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Teaching Quality Evaluation Model for Human Resource Development and Management Major under the Background of Big data 5

Teaching Quality Evaluation Model for Human Resource Develop...

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5th international conference on smart Grid and Electrical Automation (ICSGEA)

作者： Lyu, Bu Chen, Jianhui Wang, Na Bengbu Univ Sch Econ & Management Bengbu 233030 Anhui Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China

ISBN: (纸本)9781728182780

The evaluation of teaching quality in colleges relies on reliable and comprehensive evaluation data, which is also an important basis for educational decision-making. The application of big data technology can realize scientific analysis and in-depth mining of a large number of data, analyze the value and connection implied in the data on a multidimensional level, and enable the evaluation of teaching quality to transform from the previous analysis of segment information and small sample data to the whole process of all-round data decision-making. This paper proposes a learning quality evaluation model based on big data in cloud computing environment, using the structure method, AHP, fuzzy comprehensive evaluation and multi-objective optimization method. It is also combined with a variety of theories such as user perception theory and user experience theory, and the development of this model is studied. Finally, we develop and design the learning quality evaluation system under cloud computing, to test the system and prove the credibility of our scheme.

关键词： big data cloud computing teaching quality human resource

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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 ...

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

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

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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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The Impact of Technology Readiness on the Big data Adoption Among UAE Organisations 3rd

The Impact of Technology Readiness on the Big Data Adoption ...

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

作者： Haddad, Adel Ameen, Ali Isaac, Osama Alrajawy, Ibrahim Al-Shbami, Ahmed Chakkaravarthy, Divya Midhun Lincoln Univ Coll Kota Baharu Selangor Malaysia

ISBN: (纸本)9789811393648;9789811393631

Big data is an important player in offering a highly competitive advantage, specialty, in contemporary organisations. The theory of technology readiness can be used for measuring the readiness of an organisation to adapt big data. Structural equation modelling is used in this study through AMOS to analyse 381 valid questionnaires to evaluate the proposed model built on the Theory of Technology Readiness to determine the factors that could affect big data adoption. This research concentrates on one of Abu Dhabi's public organisations (ADPO). In this model, the key independent constructs are comparable to Innovativeness, Optimism, Insecurity and Discomfort pertaining to these organisations' readiness for exploiting this massive data amounts. The dependent constructs are based on the adopted big data's readiness in ADPO. The relations between the different constructs are defined in this research. This work has enhanced our insights regarding the online social networking model. The results showed that all four independent variables considerably helped to predict the adoption of big data with different percentages. The model that was put forward explained 50% of the variance occurring in the adoption of big data.

关键词： Optimism Innovativeness Discomfort and insecurity Big data Adopting Theory of technology readiness Public sector UAE

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

Multi-regional logistics distribution demand forecasting met...

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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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Collaborative Computation Offloading for smart Cities in Mobile Edge computing 13

Collaborative Computation Offloading for Smart Cities in Mob...

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IEEE 13th international conference on Cloud computing (CLOUD)

作者： Huang, Hualong Peng, Kai Xu, Xiaolong Huaqiao Univ Coll Engneering Quanzhou Peoples R China Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing Peoples R China

ISBN: (纸本)9781728187808

With the emergence of the Internet of things (IOT), smart cities have changed from concept to reality. Meanwhile, those countless IOT devices are scattered in every corner of the city which generate a mass of sensing big data and IoT services every minute. However, the computing capability of IoT devices is so constrained that IoT devices fail to process computational-intensive services. Mobile edge computing (MEC) is an emergent architecture for reinforcing the computing capabilities of IoT devices to cope with the resource-hungry IoT services. In the MEC system, IoT devices are capable of offloading part of IoT services to the cloudlet for execution. Although offloading can prominently mitigate the computing burden on IoT devices, it may result in enormous transmission cost and consuming resource of cloudlets. Therefore, it poses major challenges to how to achieve trade-offs in terms of time cost, energy cost of IOT devices and resource utilization of cloudlets. In consideration of the challenge, we devise a collaborative computation offloading approach to acquire the above trade-off in the collaboration of IoT device-cloudlet-cloud three ends. Firstly, the balancing strategies of the trade-off are obtained by leveraging the multi-objective evolutionary algorithm based on decomposition (MOEA/D). We then employ a multi-criterion decision-making method that combines the entropy weight (EW) method and technique for order preference by similarity to an ideal solution (TOPSIS), is known as (EW-TOPSIS), to acquire the optimum offloading decision in the acquired balancing strategies. Finally, extensive experiments and theoretical analysis validate the effectiveness of the proposed method.

