize the cross-domain sharing and reusing of data resources are the key issues of IoT data processing field. By introducing the semantic technology, this paper constructs domain ontology and automatic semantic annotation model for IoT data, and then based on the semantic similarity mapping algorithm, adds semantic tags for the raw IoT data. Moreover, an association model with the core content of 'data resource-observation time' is constructed, so that to obtain high-quality IoT data information further. In the end, under the background of a smart greenhouse instance, by calculating the values of the three evaluation indicators of precision rate P, recall rate R and F value to analyze the performance of the annotation model, the availability of the proposed method is verified. In addition, the implementation of annotation semantic tagging system proves that the proposed method can quickly and accurately complete IoT data annotation, which further presented the good performance and practical value of the annotation method. Which would lay a theoretical foundation and technical support for the intelligent decision-making and inferential retrieval of the Internet of Things system. © 2020 IEEE.

关键词： Internet of things

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international conference on Cyber Security Intelligence and analytics, CSIA 2020

International Conference on Cyber Security Intelligence and ...

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international conference on Cyber Security Intelligence and analytics, CSIA 2020

ISBN: (纸本)9783030433055

The proceedings contain 105 papers. The special focus in this conference is on Cyber Security Intelligence and analytics. The topics include: Computer Aided Diagnosis of Early Breast Cancer by Multimodal Imaging;machine Vision Based Macro Measurement System Detection;genetic Algorithm of the Mutual Selection Between Teachers and Students in Online Learning;data Mining Method of Logistics Economy Based on Neural Network Algorithm;application of Multimedia Computer Aided Techniques in Football Teaching and Training;investigation on Art Design Assistant Method Based on Artificial Intelligence;the Computer Information Technology Application of Big data Era in Urban Planning and Design;fault Detection of Club Fitness Equipment Based on Image Processing Technology;multi-objective Optimization of Construction Project Based on Multi Ant Colony Algorithm;sports Game Management Method Based on intelligent Computer computing;public Key Digital Authentication in Mobile Communication;artificial Intelligence Knowledge Transfer and Artificial Intelligence New Product Development Quality Under Knowledge Leadership;algorithm for Motion Video Based on Basketball Image;Heart Rate Variability Analysis Method Based on KNN Classification;three-Dimensional Human Motion Simulation and Video Analysis System for Physical Training;a Regression Model of Color Value and Substance Concentration of Colored Solution Based on Lambert Beer’s Law;a Novel Steffensen Type Method with Memory;investigation on Network Security Access Mechanism Based on Sqlserver database;Information Security Risk Assessment Based on Cloud computing and BP Neural Network;Security Technology of Wireless Sensor Network Based on IPSEC;enterprise Internal Audit data Network Security System in the Information Age;intelligent Pathfinding Algorithm in Web Games.

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Travel Time Prediction in Missing data Areas: Feature-based Transfer Learning Approach 22

Travel Time Prediction in Missing Data Areas: Feature-based ...

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作者： Elmi, Sayda Tan, Kian-Lee National University of Singapore School of Computing Singapore Singapore

ISBN: (纸本)9781728176499

Travel time prediction based on deep learning techniques has received much attention in recent years. It is of great importance to advanced traffic management systems but remains a challenging problem as historical data are available in some but not all areas. Our preliminary experiments on New York City road network demonstrated that GPS pings recorded by in-vehicle GPS devices providing speed, position and time, do not cover the whole road network, thus, travel time in some regions could not be predicted accurately. However, predicting accurate travel time in each and every region in a city is fundamental to intelligent transportation systems. To predict travel time of target areas where little historical traffic data are available, we propose a transfer learning framework that exploits historical data of some source areas which are data-Abundant regions. To accurately predict travel time in source areas, we propose a hybrid architecture that models the spatial and temporal closeness as well as the road network typology. Experimental results on New York City road network show that our proposed model can achieve competitive performance comparing to baseline methods and classic regression models. © 2020 IEEE.

关键词： Travel time

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Design and Implementation of intelligent Tour Guide System in Large Scenic Area Based on Fog computing

Design and Implementation of Intelligent Tour Guide System i...

