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共 1358 条记录，以下是231-240 订阅

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Internet of Things: A Survey on Air Pollution Monitoring, Challenges and Applications 7

Internet of Things: A Survey on Air Pollution Monitoring, Ch...

引用

7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on big data and Computational intelligence, ICBDCI 2022

作者： Jiyal, Sarita Sheetlani, Jitendra Saini, Rakesh Kumar Sri Satya Sai University of Technology and Medical Sciences School of Computer Application M.P Bhopal India DIT University School of Computing Uttarakhand Dehradun India

ISBN: (纸本)9798350309904

Due to urbanization and the development of cities, many forests have been cut down for the accommodation and conveyance of human beings. Demand for vehicles is growing exponentially due to the excessive use of resources. Due to how air pollution occurs from vehicles, we are using resources excessively, which is not good for us and our future generations. We are burning fossil fuel and coal for factories, which causes air pollution due to many diseases that occur in human beings, such as respiratory diseases and cardiovascular diseases. Smart sustainable cities are a good concept for every country using the internet of things. In this paper, we discussed air pollution and how it can be monitored using the internet of things. We use gas sensors and dust sensors, which are placed in street lights and signals along city roads, to sense the data. The Arduino is used for analysing the data;the data is displayed on an LCD display and sent to the cloud. People can know the status of air pollution using an Android application. © 2022 IEEE.

关键词： Air quality

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Air Pollution Prediction Method Based on Deep Learning 5

Air Pollution Prediction Method Based on Deep Learning

引用

5th international conference on Robotics, Intelligent Control and Artificial intelligence, RICAI 2023

作者： Qiang, Wang Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines College of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9798350357950

The formation mechanism and process of PM2.5 are relatively complex, with nonlinear, non-stationary, and multi-dimensional characteristics in both time and space, which increases the difficulty of long-term prediction. This article takes PM2.5 pollution as the research object and proposes a PM2.5 air pollution prediction method based on CNN-LSTM deep learning model. It integrates the feature learning and extraction advantages of CNN and the temporal memory advantages of LSTM to improve the accuracy of long-term prediction of PM2.5 pollution in small areas. Through experimental research, it has been shown that the integrated CNN-LSTM hybrid prediction model can fully explore the temporal and interactive characteristics of air pollutants and meteorological factor data, and has higher prediction accuracy and accuracy than a single LSTM prediction model. It is suitable for deep learning hybrid models in processing data such as meteorological data, stock market data, economic data, transportation data and other big data with time series characteristics provides reference. © 2023 IEEE.

关键词： CNN Deep learning LSTM PM2.5 pollution prediction

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Research on the Application of big data in the Construction of Smart Campus in Higher Vocational Colleges 2

Research on the Application of Big Data in the Construction ...

引用

2022 2nd international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2022

作者： Huang, Limin Wang, Jun Organization and Personnel Department Shandong Institute of Commerce and Technology Jinan China College of Cloud Computing Technology Application Industry Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9783800760268

The construction of campus informatization is the need of the development of higher vocational colleges. The informatization of higher vocational colleges has improved the running level of higher vocational colleges. In the process of informatization, higher vocational colleges have produced a large number of data. The data structure is complex and there are many kinds. It is necessary to improve the functions of data collection, management, analysis and processing. big data technology is one of the main technical means applied in the construction of smart campus because of its important role in data acquisition, collection, sorting, analysis and mining. big data technology monitors and analyzes the data generated by higher vocational colleges every day in the whole life cycle, which will provide more ideas and decision support for the governance of higher vocational colleges. This article analyzes and studies the application of big data in the construction of smart campus in higher vocational colleges. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Informatization

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Construction of Intelligent Dispatching System for Port Logistics in the Era of big data Internet 1

Construction of Intelligent Dispatching System for Port Logi...

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1st international conference on Applied Artificial intelligence and computing, ICAAIC 2022

作者： Zhai, Yu Deng, Chun School of Finance and Economics Hainan Vocational University of Science Hainan Haikou570000 China Hainan Open University Hainan Haikou570000 China

ISBN: (纸本)9781665497107

Port production scheduling is the core of port logistics management. In-depth research on port scheduling problems is conducive to further improving the efficiency of scheduling work, and how to solve the scheduling problems is the key to researching port scheduling problems. The successful application of the whole-process tracking smart logistics system, the Hong Kong-wide visual security emergency monitoring system and the multi-port linkage one-stop service system should be fully considered. The combination of technology and scheduling algorithm adopts the scheduling method based on 'real-time intelligence' to solve the scheduling problem of the port. The experimental results show that the scheduling efficiency is increased by 7.3%. © 2022 IEEE.

