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Multi-regional logistics distribution demand forecasting method based on big data analysis

Multi-regional logistics distribution demand forecasting met...

引用

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

作者： Lv, Dongmei Hunan Vocational College of Railway Technology Zhuzhou China

ISBN: (纸本)9789811525674

When the conventional logistics distribution demand forecasting method predicted the multi-region logistics distribution demand, there was a shortcoming of low analysis accuracy. To this end, a multi-region logistics distribution demand forecasting method based on big data analysis was proposed. Big data technology was introduced, a multi-regional logistics distribution demand forecasting framework was built, and the construction of multi-regional logistics distribution model was achieved;Relying on the different needs of logistics distribution between regions, the demand forecasting model was embedded to realize the forecasting and analysis of multi-regional logistics distribution demand. The test data shows that the proposed big data forecasting method is 57.23% higher than the conventional method, which is suitable for the forecast of logistics distribution demand in different regions and regions. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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A New Energy Aware Task Consolidation Scheme for Geospatial Big data Application in Mist computing Environment

A New Energy Aware Task Consolidation Scheme for Geospatial ...

引用

international conference on computing for Sustainable Global Development (INDIACom)

作者： Rabindra K. Barik Sudhansu Shekhar Patra Priya Kumari Sachi Nandan Mohanty Abdulsattar Abdullah Hamad School of Computer Applications KIIT Deemed University Bhubaneswar India College of Engineering Pune Pune India College of Science Tikrit University Tikrit Iraq

The Internet of Spatial Things (IoST) are expanding rapidly for geospatial big data applications in today's digital world. A large volume of geospatial data is produced between IoST and the mist assisted cloud environment. Mist computing is a leveraging technology that stands extremely nearer to the edge of the network where geospatial big data are processing and then transferring to the cloud storage system via the fog nodes. By implementing this mist assisted cloud infrastructure, it has the greatest capabilities of reducing latency and traffic, respectively. Because of this unique model, it has a high requirement in smart geospatial healthcare applications, smart cities, geospatial location analytics and intelligent transportation system. Microcomputers are used at the mist layer in the mist assisted cloud network. Task allocation to the microcomputers in the mist servers is an NP-hard problem. The present research paper suggests a new energy aware task consolidation scheme by reducing the unused microcomputers in the mist layer of mist assisted cloud computing environment. It employs the metaphor-less Rao-1 algorithm and studies the behaviour of the algorithm with earlier evolutionary algorithms. It also reveals better performance in terms of maximizing CPU utilization.

关键词： Cloud computing smart cities NP-hard problem Medical services Microcomputers Big data applications Geospatial analysis

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A Managerial Framework for intelligent Big data analytics 2019

A Managerial Framework for Intelligent Big Data Analytics

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big data and smart computing (ICBDSC)

作者： Sun, Zhaohao Huo, Yanxia PNG Univ Technol Dept Business Studies Private Mail Bag Lae 411 Morobe Papua N Guinea PNG Univ Technol Teaching Learning Methods Unit Private Mail Bag Lae 411 Morobe Papua N Guinea

ISBN: (纸本)9781450366427

intelligent big data analytics is an emerging paradigm for integrating big data, analytics, and artificial intelligence. The objective of this paper is to provide a managerial framework of intelligent big data analytics. More specifically, this paper proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service and management for improving business decision making. Then it elaborates intelligent big data analytics for management taking into account main managerial functions: planning, organising, leading and controlling. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, business intelligence, artificial intelligence and data science.

关键词： intelligent big data analytics intelligent analytics artificial intelligence management analytics

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Building a smart and intelligent Factory of the Future with Industry 4.0 Technologies 3

Building a Smart and Intelligent Factory of the Future with ...

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3rd international conference on Science and Technology 2019, ICST 2019

作者： Mubarok, K. Arriaga, E.F. Laboratory for Industry 4.0 Smart Manufacturing Systems Department of Mechanical Engineering University of Auckland New Zealand Department of Industrial and Mechanical University of Trunojoyo Madura Indonesia

Manufacturing industries are facing new market challenges driven by high demand for personalized products and shorter product lifecycle. To cope with these challenges, a wide range of advanced Industry 4.0 technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI) have been adopted to improve the capability and effectiveness of manufacturing processes. Consequently, the new research field towards smart and intelligent manufacturing paradigm needs to be defined. Besides, only implementing new methodologies and technologies is inadequate. There is also a need to trigger the skilled workforce and manufacturing engineer's competency through education and training. This paper discusses the transformation of manufacturing paradigm, correspondence research topics, and identifies the essential skills required. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Industry 4.0

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Thoughts on human resource management of enterprises in the era of big data

Thoughts on human resource management of enterprises in the ...

引用

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

作者： Rao, Xin Fan, Bixia Hubei Business College Wuhan430079 China

ISBN: (纸本)9789811525674

In recent years, the three words of artificial intelligence, big data and driverless have become more and more popular. Therefore, in the aspect of enterprise human resource management, we also need to follow up the changes of the times and innovate constantly. With the advent of the era of big data, the human resource management model and the optimization and upgrading of management organizations are also promoted. Using the latest technology big data to optimize human resource management has undoubtedly become an important direction and choice of enterprise reform in the era of big data. The improvement of big data technology in human resource management can be in many aspects, such as in personnel recruitment, employee performance appraisal, employee skill training, and the reverse of employee leave and shift arrangement, which have very important application value. Therefore, based on big data technology, this paper studies the human resource management of enterprises. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Business Intelligence Through Big data analytics, data Mining and Machine Learning 3rd

Business Intelligence Through Big Data Analytics, Data Minin...

