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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 3243 条记录，以下是81-90 订阅

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Visual Imputation analytics for Missing Time-Series data in Bayesian Network

Visual Imputation Analytics for Missing Time-Series Data in ...

引用

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

作者： Yeon, Hanbyul Son, Hyesook Jang, Yun Sejong Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)9781728160344

Bayesian network is derived from conditional probability and is useful in inferring the next state of the currently observed variables. If data are missed or corrupted during data collection or transfer, the characteristics of the original data may be distorted and biased. Therefore, predicted values from the Bayesian network designed with incomplete data are not reliable. Various techniques have been studied to resolve the imperfection in data using statistical techniques or machine learning, but since the complete data is unknown, there is no optimal way to impute missing values. In this paper, we present a visual analytics system that supports decision-making to impute missing values occurring in incomplete time series data. The visual analytics system allows data analysts to explore the cause of missing data in incomplete datasets. The system also enables us to compare the performance of a suitable imputation model with the Bayesian network accuracy and the Kolmogorov-Smirnov test. We evaluate how the visual analytics system supports the decision-making process for the data imputation through a use case.

关键词： Visual analytics Imputation Missing data Bayesian network

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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...

引用

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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The Quest for Scalable and intelligent Trajectory data analytics Systems: Status Report and Future Directions

The Quest for Scalable and Intelligent Trajectory Data Analy...

引用

international conference on smart Applications, Communications and Networking (smartNets)

作者： Moussa, Rim Haddad, Ahmed Bejaoui, Tarek Univ Tunis LaTICE Lab Tunis Tunisia Univ Carthage MEDIATRON Lab Tunis Tunisia

ISBN: (纸本)9781538692011

A large volume of sensor networks and trajectories of mobile objects are collected. Such data offer us high value knowledge to understand moving objects and locations, fostering a broad range of applications in smart cities, enabling intelligent transportation systems and intelligent urban computing. The next generation of roads needs to be intelligent to accommodate a future transition to fully autonomous vehicles. Consequently, we need to engineer scalable and smart Trajectory data analytics Systems in order to analyze both historical data and real-time data flows, derive insights and convert insights into decisions and actions. The purpose of this paper is first to identify key functional and non-functional requirements that a Trajectory data Analytical system must provide and second to survey open-source technologies designed for the analysis of general geo-referenced data.

关键词： data analytics

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intelligent Monitoring for Efficient Use of Energy in Buildings 3

Intelligent Monitoring for Efficient Use of Energy in Buildi...

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3rd international conference on smart Grid and smart Cities (ICSGSC)

作者： Chou, Jui-Sheng Ngoc-Tri Ngo Natl Taiwan Univ Sci & Technol Taipei Taiwan Univ Danang Univ Sci & Technol Danang Vietnam

ISBN: (纸本)9781728138480

Efficient use of energy in buildings has become a major concern of a sustainable society. Minor shifts in peak demand have major implications in terms of savings for both consumers and utilities. This study develops a cloud computing-based big data analytics framework for a building energy efficiency monitoring (BEEM) system that enables managers to facilitate efficient energy use in multiple residential buildings. The framework incorporates smart meter technology, remote sensing and monitoring, Bluetooth technology, big data analytics, cloud computing technology, optimization algorithm, web-based information technology, and electricity pricing policies. Initial experiment was performed to verify potential benefits of the proposed framework. Particularly, a smart grid infrastructure and sensors were installed in a building to retrieve data. An intelligent machine learning-based prediction model was developed to predict future usage of energy in buildings. The BEEM system can support managers and encourage the end-users to effectively monitor daily and monthly energy usage of buildings.

关键词： building energy management smart grid and sensors intelligent monitoring cloud computing machine learning

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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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computing Platforms for Big data analytics in Electric Vehicle Infrastructures 4

Computing Platforms for Big Data Analytics in Electric Vehic...

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4th international conference on Big data computing and Communications (BIGCOM)

作者： Hussain, Md Muzakkir Beg, M. M. Sufyan Alam, Mohammad Saad Krishnamurthy, Mahesh Ali, Qazi Mazhar

ISBN: (纸本)9781538680216

With the emergence of ever-growing smart vehicular applications and ubiquitous deployment of IoT devices across different architectural layers of intelligent Transportation System (ITS), data-intensive analysis emerges to be a major challenge. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. In this paper, we consider the case of Electric Vehicle (EV) to smart Grid (SG) integration. The EVs are key players for Transport Oriented smart Cities (TOSC) as they help cities to become greener by reducing emissions and carbon footprint. We analyze different use-cases in EV to SG integration to show how Big data analytics (BDA) platforms can play a vital role towards successful EV rollout. We then present two computing platforms namely, distributed cloud computing and edge/fog computing. We highlighted the distinguishing features of each towards supporting BDA activities in EV integration. Finally, we provide a detailed overview of opportunities, trends, and challenges of both these computing techniques.

