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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Research on an Intelligent Structure for Push Control of Multi-Center Cloud data Pool

Research on an Intelligent Structure for Push Control of Mul...

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Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Huang Ying Department of Information Engineering Armed Police Engineering University Xi'an China

The intelligent control of complex system must first solve the problem of data calculation, application and real-time support, which is not only the difficulty and focus of control system design, but also the key to artificial intelligence and intelligent manufacturing. It is found that the essence of control system are both processing data and controlling the direction of data flow. So, the connotation of intelligence lies in an open data processing system to increase the control of data flow, thus forming the basis of real-time data processing. Therefore, this paper proposes an intelligent push control structure of multi center cloud data pool. The structure takes multi-layer cloud data pool as the center, vertical cloud data pool processes global parameters, and uses block chain technology to keep data synchronization. Horizontal cloud data pool collects status data by different function, and parameters required by control loop are pushed by cloud data pool. So, cloud computing ensures the separation of data computing and control, and multi-layer cloud data pool can form sensor networks. The structure realizes the intelligent control target of complex large-scale system that can not only implement big data calculation, but also control the flow direction of data.

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Integration of Cloud computing, IoT, and Big data for the Development of a Novel Smart Agriculture Model

Integration of Cloud Computing, IoT, and Big Data for the De...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Molli Srinivasa Rao Sanjay Modi Rajat Singh K. Lakshmi Prasanna Shakir Khan C. Ushapriya Department of CSE Dadi Institute of Engineering & Technology Anakapalle Visakhapatnam India Lovely Professional University Phagwara Punjab India Uttaranchal Institute of Technology Uttaranchal University India School of Agriculture SR University Warangal Telangana College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Department of Computer Science and Engineering University Centre for Research and Development Chandigarh University Mohali India Department of CSE St. Martin's Engineering College Secunderabad Telangana India

Since the traditional data paradigm cannot handle the volume of information generated by IoT (Internet - of -things) gadgets, cloud storage is now required. These data have been examined using big mining techniques. When evaluating the viability of smart agriculture, the Internet of Farmers must use technologies of information and communication (ICT) in their daily lives to get agricultural information. Crop growth observing, fertilizer classification and irrigation support systems use IoT. This article investigates and optimizes the large amounts of data produced during the farming process, but it seems to be analyzing data mining using the automated k-means system according to the maximum speed. The crop growth curve is intended to simulate the earliest K-means methodology. The experimental findings support the idea that clustering algorithms provide overall benefits in the F mutual information of 7.67% and just a decrease in the total period of 0.23 seconds is supported. Because it can more efficiently realize the operational processes of reliable information communication and data processing, the algorithm presented in this article has a major effect on advancing agronomic information technology.

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Research on the Construction of an Accurate Procurement System for Library e-Resources in Foreign Language Under Dig data Analysis 4th

Research on the Construction of an Accurate Procurement Syst...

引用

4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Luo, Ke Zhang, Xiujun Department of Library Shaoyang University Hunan Shaoyang China Network Information Centre Nanchang Institute of Technology Jiangxi Nanchang China

ISBN: (纸本)9783031201011

Due to the limited funds for the acquisition of university library resources, in order to improve the collection resource profitability and reduce the labor cost of libraries, a method of text classification using natural language processing is proposed to build an accurate procurement system for electronic book resources. Feature extraction is performed for the text information of the book collection, a feature vector matrix is established, and classification prediction is performed by machine learning algorithms. By optimizing the prediction model and evaluating the results with ten cross-validations, the accuracy of the LightGBM model testing reached 77.49%, so the acquisition system constructed based on this model can make effective decisions and thus optimize the structure of the collection resources. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Comparative Analysis of Cryptographic algorithms for data Security in Cloud computing: A Performance Evaluation

Comparative Analysis of Cryptographic Algorithms for Data Se...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Ishu Rajan Lave Kumar Vanshika Srivastava Manoj Singhal Rajani Singh Department of Information Technology GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India

In the modern era, people are sharing their data online. securing this data in today's world is a crucial task i.e., anyone can get the data without the concern of the respective authority. Users store their sensitive/non-sensitive data on the cloud only by trusting the Terms & Conditions and the user agreement offered by the service provider. Cloud service providers are known to reject the responsibility for any data breaches that may happen. This paper proposes a method to strengthen the confidentiality of data in cloud. We have done comparative study between several cryptographic algorithms on the basis of their encryption and decryption time, performing ondifferent data formats like text, image, and videos.