引用

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

作者： Yafooz, Wael M. S. Abu Bakar, Zainab Binti Fahad, S. K. Ahammad Mithun, Ahamed. M. Al Madinah Int Univ Fac Comp & Informat Technol Kuala Lumpur Malaysia

ISBN: (纸本)9789811393648;9789811393631

There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information's are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.

关键词： data mining Business intelligence Unstructured data Structured information

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Scaling Mentoring Support with Distributed Artificial Intelligence 1

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16th international conference on intelligent Tutoring Systems (ITS)

作者： Klamma, Ralf de Lange, Peter Neumann, Alexander Tobias Hensen, Benedikt Kravcik, Milos Wang, Xia Kuzilek, Jakub Rhein Westfal TH Aachen Aachen Germany DFKI Berlin Germany

ISBN: (数字)9783030496630

ISBN: (纸本)9783030496630;9783030496623

Mentoring is the activity when an experienced person (the mentor) supports a less knowledgeable person (the mentee), in order to achieve the learning goal. In a perfect world, the mentor would be always available when the mentee needs it. However, in the real world higher education institutions work with limited resources. For this, we need to carefully design socio-technical infrastructures for scaling mentoring processes with the help of distributed artificial intelligence. Our approach allows universities to quickly set up a necessary data processing environment to support both mentors and mentees. The presented framework is based on open source standards and technologies. This will help leveraging the approach, despite the organizational and pedagogical challenges. The deployed infrastructure is already used by several universities.

关键词： Mentoring support Learning analytics Cloud computing Distributed architectures Infrastructuring intelligent mentoring bots

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Exploring Human Activities Using eSense Earable Device

Exploring Human Activities Using eSense Earable Device

引用

international conference on Activity and Behavior computing (ABC)

作者： Islam, Md Shafiqul Hossain, Tahera Ahad, Md Atiqur Rahman Inoue, Sozo Kyushu Inst Technol Dept Human Intelligence Syst Kitakyushu Fukuoka Japan Kyushu Inst Technol Appl Sci Integrated Syst Engn Kitakyushu Fukuoka Japan Univ Dhaka Dept EEE Dhaka Bangladesh Osaka Univ Dept Media Intelligent Suita Osaka Japan

ISBN: (纸本)9789811589461;9789811589447;9789811589430

Detecting head- and mouth-related human activities of elderly people are very important for nurse care centers. They need to track different types of activities of elderly people like swallowing, eating, etc., to measure the health status of elderly people. In this regard, earable devices open up interesting possibilities for monitoring personal-scale behavioral activities. Here, we introduce activity recognition based on an earable device called 'eSense'. It has multiple sensors that can be used for human activity recognition. 'eSense' has a 6-axis inertial measurement unit with a microphone and Bluetooth. In this paper, we propose an activity recognition framework using eSense device. We collect accelerometer and gyroscope sensor data from eSense device to detect head- and mouth-related activities along with other normal human activities. We evaluated the classification performance of the classifier using both accelerometer and gyroscope data. For this work, we develop a smartphone application for data collection from the eSense. Several statistical features are exploited to recognize head- and mouth-related activities (e.g., head nodding, head shaking, eating, and speaking), and regular activities (e.g., stay, walk, and speaking while walking). We explored different types of machine learning approaches like Convolutional Neural Network (CNN), Random Forest (RnF), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), etc., for classifying activities. We have achieved satisfactory results. Our results show that using both accelerometer and gyroscope sensors can improve performance. We achieve accuracy of 80.45% by LDA, 93.34% by SVM, 91.92% by RnF, 91.64% by KNN, and 93.76% by CNN while we exploit both accelerometer and gyroscope sensor data together. The results demonstrate the prospect of eSense device for detecting human activities in various healthcare monitoring system.

关键词： Activity recognition Earables eSense Health care Wearable

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"Internet+" in the Marketing Mode of Electric Power Electricity Value-Added Services to Users

"Internet+" in the Marketing Mode of Electric Power Electric...

引用

international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Jin, Liangfeng Wang, Zhengguo Ding, Qi Shen, Ran Liu, Huan Power Science Research Institute of State Grid Zhejiang Electric Power Co. Ltd. Hangzhou31200 China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou312000 China

ISBN: (纸本)9783030627423

With the wide use of mobile Internet, it brings great convenience to our production and life. With the advent of the information age, the application upgrading and breakthrough of artificial intelligence and big data, the changes of communication, digital and network technology are mostly attributed to the development of the network era based on computer technology. The purpose of this paper is to explore the value added service in the electricity marketing mode under the background of "Internet+". Through the "Internet+" power of cross boundary products, the unity of traditional power industry and Internet technology and information technology will be combined. In this paper, we will use the research method of specific analysis to compare the data and come to a conclusion. The results of this paper show that Power Grid Corp will fully grasp its own advantages and explore potential value of end-users in the "Internet+" mode. Starting from the marketing theory of Power Grid Corp and user relationship theory, this paper analyzes the current situation and problems of the power grid and end-user marketing management mode, and puts forward the core value orientation requirements of the "Internet+" and the essence of the new Internet transformation "Internet+" mode. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data fusion

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Realising Edge analytics for Early Prediction of Readmission: A Case Study 8

Realising Edge Analytics for Early Prediction of Readmission...

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8th IEEE international conference on Cloud Engineering (IC2E)

作者： Nan, Yucen Li, Wei Lu, Feng Delicato, Flavia C. Zomaya, Albert Y. Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW Australia Huazhong Univ Sci & Technol Sch Comp Sci Wuhan Peoples R China Fluminense Fed Univ Dept Comp Sci Niteroi RJ Brazil

ISBN: (纸本)9781728110998

The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called P-smart (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.

关键词： Internet of things Edge computing Medical application

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