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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

共 3255 条记录，以下是2031-2040 订阅

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Extraction Method of Classification Rules in Decision Tree Based on Attribute Selection Metric of Rough Set

Extraction Method of Classification Rules in Decision Tree B...

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international conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Wang, Lan Xu, Hongsheng Luoyang Normal University Luoyang471934 China Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang471934 China

ISBN: (纸本)9783030251277

In this paper, the kernel algorithm of decision tree is used as a tool, and on the basis of constructing a good data source group for classification, the characteristics of decision tree classification algorithm are analyzed and experimented. This paper introduces a rough set attribute selection metric, which selects attributes for classification from the point of view of improving the accuracy of classification and the purity of sub-databases. Association rule mining is the mining of interesting association rules or relationships between indexes (items) in the database. The paper present extraction method of classification rules in decision tree based on attribute selection metric of rough set. © 2020, Springer Nature Switzerland AG.

关键词： Association rules

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data-driven sustainable smart manufacturing: A conceptual framework

Data-driven sustainable smart manufacturing: A conceptual fr...

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intelligent Systems and Computer Vision (ISCV)

作者： Fadwa Mahiri Aouatif Najoua Souad Ben Souda Signals distributed systems and Artificial Intelligence laboratory Hassan II University of Casablanca Casablanca Morocco

ISBN: (数字)9781728180410

ISBN: (纸本)9781728180427

The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today's manufacturing systems and increased the growth of data generated from manufacturing. Big data as a driver key of intelligent manufacturing empowered companies to adopt data driven approaches to enhance competitiveness, efficiency and sustainability. In this paper a historical evolution of data in manufacturing is overviewed, the smart manufacturing its key technologies and sustainability are related, and a conceptual framework from lifecycle product perspective was proposed to show potential applications of big data analytics in sustainable smart manufacturing.

关键词： Big data Production Sustainable development smart manufacturing Cloud computing Internet of Things

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Internal Control of Enterprise Computer Accounting Information System in the Age of Big data

Internal Control of Enterprise Computer Accounting Informati...

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international conference on Cyber Security Intelligence and analytics, CSIA 2020

作者： Ye, Zhenying Hu, Jingjian Jingdezhen Ceramic University College of Science and Technology JingdezhenJiangxi333001 China

ISBN: (纸本)9783030433086

With the advent of the era of big data, the development of the network economy, the advancement of science and technology has undoubtedly accelerated the process of social information. Enterprise accounting information processing has become the trend of the times. The traditional manual bookkeeping method of enterprises has gradually been replaced by accounting computerization and officially entered the stage of computer informationization. In the process of enterprise development, accounting information statistics can affect the success or failure of business operations to a certain extent. This paper analyzes the problems existing in the internal control of computer accounting information system and proposes corresponding preventive measures. © Springer Nature Switzerland AG 2020.

关键词： Big data

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Scrambling and Descrambling of Document Image for data Security in Cloud computing 3rd

Scrambling and Descrambling of Document Image for Data Secur...

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3rd international conference on smart computing and Informatics (SCI)

作者： Salimath, Nagesh Mallappa, Satishkumar Padhy, Neelamadhab Sheetlani, Jitendra SSSUTMS Dept Comp Sci Sehore MP India Gulbarga Univ Dept Comp Sci Kalaburagi KA India GIET Univ Sch Engn & Technol CSE Gunupur 762022 Odisha India

ISBN: (纸本)9789813296909;9789813296893

This paper presents the issue of security for the document image, which may store in the cloud environment. To provide the protect to the document image is gained by using the Arnold Transform scrambling and descrambling method. In today's scenario, we are witnesses of the rapid development of the technology. The security of data is becoming the most critical factor. The Document image is treats as the digital property of any individual person, organization or any Government sector. The digitized documents are stored from any place and allow the access also from any place on the globe, by using Cloud computing technologies. Hence, there has been an issue of security, which plays the important role. The security issue still exists in cloud computing. This security issue is motivated to address in this paper. We have considered the Arnold Transformation method for scrambling the document image. The image may send using the medium of internet technology or store in the cloud. By using thiswork, the security applied to the document image and this ensures un-authorized persons should not be able to read or exploit the document image information. To retrieve the original image descrambling process is applied. The proposed method exhibited encouraging results.

