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

作者： Lee, Sukhan Lee, Yeonho Sungkyunkwan Univ Intelligent Syst Res Inst Suwon 16419 South Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160344

The real-time pick and place of 3D industrial parts randomly filed in a part-bin plays an important role for manufacturing automation. Approaches based solely on the conventional engineering discipline have been shown limitations in terms of handling multiple parts of arbitrary 3D geometries in real-time. In this paper, we present a hybrid approach of deep learning and engineering as a means of breaking through the current limitations of industrial bin picking toward enabling the real-time pick and place of multiple 3D parts of arbitrary geometries. The proposed approach, first, makes use of deep learning based object detectors configured in a cascaded form for both detecting parts in a bin and extracting features associated with the individual parts detected. The concatenation of the part label and the feature labels and their positions associated with the part allows the subsequent part net to have its part recognition rate close to 100%, Furthermore, the part features and their positions arc to be fed directly into the estimation of the 3D pose of the corresponding part in a bin as well as its degree of occlusion. The experimental results demonstrate that the proposed approach is able to perform a real-time multiple part bin picking operation for multiple 3D parts of arbitrary geometries with a high precision.

关键词： Bin Picking 3D Objects/Parts Deep Learning Network 3D Pose Estimation

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Numerical Weather Analysis Using Statistical Modelling as Visual analytics Technique 1

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2nd international conference on computing, Communications and data Engineering (CCODE)

作者： Sudha, Govindan Thangaraj, Muthuraman Sangaiah, Suguna Sri Meenakshi Govt Arts Coll Women A Madurai Tamil Nadu India Madurai Kamaraj Univ Dept Comp Sci Madurai Tamil Nadu India

ISBN: (数字)9789811501357

ISBN: (纸本)9789811501357;9789811501340

This research work focuses on analysing weather data, described by plenty of attributes, creating a high-dimensional data. After fetching the real-time observations recorded at the city on interest, the data is pre-processed for the purpose of feature selection;this also ensures data correctness, consistency and prepares data for analysis. Statistical model arrives at the aggregate of the insights observed in the empirical data. The statistical model delivers the parameters which are used to estimate the future weather attributes. Substitution of results in underlying statistical model fitness equation, the predictions can be obtained.

关键词： Numerical weather forecasting Regression Correlation

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Precision marketing method and strategy based on big data analysis in e-commerce environment

Precision marketing method and strategy based on big data an...

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

作者： Fu, Lei Shenyang Polytechnic College Shenyang China

ISBN: (纸本)9789811525674

With the rapid development of e-commerce, a large amount of consumption data has been generated, and consumers have also put forward personalized requirements for the purchased goods, so the traditional marketing model has not been able to better meet the needs of customers. Therefore, it is very important to study the precision marketing method in the e-commerce environment. This paper first expounds the significance of precision marketing, then summarizes the process of data mining in e-commerce, and proposes a precision marketing model based on big data analysis. Finally, from the aspects of timeliness of data, multi-dimensional data collection, and improvement of user cohesion, the paper gives the precise marketing strategy in the e-commerce environment. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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smart Farming Using IoT

Smart Farming Using IoT

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international conference on Machine Intelligence and Soft computing, ICMISC 2020

作者： Bhupal Naik, D.S. Ramakrishna Sajja, V. Jhansi Lakshmi, P. Venkatesulu, D. Department of Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur India

ISBN: (纸本)9789811595158

With the rapid development of the world population, large area of land is utilized to develop housing and the ability of producing food is reduced. Farming has become crucial in present trend and keeps food on the tables. Farming with IoT helps in mitigating the shortage of food by demanding the existing land for stronger utilization at minimum cost. smart farming is a notion that quickly snaps on the agricultural field. This offers an automated farming techniques, useful data collection and high-rigor crop control. Sophisticated sensor-based architecture is proposed to monitor the conditions of the farms by using sensors, and the information extracted from these sensors is stored on the Internet. This stored information is obtained and evaluated to forecast the condition of farms, according to a period of time. Based on this evaluation, the necessary improvements can be made with better farming conditions in future. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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City Assessment Tool to Measure the Impact of Public Policies on smart and Sustainable Cities. The Case Study of the Municipality of Alcobendas (Spain) Compared with Similar European Cities 3rd

City Assessment Tool to Measure the Impact of Public Policie...

