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data mining on open public transit data for transportation analytics during pre-COVID-19 era and COVID-19 era 12th

Data mining on open public transit data for transportation a...

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

作者： Leung, Carson K. Chen, Yubo Shang, Siyuan Wen, Yan Hryhoruk, Connor C. J. Levesque, Denis L. Braun, Nicholas A. Seth, Nitya Jain, Prakhar University of Manitoba WinnipegMB Canada

ISBN: (纸本)9783030577957

As the urbanization of the world continues and the population of cities rise, the issue of how to effectively move all these people around the city becomes much more important. In order to use the limited space in a city most efficiently, many cities and their residents are increasingly looking towards public transportation as the solution. In this paper, we focus on the public bus system as the primary form of public transit. In particular, we examine open public transit data for the Canadian city of Winnipeg. We mine and conduct transportation analytics on data prior to the coronavirus disease 2019 (COVID-19) situation and during the COVID-19 situation. By discovering how often and when buses were reported to be too full to take on new passengers at bus stops, analysts can get an insight of which routes and destinations are the busiest. This information would help decision makers make appropriate actions (e.g., add extra bus for those busiest routines). This results in a better and more convenient transit system towards a smart city. Moreover, during the COVID-19 era, it leads to additional benefits of contributing to safer buses services and bus waiting experiences while maintaining social distancing. © Springer Nature Switzerland AG 2021.

关键词： COVID-19

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A design of real-time status updated charging service APP

A design of real-time status updated charging service APP

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2020 international conference on computing and data Science, CDS 2020

作者： Li, Ming Zhang, Xiangfei Liu, Guojing Research and Development Department Beijing Smart-chip Microelectronics Technology Ltd. Beijing China

ISBN: (纸本)9781728171067

Under the background that the country advocates green travel and vigorously promotes the development of new energy industry, electric vehicles are bound to be gradually popularized in China, and the demand of electric vehicle users for charging service is also increasing. In order to let electric vehicle users know the location and availability of the charging pile station nearby in real time, it is a feasible scheme to build an intelligent pile station management platform by applying the rapid development of cloud computing technology and mobile app based on map navigation function. However, with the increase of the number of charging piles and charging stations, it is bound to put forward higher requirements for the performance of the management platform. This paper proposes an optimization scheme based on LBS cloud retrieval technology to update the available status of a large number of piles. © 2020 IEEE

关键词： Piles

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intelligent video surveillance of sand mining based on object detection and tracking 5

Intelligent video surveillance of sand mining based on objec...

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5th international conference on Water System Control and smart City, WSCSC 2022

作者： Gao, Yueming Lin, Qinghua Xu, Chaoyang Lin, Qinghua Guangdong South China Hydro-power High-Tech Development Co. Ltd Guangdong Guangzhou510000 China

In order to improve the regulatory capability of sand mining and make full use of the video resources collected by video surveillance systems, an object detection and tracking algorithm that combines YOLOv4 and Kalman filter is proposed to identify sand dredgers. Initially, an object detection dataset of common ships is finely established through camera capture, manual and automatic annotation, and data augmentation. Then, the obtained dataset is used to train YOLOv4. The results show that the trained YOLOv4 achieves a mean Average Precision of 92.26% over all classes. Although Kalman filter does not increase the precision of YOLOv4, it supplements the association information between images in an frame stream, so as to solve the target loss problem of YOLOv4 caused by occlusion. Finally, the proposed algorithm is deployed in edge computing terminal devices to form an intelligent video surveillance system for sand dredgers. The intelligent system realizes the 24-hour real-time monitoring of sand mining, which can help to stop illegal sand mining in time, and provide strong technical support for relevant departments to fulfill their management obligations. © Published under licence by IOP Publishing Ltd.

关键词： Kalman filters

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Tweets Analysis with Big data Technology and Machine Learning to Evaluate smart and Sustainable Urban Mobility Actions in Barcelona 14th

Tweets Analysis with Big Data Technology and Machine Learnin...

