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

共 6952 条记录，以下是291-300 订阅

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An Effectual Method for Clustering of Bigdata on the Cloud using Fuzzy c-means Algorithm

An Effectual Method for Clustering of Bigdata on the Cloud u...

引用

2023 IEEE Global conference on Information Technologies and Communications, GCITC 2023

作者： Gupta, Vishan Kumar Gupta, Avdhesh Agrawal, Madhup Sardana, Anjali Shukla, Surendra Kumar Joshi, Kireet Amity University Punjab Department of Cse Mohali India Ajay Kumar Garg Engineering College Department of Cse Ghaziabad India Ajay Kumar Garg Engineering College Department of It UP Ghaziabad India Abes Engineering College Department of Cse UP Ghaziabad India Svkms Nmims Mpstme Department of Computer Engineering India Graphic Era Deemed to Be University Department of Cse Dehradun India

ISBN: (纸本)9798350308167

Big data clustering on cloud platforms has become increasingly important due to the exponential growth of data and the need for scalable and efficient processing. This review examines a paper that focuses on the application of clustering algorithms in a cloud computing environment to analyze and cluster large-scale datasets. The paper explores the implementation and evaluation of various clustering algorithms in a cloud-computing environment. It introduces the problem of big data clustering and highlights the significance of leveraging cloud resources to handle massive volumes of data efficiently. The authors propose a novel approach that utilizes cloud infrastructure to perform distributed clustering and present experimental results using real- world datasets. The paper provides a comprehensive literature review on big data clustering and cloud computing. It discusses relevant prior research, highlighting the gaps and challenges addressed by the proposed approach. The research provides evidence for the scalability and effectiveness of the suggested method when working with sizable datasets. It showcases the advantages of cloud-computing in terms of resource utilization and processing time. © 2023 IEEE.

关键词： bigdata cloud computing clustering data mining exploratory data analysis mapreduce optimal fuzzy c-means clustering algorithm

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A Provisional Study of data Mining Classification algorithms in Predicting Credit Card Defaulters Using Weka Tools 8

A Provisional Study of Data Mining Classification Algorithms...

引用

8th IEEE international conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Sakina Rosdi, Nur Fadhlin Shafiqah Ibrahim, Nur Shamsudin, Intan Hajariah Mutalib, Sofianita Abdul-Rahman, Shuzlina School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9798350315516

data mining is a field of study that seeks to draw out significant patterns from large amounts of data. This study explores the classification algorithms for spotting new trends in massive volumes of credit card data. This work covers the performance analysis of various data mining techniques, namely, Naive Bayes, J48, and Support Vector Machine (SVM), in predicting credit card defaulters the dataset is gathered from Kaggle, with 25 attributes and 30,000 instances used to assess the performance of algorithms. Additionally, each classification algorithm's effect on feature selection is mentioned. A variety of data mining classification approaches is used to compare the models in this study the experiment finds J48 and SVM as the best algorithms at 81.56% and 80.92%, respectively. © 2023 IEEE.

关键词： Support vector machines

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Design and implementation of data upload module based on ARINC615A 8

Design and implementation of Data upload module based on ARI...

引用

8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Wang, Kuisheng Feng, Jiaxu Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9798350302455

With the continuous development of avionics technology, many data file loading and unloading technologies have appeared for updating and maintaining the configuration in the airborne network. Due to the continuous increase in the amount of airborne network data, the original data file loading technology can no longer meet the requirements of use. In this study, ARINC615A is selected as the data file loading and unloading protocol. After the analysis and research of the protocol, the data uploading module of the data loading and unloading software is designed and implemented on the AFDX network to meet the data file uploading requirements. © 2023 IEEE.

关键词： Unloading

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Collaboration of Mixed Reality for Interactive Visualization of Ocean Mapping 2

Collaboration of Mixed Reality for Interactive Visualization...