关键词： big data

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A Novel Approach to Panel data Clustering: Integrating Feature Extraction and Weighted Distance Functions 4

A Novel Approach to Panel Data Clustering: Integrating Featu...

引用

4th international conference on Artificial intelligence and Computer Engineering, ICAICE 2023

作者： Ao, Xiqin Abisado, Mideth College of Computing and Information Technologies National University Manila Philippines Institute of Big Data and Artificial Intelligence Anhui Xinhua University Anhui China

ISBN: (纸本)9798400708831

The traditional clustering method for two-dimensional information is unsuitable for processing panel data. A weighted panel data clustering method based on feature extraction is proposed based on the investigation of multiple information features of panel data. The technique first defines a distance function that considers the absolute value of the indicator, the growth rate of adjacent time points, and the degree of fluctuation variation. Then, the indicator weights and time weights are aggregated into a comprehensive weighted distance through a linear model, and the clustering process of panel data is implemented based on the Ward clustering method. Finally, empirical analysis using consumption expenditure data of various regions in China shows that this clustering analysis method has better discrimination ability and can improve the accuracy of sample clustering. © 2023 ACM.

关键词： Feature extraction

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DETECTION OF FASHION LANDMARKS BASED ON POSE ESTIMATION AND HUMAN PARSING 16

DETECTION OF FASHION LANDMARKS BASED ON POSE ESTIMATION AND ...

引用

16th international conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th international conference on Connected Smart Cities, CSC 2022, 7th international conference on big data Analytics, data Mining and Computational intelligence, bigDaCI 2022, and 11th international conference on Theory and Practice in Modern computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： He, Honghong Zhou, Yanghong Fan, Jin-Tu Mok, P.Y. Institute of Textiles and Clothing The Hong Kong Polytechnic University Hunghom Hong Kong Laboratory for Artificial Intelligence in Design Special Administrative Region Hong Kong Hong Kong

ISBN: (纸本)9789898704429

Locating fashion items on any input images is referred as 'fashion image understanding', which is often a preliminary or initial step that supports various other visions tasks. The study of fashion image understanding has been widely benefited from the rapid advancements in deep learning-based new models and the availability of large datasets covering real-world fashion images. However, the real value of these datasets is in doubt, because different annotation strategies have been adopted in these datasets, resulting in different landmarks for different clothing styles and low compatibility of these datasets. In this paper, we propose a pose-aware segmentation-based method to locate key points of fashion items on fashion images by taking advantage of clear correspondence between clothing and human body, that can be applied to locate key points of fashion items and to re-annotate existing datasets for cross dataset learning. The validity of the method was validated on a subset of the DeepFashion2 dataset. © MCCSIS *** rights reserved.

关键词： Graphic methods

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3D POSE ESTIMATION BY GROUPED FEATURE FUSION AND MOTION AMPLITUDE ENCODING 16

3D POSE ESTIMATION BY GROUPED FEATURE FUSION AND MOTION AMPL...

引用

作者： Peng, Jihua Zhou, Yanghong Mok, P.Y. Institute of Textiles and Clothing The Hong Kong Polytechnic University Hunghom Hong Kong Laboratory for Artificial Intelligence in Design Special Administrative Region Hong Kong Hong Kong

ISBN: (纸本)9789898704429

3D human pose estimation is challenging and converting it to a local pose estimation problem by dividing human body into different groups based on anatomical relationships can improve the accuracy of the resulting 3D pose estimation. Joint features of different groups are fused to predict complete pose information of entire body, in which a joint feature fusion scheme must be used for this purpose. However, the joint feature fusion adopted in existing methods has to learn a large number of parameters and is computational expensive. In this paper, we propose an optimized feature fusion (OFF) module, which requires fewer parameters and less calculations while ensures prediction accuracy. Moreover, we also propose a motion amplitude encoding (MAE) method to improve the prediction accuracy for small ranged movements. Experiments have shown that our method outperforms previous state-of-the-art results on Human3.6M dataset. © MCCSIS *** rights reserved.