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14th international conference on Complex, intelligent and Software Intensive Systems (CISIS)

作者： Di Martino, Beniamino Cante, Luigi Colucci Graziano, Mariangela Enrich Sard, Regina Univ Campania Luigi Vanvitelli Dept Engn Aversa Italy Eurecat Technol Ctr Barcelona Spain Asia Univ Taichung Taiwan

ISBN: (纸本)9783030504540;9783030504533

Social media can become a valuable data source to gather user opinions on topics of interest. Setting the focus on Twitter as one of the most popular social media channels to share opinions, three challenges have been identified in this work: to obtain users' sentiment: to classify the topics of interest, to decide whether the opinion is positive or negative by applying sentiment analysis to natural language in written form, and to handle in an efficient manner the huge volume of data generated by Twitter. This paper shows how machine learning and big data technologies have been applied to classify user opinions on electromobility in Barcelona. Supervised and unsupervised approaches have been compared in terms of accuracy and a big data framework based on Spark has been implemented for real time processing combinedwith batch modelling. The results obtained show potential to apply them as a complementary mechanism to surveys.

关键词： Big data

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An Improved Apriori Algorithm Research in Massive data Environment

An Improved Apriori Algorithm Research in Massive Data Envir...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Xu, Yu Zhan, Ranzhi Tan, Gang Chen, Lu Tian, Bojin State Grid Chongqing Elect Power Co Informat Commun Branch Chongqing 400000 Peoples R China

ISBN: (纸本)9783030152352;9783030152345

smart grid computing environment is an information platform that has lots of production data, data management, and the real-time and non real-time data. Under such massive data environment, the classic Apriori algorithm of mining association rules has a significant performance bottleneck. After analyzing the Apriori algorithm, the MapReduce programming model is used to realize the parallel Apriori algorithm. In order to improve the mining efficiency further, auxiliary tables and attribute columns are added and parallel strategy is improved in the process of candidate itemsets generation. Simulation experiments show that the improved Apriori algorithm can effectively reduce the algorithm execution time and improve the efficiency of data mining under the massive data environment.

关键词： data mining Apriori algorithm MapReduce smart grid

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international KES conference on Human Centred intelligent Systems, KES-HCIS 2020

International KES Conference on Human Centred Intelligent Sy...

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international KES conference on Human Centred intelligent Systems, KES-HCIS 2020

ISBN: (纸本)9789811557835

The proceedings contain 35 papers. The special focus in this conference is on Human Centred intelligent Systems. The topics include: A novel hand gesture recognition method based on illumination compensation and grayscale adjustment;architecting intelligent digital systems and services;A human-centric perspective on digital consenting: The case of GAFAM;an industrial production scenario as prerequisite for applying intelligent solutions;spectrum management of power line communications networks for industrial applications;innovations in medical apps and the integration of their data into the big data repositories of hospital information systems for improved diagnosis and treatment in healthcare;Automatic classification of rotating machinery defects using machine learning (ML) algorithms;potentials of emotionally sensitive applications using machine learning;a brief review of robotics technologies to support social interventions for older users;the human–robot interaction in robot-aided medical care;challenges of adopting human-centered intelligent systems: An organizational learning approach;experiment protocol for human–robot interaction studies with seniors with mild cognitive impairments;designing robot verbal and nonverbal interactions in socially assistive domain for quality ageing in place;ioT in smart farming analytics, big data based architecture;Review of internet of things and design of new UHF RFID folded dipole with double U slot tag;smart water distribution system based on IoT networks, a critical review;performance analysis of mobile network software testbed;Method for assessing the applicability of AI service systems;how will 5G transform industrial IoT: Latency and reliability analysis;Business process-based IS development as a natural way to human-centered digital enterprise architecture;digital architecture in startups.

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Using GitLab Interactions to Predict Student Success When Working as Part of a Team 23rd

Using GitLab Interactions to Predict Student Success When Wo...