引用

2nd international conference on Advancements in Electrical, Electronics, Communication, computing and Automation, ICAECA 2023

作者： Rithish, Ramamoorthy Sathya Vaishali, V. Balavedhaa, S. Sushmita, V. Sabitha, B. Ug Mechatronics Engineering Kumaraguru College of Technology Coimbatore India UG Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India Mechatronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

Technological advancements in recent times have led to the combination of Artificial Intelligence (AI) and Mixed Reality (MR), making it a powerful tool to address major challenges in a variety of fields. Ocean mapping includes a wide network of research associations which collect ocean data for analysis. This information about ocean floors is vital for various industrial and education fields. A few machine learning algorithms and data pre-processing techniques help to collect, clean, and manipulate data faster and at a lower cost due to the increased speed and efficiency it offers. This allows us to create MR infographics which can be used by research associates to visualize and view the data in a better way. This immersive technology enhances the method of visualization and makes it easier for researchers to conclude a final decision from the analysis. This paper includes an introduction of key terminologies, followed by the current ocean mapping methods i.e., Bathymetry and Multibeam Mapping, along with their respective visualization techniques, and how MR can be used for visualizing the collected data for further study purposes. This paper also briefs about how the implementation of MR in ocean mapping can be advantageous to a wide range of industries. © 2023 IEEE.

关键词： Mixed reality

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A data-Driven Study on the Hawthorne Effect in Sensor-Based Human Activity Recognition

A Data-Driven Study on the Hawthorne Effect in Sensor-Based ...

引用

ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Hoelzemann, Alexander Bock, Marius Valladares, Ericka Andrea Touhami, Salma El Ouazzani Nassiri, Kenza Van Laerhoven, Kristof Univ Siegen Siegen Germany

ISBN: (纸本)9798400702006

Known as the Hawthorne Effect, studies have shown that participants alter their behavior and execution of activities in response to being observed. With researchers from a multitude of human-centered studies knowing of the existence of the said effect, quantitative studies investigating the neutrality and quality of data gathered in monitored versus unmonitored setups, particularly in the context of Human Activity Recognition (HAR), remain largely underexplored. With the development of tracking devices providing the possibility of carrying out less invasive observation of participants' conduct, this study provides a data-driven approach to measure the effects of observation on participants' execution of five workout-based activities. Using both classical feature analysis and deep learning-based methods we analyze the accelerometer data of 10 participants, showing that a different degree of observation only marginally influences captured patterns and predictive performance of classification algorithms. Although our findings do not dismiss the existence of the Hawthorne Effect, it does challenge the prevailing notion of the applicability of laboratory compared to in-the-wild recorded data. The dataset and code to reproduce our experiments are available via https://***/mariusbock/hawthorne_har.

关键词： human activity recognition hawthorne effect data collection

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Comparative Study on Calculating CPU Burst Time using Different Machine Learning algorithms 14

Comparative Study on Calculating CPU Burst Time using Differ...

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Vayadande, Kuldeep Agarwal, Naman Shevate, Srushti Sawakare, Chinmayee Parakh, Piyush Khandare, Snehankit Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. Throughout the study, we discovered that the effectiveness of different machine-learning approaches relies on the applications to which they are put. Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data. © Grenze Scientific Society, 2023.

关键词： Scheduling algorithms

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Implementing An Outsourced Dual-Proxy Signing and Decryption Scheme in Mobile Cloud computing

Implementing An Outsourced Dual-Proxy Signing and Decryption...

引用

37th IEEE international Parallel and Distributed processing Symposium (IPDPS)

作者： Fugkeaw, Somchart Thammasat Univ Sirindhorn Int Inst ofTechnol Sch ICT Pathum Thani Thailand

ISBN: (纸本)9798350311990

Existing access control schemes that support fine-grained and secure data sharing for mobile cloud computing system typically focus on the design and development of secure and lightweight cryptographic protocols to enable data users efficiently decrypt the data by using mobile devices. However, some business requirements such as industrial cloud users may require the proof of authenticity of the data owner. Accordingly, both privacy-preserving and strong authenticity that data users can verify the authenticity of data owners to make trust the shared data are essential. To this end, we proposed a secure and efficient access control featured with efficient revocation in mobile cloud computing environment. Our proposed scheme is based on the outsourced dual proxy model where the expensive digital signing, encryption and decryption are partially offloaded to be executed by the delegated proxies in the cloud. We employed the symmetric encryption and the ciphertext policy attribute-based encryption (CP-ABE) as a core encryption scheme. In addition, we introduced a user revocation algorithm to enable the efficient user revocation in the mobile cloud computing. For the evaluation, we run the experiments to measure the performance of our core access control algorithms and the results demonstrated that our proposed scheme is efficient.