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2020 international conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2020

作者： Dong, Hongyan Zhang, Xiaozhong Kong, Dehua Zhang, Bo Wuhan Donghu University School of Computer Science Wuhan China Technical Center DongFeng Motor Corporation Wuhan China

ISBN: (纸本)9781728164991

In recent years, the tourism industry has developed rapidly, and there are more and more large-scale tourist parks. There is an urgent need for intelligent guide systems that take tourists as the center and integrate multiple functions into one. Based on the basic requirements of smart tourism, this paper proposes an intelligent guide system based on fog computing. The overall architecture and system functions of the system are designed. The tasks undertaken by each level and the implementation methods of each function are analyzed in detail. This intelligent tour guide system can provide high-quality services such as location-based and real-scenery commentary, intelligent tour guides, program reminders, and consulting exchanges to bring tourists a more satisfactory travel experience. © 2020 IEEE.

关键词： Fog computing

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Identity and Policy Based Signcryption Scheme for AMI Downlink Transmission

Identity and Policy Based Signcryption Scheme for AMI Downli...

引用

2020 IEEE international conference on Power, intelligent computing and Systems, ICPICS 2020

作者： Wang, Changji Yuan, Yuan School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China School of Mathematics and Statistics Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781728198736

The smart grid is an upgraded power network that integrates advanced metering infrastructure to provide seamless two-way communication between consumers and suppliers. Many researchers have proposed solutions to confidentiality, integrity, and authenticity in uplink transmission, but there are few pieces of literature on the security of downlink multicast communication. In this paper, we first introduce a new cryptographic concept called identity and policy based signcryption (IPSC) scheme. Then we give a concrete IPSC scheme, and prove the proposed scheme is IND-CPA secure under the DBDH assumption and UF-CMIA secure under the ECDL assumption. Finally, we present a secure and efficient smart grid downlink transmission framework by exploiting our IPSC scheme, which can ensure origin non-repudiation, data confidentiality and integrity, and can also provide fine-grained access control on downlink multicast information. © 2020 IEEE.

关键词： smart power grids

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Design of a smart home system using bluetooth protocol

Design of a smart home system using bluetooth protocol

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2020 international conference on Artificial Intelligence, Big data, computing and data Communication Systems, icABCD 2020

作者： Zcodo, Sandile Ogudo, Kingsley Umenne, Patrice Department of Electrical Engineering and Electronic Engineering University of Johannesburg Johannesburg South Africa Department of Electrical and Mining Engineering University of South Africa Johannesburg South Africa

ISBN: (纸本)9781728167701

Home automation is an intelligent, functional as a unit system that facilitates home processes without unnecessarffy complicating the user's life. Devices can be connected, which in turn connect and talk through II centralized control unit. which are accessible via mobile phones. These devices include lights, appliances. security systems, alarms and many other sensors and devices. This paper presents the design and implementation of a Bluetooth based smart home automation system which uses a Peripheral interface controller (PIC) microcontroller (16F1937) as the main processer and the appliances are connected to the peripheral ports of the microcontroller via relays. The circuit in the project was designed in Diptrace software. The PCB layout design was completed. The fully functional smart home prototype was built and demonstrated to functional. © 2020 IEEE.

关键词： Bluetooth

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Research on the Framework Design of NQI Service Cloud Platform in the Field of smart Grid Measurement 6

Research on the Framework Design of NQI Service Cloud Platfo...

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2020 6th international conference on Materials, Mechanical Engineering and Automation Technology, MMEAT 2020

作者： Fang, Xu Liu, Yongguang Wang, Yong Wang, Jun Intelligent Power Sub Center XJ Group Corporation Xuchang City China Metrology Center State Grid Henan Electric Power Company Zhengzhou City China Intelligent Power Sub Center XJ Group Corporation Xuchang City China State Grid Henan Electric Power Company China

The quality control of smart meters exists in production enterprises and power grid companies respectively. Their data are not connected, which leads to the lack of comprehensive control in the whole life cycle of smart meter design, manufacturing and operation scrap, which results in the overall quality level of smart meter industry is limited. The NQI service cloud platform in the field of smart grid measurement takes cloud computing technology as the core and NQI quality service as the process to mine and analyze the quality data of smart meters in the whole life cycle, so as to achieve the overall quality improvement of smart meter industry. In this paper, the service cloud platform architecture is designed by analyzing the service scenario, the quality evaluation index system and the data model of intelligent power meter. © Published under licence by IOP Publishing Ltd.

关键词： smart meters

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