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23rd international conference on Interactive Collaborative Learning (ICL) / 49th IGIP international conference on Engineering Pedagogy - Educating Engineers for Future Industrial Revolutions

作者： Ekuban, Audrey Beatrice Mikroyannidis, Alexander Third, Allan Domingue, John Open Univ Knowledge Media Inst Milton Keynes MK7 6AA Bucks England

ISBN: (纸本)9783030681982;9783030681975

This paper explores machine learning algorithms that can be used to predict student results in an assignment of a Software Engineering course, based on weekly cumulative average source code submissions to GitLab. GitLab is a source code version control system, commonly used in Software Engineering courses in Higher Education. The aim of this work is to create models that can be used to predict if a group of students in a team will pass or fail an assignment. In this paper, we present results from Decision Tree, Random Forest, Extra Trees, Ada Boost and Gradient Boosting machine learning models. These models were evaluated using cross-validation, with Ada Boost achieving the highest average score.

关键词： Learning analytics Educational data Mining Machine learning

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Experimental Evaluation of Deep Learning Based Liver Cancer Identification Scheme Using Hybrid Classification Logic

Experimental Evaluation of Deep Learning Based Liver Cancer ...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Mouleeswaran SK J. Bhuvaneswari Sakthi Saravanan. N S. Hemajothi Sivabalan A K. Vijayakumar Department of CSE Dayananda Sagar University Bangalore Department of Artificial Intelligence and Data Science RMK college of Engineering and Technology Chennai Department of EEE S. A. Engineering College Chennai Department of ECE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Easwari Engineering College Chennai Department of Information Technology St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Companies in the healthcare industry would be foolish to disregard medical analytics and decision-making tools that employ multi-modal intelligence. Liver cancer, among the most prevalent malignancies, can only be effectively treated if caught early. The contrast and brightness of liver tumours are identical to those of the surrounding tissues. Also, depending on the stage of cancer and the kind of tumour, abnormal tumour forms might be a major cause for concern. Segmenting a liver tumour entails first locating the cancer, and then cutting it into smaller pieces. However, while traditional methods of interactive segmentation require a large number of intensity levels, newer methods based on convolutional neural networks (CNN) and the Learning Scheme for Liver Cancer Identification (LSLCI) have limited presentation on images of liver tumours. This study showcases a new method for segmentation utilizing Coot Extreme Learning Model, which is based on deep learning, and it shows great efficiency in identifying tumors using accessible information of liver scans. The research process the first segmentation with minimal further user clicks in order to optimize the segmentation process. Doing so allows the suggested geodesic distance encoding method to incorporate inner boundary points. Liver cancer survival rates can be improved with early detection and treatment. The gold standard for liver tumour differential diagnosis is dynamic contrast-enhanced magnetic resonance imaging (MRI). But since MRI diagnoses are susceptible to human error, deep learning may provide a fresh approach to the diagnostic process. We trained a deep learning system using LSLCI and CNN to identify liver tumours from a variety of sources, including enhanced and unenhanced magnetic resonance images, as well as textual and numerical clinical data, as well as the results of laboratory tests.

关键词： Deep learning Liver cancer Image segmentation Magnetic resonance imaging Liver Magnetic resonance Medical services Convolutional neural networks Medical diagnostic imaging Tumors

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smart Home Security Based on the Internet of Things

Smart Home Security Based on the Internet of Things

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

作者： Zhao, Kejie Zhong, Jiezhuo Ye, Jun School of Computer Science and Cyberspace Security Hainan University Haikou China

ISBN: (纸本)9783030627454

With the rapid development of Internet of Things, Internet of Things technology has been widely applied in many aspects. However, with the continuous popularization of intelligence and digitalization, the security problem of smart home in the Internet of Things environment is increasingly significant, and there are no specific laws, regulations and standards to protect the security of smart home. Therefore, smart home security has become one of the indispensable research fields of the Internet of Things. At first, this paper describes the development stage of smart home in recent years, then according to the literature at home and abroad in recent years, the Internet of things brought by smart home security hidden danger, respectively from two aspects of device security, communication security for a description, and sums up the harm of security vulnerabilities and the lack of research are summarized. Finally, based on the research status of smart home security, the challenges and opportunities of smart home security in the future are proposed, which is of great reference significance to the development of smart home industry in the Internet of Things. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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