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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 3255 条记录，以下是2011-2020 订阅

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Severity Prediction of Software Vulnerabilities Using Textual data

Severity Prediction of Software Vulnerabilities Using Textua...

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international conference on Recent Trends in Machine Learning, IOT, smart Cities and Applications, ICMISC 2020

作者： Malhotra, Ruchika Vidushi Delhi Technological University Shahbad Daulatpur Main Bawana Road Delhi110042 India

ISBN: (纸本)9789811572333

Nowadays, all essential activities carried out by society are dependent on software systems. These systems with time have become more and more complex. Increase in complexity leads to introduction of vulnerabilities in the software system. Vulnerabilities are being reported every year and are increasing exponentially with time. These vulnerabilities required to be patched and fixed before getting exploited. To achieve this goal, textual data available on vulnerabilities is needed to be exploited to retrieve valuable information from it. Therefore, in this paper, we aim to develop a prediction model which will take textual description of Apache Tomcat vulnerabilities as input and will predict the severity of the vulnerabilities. Text mining techniques and machine learning algorithms are used to perform this task. The huge volume of textual data available on vulnerabilities has to be prioritized so that more severe vulnerabilities can be targeted first and limited resources can be put to its best use. Hence, it is essential to use text mining techniques for feature selection to reduce the volume of the data. The model being developed uses chi-square and information gain for feature selection, and among the machine learning algorithm, bagging technique, random forest, naïve Bayes, support vector machine are used for prediction. From results, it is observed that comparatively information gain gave better results among feature selection technique. Model based on naïve Bayes algorithm along with information gain as feature selection techniques performed good with the average values of 0.91, 90.90%, and 92.30%, respectively, for AUC, sensitivity, specificity, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Exploiting Satellite data for Solar Performance Modeling

Exploiting Satellite Data for Solar Performance Modeling

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2020 international conference on Communications Control and computing Technologies for smart Grids

作者： Bansal, Akansha Singh Irwin, David Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781728161273

Developing accurate solar performance models, which infer solar power output in real time based on the current environmental conditions, are an important prerequisite for many advanced energy analytics. Recent work has developed sophisticated data-driven techniques that generate customized models for complex rooftop solar sites by combining well-known physical models with both system and public weather station data. However, inferring solar generation from public weather station data has two drawbacks: not all solar sites are near a public weather station, and public weather data generally quantifies cloud cover-the most significant weather metric that affects solar-using highly coarse and imprecise measurements. In this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth's surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km(2) area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy.

关键词： Geostationary satellites

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A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud data Centers 22

A Reinforcement Learning-Based Virtual Machine Placement Str...

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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： Long, Saiqin Li, Zhetao Xing, Yun Tian, Shujuan Li, Dongsheng Yu, Rong School of Computer Science Xiangtan University Key Lab. of Hunan Prov. for Internet of Things and Info. Secty. Hunan Intl. Sci. and Technol. Coop. Base of Intelligent Netwk. Xiangtan China Xiangtan University School of Computer Science Xiangtan China National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9781728176499

With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms. © 2020 IEEE.

关键词： Energy conservation

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A Vision of smart Traffic Infrastructure for Traditional, Connected, and Autonomous Vehicles

A Vision of Smart Traffic Infrastructure for Traditional, Co...

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international conference on Connected and Autonomous Driving (MetroCAD)

作者： Ranka, Sanjay Rangarajan, Anand Elefteriadou, Lily Srinivasan, Siva Poasadas, Emmanuel Hoffman, Dan Ponnulari, Raj Dilmore, Jeremy Byron, Tom Univ Florida Gainesville FL 32611 USA City Gainesville Gainesville FL USA Florida Dept Transportat Tallahasee FL USA

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160597

This smart city traffic management approach seeks to use edge-based video-stream processing (using multicore and GPU processors) at intersections and in public vehicles (city buses, fire trucks, ambulances, school buses) to convert video data into space-time trajectories of individual vehicles and pedestrians that are transmitted to a cloud-based system. Key information is then synthesized in the cloud from them to create a real-time city-wide traffic palette. Real-time or offline processing both at the edge and the cloud will then be leveraged to optimize intersection operations, manage network traffic, identify near-collisions between various units of traffic, provide street parking information, and a host of other applications. Additional information such as weather and environment will also be leveraged. The use of edge-based real-time machine learning (ML) techniques and videostream processing has several significant advantages. (1) Because there is no need to store copious amounts of video (few minutes typically suffice for edge-based processing), it automatically addresses concerns of public agencies who do not want person-identifiable information to be stored for reasons of citizen privacy and legality. (2) The processing of the video stream at the edge will allow for the use of low bandwidth communication using wireline and wireless networks to a central system such as a cloud, resulting in a compressed and holistic picture of the entire city. (3) The real-time nature of processing enables a wide variety of novel transportation applications at the intersection, street, and system levels that were not possible hitherto, significantly impacting safety and mobility.