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3rd international conference on smart and Sustainable Planning for Cities and Regions, SSPCR 2019

作者： Sisto, Raffaele García López, Javier Lumbreras Martín, Julio Mataix Aldeanueva, Carlos Ramos Ferreiro, Linos Escuela Técnica Superior de Ingenieros Industriales Universidad Politécnica de Madrid Calle de José Gutiérrez Abascal 2 Madrid Spain North America At Technical University of Madrid 79 John F. Kennedy St Cambridge United Kingdom Av. Complutense s/n. Ciudad Universitaria Madrid Spain Planning and Evaluation at City Council of Alcobendas Plaza Mayor 1 Alcobendas Spain

ISBN: (纸本)9783030577636

data analytics is a key resource to analyze cities and to find their strengths and weaknesses to define long-term sustainable strategies. On the one hand, urban planning is geared to adapting cities’ strategies towards a qualitative, intelligent, and sustainable growth. On the other hand, institutions are geared towards open governance and collaborative administration models. In this context, sustainability has become a global concern for urban development, and the sustainable development goals (SDGs), defined by United Nations, are the framework to be followed to define the new city goals and to measure the advances of the policies implemented over recent years. The main objective of this research is to explain the methods and results of the application of a city assessment tool for measuring the impact of public policies on the socioeconomic and environmental structure of a city. It addresses the case study of the evaluation of the strategic plan "Diseña 2020" of the municipality of Alcobendas (Madrid, Spain, with 116.037 inhabitants), the document used to communicate the actions needed to achieve the city goals during the planning exercise. A selection of urban indicators has been aligned with the SDGs defined in the Agenda 2030 to develop a tool for the measurement of the impacts of policies in economic, social, and ecological terms. Through this set of indicators, the tool is able to quantify the impact of the policies on the city and the SDGs and to support the decision-making processes of the administration. The set of urban indicators is divided into five areas: economic development and employment, sustainable development, open government, social responsibility, and quality of life. The data evolution, across the recent years 2012–2018, is used to monitor and benchmark the effects of the applied policies. In addition, Alcobendas can be compared with other Spanish and European cities with similar characteristics;it makes possible assessing the achievement of

关键词： data analytics

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Multi-source Transfer Learning for Human Activity Recognition in smart Homes 6

Multi-source Transfer Learning for Human Activity Recognitio...

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6th IEEE international conference on smart computing (smartCOMP)

作者： Niu, Hao Nguyen, Duc Yonekawa, Kei Kurokawa, Mori Wada, Shinya Yoshihara, Kiyohito KDDI Res Inc Tokyo Japan

ISBN: (纸本)9781728169972

With the deployment of smart homes, we find that human activity recognition (HAR) is essentially important to many applications, e.g., child/senior care, intelligent information push and exercise promotion. Although it is always better to build HAR model for each smart home to resolve the practical problem that homes have different floorplans or adopted sensors, it is intractable to acquire labeled data for each home due to cost and privacy. We thus propose a method to transfer the HAR model from multiple labeled source homes to the unlabeled target home. Specifically, we first generate transferable representations for the sensors of these homes, based on which we build the HAR model using the data of labeled source homes. Then, we employ the built HAR model into the unlabeled target home. Experiment results on CASAS dataset illustrate that our proposed method outperforms baseline methods in general and also avoids potential negative transfer caused by using only one source home.

关键词： smart homes human activity recognition transfer learning Word2Vec LSTM

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Explainable Machine Learning and Mining of Influential Patterns from Sparse Web

Explainable Machine Learning and Mining of Influential Patte...

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IEEE/WIC/ACM international Joint conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Leung, Carson K. Jiang, Fan Zhang, Yibin Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Northern British Columbia UNBC Dept Comp Sci Prince George BC Canada Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9781665419246

With advancements in modern technology in the current era, very large volumes of big data have been generated and collected in numerous real-life applications. These have formed a connected world comprising webs of agents, data, people, things and trust. Some of these webs have also emerged in health and smart living. As valuable information and knowledge is embedded in these rich sets of webs, web intelligence is in demand. In this paper, we focus on a data science task of web usage mining. In particular, we present a web intelligent solution to conduct explainable machine learning and mining of influential patterns from sparse web. It provides a compressed representation of sparse web, discovers influential websites and/or web pages that are frequently browsed or surfed by web surfers, and recommends these influential websites and/or web pages to other web surfers. Evaluation results show the effectiveness (especially, in data compression), interpretability and practicality of our solution.