关键词： Mobile Edge computing smart Cities Computation Offloading Resource Utilization

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Machine learning based diagnosis of alzheimer’s disease

Machine learning based diagnosis of alzheimer’s disease

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international conference on Image Processing and Capsule Networks, ICIPCN 2020

作者： Karthiga, M. Sountharrajan, S. Nandhini, S.S. Sathis Kumar, B. Bannari Amman Institute of Technology Sathyamangalam India VIT Bhopal University Madhya Pradesh India VIT University Chennai Tamilnadu India

ISBN: (纸本)9783030518585

Alzheimer’s disease is the most dreadful. Despite the various Alzheimer’s treatments available nowadays, the survival rate of Alzheimer’s patients is very much low. Detection of Alzheimer’s disease in an earlier stage is one of the most important things to reduce the death rate. Magnetic Resonance Imaging (MRI) is an important tool in medical informatics and clinical diagnosis. This MRI image helps to diagnose and detect Alzheimer’s disease at advanced stages. There are several approaches implemented to discover Alzheimer’s by MRI data. Features are extracted discriminately and combinations of different classification techniques for classification is implemented in the proposed work. In this paper, the curvelet-based transform technique is utilized for extracting the features. The AdaBoost classifier is utilized for combining multiple weak classifiers into one strong classifier to improve the accuracy of the result. To improve the efficiency of the result, AdaBoost classifier with SVM was used to obtain better results than existing ones. The proposed model with 95.66% accuracy predicts the AD from the brain MRI images at an earlier stage. This accuracy value is far better than other classifiers like Decision Tree and Random Forest. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： data analytics

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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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Influence of smart Interactive Advertising Based on Age and Gender: A Case Study from Sri Lanka 4th

Influence of Smart Interactive Advertising Based on Age and ...

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

作者： Wickramaarachchi, Wiraj Udara Weerasinghe, W. M. S. L. Rathnayaka, R. M. K. T. Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Rajarata Univ Sri Lanka Fac Appl Sci Mihintale Sri Lanka Sabaragamuwa Univ Sri Lanka Fac Appl Sci Belihuloya Sri Lanka

ISBN: (纸本)9783030335823;9783030335816

Advertising media plays a major role in marketing. Sometimes, the consumers tend to get advertisements as a disturbance when they do not show what consumers need to know or buy at right time. Also, some advertisements cannot watch with the children together due to the reasons like nudity. The smart interactive advertising (SIA) concept is a solution to these issues. The study discusses the influence of SIA concept to Sri Lankan people. A prototype system of SIA to detect age and gender of the particular person and to shows advertisements according to detected age and gender has developed. A questionnaire was used to find the influence of SIA concept. The empirical findings suggest that the SIA concept is a good solution for the issues mentioned above. Specifically, it is influencing in better way to Sri Lankan People.

关键词： Advertisements Age estimation Gender estimation Image processing smart interactive advertising

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IoT Based smart Farming Using Thingspeak and MATLAB 3rd

IoT Based Smart Farming Using Thingspeak and MATLAB

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3rd international conference on Communications and Cyber Physical Engineering, ICCCE 2020

作者： Penchalaiah, N. Nelson Emmanuel, Jaladanki Suraj Kamal, S. Lakshmi Narayana, C.V. University of Technology Jaipur India Department of CSE AITS Rajampet India Department of CSE University of Technology Jaipur India

ISBN: (纸本)9789811579608

Climate changes have contributed to the growing importance of monitoring of the climate. A continuous monitoring of the environmental parameter is important to assess the value of the atmosphere. The IoT technology had taken revolution to any area of human life, making it digital and insightful. IoT is a collection of things that make up a network for self-configuration. Since the IoT is the most advanced technology, the collection of data from the sensor system plays a key role. This paper presents a Arduino UNO Wi-Fi module (ESP8266) which helps processing and transfers sensed information to the thing speak cloud, usually comprised of various sensors such as temperature, humidity and moisture, etc. Then, the obtained parameters are stored on the cloud server. A cloud computing system tracks environmental changes as a repository. Things talk will provide a function for a public channel, which is measured and calculated by the general public. A free access to measurement parameters is provided with an Android framework. This paper is aimed at proposing a new smart IoT based farming that supports farmers in obtaining live data (temperature, soil humidity) for successful environmental monitoring so that they can make smart farming and increase overall production and value of their products. The Novel intelligent IoT Farming is embedded with Arduino Technology and the breadboard can be obtained from *** with different sensor modules and live data feeds. A supporting, open API platform for IoT’s Thingspeak’s internet services is a host for a range of sensor systems that manage sensitive information at cloud level and incorporated a special feature of transferring sensed data to MATLAB R2019a. A Channel ID and API key assigned through the services can be used to monitor data quality at specific intervals. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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