关键词： Signal encoding

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An Efficient Reference-Point Based k Neighbors Algorithm for Imbalanced data 7

An Efficient Reference-Point Based k Neighbors Algorithm for...

引用

7th international conference on Cloud computing and big data Analytics, ICCCBDA 2022

作者： Wang, Junkuan Wu, Yanmin Qi, Jinli Chen, Zizhong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data Chongqing China ChongQing College of Electronic Engineering Department of General Education and International Studies Chongqing China

ISBN: (纸本)9781665487115

The kNNs are very simple, effective and online mul-ticategory learning algorithms, which makes them widely applica-ble to various fields. However, almost all kNNs have the problem of low accuracy in imbalanced data. In this paper, an interesting point is observed that the nearest neighbors might not always be the best samples for prediction, and a reference-point based k Neighbors algorithm (RPkN) is proposed. The proposed algorithm uses some reference-points to measure the distance between two data points rather than directly computing the distance. This makes it able to find better neighbors than their nearest ones, and the problem of low accuracy in imbalanced data sets could be solved. Therefore, the proposed method can achieve higher aver-age accuracy than the existing exact kNNs. In addition, it avoids the direct computation of distances between each pair of data points without using any tree structure, and this makes the RPkN decrease the time complexity to O(nlogn). The time complexity to classify a new data point is O(logn). The time complexity is lower than almost all the existing exact kNNs. The low time complexity and not relying on tree structures makes it very suitable to be paralleled for large-scale data processing when compared with other fast exact kNNs using tree structure. © 2022 IEEE.

关键词： Training Cloud computing big data Prediction algorithms data processing Mathematical models Classification algorithms

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Inferencing big data with Artificial intelligence & Machine Learning Models in Metaverse

Inferencing Big Data with Artificial Intelligence & Machine ...

引用

2022 international conference on Smart Applications, Communications and Networking, SmartNets 2022

作者： Siwach, Gautam Haridas, Adinarayana Bunch, Don Distinguished Data Scientist Ibm Southbury United States Site Reliability Engineer Ibm Hyderabad India Infrastructure Specialist Ibm Costa Mesa United States

ISBN: (纸本)9781665487580

This quantitaive study provides different methods of visualization for processing big data sets in augmented and virtual reality. The goal is to provide the detailed implementation of the statistical methods and modeling techniques i.e. using machine learning algorithms and artificial intelligence. The Statistical analysis is performed on the big data sets in Metaverse, that points towards real time inferencing infrastructure and techniques. Also, This paper elaborates the importance of pervasive and Ubiquitous computing Architecture and Applications that enable High-end computing on big data sets. In this paper, we evaluate the need for a combination of cognitive mechanism and high-end infrastructure to address the performance, latency, and security issues when working on big data sets in environments like virtual or Augmented reality. Despite the modernization and advancement in technology there are increasing number of breaches in cloud and hybrid infrastructures, this justifies the need to meet the requirements of strengthening security, safeguard mechanisms and privacy. This paper discusses methods beyond basic visualizations through a deep dive exploration on improvising the applications of existing analytical methods, and use of advanced exploratory tools and visualization techniques on the next generation platforms. © 2022 IEEE.

关键词： Machine learning

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Research on big data Acquisition and Application of Power Energy Based on big data Cloud Platform

Research on Big Data Acquisition and Application of Power En...

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2021 international conference on Computer Graphics, Artificial intelligence, and data Processing, ICCAID 2021

作者： Liu, Rui School of Management Tianjin University of Technology Tianjin China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Under the background of the new era, technologies such as big data and cloud computing have gradually penetrated into the power industry, providing strong support for data collection and quality improvement of power energy. In this regard, this article uses a distributed architecture of electricity consumption information collection system to collect electricity consumption data and establish a big data cloud platform. On this basis, through BP neural network algorithm and other big data analysis methods, accurate load forecasting and environmental pollution prevention and control are implemented., To improve the effectiveness of line loss management, and reasonable prospects for the future development of power energy big data, hoping to better play the value and role of power energy big data, and support the long-term stable development of the power industry to provide a certain reference and reference. © 2022 SPIE.

关键词： data handling

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