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Sewing and Weaving data: Analyzing Fiber Crafts as Context for Performing data processing and Storing 17

Sewing and Weaving Data: Analyzing Fiber Crafts as Context f...

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17th international conference of the Learning Sciences, ICLS 2023

作者： Keune, Anna Technical University of Munich Germany

ISBN: (纸本)9781737330677

Fiber crafts, including sewing, are connected to the history and future of computing. Yet, they are underrepresented in computing education. This qualitative study analyzed performed an iterative thematic analysis of artifact analysis sessions in which computer science experts examined middle school students' craft projects for evidence of challenging computer science concepts. This paper shows a weaving and sewing craft as context for performing data processing and storing with implications for computing education. © ISLS.

关键词： Word processing

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Pavement Distress Detection Using Image processing from Unmanned Aerial Vehicle data

Pavement Distress Detection Using Image Processing from Unma...

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international conference on Innovative Methods and Practical Applications for Cognizant Transportation Systems, IMPACTS 2023

作者： Manjusha, M. Rudhra, A. Sunitha, V. Department of Civil Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819772995

The scale of public roads, particularly highways, has rapidly increased in many nations over the last few decades. To increase the road’s usability and thus its social and economic benefits, road maintenance has received more attention lately. Road inspections were done manually, the old-fashioned way takes a lot of time, and work, and is subjective. For proper road maintenance, deteriorated roads must be repaired as quickly as possible. Therefore, there is an urgent need for high-performance, low-cost technology to identify road surface distress. Unmanned aerial vehicles (UAVs), on the other hand, are an exciting option for monitoring the status of roads. They have the benefits of great flexibility, cheap cost, and simple agility. The data was collected in a 5 km road stretch of NH-336, Tamil Nadu. In this study, some of the computer vision tools including OpenCV, CUDA, etc. are tried to detect and localize the distress. It includes the detection and classification of pavement distress by using Object detection algorithms like Faster RCNN, Yolov3, and Yolov4 were used for detection and classification of road distresses. The main focus is on the detection and classification of potholes and cracks. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real-time. The application of deep learning techniques in transportation engineering problems is very rare. This study makes an effort to use deep learning techniques in the classification of the distresses and localizing them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Co-Tuning of Cloud Infrastructure and Distributed data processing Platforms

Co-Tuning of Cloud Infrastructure and Distributed Data Proce...

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IEEE international conference on Big data

作者： Isuru Dharmadasa Faheem Ullah The University of Adelaide Australia

Distributed data processing Platforms (e.g., Hadoop, Spark, and Flink) are widely used to store and process data in a cloud environment. These platforms distribute the storage and processing of data among the computing nodes of a cloud. The efficient use of these platforms requires users to (i) configure the cloud i.e., determine the number and type of computing nodes, and (ii) tune the configuration parameters (e.g., data replication factor) of the platform. However, both these tasks require in-depth knowledge of the cloud infrastructure and distributed data processing platforms. Therefore, in this paper, we first study the relationship between the configuration of the cloud and the configuration of distributed data processing platforms to determine how cloud configuration impacts platform configuration. After understanding the impacts, we propose a co-tuning approach for recommending optimal co-configuration of cloud and distributed data processing platforms. The proposed approach utilizes machine learning and optimization techniques to maximize the performance of the distributed data processing system deployed on the cloud. We evaluated our approach for Hadoop, Spark, and Flink in a cluster deployed on the OpenStack cloud. We used various benchmarking workloads in our evaluation. Our results reveal that, in comparison to default settings, our co-tuning approach reduces execution time by 17.5% and ${\$}$ cost by 14.9% solely via configuration tuning.