关键词： Electric Vehicles intelligent Transportation System (ITS) Internet of Things (IoT) Edge computing Big data analytics (BDA)

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The Research on Street Landscape Design in smart City Based on Big data

The Research on Street Landscape Design in Smart City Based ...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Wang, Wenjun WUST City Coll Wuhan Hubei Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The traditional urban planning and management model can no longer meet the needs of the contemporary people. More and more cities are moving towards smarter. Wisdom is an important part of urban planning and a technical basis for urban development. In the process of development, a lot of financial and material resources are invested to build a smart city street landscape. The continuous development of street landscape construction has played a positive role in promoting the development of smart cities. The emergence of smart cities is not only an effective means of urban planning, but also an urban development. premise. The article first introduces the background and significance of the research, analyzes the current situation of the application of street landscape design in smart city planning, and combines the current practical characteristics to give a plan for smart city street landscape planning.

关键词： smart city Big data Street landscape Design

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AMI smart Meter Big data analytics for Time Series of Electricity Consumption 17

AMI Smart Meter Big Data Analytics for Time Series of Electr...

引用

17th IEEE international conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom) / 12th IEEE international conference on Big data Science and Engineering (IEEE BigdataSE)

作者： Rashid, Mohammad Harun Pace Univ New York NY 10038 USA

ISBN: (纸本)9781538643877

AMI smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time interval, e.g., every 5 minutes, 15 minutes etc. The data are very sizable, and might be from different sources, along with the other social-economic metrics, which make the data management very complex. A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. Deployment of smart grids gives space to an occurrence of new methods of machine learning and data analysis. smart grids can contain a millions of smart meters, which produce a large amount of data of electricity consumption (long time series). Big data technologies offers suitable solutions for utilities. This paper presents a thorough analysis of 5-minutes 100 anonymized commercial buildings meter data sets to explore time series of electricity consumption and on the creation of a simple forecast model, which uses similar day approach. Our big data analytics can help energy companies to improve the management of energy and services, support intelligent grid control, make an accurate forecast or to detect anomalies.

关键词： smart meter AMI smart grid time series big data electric consumptions forcast data analytics load profile

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A Framework for Big data Analysis in smart Cities 3rd

A Framework for Big Data Analysis in Smart Cities

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3rd international conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Elhoseny, Hisham Elhoseny, Mohamed Riad, A. M. Hassanien, Aboul Ella Mansoura Univ Fac Comp & Informat Mansoura Egypt Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt SRGE Cairo Egypt

ISBN: (纸本)9783319746906;9783319746890

Due to the rapid change in technologies, new data forms exist which lead to a huge data size on the internet. As a result, some learning platforms such as e-learning systems must change their methodologies for data processing to be smarter. This paper proposes a framework for smoothly adapt the traditional e-learning systems to be suitable for smart cities applications. Learning analytics (LA) has turned into a noticeable worldview with regards to instruction of late which embraces the current progressions of innovation, for example, cloud computing, big data processing, and Internet of Things. LA additionally requires a concentrated measure of preparing assets to create applicable investigative outcomes. Be that as it may, the customary methodologies have been wasteful at handling LA difficulties.

关键词： Big data E-learning smart learning smart systems smart cities Learning analytics Internet of Things

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A Comparative Study of Different smart University Frameworks 4

A Comparative Study of Different Smart University Frameworks

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4th international Mobile, intelligent, and Ubiquitous computing conference

作者： ElRashidy, Hussein ElSabry, ElHassan Nile Univ Grad Sch Management Technol Giza Egypt

ISBN: (纸本)9798350367782;9798350367775

In recent years, the concept of smart Universities has emerged as a significant development in the digital transformation of higher education. Leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and big data analytics, smart universities aim to improve educational experiences, optimize administrative processes, and enhance research capabilities. This paper provides a comparative analysis of various smart university frameworks, highlighting their architectural designs, technological enablers, key features, and the challenges associated with their implementation. The study concludes with insights into the future of smart university frameworks and their potential to revolutionize higher education.

关键词： smart University smart infrastructure education technology IoT

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