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A Review on Video analytics Its Challenges and Applications

A Review on Video Analytics Its Challenges and Applications

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international conference on computing, Power and Communication Technologies (GUCON)

作者： Ansari, Shaharyar Alam Zafar, Aasim Aligarh Muslim Univ Aligarh Uttar Pradesh India

ISBN: (纸本)9789811503399;9789811503382

Nowadays the vast amount of video data is generated, which needs to be processed fast for getting correct insights into it. Manpower is not enough to process all of it in a short span of time due to their limited capacity. Video analytics facilitates useful tools to extract more valuable video data. This paper lays stress on different videos analysis activities. Here we explore various techniques which are used in video analysis along with presenting a comparison with image analysis techniques. Further, the application of video analytics along with the challenges being faced while analyzing the real time videos is discussed.

关键词： Video processing Image processing Object detection Object tracking Pattern recognition Action recognition

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Framework for Resource Management in Cloud computing 4th

Framework for Resource Management in Cloud Computing

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4th international conference on Information and Communication Technology for intelligent Systems, ICTIS 2020

作者： Kaur, Gagandeep Panjab University Chandigarh India

ISBN: (纸本)9789811570612

Cloud computing has become popular in these days as it provides on-demand availability of computer resources such as processor, storage, and bandwidth throughout the world. Virtualization helps in the growth of data and computation centers and makes workload balancing much simpler and easier. Virtualization in modern days makes the things easier for cloud users as they no longer need to make large upfront investments and get rapid access to low-cost and flexible resources. Virtualization aims to reduce end-user apprehensions concerning server upkeep, constraints, and scalability. Cloud computing uses the concept of virtualization and uses IT assets as utilities in todays’ world. Cloud computing uses cloud marketplace to access and integrate the services and offerings. Cloud users and cloud providers have different aims when managing the resources, policies, and demand patterns in real-world scenario. In this paper, a new three-tier architecture has been presented to manage the resources in economic cloud market considering deadline and execution time of the tasks for users. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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A smart Crutch Tip for Monitoring the Activities of Daily Living Based on a Novel Neural-Network intelligent Classifier 15th

A Smart Crutch Tip for Monitoring the Activities of Daily Li...

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15th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Brull, Asier Zubizarreta, Asier Cabanes, Itziar Torres-Unda, Jon Rodriguez-Larrad, Ana Faculty of Engineering of Bilbao University of the Basque Country UPV/EHU Bilbao Spain Department of Physiology Faculty of Medicine and Nursing University of the Basque Country UPV/EHU Bilbao Spain

ISBN: (纸本)9783030578015

The determination of the objectives of gait rehabilitation therapies is usually based on partial data provided by clinical tests carried out in specific scenarios and the subjective perception of both the therapist and the patient. However, recent studies have shown that individualization is mandatory to maximize the effect of the therapy on the patient. This requires monitoring the Activities of Daily Living of the patient using objective indicators and measurements, which can be achieved using instrumented devices or wearable sensors. In this work, a smart crutch tip is proposed, which integrates a novel neural-network based intelligent Activities of Daily Living classifier. Based on the sensors integrated on the tip, the classifier is able to detect four typical activities (walking, standing still, going up stairs and going down stairs). In order to design the classifier, data from a group of 13 volunteers is used and different network architectures are evaluated in order to consider the most computationally efficient design, obtaining a success rate of 95%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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Extraction of Tabular data from PDF to CSV Files 4th

Extraction of Tabular Data from PDF to CSV Files

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

作者： Bhatia, Gresha Tewari, Abha Gurbani, Grishma Gokhale, Sanket Varyomalani, Naman Kirtikar, Rishil Bhatia, Yogita Athavale, Shefali CMPN Department VESIT-Vivekanand Education Society’s Institute of Technology Mumbai India

ISBN: (纸本)9789811556159

Companies generate their reports in the form of PDF files. For further data analysis, the statistics or quantitative data in these reports have to be converted to CSV (.csv) or Excel (.xlsx) files. This is done manually by companies. This consumes a lot of time and manual work which can be reduced for better utilization of resources. Forecomp is a web application to automatically convert the tables in the PDF to CSV files. The tables could be present in text format or as an image. The web application is built keeping flexibility in mind such that the user can select the process used to convert the PDF into CSV files based on the tables in their PDF. Different technologies used in this application include YOLO model for machine learning, Tesseract OCR, Tabula, and an inbuilt snipping tool. This paper introduces the concepts behind Forecomp focussing on the methodology employed and the various results obtained. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Utilizing Timed Petri Nets to Guide data-Driven Fault Diagnosis of PLC-Timed Event Systems 11

Utilizing Timed Petri Nets to Guide Data-Driven Fault Diagno...