关键词： web intelligence web of data data science data analytics data mining web mining web usage mining data compression frequent patterns recommendation explainable machine learning explainable artificial intelligence (XAI)

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Improving malicious PDF classifier with feature engineering: A data-driven approach

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2021年 115卷 314-326页

作者： Falah, Ahmed Pan, Lei Huda, Shamsul Pokhrel, Shiva Raj Anwar, Adnan Deakin Univ Sch Informat Technol Fac Sci Engn & Built Environm Geelong Vic 3220 Australia

Several approaches and tools have been developed to analyse and detect the presence of malicious content within the PDF;however, the fundamental approach in designing the existing tools and techniques has not been entirely considerate. Existing tools are based on the available datasets and the observation made during the maldoc manual analysis, making them susceptible to various types of attacks such as Mimicry and Parser confusion. We aim to enhance PDF maldoc classification by identifying the most conclusive feature-set required for accurately classifying PDF maldocs. We extract features using two popular PDF analysis tools and derive a set of features backed by data that further complements classification. We subsequently evaluate all features through a wrapper function. The features with the highest importance values are used to construct a classifier that outperforms the baseline models in terms of classification accuracy and efficiency. Our proposed method helps us identify a useful set of tool-independent features that prolong the current tools' lifespan and usability. It provides us with an in-depth understanding of how these chosen features cumulatively impact the classification. In addition, we evaluate our findings using real-world samples from VirusTotal. Using our proposed technique, we managed to decrease the size of the feature-set by more than 60% while increasing the classification accuracy by around 2%. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Feature engineering Feature aggregation Machine learning Malicious PDF Malware analysis

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Research on the data Mining Technology in College Students' Attendance System Based on the Big data Architecture

Research on the Data Mining Technology in College Students' ...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Jian, Zhong Guangdong Polytech Normal Univ Tianhe Coll Guangzhou 510540 Guangdong Peoples R China

ISBN: (纸本)9783030152352;9783030152345

With the development and improvement of the information technologies, the increasing of the upper application systems and the rapid expansion of the data accumulated in the campus information environment, a typical campus big data environment has initially been formed. Because of the characteristics of the higher education, students' mobility is great and their learning environment is uncertain, so that the students' attendance mostly used the manual naming. The student attendance system based on the big data architecture is relying on the campus network, and adopting the appropriate sensors. Through the data mining technology, combined with the campus One Card solution, we can realize the management of the attendance without naming in class. It can not only strengthen the management of the students, but can also improve the management levels of the colleges and universities.

关键词： Big data architecture system College students Attendance system data mining technology

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ResNet Based Parallel intelligent System Design for Marine Search and Rescue 22

ResNet Based Parallel Intelligent System Design for Marine S...

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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： Su, Zhendong Liu, Darong Mu, Lin University of Chinese Academy of Sciences School of Economics and Management Beijing China China University of Geosciences College of Marine Science and Technology Wuhan China

ISBN: (纸本)9781728176499

Among numerous emergency responses to maritime emergencies, the most critical part is the search and rescue (SAR) for victims in distress on the sea. Maritime search and rescue missions are usually divided into two: search and rescue, and the most complex and difficult part is searching for targets in distress at sea. Traditional numerical models can provide accurate drift trajectories of targets in distress. However, owing to the mathematical mechanism, even with high-performance computing technology, the searching time is still far from meeting the real-Time requirements of sea rescue missions. In view of this situation, in this paper, a parallel intelligent system is proposed for SAR targets in distress at sea based on a deep residual neural network. A large amount of natural system observation data is utilized to train the proposed ResNet network with the advantages of high-performance computing technique which can accelerate the training process. Furthermore, mathematical model simulation results are used to tune the parameters and optimize the results of the neural network. The precision, recall, and F1 scores of the predictions for drifting of the targets in distress can reach more than 90%. Moreover, the ResNet network-based parallel intelligent system combined with high-performance parallel computing technology reduces the time by about 1%. When compared with traditional numerical model systems in predicting target drifting results, the proposed system can enhance the timeliness of the maritime search and rescue process. © 2020 IEEE.

关键词： intelligent systems

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