关键词： video processing sensor data machine learning big data analytics intelligent transportation smart city

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smart Farming System Based on Fog computing and LoRa Technology 1st

Smart Farming System Based on Fog Computing and LoRa Technol...

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1st international conference on Embedded Systems and Artificial Intelligence, ESAI 2019

作者： Baghrous, Mohamed Ezzouhairi, Abdellatif Benamar, Nabil Renewable Energies and Intelligent Systems Laboratory Faculty of Science and Technology Sidi Mohammed Ben Abdellah University Fez Morocco National School of Applied Sciences Sidi Mohamed Ben Abdellah University Fez Morocco School of Technology University Moulay Ismaïl Meknes Morocco

ISBN: (纸本)9789811509469

Nowadays, the increased agricultural production at a lower cost is more and more driven by the Internet of Things (IoT) and the cloud computing paradigms. Many researches and projects have been elaborated so far in this context. It aims at reducing human efforts as well as resources and power consumption. Such projects are mainly based on collecting various data pertaining to the agricultural area and sending it to the cloud for further analysis. However, the long distance between sensors/actuators and the cloud leads to a significant increase in latency, which leads to a decrease of performances of the irrigation systems, pesticide monitoring, etc. This paper presents an alternative solution based on Fog Nodes and LoRa technology to optimize the number of nodes deployment in smart farms. Our proposed solution reduces the total latency induced during data transmission toward the cloud for processing. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Agriculture

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Research on the status quo and problems of power grid production in China 13th

Research on the status quo and problems of power grid produc...

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13th international conference on Genetic and Evolutionary computing, ICGEC 2019

作者： Xie, Bo-Lin Lin, Yuh-Chung Pan, Jeng-Shyang Chang, Kuo-Chi Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Information Science and Engineering Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811533075

The intelligent production of the power grid is the core of the development of smart grids. The current production status of the power grid is in a transitional phase. During the transition period, there are many problems in the power grid foundation due to the application of new technologies. After our investigation, the big data storage and protection problems, the different generation devices coexistence problem, and scene image classification and processing problems taken by drone inspection are the most prominent problems in the current power grid. This paper elaborates on the above issues and proposes feasible solutions according to the development of modern technology. The possible solutions for our observed problems are to establish a big data cloud platform inside the power grid, to propose a unified standard for the power grid device, and to effectively utilize the two-dimensional code for image classification. This will not only effectively solve the various problems faced by the power grid at the current stage, but also lay the foundation for the development of smart grid. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS data 3rd

Analyzing Linear Relationships of LST with NDVI and MNDISI U...

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international conference on data Management, analytics and Innovation (ICDMAI)

作者： Govil, Himanshu Guha, Subhanil Diwan, Prabhat Gill, Neetu Dey, Anindita Natl Inst Technol Raipur Dept Appl Geol Raipur Madhya Pradesh India Chhattisgarh Council Sci & Technol Raipur Madhya Pradesh India Nazrul Balika Vidyalaya Dept Geog Guma W Bengal India

ISBN: (纸本)9789813299498;9789813299481

The present study used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Impervious Surface Index (MNDISI) to determine the linear relationship between Land Surface Temperature (LST) distribution and these remote sensing indices under various spatial resolutions. Four multi-date Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images of parts of Chhattisgarh State of India were used from four different seasons (spring, summer, autumn and winter). The results indicate that LST established moderate to strong negative correlations with NDVI and weak negative to moderate positive correlations with MNDISI at various spatial resolutions (30-960 m). Generally, the coarser resolutions (840-960 m) possess stronger correlation coefficient values due to more homogeneity. The autumn or post-monsoon image represents the strongest correlation for LST-NDVI and LST-MNDISI at any resolution levels. The image of winter season reveals the best predictability of LST distribution with the known NDVI and MNDISI values.

关键词： Land surface temperature (LST) Normalized difference vegetation index (NDVI) Modified normalized difference impervious surface index (MNDISI) Landsat Spatial resolution

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Internet of Energy: Ensemble Learning through Multilevel Stacking for Load Forecasting 18

Internet of Energy: Ensemble Learning through Multilevel Sta...

