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

共 6946 条记录，以下是4661-4670 订阅

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Scout: An Efficient Federated Learning Client Selection Algorithm Driven by Heterogeneous data and Resource

Scout: An Efficient Federated Learning Client Selection Algo...

引用

IEEE international conference on Joint Cloud computing (JCC)

作者： Ruilin Zhang Zhenan Xu Hao Yin Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

Federated Learning is a novel distributed machine learning paradigm that leverages the computing power of numerous decentralized data sources for jointly training machine learning models while ensuring user privacy. In the most commonly used cross-device scenarios, the client cluster typically cover a vast number of heterogeneous end devices. Due to physical limitations such as bandwidth, only a few clients can participate in each round of training. The core issue of the client selection is to determine an appropriate client set for each training round. However, existing selection algorithms, especially the widely adopted random selection, suffer from a number of issues that prevent them from achieving a good balance between training efficiency and speed. Therefore, we propose Scout, which utilizes the heterogeneity features of clients’ data and resources to jointly model the utility function, and enhances the utilization of correlation among clients and the diversity among selected clients to achieve better training efficiency and speed. Furthermore, Scout maintains the scalability and fairness. Our experiments demonstrate that in large-scale heterogeneous clients scenarios, Scout outperforms three baseline algorithms and the state-of-the-art dual-feature dimension algorithm Oort in evaluation metrics.

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Research on Demand Response data Compression Based on Neural Network under 5G Environment

Research on Demand Response Data Compression Based on Neural...

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IEEE international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Yuqing Pan Bing Qi Bin Li Enguo Zhu Yan Liu Chunguang Lu Chaoliang Wang School of Electrical and Electronic Engineering North China Electric Power University Beijing China China Electric Power Research Institute Beijing China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

In the context of 5G, data volume is exploding, and data transmission and processing are facing challenges. In this paper, a data compression algorithm based on neural network is proposed to solve the problem of large scale and high dimension of demand response data in the process of transmission and storage. By mapping demand response data to low dimensional space for representation, the algorithm effectively reduces the amount of data and improves the efficiency of data transmission and storage. The algorithm uses Long Short Term Memory network for data compression and restoration. The experimental results show that the data compression algorithm based on neural network has good compression effect and accuracy. At the same time, the algorithm has good versatility and adaptability, and can be applied to the compression and restoration of data sets of different types and sizes, providing an effective solution for efficient demand response data processing.

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Clustering the Various Categorical data: An Exploration of algorithms and Performance Analysis

Clustering the Various Categorical Data: An Exploration of A...

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Emerging Technology (INCET), international conference for

作者： Raman Kumar Peeta Basa Pati K. Deepa Suresh Yanan Department of Computer Science and Engineering Amrita School of Computing Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm.

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Adequate Basis for the data-Driven and Machine-Learning-Based Identification 1

引用

22nd international conference on Artificial Intelligence and Soft computing, ICAISC 2023

作者： Rojahn, Marcel Ambros, Maximilian Biru, Tibebu Krallmann, Hermann Gronau, Norbert Grum, Marcus University of Potsdam Potsdam Germany Krallmann AG Berlin Germany

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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data Protection Between IoT Devices and Cloud computing: Using PRINCE Lightweight Algorithm

Data Protection Between IoT Devices and Cloud Computing: Usi...

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international conference on Engineering Technology and their Applications (IICETA)

作者： Anaam Ghanim Hilal Mehdi Ebady Manaa Department of Information Networks University of Babylon Babylon Iraq

The Internet of Things (loT) is a fast-growing field that involves connecting a wide variety of devices and sensors to the Internet to collect and share data. The vast amount of data generated by these devices requires secure storage and processing, which is where cloud computing comes in. Cloud computing provides scalable, on-demand storage and processing resources that can handle the massive amount of data that loT devices produce. By integrating loT and cloud computing, organizations can effectively store, manage, and analyze the data generated by loT devices, leading to more efficient and effective operations. The main disadvantage is security, which is the most important issue nowadays The primary goal of this paper is to make data more secure and reduce latency when data is transported from loT devices to cloud computing, must using efficient lightweight cryptography algorithm suitable in loT environment. In this paper, we will focus on the PRINCE lightweight encryption algorithm. evaluating the proposed system using main parameters so that one can understand the security and performance aspects of the suggested system such as Execution time, and Entropy. The result showed in the last reading as the following execution time is 0.202994713Sec and Entropy is 7.97083942.

