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Content-based analytics: Moving Beyond data Size 6

Content-based Analytics: Moving Beyond Data Size

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6th IEEE international conference on Big data computing Service and Applications (IEEE BigdataService)

作者： Tsoumakos, Dimitrios Giannakopoulos, Ioannis Ionian Univ Dept Informat Corfu Greece NTUA Sch Elect & Comp Engn Comp Syst Lab Athens Greece

ISBN: (纸本)9781728170220

Efforts on Big data technologies have been highly directed towards the amount of data a task can access or crunch. Yet, for content-driven decision making, it is not (only) about the size, but about the "right" data: The number of available datasets (a different type of volume) can reach astronomical sizes, making a thorough evaluation of each input prohibitively expensive. The problem is exacerbated as data sources regularly exhibit varying levels of uncertainty and velocity/churn. To date, there exists no efficient method to quantify the impact of numerous available datasets over different analytics tasks and workflows. This visionary work puts the spotlight on data content rather than size. It proposes a novel modeling, planning and processing research bundle that assesses data quality in terms of analytics performance. The main expected outcome is to provide efficient, continuous and intelligent management and execution of content-driven data analytics. intelligent dataset selection can achieve massive gains on both accuracy and time required to reach a desired level of performance. This work introduces the notion of utilizing dataset similarity to infer operator behavior and, consequently, be able to build scalable, operator-agnostic performance models for Big data tasks over different domains. We present an overview of the promising results from our initial work with numerical and graph data and respective operators. We then describe a reference architecture with specific areas of research that need to be tackled in order to provide a data-centric analytics ecosystem.

关键词： big data data quality Machine Learning modeling scheduling

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Identification of Significant Challenges Faced by the Tourism and Hospitality Industries Using Association Rules 4th

Identification of Significant Challenges Faced by the Touris...

引用

4th international conference on data Management, analytics and Innovation, ICDMAI 2020

作者： Bafna, Prafulla B. Saini, Jatinderkumar R. University PuneMaharashtra India

ISBN: (纸本)9789811556159

The tourism and hospitality sector is extending enormously resulting in generating revenues and employability. There are several challenges with respect to the tourism industry stated in the literature. In this paper, a novel approach is presented to generate association mining rules. This work comes up with the important challenges to be worked upon to get profit in the tourism industry which in turn will improve the economies of the countries. Thirty research papers published in the area of tourism during the last decade were analyzed and ten core challenges were identified from them. The proposed approach selects the top 4 critical factors as challenges which are affecting tourism practices. The Apriori algorithm was used to generate rules and in turn significant factors. © 2021, Springer Nature Singapore Pte Ltd.

关键词： data mining

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Design of Unified Management Platform for Power Operation and Maintenance Based on Cloud computing Technology

Design of Unified Management Platform for Power Operation an...

引用

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

作者： Li, Mingming Wang, Ruiqi Yan, Zhigang Wan, Tao Tang, Xin Huang, Hengshuo State Grid Zhumadian Power Company Zhumadian463000 China

ISBN: (纸本)9783030627423

On March 25, 2015, several opinions of the Central Committee of the Communist Party of China on further deepening the reform of the electric power system (Zhongfa [2015] No. 9) was issued, abbreviated as "No. 9 document of power reform". The focus of the new power reform is "three deregulation, one independence and three strengthening": the liberalization of operating electricity price, electricity sales business, distribution business, power generation and power supply plans other than public welfare and regulatory nature, and the transaction is stable. This electricity reform will be conducive to restoring the commodity attribute of electric power, realizing the marketization of electric power trading, thus stimulating the market vitality of electric power enterprises, reshaping the value chain of electric power industry, and promoting the development of the following businesses. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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Study of the Structural Renewal of "Snow Bud" in Shutao Company Based on intelligent Systems and Big data analytics

Study of the Structural Renewal of "Snow Bud" in Shutao Comp...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Xu, Yiming Gu, Siyang Li, Pinwu Sichuan Agr Univ Chengdu 611130 Sichuan Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The improved "Snow Bud" packaging mainly includes the medium packing and the external packaging, with the cuboid as the main structure, and the paper as the main material. The inner packaging uses the tin foil paper bag. Because of the thin thickness of the carton used and the problems in colors, patterns, character design, structures and other aspects, the purpose of the improvement is to use the new structures to enhance the practical functions of the packaging and to explore the application of the new materials in the tea packaging.