关键词： Scrambling Descrambling Arnold transform Document image Cloud computing

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Enhancing Trust and Immutability in Cloud Forensics 5th

Enhancing Trust and Immutability in Cloud Forensics

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5th international conference on ICT for Sustainable Development, ICT4SD 2020

作者： Chauhan, Pranay Bansal, Pratosh Department of Information Technology Institute of Engineering and Technology Devi Ahilya Vishwavidyalaya IndoreMP India

ISBN: (纸本)9789811582882

We are living in a digital era where most of us have an online presence with all their data on cloud. "Easy access of data from anywhere" is the biggest advantage of cloud but this same point becomes the cause of cloud-based crimes. There has been a drastic increase in cloud-based crime in the recent past. The immoral from any geolocation can target the victim of any geolocation, even cross-borders. Moreover, hunting down the immorals/suspects in these cross-border attacks is more difficult as suspects are easily able to coat themselves. In the cloud, data is stored at multiple geolocations, and this cross-border cloud data storage is the biggest challenge for law enforcement agencies. When the crime is committed from a different country this geographically sparse data becomes a major hindrance for the forensics investigation. LEA had to request the cloud service providers of all the involved locations (including the ones which are outside the country) to provide data for fetching out the right pieces of evidence. In the current state, LEA performs these forensic investigation requests through web portals or through email conversations. These requests take days or weeks to process which in turn delays the investigation process, and sometimes victims are left out without justice. Also, these web portals/applications through which the LEA demands the pieces of evidence are also based on cloud and thus are highly insecure. These not so secure portals raise a big question on the authenticity of the data and evidence received on them. Evidence could be easily tailored or removed (partially or fully) from the cloud storage. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： smart contract

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Democratization of AI to Small Scale Farmers, Albeit Food Harvesting Citizen data Scientists, that Are at the Bottom of the Economic Pyramid 3rd

Democratization of AI to Small Scale Farmers, Albeit Food Ha...

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3rd international conference on intelligent Human Systems Integration (IHSI) - Integrating People and intelligent Systems

作者： Vuppalapati, Chandrasekar Ilapakurti, Anitha Kedari, Sharat Vuppalapati, Rajasekar Vuppalapati, Jayashankar Kedari, Santosh Hanumayamma Innovat & Technol Inc Fremont CA 94536 USA Hanumayamma Innovat & Technol Private Ltd Hyderabad India

ISBN: (纸本)9783030395124;9783030395117

Climate change is impacting milk production worldwide. For instance, increased heat stress in cows is causing average-sized dairy farms losing thousands of milk gallons each year;drastic climate change, especially in developing countries, pushing small farmers, farmers with less than 10 to 25 cattle, below the poverty line and is triggering suicides due to economic stress and social stigma. It's profoundly clear that current dairy agriculture practices are falling short to counter the impacts of climate change. What we need are innovative and intelligent dairy farming techniques that employ best of traditional practices with data infused insights to counter negative effects of climate change. To achieve innovative and intelligent dairy farming techniques, we need to disseminate state of art data science to masses. In other word, provide the state of art data science algorithms to every food harvesting citizen data scientist, farmer. The democratization of artificial intelligence to farmers, importantly, is not only empowers farmers to understand the patterns and signatures of climate change but also provides the ability to forecast the impending climate change adverse events and recommends data driven insights to counter the negative effects of climate change. With the availability of new data tools, farmers can not only improve their standard of life but also conquer, importantly, perennial "climate change related suicide" issue. It's our staunch believe that the gold standard for the success of the democratization of artificial intelligence is no farmer life loss due to negative effects of climate change. In this paper, we propose an innovative machine learning Sensor edge approach that considers the impact of climate change and develops artificial intelligent (AI) models that is validated globally but enables localized solution to thwart impacts of climate change. The paper presents prototyping dairy IoT sensor solution design as well as its application and certa

关键词： Internet of Things (IoT) Machine Learning Climate Change Climate change related suicides Decision tree Regression analysis Embedded device Edge analytics Hanumayamma dairy IoT sensor Climate models Farmer suicides

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CNNs for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications 18

CNNs for Heart Rate Estimation and Human Activity Recognitio...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Brophy, Eoin Muehlhausen, Willie Smeaton, Alan F. Ward, Tomas E. Dublin City Univ Insight Ctr Data Analyt Dublin Ireland Univ Coll Cork Infant Res Ctr Cork Ireland