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18th IEEE international conference on Pervasive intelligent and computing (PICom)

作者： Singh, Shailendra Yassine, Abdulsalam Benlamri, Rachid Lakehead Univ Dept Elect & Comp Engn Thunder Bay ON Canada Lakehead Univ Dept Software Engn Thunder Bay ON Canada

ISBN: (数字)9781728166094

ISBN: (纸本)9781728166094

In the Internet of Energy (IoE) ecosystem, an accurate electricity load forecasting is critically important to all the participants in the smart grids, such as manufacturers, utility companies, renewable energy generators, consumers and prosumers. It is a vital component for a stable and reliable operation of the electricity grid, effective demand side management, and the success of energy efficiency programs. We present a novel ensemble learning mechanism through multi-level stacking for the load forecasting. Our proposed architecture has four layers utilizing strong learners at each level (base and meta) with an input features set, at all the levels, that includes initial (original) variables/features from the dataset along with meta-features extracted during stacking. We use computational intelligence (CI) techniques such as Random Forest, Cubist, k-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Support Vector Machine Regression (SVM-R), Multivariate Adaptive Regression Splines (MARS), and Principal Component Regression (PCR) to train participating prediction models at various levels of stacking. The results show significant improvement in performance with Root Mean Square Error (RMSE) reduced by 36.81%, a 38.82% reduction in Mean Absolute Error(MAE), and R2 increased from 0.787 to 0.923 when compared to single forecasting model.

关键词： Ensemble Learning Stacking Multilevel stacking smart Grids smart Meter Load forecasting Big data Energy Time Series data Mining

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A Study on Securing Non-financial data Using Blockchain Mechanism 2nd

A Study on Securing Non-financial Data Using Blockchain Mech...

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2nd international conference on computing analytics and Networking, ICCAN 2019

作者： Sontakke, Abhyarthna Shadangi, Shivam Verma, Abhavya Pradhan, Roshni Kalinga Institute of Industrial Technology Deemed University BhubaneswarOdisha India

ISBN: (纸本)9789811524134

Blockchain is a sequence of blocks that contains information. It is originally designed intended to timestamp digitally notary documentation. It was developed by a group of researchers and later it was discovered by Nakamoto Satoshi. Block chain is a distributed public ledger which permits to approach and records the data which are difficult to eradicate. It is a technology intended for industries and companies to cooperate with faith as well as lucidity in work in a safe way. The well-known use of blockchains is a cryptographic currency like bitcoin along with some other uses. In Blockchain, each block contains data, hash of the data, and hash of the previous block. Hash is a fingerprint that identifies a block and block of data containing within it. After the block is created the hash is calculated and it is connected to the previous hash like a chain or necklace. Blockchain in a very simple language is like a garland that secures data with high cryptocracy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Bitcoin

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Keynote: Edge Intelligence - Emerging Solutions and Open Challenges

Keynote: Edge Intelligence - Emerging Solutions and Open Cha...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Mario Luca Bernardi Dept. of Engineering University of Sannio Benevento Italy

ISBN: (纸本)9781665404242;9781665447249

Recent advances in AI have led to a number of intelligent applications, such as intelligent personal assistants, context-related and personalized buying advice, automated video surveillance and smart appliances. Such applications quickly rose to prominence and gained tremendous popularity in a short time as they have had a positive impact on people's lives by improving the productivity and generally efficiency of human-based processes. With the proliferation of mobile, pervasive computing and the IoT, the trend that had seen the cloud as the largest data concentrator is reversing. In fact, bringing the enormous volumes of data to a completely centralized AI is unthinkable given the size of the phenomena taking place. The edge ecosystem plays a fundamental role in enabling the creation of intelligent applications and systems that start from the periphery and integrate with centralized AI systems. It is therefore necessary to push the boundary of AI towards the edge ecosystem that basically lives in the last mile of the Internet. This, however, is an extremely difficult task due to the non-trivial issues of performance, cost, privacy and integration. Transferring huge amounts of data across Wide Area Networks raises costs, slows down operations, and makes ensuring privacy extremely complex. Energy efficiency also plays a key role in this context. In fact, AI services require computing power which must therefore be shared in the periphery, appropriately guaranteeing all systems, even the limited ones, to be able to use them. It is therefore necessary to exploit the physical proximity between the sources of processing and generation of information through decentralized architectures capable of cooperating: this allows to obtain numerous advantages compared to the traditional processing paradigm which is entirely based on cloud. From this close integration between edge computing and AI, a new research area has arisen, namely “edge intelligence”. Edge intelligence aims to

关键词： Pervasive computing Wide area networks Cloud computing Visual analytics Ecosystems Video surveillance

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