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Stock Market Prediction Approach: An Analysis

Stock Market Prediction Approach: An Analysis

引用

Artificial Intelligence and Smart Communication (AISC), international conference on

作者： Mayank Joshi Gaurav Goel Department Of Computer Science and Engineering Chandigarh Group of College India

data mining is a method that can glean meaningful information from a wealth of data. The data mining method known as prediction analysis uses current data to forecast potential future outcomes. Pre-processing the image, to extract the features, and classify the image are just a few of the operations used in the prediction analysis methodology. The stock market forecast is the foundation of this review essay. This study reviews a number of classification-based stock market prediction systems. Python is used to put the stock market forecasting concepts into practice.

关键词： Feature extraction Prediction algorithms Classification algorithms data mining Stock markets Forecasting Artificial intelligence

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Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data

Machine learning-based prediction of sorghum biomass from UA...

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computing and Communication Systems (I3CS), international conference on

作者： Boubacar Gano Nurzaman Ahmed Nadia Shakoor Donald Danforth Plant Science Center Saint Louis MO USA

Unmanned aerial vehicle (UAV)-based remote sensing applications in plant phenotyping have received attention in modern plant breeding programs that increasingly have the need to automate time-consuming manual measurements of agronomic traits. This paper focuses on the prediction of sorghum biomass using machine learning algorithms such as Linear Regression, KNeighbors Regressor, and the XGBoost Regressor. Results from a field experiment of 344 sorghum genotypes conducted at the Donald Danforth Plant Science Center (Saint Louis, MO, USA) showed accurate prediction models. The K-Neighbors Regression model performed better than the other two models (R 2 =0.65, RMSE =4968.60kg/ha). The developed approach in this study could be used as a decision support tool for sorghum biomass phenotyping in breeding programs.

关键词： Machine learning algorithms Biological system modeling Computational modeling Vegetation mapping Predictive models Autonomous aerial vehicles data models

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Deriving Pipeline for Emergency Services Using Natural Language processing Techniques 11th

Deriving Pipeline for Emergency Services Using Natural Langu...

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11th international conference on Recent Trends in computing, ICRTC 2022

作者： Anand, Akshat Rajeswari, D. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789811988240

Communications have a crucial role in times of crisis, particularly in times of emergency. Whenever a region is affected by any disaster, social media sites such as Twitter, etc., are a great way to get information out to those who need it the most. Our paper suggests that in times of crisis, when people are worried about finding the right emergency contacts or are experiencing a transformation in their psychological response, they should manually enter their experience into an app to obtain contact information for the nearest SOS. Alternatively, the proposed application suggests scraping hashtags from around the world with web scraping scripts to retrieve geo-referenced tweets in real time. In order to gather accurate data, Twitter's API or some third-party application can be used in the development of this software. When there is a major crisis, social media user behavior plays a vital role, owing to its efficiency and high reliability with swift data dissemination. Anxiety over finding the right emergency team contact may cause people to lose their cool and become unclear about which authority they should call when an emergency occurs. Eventually, our application bridges the gap between clients in need of help and the approved agency that can really provide that support in different circumstances. In the case of, for example, a lost child or a flooded area, our application helps you get in touch with an organization that can assist you in dealing with the situation. We have used CNN-1-D + LSTM, CNN-1-D + Bi-LSTMs, and CNN-1-D + LSTM + Glove-100-D to get maximum accuracy for the classification model. It is possible to use the prototype solution in a novel way to organize immediate assistance with authorities in comprehensive administration, while integrating Twitter or other social platforms into the application can make it a feasible platform for preventive intervention and catastrophe management. © 2023, The Author(s), under exclusive license to Springer Nature

关键词： Emergency services

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