引用

Joint 11th international conference on Soft computing and intelligent Systems and 21st international Symposium on Advanced intelligent Systems (SCIS-ISIS)

作者： Cohen, Joseph Jiang, Baoyang Ni, Jun Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Foxconn Ind Internet Technol Serv Business Grp Shenzhen Peoples R China

ISBN: (纸本)9781728197326

Highly synchronized timed event systems managed by programmable logic controllers are ubiquitous in manufacturing. Discrete event system methods such as finite state machines and Petri Nets have been useful for diagnosing known faults in such systems. However, existing methods do not scale as well for practical applications where faulty behavior is not fully known and event timing may vary. With the advent of big data, a new methodology is presented that strengthens fault diagnosis capabilities utilizing machine learning and Timed Petri Nets. The hybrid approach consists of building a Timed Petri Net model of the normal process to help select discriminating features based on timed event sequences. This work focuses on modeling Timed Petri Nets to serve as knowledge representations of the nominal process behavior to guide data-driven fault diagnosis of timed event systems. A methodology based on identification of observable events is introduced, consisting of three different and complementary heuristics including demarcating periods, modeling concurrencies, and imposing synchronizations. Then, using features selected by using the Timed Petri Net, a nonlinear SVM is implemented for multiclass fault classification. The proposed framework is applied to an existing semiconductor manufacturing process with events timed via binary programmable logic controller signals. With over 97% validation accuracy achieved for fault diagnosis, the hybrid modeling approach shows promise for smart manufacturing applications.

关键词： artificial intelligence Timed Petri Nets fault diagnosis fault classification smart manufacturing programmable logic controllers support vector machines discrete event systems

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Edge-enhanced analytics via latent space dimensionality reduction 7

Edge-enhanced analytics via latent space dimensionality redu...

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7th IEEE/ACM international conference on Big data computing, Applications and Technologies (BDCAT)

作者： Ndubuaku, Maryleen U. Ali, Muhammad K. Anjum, Ashiq Liotta, Antonio Reiff-Marganiec, Stephan Univ Leicester Coll Sci & Engn Leicester Leics England Univ Leicester Coll Sci & Engn Derby England Free Univ Bozen Bolzano Fac Comp Sci Bozen Bolzano Italy Univ Derby Coll Engn & Technol Derby England

ISBN: (纸本)9780738123967

With the Internet of Things technology, almost any remote sensing devices, wearables, and smart objects are equipped to transmit large volumes of data in continuous streams. In conventional cloud-centric analytics, all the raw data is transferred to a data centre and processed in real-time, near real-time, or in batches. However, this approach is usually not very responsive to real-time analytics due to the latency in transmission alongside network traffic, bandwidth and data transmission costs. To tackle this, edge-enhanced analytics ensures that raw data can be preprocessed at the edge and sent across the network channel in a more compact form. A specific category of deep learning model, autoencoder, can help to achieve this by transforming high-dimensional data into compact representation. We propose an edge-enhanced framework which deploys a deep autoencoder model on the network edge for data compression. After training of models in the cloud, the encoder part of the autoencoder is deployed on the edge for data reduction while the decoder remains on the cloud to reconstruct the data for an image classification task on the cloud. We applied supervised fine-tuning using the intrinsic dimensionality of the data to achieve an accuracy that surpasses the baseline cloud model. The solution was explored in the context of an image recognition problem using the MNIST and FASHION-MNIST datasets. The framework was validated on an event simulator to estimate the network savings of the proposed method in terms of bandwidth and latency. The edge-enhanced approach saves up to 74% bandwidth compared to the centralised analytics. In addition, real-time analytics is further improved by taking 25% less time to complete the task.