ISBN: (纸本)9781728147161

Wrist-worn smart devices are providing increased insights into human health, behaviour and performance through sophisticated analytics. However, battery life, device cost and sensor performance in the face of movement-related artefact present challenges which must be further addressed to see effective applications and wider adoption through commoditisation of the technology. We address these challenges by demonstrating, through using a simple optical measurement, photoplethysmography (PPG) used conventionally for heart rate detection in wrist-worn sensors, that we can provide improved heart rate and human activity recognition (HAR) simultaneously at low sample rates, without an inertial measurement unit. This simplifies hardware design and reduces costs and power budgets. We apply two deep learning pipelines, one for human activity recognition and one for heart rate estimation. HAR is achieved through the application of a visual classification approach, capable of robust performance at low sample rates. Here, transfer learning is leveraged to retrain a convolutional neural network (CNN) to distinguish characteristics of the PPG during different human activities. For heart rate estimation we use a CNN adopted for regression which maps noisy optical signals to heart rate estimates. In both cases, comparisons are made with leading conventional approaches. Our results demonstrate a low sampling frequency can achieve good performance without significant degradation of accuracy. 5 Hz and 10 Hz were shown to have 80.2% and 83.0% classification accuracy for HAR respectively. These same sampling frequencies also yielded a robust heart rate estimation which was comparative with that achieved at the more energy-intensive rate of 256 Hz.

关键词： deep learning transfer learning photoplethysmography

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Application problems and solutions of intelligent airport service robots 10

Application problems and solutions of intelligent airport se...

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2020 10th Chinese Geosynthetics conference and international Symposium on Civil Engineering and Geosynthetics, ISCEG 2020

作者： Dai, Yanyan Chen, Meng Department of College Students Innovation and Entrepreurship and Development Center Shenyang Jianzhu University SJZU Shenyang China Vice-general Manager Shenyang Ivshang Technology Co. Ltd LISUM Shenyang China

With the development of new technologies such as artificial intelligence, big data, and cloud computing, the "intelligent airport"is considered to be an effective means to solve or alleviate the current industry problems such as large-scale airport business, the large number of operating entities, and the complicated operation conditions. This paper is about the collaboration between universities and enterprises based on the concept of service design. Relying on big data and cloud computing technology, this paper addresses the problems of airport service robots in inquiries, blind spots of security inspection, and full monomer smart navigation diffluence, combined with the basic technology of service robot artificial intelligence and the third-party interface to design solutions to effectively solve the problems of process. © The Authors, published by EDP Sciences 2020.

关键词： Airports

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Automatic Patents Classification Using Supervised Machine Learning 4th

Automatic Patents Classification Using Supervised Machine Le...

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4th international conference on Soft computing and data Mining (SCDM)

作者： Shahid, Muhammad Ahmed, Adeel Mushtaq, Muhammad Faheem Ullah, Saleem Matiullah Akram, Urooj Govt Sadiq Egerton SE Coll Dept Phys Bahawalpur Pakistan Quaid I Azam Univ Dept Comp Sci Islamabad Pakistan Khwaja Fareed Univ Engn & Informat Technol Fac Comp Sci & Informat Technol Rahim Yar Khan Pakistan

ISBN: (纸本)9783030360566;9783030360559

Every year, approximately one million patent documents are issued with unique patent number or symbol. In order to find the relevant patent document, several users query the IPC documents using IPC symbols. So, there is a need of automatic classification and ranking of patent documents w.r.t. user query. Automatic classification is only possible through supervised machine learning techniques. In this paper, we classified patent documents using common classifiers. We collected 1625 patent documents related to eight different classes taken from IPC website using web crawler in an unstructured text. We considered 90% of training and 10% of test samples of the total patents. We built a feature matrix using tf-idf, smart notations and BM25 weighting schemes. This feature matrix is given to each classifier as input and output of each classifier consists of correctly classified and incorrectly classified instances. Finally, we evaluated the accuracy of each classifier using precision, recall and F-measure. We performed comparative analysis of classifiers and observed that by adding more features to each classifier, accuracy of classifier can be improved.

关键词： Classification Supervised learning Unigram BM25 TF-IDF smart notations

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Practical User and Entity Behavior analytics Methods for Fraud Detection Systems in Online Banking: A Survey 10th

Practical User and Entity Behavior Analytics Methods for Fra...

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10th Annual international conference on Biologically Inspired Cognitive Architectures, BICA 2019

作者： Slipenchuk, Pavel Epishkina, Anna Group-IB Moscow Russia Moscow Russia

ISBN: (纸本)9783030257187

User and entity behavior analytics (UEBA) methods in fraud detection and advertising are widely used cognitive science methods in modern online banking systems. But profusion of marketing papers complicates true situation. Most of academic papers contain a systematic error: "Correct sample choice error". Fed to the input the real data of user behavior in online banking do not nothing benefit. The paper will be submitted to the criticism of several methods on "mouse track analysis" and "keystroke dynamics" algorithms. New type of algorithms will be present: "preference-behavioral chain" methods. One "preference-behavioral chain" algorithm for social engineering detection will be presented. © 2020, Springer Nature Switzerland AG.

关键词： Online systems

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