关键词： access control data sharing cloud digital signing symmetric encryption cp-abe mobile cloud computing

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SymED: Adaptive and Online Symbolic Representation of data on the Edge 29th

SymED: Adaptive and Online Symbolic Representation of Data o...

引用

29th international conference on Parallel and Distributed computing (Euro-Par)

作者： Hofstaetter, Daniel Ilager, Shashikant Lujic, Ivan Brandic, Ivona Vienna Univ Technol Vienna Austria Ericsson Nikola Tesla Zagreb Croatia

ISBN: (纸本)9783031396977;9783031396984

The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-constrained edge devices. Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols. Also, they allow data analytics (e.g., anomaly detection and trend prediction) directly on symbols, benefiting large classes of edge applications. However, existing SR algorithms are centralized in design and work offline with batch data, which is infeasible for real-time cases. We propose SymED - Symbolic Edge data representation method, i.e., an online, adaptive, and distributed approach for symbolic representation of data on edge. SymED is based on the Adaptive Brownian Bridge-based Aggregation (ABBA), where we assume low-powered IoT devices do initial data compression (senders) and the more robust edge devices do the symbolic conversion (receivers). We evaluate SymED by measuring compression performance, reconstruction accuracy through Dynamic Time Warping (DTW) distance, and computational latency. The results show that SymED is able to (i) reduce the raw data with an average compression rate of 9.5%;(ii) keep a low reconstruction error of 13.25 in the DTW space;(iii) simultaneously provide real-time adaptability for online streaming IoT data at typical latencies of 42ms per symbol, reducing the overall network traffic.

关键词： Internet of Things Edge computing Symbolic data representation Edge storage and analytics data compression Time series

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Edge AI Deployment and Stream-Based Time Series Classification Within ARTHUR 12th

Edge AI Deployment and Stream-Based Time Series Classificati...

引用

13th international conference on Cloud computing and Services Science (CLOSER)

作者： Schneider, Niels Ruf, Philipp Lermer, Matthias Reich, Christoph Furtwangen Univ Appl Sci Inst Data Sci Cloud Comp & IT Secur Robert Gerwig Pl 1 D-78120 Furtwangen Germany

ISBN: (纸本)9783031681646;9783031681653

The convergence of Artificial Intelligence (AI) and manufacturing has given rise to transformative opportunities in enhancing operational efficiency and productivity. This study delves into the concept of edge computing, where AI algorithms are deployed closer to data sources within a distributed architecture. The ARTHUR framework, a versatile data Acquisition System with Distributed Sensors, developed under the slogan "test before invest", is introduced as a platform to realise this approach. By employing low-cost hardware, event-based communication, and in-database AI operations, ARTHUR enables real-time data processing and analysis for research, education and industrial applications. A case study demonstrates the deployment of a machine learning model for time series classification using vibration data from CNC machines. The results underscore the potential of edge-based AI in manufacturing, highlighting its capability to empower adaptive and responsive production systems.

关键词： Distributed monitoring system Machine learning Cloud data acquisition IoT-cloud integration in-database operations

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Optimization and Application of SPRINT Big data Classification Algorithm in Cloud computing Framework 6

Optimization and Application of SPRINT Big Data Classificati...

引用

2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Han, Wenqi Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350313444

When processing massive data, existing classification methods frequently take too long to compute, making their performance challenging to satisfy the actual needs of big data applications. In this paper, an SPRINT optimization classification algorithm based on a cloud computing framework is studied. The idea is to build a three-level cloud computing framework, and the optimization SPRINT classification algorithm is implanted into it so that the SPRINT classification algorithm has better stability and communication efficiency, as well as a higher-performance parallel computing environment in the process of processing big data. SPRINT classification algorithm optimization the process is as follows: First, the segmentation of GiNi value nodes is used to enhance the parallel computing capability of the classification algorithm and the optimization ability of continuous attributes. Next, the subsets of the big data set are reclassified, and the decision tree construction is finished based on the optimal segmentation point to enhance the big data classification processing ability. According to the experimental findings, the cloud computing framework's optimized SPRINT classification algorithm outperforms the more established big data classification algorithm in terms of classification performance and efficiency, as well as execution rate and parallel performance. © 2023 IEEE.

关键词： Child node cloud computing framework GiNi value optimizing SPRINT classification algorithm

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