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Brain Cancer Classification using MR Images Based on VGG-19 Feature Extraction and Ensemble Classifier

Brain Cancer Classification using MR Images Based on VGG-19 ...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Chintam Anusha Rajendra Kumar G Praveen M. A Computer Science and Engineering Vignan's Institute of Information Technology Visakhapatnam andhrapradesh India

One of the most studied disorders in modern medicine is the brain tumor. A correct cancer type diagnosis enables the professionals to select the best course of action and save the patient's life. The significance of an automated tumor classification system incorporating image processing should go without saying. The classification of different forms of brain tumors using magnetic resonance images (MRI) has been improved in this paper utilizing deep learning and a group of machine learning algorithms. The Brain Tumor MRI dataset can be divided into four groups, which include three types of brain cancer (Glioma, Meningioma, and Pituitary), as well as non-cancerous, or normal type, tumors. o extract detailed information from the MRIpictures, a VGG-19 convolutional neural network is created. Then, to classify among various cancer kinds, these filtered deep characteristics are put into multi-class ML classifiers. The outputs of each ML classifier are thencombined to improve performance using a weighted average ensemble of classifiers. A total of 7022 MRI scansfrom four classes make up the dataset for the system. The accuracy of the Brain Tumor MRI dataset has improved, with a a Meningioma class accuracy of 97.88%, a Pituitary class accuracy of 98.76%, Glioma class accuracy of 99.78%, a normal class accuracy of 99.00% and an overall accuracy of 98.87%.

关键词： Deep learning Image quality Machine learning algorithms Magnetic resonance imaging Training data Magnetic resonance Information filters

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Exploring Domain Randomization’s Effect on Synthetic data for Activity Detection*

Exploring Domain Randomization’s Effect on Synthetic Data f...

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Metaverse computing, Networking and Applications (MetaCom), IEEE international conference on

作者： Megani Rajendran Chek Tien Tan Indriyati Atmosukarto Aik Beng Ng Andrew Grant Eric Cameracci Simon See NVIDIA AI Technology Center NVIDIA Singapore Center for Immersification Singapore Institute of Technology Singapore NVIDIA Canada

The construction and operation of Metaverse virtual environments, e.g., 3D reconstruction and activity detection is an important supporting technology of computer vision. Recently, synthetic data has seen a surge in adoption for model training in computer vision. Prior research generally show a positive correlation between the volume of synthetic training data and inference accuracy. This paper focuses on the domain of activity detection, and explores how to improve the performance of such algorithms using synthetic data. In particular, we present an overview of the state-of-the-art in using domain randomization approaches for synthetic data generation. This paper presents initial inference accuracies of a model trained on initial attempts at domain randomized synthetic data (7.2%), compared to a model trained on real-world data (9.2%). The synthetic data, although performed worse, indicated promising trajectories for future work, approximately 2% away from the real-world result.

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A Perceptual data Cleansing Model (SDCM) for Reducing the Dirty data

A Perceptual Data Cleansing Model (SDCM) for Reducing the Di...