关键词： Autoencoder, Cloud computing, Deep Learning, Dimensionality Reduction, Edge computing

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FengHuoLun: A Federated Learning based Edge computing Platform for Cyber-Physical Systems 18

FengHuoLun: A Federated Learning based Edge Computing Platfo...

引用

IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Zhang, Chong Liu, Xiao Zheng, Xi Li, Rui Liu, Huai Deakin Univ Sch Informat Technol Geelong Vic Australia Macquarie Univ Dept Comp Sydney NSW Australia Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic Australia

ISBN: (纸本)9781728147161

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge computing platform for CPS, named "FengHuoLun". Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework;2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.

关键词： Federated Learning Edge computing Cyber-Physical Systems Trustworthy Microservices

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A Survey of Application of ML and data Mining Techniques for smart Irrigation System 2

A Survey of Application of ML and Data Mining Techniques for...

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2nd international conference on Inventive Research in computing Applications, ICIRCA 2020

作者： Nandanwar, Himanshu Chauhan, Anamika Pahl, Dushyant Meena, Harshit Information Technology Delhi Technological University Delhi India

ISBN: (纸本)9781728153742

This paper reviews our current research in agriculture analytics on an open-source platform using data mining and machine learning techniques. Various sensors are used to collect data that provides real-time analytics on the weather forecast, soil moisture, air temperature, PH, humidity. The smart irrigation system is paired with different hardware and development application. The science of machine learning and data mining plays a significant role here. It is a data analysis process using multiple models and algorithms to trained data directly. Machine learning and data mining to hold the irrigation system in r leaner manner. For instance, it optimizes the use of water and provides a critical amount of water and fertility to increase production efficiency, reduce manpower involvement, and reduce crop diseases. The survey tests the effect of applicable techniques and how these techniques boost efficiency. © 2020 IEEE.

关键词： data mining

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8th international conference on Computers Communications and Control, ICCCC 2020

8th International Conference on Computers Communications and...

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8th international conference on Computers Communications and Control, ICCCC 2020

ISBN: (纸本)9783030536503

The proceedings contain 26 papers. The special focus in this conference is on Computers Communications and Control. The topics include: Experimenting with beta distributions for approximating hammocks’ reliability;general frameworks for designing arithmetic components for residue number systems;the shape of the reliability polynomial of a hammock network;Queuing theory application on DTN buffer management;Redesign of a conference from in-person to online. case study: ICCCC;proposal of a multi-standard model for measuring maturity business levels with reference to information security standards and controls;IT solutions for big data processing and analysis in the finance and banking sectors;automatons immersed in ocean currents for transformation of biomass into fuel;challenges porting blockchain library to opencl;using two-level context-based predictors for assembly assistance in smart factories;visual analysis of multidimensional scaling using geogebra;edge computing in real-time electricity consumption optimization algorithm for smart grids;the study of trajectories of the development of state capacity using ordinal-invariant pattern clustering and hierarchical cluster analysis;a micro simulation approach for a sustainable reduction traffic jam;empirical versus analytical solutions to full fuzzy linear programming;on recursively defined combinatorial classes and labelled trees;critical analysis of faults in operation of energy systems using fuzzy logic;fuzzy-logic based diagnosis for high voltage equipment predictive maintenance;making a multi-criteria analysis model for choosing an erp for smes in a km world;a multi-objective model for devices procurement with application in health care.

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Practice and Exploration of Extracurricular Education in Colleges and Universities Under the Background of Big data

Practice and Exploration of Extracurricular Education in Col...

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international conference on Cyber Security Intelligence and analytics, CSIA 2021

作者： Zhang, Guifang He, Zhiwei Dong, Xiaowei Sichuan University Jinjiang College Meishan Sichuan China

ISBN: (纸本)9783030700416

Colleges and universities are frontiers with active ideas, intensive knowledge and full use of network information technology. With the advent of the era of big data, the extracurricular education work in colleges has been seriously affected and severely challenged. Colleges should deal with the challenge and take effective measures, such as creating themed education activities with students as the main body, leading the mainstream culture of youth in effective ways;creating a social practice "talent shop" to promote the marketization of social practice;building an innovation and entrepreneurship education network platform to lead students are close to the forefront;focus on the collection of value data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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