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IntelliBVR - intelligent large-scale video retrieval for objects and events utilizing distributed deep-learning and semantic approaches

IntelliBVR - Intelligent large-scale video retrieval for obj...

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

作者： Alam, Aftab Khan, Muhammad Numan Khan, Jawad Lee, Young-Koo Kyung Hee Univ Dept Elect & Comp Engn Gyeonggi Do South Korea

ISBN: (纸本)9781728160344

There is increasing reliance on the intelligent CCTV systems for effective analysis and interpretation of the streaming data to recognize activities and to ensure public safety. Monitoring videos captured by surveillance cameras is always a challenging and time-consuming task. There is a need for automated analysis using computer vision methods in order to extract spatial and temporal features to assist the authorities. Once videos are processed using computer vision technologies, another issue is how to index the extracted low-level features to search, analyze, and browse? How to bridge the semantic gap between the low-level features in Euclidean space and temporal relation across videos in a multi-stream environment? Similarly, how to deal with petascale video in the cloud while extracting the low-level and high-level features? In order to address such issues, in this paper, we propose a layered architecture for large-scale distributed intelligent video retrieval while exploiting deep-learning and semantic approaches called IntelliBVR. The base layer is responsible for large-scale video data curation. The second and the third layer is supposed to process and annotate videos, respectively while using deep learning on the top of distributed in-memory computing engine. Finally, the knowledge curation layer, where the extracted low-level and high-level features are mapped to the proposed ontology so that it can be searched and retrieved using semantic rich queries. Finally, we implement and show results, which project the effectiveness of IntelliBVR.

关键词： ontology deep-learning video analytics distributed computing spark intelligent video search video ontology

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CHMP-CNN: To Implement the IoT Based Crops Health Monitoring and Prediction Using Deep Learning

CHMP-CNN: To Implement the IoT Based Crops Health Monitoring...

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Advances in computing Research on Science Engineering and Technology (ACROSET), international conference on

作者： Vivek Pandey Yukta Patil Ramya Kanagaraj Kaustubh Dabhilkar Ritesh Darade Hitesh Patil Department of Computer Engineering University of Mumbai Mumbai India Department of Information Technology University of Mumbai Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Agriculture is entering a new era marked by the Internet of Things (IoT), which is completely changing how we monitor and manage crops. The adoption of remote monitoring and control has resulted in reduced operational expenses and increased production. The significance of agriculture in maintaining human existence makes it necessary to adopt technical innovations to guarantee its effectiveness and sustainability. This research provides a novel approach to IoT-based smart agriculture that addresses multiple agricultural characteristics and maximizes crop productivity. The sophisticated network of sensors that make up the hardware part of the proposed system includes the DHT11 temperature and humidity sensor, raindrop sensors that sense the presence of rain, ultrasonic sensors that monitor water level, and the ESP32-CAM wireless image transmission sensor. Real-time data gathering and analysis are made possible by the combination of these sensors and the Arduino Mega processing unit. In the proposed model uses chili plant disease dataset and apply a convolutional neural network on which tune the performance and improve the accuracy more than the traditional existing model. The software component for plant disease identification uses Convolutional Neural Network (CNN) techniques to improve farmers' predictive analytics and decision-making.

关键词： Temperature sensors Temperature measurement Image sensors Wireless sensor networks Crops Sensor phenomena and characterization Sensor systems Convolutional neural networks Monitoring intelligent sensors

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Predicting the Getting-on and Getting-off Points Based on the Traffic Big data

Predicting the Getting-on and Getting-off Points Based on th...

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

作者： Wang, JinLei Chen, Feng Yan, Keyu Wang, Lu Zhang, FangChen Xia, Li Zhao, Zhen The North China Sea Data and Information Service SOA Qingdao China Information Science and Technology School Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9783030627423

Taxi is an important part of urban passenger transportation. Taxi traffic big data has become an important research topic in smart transportation cities. In order to dispatch taxi resources more reasonably, this paper based on the GPS big data of Baota District of Yan’an City, predicting the getting-on point and getting-off point. Firstly, the historical data is cleaned, and then the DBSCAN clustering algorithm is used to divide the area with sufficient high density into several clusters. Finally, the high-definition map API is used to visualize the prediction results, which solves the problem of forecasting the taxi loading point. In real life, it has a practical reference value for the travel of local residents. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Temporal Based intelligent LRU Cache Construction 27

Temporal Based Intelligent LRU Cache Construction

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27th IEEE international conference on High Performance computing, data, and analytics (HiPC)

作者： Nittur, Pavan Kanukotla, Anuradha Mutyala, Narendra Samsung Res Inst Bangalore OnDeviceAI Bengaluru India