关键词： Shutao "Snow Buds" Packaging structure Packaging materials

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Optimal Hyperparameter Tuning using Meta-Learning for Big Traffic datasets

Optimal Hyperparameter Tuning using Meta-Learning for Big Tr...

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IEEE international conference on Big data and smart computing (BigComp)

作者： Bui, Khac-Hoai Nam Yi, Hongsuk Korea Inst Sci & Technol Informat Ctr Supercomp Applicat Daejeon South Korea

ISBN: (纸本)9781728160344

Big traffic data is an emergent issue of intelligent Transportation System (ITS) since a large of data can be produced every day. Consequently, traffic flow prediction becomes the main component for developing ITS in terms of providing accurate and timely traffic information. In this study, we take the traffic forecasting problem into account using Deep learning (DL) models. Specifically, traffic data are from many devices on the roads in which training by DL models have to face with expensive problems (e.g., time-consuming and human expertise). Therefore, we focus on hyperparameter tuning for training big traffic datasets using meta-learning to improve the automatic learning process and reduce time-consuming tasks. Regarding the experiment, we take data from the Vehicle Detection System (VDS) as the case study for evaluating our approach. Specifically, data have collected from 21 sensors which are located in an urban area. The experiments show promising results of our proposed approach for training multiple traffic datasets.

关键词： intelligent transportation system big traffic datasets meta-learning hyperparameter optimization traffic prediction vehicle detection systems

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A Survey of Common IOT Communication Protocols and IOT smart-X Applications of 5G Cellular 1

引用

16th international conference on intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th international conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Chu, Kai-Chun Turatsinze, Elias Chang, Kuo-Chi Zhou, Yu-Wen Chang, Fu-Hsiang Wang, Ming-Tsung Department of Business Management Fujian University of Technology Fuzhou China School of Information and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Tourism Shih-Hsin University Taipei Taiwan Institute of Communications Engineering National Yunlin University of Science and Technology Yunlin Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

New technologies and the potential development of Internet-connected devices have made the IoT one of the main concerns in the areas of computing. In this review paper, Bluetooth, Zigbee, Z-Wave, Wi-Fi, 5G cellular, NFC, and LoRaWAN were described. Furthermore, IOT applications like smart city, smart energy and grids, smart transport, smart buildings, smart home, smart industrial manufacturing, smart Health, and smart farming were also described. However, every communication technology has its own merits and demerits when we consider scalability, cost, and network requirements. The survey provided sufficient knowledge to select the best communication protocol suitable for the application based on network reliability, roaming capabilities, recovery mechanisms, and costs. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zigbee

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A data-Driven Dynamic Discretization Framework to Solve Combinatorial Problems Using Continuous Metaheuristics 1

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11th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Cisternas-Caneo, Felipe Crawford, Broderick Soto, Ricardo de la Fuente-Mella, Hanns Tapia, Diego Lemus-Romani, José Castillo, Mauricio Becerra-Rozas, Marcelo Paredes, Fernando Misra, Sanjay Pontificia Universidad Católica de Valparaíso Valparaíso Chile Universidad Diego Portales Santiago Chile Covenant University Ota Nigeria

ISBN: (数字)9783030736033

ISBN: (纸本)9783030736026

Combinatorial optimization problems are very common in the real world but difficult to solve. Among the promising algorithms that have been successful in solving these problems are metaheuristics. The two basic search behaviors used in metaheuristics are exploration and exploitation, and the success of metaheuristic search largely depends on the balance of these two behaviors. Machine learning techniques have provided considerable support to improve data-driven optimization algorithms. One of the techniques that stands out is Q-Learning, which is a reinforcement learning technique that penalizes or rewards actions according to the consequence it entails. In this work, a general discretization framework is proposed where Q-Learning can adapt a continuous metaheuristic to work in discrete domains. In particular, we use Q-learning so that the algorithm learns an optimal binarization schemEqe selection policy. The policy is dynamically updated based on the performance of the binarization schemes in each iteration. Preliminary experiments using our framework with sine cosine algorithm show that the proposal presents promising results compared to other algorithms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Combinatorial optimization

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Online Credit Card Fraud analytics Using Machine Learning Techniques 4th

Online Credit Card Fraud Analytics Using Machine Learning Te...