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Smart computing and Application (ICSCA), international conference on

作者： Mohammad Azmi Al-Madi Ahmed Abdel-Wahab Mohammed AlShanty Shaikhan Bawazeer Mohammed AlZahrani Department of Computer Sciences Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Faculty of Computer Studies Arab Open University Riyadh Saudi Arabia Deprtment of Systems and Computers Engineering Al-Azhar University Cairo Egypt

data cleansing approaches aim at revealing and reducing different types of outsourced errors. Such errors introduce a major issue as data cleansing often involves costly computations and time consumption. data cleansing is complicated as most of the errors within the obtained data emerge in different forms, such as typos, duplicates, noncompliance with business rules, outdated data, and missing values. In this paper, the Supervised dataset Cleaning Model (SDCM) is proposed in order to detect and reduce different types of outsourced errors that are stored in the data repository according to the supervised cleaning rules that are practiced from the previous cleaning processes. The findings indicates that the cleaning execution time can be reduced with this supervised model and the accuracy of the model is also increased when it covers the entire training rules and classified error types derived from the data warehouse. There are some expected future directions, which include: implementing full scale of SDCM and comparing the results obtained with different current methods. By comparing different data cleaning algorithms with SDCM, a knowledge gap is likely to arise in the search for further future improvements.

关键词： Training Computational modeling Supervised learning Clustering algorithms data warehouses Cleaning data models

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13th international Symposium on Business Modeling and Software Design, BMSD 2023

13th International Symposium on Business Modeling and Softwa...

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13th international Symposium on Business Modeling and Software Design, BMSD 2023

ISBN: (纸本)9783031367564

The proceedings contain 29 papers. The special focus in this conference is on Business Modeling and Software Design. The topics include: Preface;modeling Change in Business Processes;Development of a Capability Maturity Model for Organization-Wide Green IT Adoption;From Conceptual Specification to OO Specification;Architecting Agility: Unraveling the Impact of AI Capability on Organizational Change and Competitive Advantage;enterprise Architecture Artifacts’ Role in Improved Organizational Performance;A Semiotic Analysis of the Representativeness of BPMN Graphic Elements;A Systematic Approach to Derive User Stories and Gherkin Scenarios from BPMN Models;composing an Initial Domain-Specific Modeling Language Notation by Reusing Icons;validating Trust in Human Decisions to Improve Expert Models Based on Small data Sets;how is Affect Social Justice Tensions: A Case Study of Asylum Management;applying Augmented Reality in Tourism: Analyzing Relevance as It Concerns Consumer Satisfaction and Purchase Intention;realizing Appropriate Process Standardization – Basis for Effective Digital Transformation;comparing Sensor-Based computing and Predictive data Analytics for Usage in Context-Aware Applications;a Model of a Multi-sensor System for Detection and Tracking of Vehicles and Drones;a Web-Based Approach for Traceability in Rule-Based Business Information Systems;a Conceptual Model for the Selection of Methods for Software Engineering Process Improvement;Towards Log-Driven Monitoring of Technical Degradation: An ERP Perspective;A Development Example: From Conceptual Specification to OO Specification;managing database Trigger Design;a View on Vulnerabilities Within IoT Devices in the Smart Home Environment;on Tools for Practical and Effective Security Policy Management and Vulnerability Scanning;linking Computers to the Brain: Overview of Cybersecurity Threats and Possible Solutions;serious Game-Based Haptic Modeling - An Application-Oriented Approach for Sequentially Devel

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Disease Prediction and Classification using Intelligent algorithms

Disease Prediction and Classification using Intelligent Algo...

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Artificial Intelligence and Smart Systems (ICAIS), international conference on

作者： P. Mohana Priya Deparment of IT School of Computing SASTRA Deemed to be University Thanjavur India

Cardio Vascular Diseases are the major risk factors for human survival. Cardio Vascular Diseases are comprised with many forms such as abnormal heart rhythms, congenital heart disease, deep vein thrombosis, heart attack, heart failure etc. These diseases are identified using scanning devices and doctors can analyze the report by observing heart x-ray images. This research work is carried out using various intelligence algorithms for predicting and classifying the type of heart disease. It is observed that Support Vector Classifier predicts with an accuracy of 99%, Logistic Regression predicts with an accuracy of 85%, Multi Layer Perceptron prediction accuracy is 86.4%, Simple Logistic prediction accuracy is 85.8%, J48 prediction accuracy as 88.62% and Random Forest classification as 100% based on the training data.

关键词： Heart Veins Training data Support vector machine classification Prediction algorithms Classification algorithms Thrombosis

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