ISBN: (纸本)9781665422925

In the Android platform, the cache-slots store applications upon their launch, which it later uses for prefetching. The Least Recently Used (LRU) based caching algorithm which governs these cache-slots can fail to maintain essential applications in the slot, especially in scenarios like memorycrunch, temporal-burst or volatile environment situations. The construction of these cache-slots can be ameliorated by selectively storing user critical applications before their launch. This reform would require a successful forecast of the user-app-launch pattern using intelligent machine learning agents without hindering the smooth execution of parallel processes. In this paper, we propose a sophisticated Temporal based intelligent Process Management (TIPM) system, which learns to predict a smart Application List (SAL) based on the usage pattern. Using SAL, we construct intelligent LRU cache-slots, that retains essential user applications in the memory and provide improved launch rates. Our experimental results from testing TIPM with different users demonstrate significant improvement in cache-hit rate (95%) and yielding a gain of 26% to the current baseline (LRU), thereby making it a valuable enhancement to the platform.

关键词： App launch performance App prediction Reinforcement Learning

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Ritchie’s smart watch data analytics and visualization

Ritchie’s smart watch data analytics and visualization

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international conference on Image Processing and Capsule Networks, ICIPCN 2020

作者： Reddy, Nandireddy Chaitanya Nath Ramesh, Aditya Rajasekaran, Rajkumar Masih, Jolly School of Computer Science and Engineering Vellore Institute of Technology Vellore India Erasmus University Rotterdam Netherlands

ISBN: (纸本)9783030518585

The popularity of smartwatches has significantly risen in the past decade owing to advancements in technology, cost reduction, materials availability, production optimization and industrial automation. These factors have compounded the growth of manufacture of smartwatches and coupled with the internet-of-things boom in recent years, this field has seen rapid development and large investments of time and resources. Consequently, with a large amount of data being generated by these smartwatches, the question arises – is it feasible to obtain this data from a watch, and then visualize it in an interpretable manner? Our work answers this with a positive "yes" and demonstrates the proof-of-concept. We propose to build a platform that can extract data from a smartwatch (Mi Band 3 used in proof-of-concept) and visualize this information on a web application dashboard in order to enable the user to obtain insights regarding their physical health and data with ease and convenience. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： data visualization

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Construction of behavior-oriented smart teaching model in mobile new media environment-take the course "nc machine tool programming and operation" as an example

Construction of behavior-oriented smart teaching model in mo...

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Shi, Lei Liaoning Equipment Manufacturing Vocational and Technical College ShenyangLiaoning China

ISBN: (纸本)9783030515553

In the mobile new media environment, the vocational education and teaching environment is constantly changing, which has had multiple effects on the teaching model. In order to make the teaching of "NC Machine Tool Programming and Operation" more efficient, it is recommended to take the action-oriented guidance and use mobile new media technology to build a smart teaching mode of "NC Machine Tool Programming and Operation" course. This can achieve effective docking and efficient linkage between teaching and learning. The way of constructing the wisdom teaching mode of the course of "NC Machine Tool Programming and Operation" under the mobile new media environment is to optimize the course standards with the guidance of behavior, create a theme learning situation, and systematically design the course teaching structure. © Springer Nature Switzerland AG 2021.

关键词： Teaching

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A Comparative Analysis of Federated Learning Towards Big data IoT with Future Perspectives

A Comparative Analysis of Federated Learning Towards Big dat...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： G. Anitha A. Jegatheesan E. Baburaj Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medicaland Technical Sciences Chennai India Department of Electrical and Computer Engineering College of Engineering and Technology Bule Hora University Ethiopia

Over the past few years, big data has come a long way, allowing us to make sense of the immense amounts of information being produced by cutting-edge services and apps, as well as a plethora of IoT devices. Only through extracting value from huge data through data analytics can big data's full potential be accomplished [6]. Machine learning (ML) plays a highly essential part in this process because of its capacity to know patterns and then deliver insights through learning from facts. Since the data may include private information about individuals, governments, or financial accounts, using a more traditional technique raises serious concerns about data confidentiality. Recently, Federated learning (FL) seemed like a potential learning procedure that could help us get beyond this obstacle. In FL, the central ML algorithm is only given the parameters from the local devices so that it can train and make predictions on a global scale. The aforementioned problems with huge data may be amenable to resolution thanks to this aspect of FL. Furthermore, a thorough assessment of FL for big data applications has forward done, leaving a void in the literature. This article delivers a summary of FL, big data, and the motives for the procedure of FL for big data by surveying its use in big data applications. In particular, it is important to thoroughly examine the usage of FL for major big data services with applications like the smart city, remote surgery, transportation, smart grid, and social networks. Additionally, briefly discuss the most pressing problems associated with this engaging subject and the most potential approaches to fixing them.