引用

4th international conference on data Management, analytics and Innovation, ICDMAI 2020

作者： Kumar, Akshi Anand, Kartik Jha, Simran Gupta, Jayansh Department of Computer Science & Engineering Delhi Technological University DelhiNew Delhi110042 India

ISBN: (纸本)9789811556159

Every year, companies and financial institutions worldwide lose billions due to credit card fraud. With the advancement in electronic commerce and communication technology and the development of modern technology, there has been an increase in the usage of credit cards and also the risks associated with it. Financial fraud detection systems are being implemented everywhere to minimize these losses. In the real world, fraudulent and real transactions are scattered all around and it is extremely difficult to distinguish between them. There is a class imbalance problem due to the minority of fraudulent transactions. The enormity of imbalanced data poses a challenge on how well Machine Learning techniques can efficiently detect fraud with a high prediction accuracy, and at the same time reduce misclassification costs. In this paper, experiments are performed to compare the most commonly used ML algorithms and investigate their performance when applied to a massive and highly imbalanced dataset. We compare six Supervised Machine learning techniques, namely, Logistic Regression, K-Nearest Neighbours, Naive Bayes, Decision Trees, Random Forest and Linear Support Vector Machine (SVM). © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Enabling smart Homes Through Health Informatics and Internet of Things for Enhanced Living Environments 8th

Enabling Smart Homes Through Health Informatics and Internet...

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World conference on Information Systems and Technologies (WorldCIST)

作者： Marques, Goncalo Pitarma, Rui Univ Beira Interior Inst Telecomunicacoes P-6201001 Covilha Portugal Polytech Inst Guarda P-6300559 Guarda Portugal Univ Beira Interior CISE Electromechatron Syst Res Ctr P-6201001 Covilha Portugal

ISBN: (纸本)9783030456962;9783030456979

As people spend most of their time inside buildings, indoor environment quality must be monitored in real-time for enhanced living environments and occupational health. Indoor environmental quality assessment is based on the satisfaction of the thermal, sound, light and air quality conditions. The indoor quality patterns can be directly used to promote health and wellbeing. With the proliferation of the Internet of Things related technologies, smart homes must incorporate monitoring solutions for data acquisition, transmission, and microsensors for several real-time monitoring activities. This paper presents a low-cost and scalable multi-sensor smart home solution based on Internet of Things for enhanced indoor quality considering acoustic, thermal and luminous comfort. The proposed system incorporates three sensor modules for data collection and use Wi-Fi communication technology for Internet access. The system has been developed using open-source and mobile computing technologies for real-time data visualization and analytics. The acquisition modules incorporate light intensity and colour temperature, particulate matter, formaldehyde, relative humidity, ambient temperature and sound sensor capabilities. The results have successfully validated the scalability, reliability and easy installation of the proposed system.

关键词： Ambient assisted living Enhanced living environments Health informatics Indoor environmental quality Internet of Things smart home

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12th international conference on intelligent Networking and Collaborative Systems, INCoS 2020

12th International Conference on Intelligent Networking and ...

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12th international conference on intelligent Networking and Collaborative Systems, INCoS 2020

ISBN: (纸本)9783030577957

The proceedings contain 48 papers. The special focus in this conference is on intelligent Networking and Collaborative Systems. The topics include: Applying a consensus building approach to communication projects in the health sector: The momento medico case study;gesture-based human-machine interface system by using omnidirectional camera;A secure group communication (SGC) protocol for a P2P group of peers using blockchain;method for detecting onset times of sounds of string instrument;PageRank for billion-scale networks in RDBMS;Welcome message from the INCoS-2020 Organizing Committee;Performance evaluation of WMNs by WMN-PSODGA simulation system considering exponential distribution of mesh clients and different router replacement methods;an algorithm to select an energy-efficient sever for an application process in a cluster of servers;suggesting cultural heritage points of interest through a specialized chatbot;considering a method for generating human mobility model by reinforcement learning;data mining on open public transit data for transportation analytics during pre-COVID-19 era and COVID-19 era;eye movement patterns as a cryptographic lock;evolutionary fuzzy rules for intrusion detection in wireless sensor networks;blockchain architecture for secured inter-healthcare electronic health records exchange;semi-automatic knowledge base expansion for question answering;mina: SeMantic virtual assistant for domain ontology based question-answering;safeEat: Extraction of information about the presence of food allergens in recipes;An integrated fuzzy-based simulation system for driving risk management in VANETs considering road condition as a new parameter;accelerated neural intrusion detection for wireless sensor networks;end-to-end security for connected vehicles;Triangle enumeration on massive graphs using AWS lambda functions.

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