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smart Bicycle Probe - An EPS@ISEP 2020 Project 23rd

Smart Bicycle Probe - An EPS@ISEP 2020 Project

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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： Boularas, Melissa Szmytke, Zuzanna Smith, Logan Isik, Kaan Ruusunen, Juho Malheiro, Benedita Justo, Jorge Ribeiro, Cristina Silva, Manuel F. Ferreira, Paulo Guedes, Pedro Polytech Porto ISEP PPorto Sch Engn Porto Portugal INESC TEC Porto Portugal INEB Inst Engn Biomed Porto Portugal

ISBN: (纸本)9783030681982;9783030681975

Air pollution kills approximately 7 million people every year and nine out of ten people are exposed to high levels of airborne pollutants. This paper describes the design of a bicycle air probe by a team of multicultural and multidisciplinary students of the European Project Semester, during the spring of 2020. This learning experience started with the analysis of the state-of-the-art, ethics, marketing and sustainability dimensions, and was followed by the design, development and simulation of a proof-of-concept solution. The result is GOairLight - a bicycle probe paired with a mobile app. The probe collects air quality, humidity and temperature data as cyclists ride, while the mobile app shares the collected data with the community, by means of a cloud database, presents relevant air quality information and suggests less polluted routes. Furthermore, it relies on a sustainable energy source - a dynamo powered by the cyclist - and automatic lighting. The latter feature improves cyclist visibility and raises the awareness towards the cyclist, contributing to increased road safety.

关键词： Collaborative learning European Project Semester Sustainability Air pollution Community Bicycle

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Trustworthy and Sustainable Edge AI: A Research Agenda 3

Trustworthy and Sustainable Edge AI: A Research Agenda

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3rd EEE international conference on Trust, Privacy and Security in intelligent Systems and Applications (TPS-ISA)

作者： Ding, Aaron Yi Janssen, Marijn Crowcroft, Jon Delft Univ Technol Delft Netherlands Univ Cambridge Cambridge England

ISBN: (纸本)9781665416238

As a fast evolving domain that merges edge computing, data analytics and AI/ML, commonly referred as Edge AI, the community of Edge AI is establishing and gradually finds its way to connect with mainstream research communities of distributed systems, IoT, and embedded machine learning. Meanwhile, despite of its well-claimed potential to transform cloud and IoT industry, Edge AI is still a complex subject that faces critical challenges from the trustworthy and sustainable concerns. To shed light on these pressing matters, this paper aims to develop a research agenda for trustworthy and sustainable Edge AI. We clarify the concepts, define the proper scoping and propose a research agenda for Edge AI to be trustworthy and sustainable. To illustrate the research agenda in practice, we highlight two active R&D projects: the SPATIAL project on trustworthy Edge AI and the APROPOS project on sustainable computing. The projects serve as concrete use cases to explore the agenda development. Our goal is to equip researchers, engineers, service providers, government and public sectors with a better understanding of the underlying concepts and to raise awareness of emerging directions in trustworthy and sustainable Edge AI.

关键词： Edge computing Edge AI IoT trustworthiness sustainable AI

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Work-in-Progress: smart-WATER, a Νovel Τelemetry and Remote Control System Infrastructure for the Management of Water Consumption in Thessaloniki 13th

Work-in-Progress: SMART-WATER, a Νovel Τelemetry and Remot...

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13th international conference on Interactive Mobile Communication, Technologies and Learning, IMCL 2019

作者： Mourtzios, Christos Kourtesis, Dimitrios Papadimitriou, Nikolaos Antzoulatos, Gerasimos Kouloglou, Ioannis-Omiros Vrochidis, Stefanos Ioannis, Kompatsiaris Research and Development Department APIFON S.A. Telecommunications Thessaloniki Greece Thessaloniki Greece

ISBN: (纸本)9783030499310

Water scarcity and water stress issues pose a serious threat to the global population. Climate change, drought, population growth and consolidation in urban centres have all been increasing the pressure on water service providers to deploy more sustainable approaches in urban water management. Real-time monitoring and control of water consumption are key ingredients for a smart water management system which will raise consumers’ environmental awareness and reduce costs. This paper introduces a smart infrastructure system which enables remote telemetry and control of water consumption via a web application. The bidirectional and reliable communication between terminal devices (smart meters and valves) and the end-user (consumers and water utility operators) is realized through a fixed hybrid network which uses multiple telecommunication protocols. The metering data is collected and further processed through modern data analytics tools to give a deeper insight on water consumption and behavioral patterns. © 2021, Springer Nature Switzerland AG.

关键词： data analytics

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