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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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1st international conference on Advances in Artificial Intelligence and Machine Learning in Big data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference on Advances in Artificial Intelligence and Machine Learning in Big data processing, AAIMB 2023

ISBN: (纸本)9783031730672

The proceedings contain 51 papers. The special focus in this conference is on Advances in Artificial Intelligence and Machine Learning in Big data processing. The topics include: Topological Navigation of Path Planning Using a Hybrid Architecture in Wheeled Mobile Robot;abnormal Behaviour Detection in Surveillance Videos;ISAApp – Image Based Smart Attendance Application;a Self-learning Ai-Based Information Leak Protection System;enhancing Abnormal Object Detection in Camera-Based Systems Through Computer Vision and Deep Learning Techniques;Detection and Classification of Brain Tumor in Magnetic Resonance Images Using CNN;diagnosis of Parkinson’s Disease Using Machine Learning and Deep Learning Techniques;a Survey on Deep Learning Based Human Activity Recognition System;a Deep Learning Approach for Non - invasive Body Mass Index Calculation;Early-Stage Detection of Alzheimer’s Disease Using MRI Scans with Deep Learning;penguin Search Optimization with Deep Learning Based Cybersecurity Malware Spectrogram Image Classification;Detection and Classification of Skin Disease Using CNN;estimation of Above Ground Biomass Using Machine Learning and Deep Learning algorithms: A Review;URL Phishing Detection Using Deep Learning and Machine Learning Techniques;enhanced Disease Recognition and Classification in Black Gram Plant Leaves Using Deep Learning;Ensemble Deep Learning Approach for Identification of DDOS Attack;ROCLT: Enhanced Text Classifier for Sentiment on Imbalanced Multiclass Tweet data Using Hybrid Deep Learning Techniques;computer Vision to Animal Footprint Classification Based on Deep Learning Model;Speech Emotion Recognition Using CNN Classifier Based on Deep Learning Model;face Detection and Recognition for Criminal Identification System Using Deep Learning.

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2nd international conference on Business data Analytics, ICBDA 2023

2nd International Conference on Business Data Analytics, ICB...

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2nd international conference on Business data Analytics, ICBDA 2023

ISBN: (纸本)9783031807770

The proceedings contain 28 papers. The special focus in this conference is on Business data Analytics. The topics include: A Novel Approach for Diabetic Prediction Using Attribute Subset Selection, K-Means and Logistic Regression;diabetic Retinopathy Detection Approach Using Convolution Neural Networks;voting Regression Model for the Air Quality Prediction;Quantitative Assessment on Investigation on the Impact of Artificial Intelligence on HR Practices and Organizational Efficiency for Industry 4.0;NIR Spectroscopy for Freshness Detection and Classification of Chicken Eggs;Human Action Recognition in Infrared Domain: A Study on IITR-IAR dataset;Software Requirements Prioritization Using Fuzzy Based TOPSIS Methods;predictive Analytics for Inventory Management in Multi-Vendor E-Commerce;data Analytics Augmented by AI in the Realm of 6G Wireless Communication;big data-Powered Analytics for Fortifying Virtualized Infrastructure Security in the Cloud;orthogonal Polynomials and Their Engineering Applications;Explainable AI for Drone data Analytics in Aerial computing;correlational Analysis of Risk-Taking Propensity in Adolescents;Analytical Insight into Cutting-Edge Image Captioning for Advanced ChatGPT Functionality;an Expert System for Talent Prediction and Enhancement of Non-Talented Cricket Performers;Predicting Early Dropouts in SWAYAM MOOCs Using Machine Learning Techniques: A Comparative Analysis;data Analysis of Social Media’s Impact on Promotion of Organic Products in Uttarakhand;enhancing Academic Performance Prediction Through K-Means Clustering and Comparative Evaluation of Machine Learning algorithms: A Case Study on Student dataset;cognitive data Underwriting with Automation: A Survey;Multi-Level Thresholding Segmentation of Chest X-ray Images of COVID-19 Patients Using Chaos Game Optimizer and Utilizing Kapur's Entropy;concrete Strength Prediction Using Machine Learning.

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Optimization of the database structure based on Machine Learning algorithms in case of increased data flow

Optimization of the database structure based on Machine Lear...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Akhatov, A. Rashidov, A. Renavikar, A. Samarkand State University Samarkand Uzbekistan NeARTech Solutions Pune India

ISBN: (纸本)9781032669663

Today, the sharp increase in the use of digital devices in the world is the reason for the creation of a large flow of data in plenty of systems. Due to the large amount of data in these systems, the process of data processing, i.e. making queries and getting the result, takes more time than traditional methods. Currently, the most popular method of real-time data processing is data processing based on a distributed computing mechanism. But one of the most important disadvantages of this method is determining the number of optimal distributions in proportion to the size of the data. Because over-distribution also has a negative effect on the efficiency of the system. In this research work, in order to solve the given problem, an approach to the optimal distribution of data flow based on Machine Learning algorithms is proposed. At the same time, during the research work, the data obtained as a result of the experiment conducted in unified computing systems based on the distributed computing mechanisms of this approach were analyzed. In addition, the efficiency indicators of 18 Machine Learning algorithms used during the ban were evaluated and the selection of the 5 most effective algorithms for the proposed approach was considered. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Digital devices

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Automatic Financial and Economic Information processing System Based on RPA and Big data algorithms

Automatic Financial and Economic Information Processing Syst...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Li, Linwei Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350313413

In the practice of financial management, the main links of financial and economic information processing include data collection, data integration, data analysis, and report preparation. Manually processing this type of information is not only inefficient but also error-prone. This article proposes an automatic financial and economic information processing system based on RPA (Robotic Process Automation) and big data algorithms, uses RPA technology to build the overall framework of the system, and conducts in-depth analysis of financial and economic information based on big data algorithms. This system includes a data collection module, a data analysis module and a report generation module, which can realize the functions of automatic collection, processing, analysis and report preparation of financial and economic information, and solve the problems of low efficiency and high error probability in traditional manual processing of financial and economic information, and provide effective data support for enterprise decision-making. The experimental results show that the work efficiency of the financial and economic information automatic processing system based on RPA and big data algorithms can reach 92.3%. The development and application of this system can improve financial work efficiency, reduce human cost input, and provide effective data support for enterprise decision-making. © 2023 IEEE.

关键词： automatic processing system big data algorithm financial and economic information robotic process automation

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LSPR spectrometer integrated software system: advanced data processing algorithms for biosensing 4

LSPR spectrometer integrated software system: advanced data ...

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4th international conference on Bioinformatics and Intelligent computing (BIC)

作者： Zhou, Mixing Geng, Zhaoxin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

ISBN: (纸本)9798400716645

The spectrometer, a crucial tool in optical research, finds wide-spread application across various disciplines. However, existing spectrometer software systems often lack the capability for collaborative work, being confined to the control of a single device and presenting challenges in supporting system integration. This paper addresses these issues by investigating spectrometer software systems and data processing algorithms. A software system tailored to the integration needs of spectrometers is designed and implemented. Additionally, a set of data processing algorithms is developed to enhance spectral data processing. Using LSPR reflectance spectroscopy as an exemplar, the integrated spectrometer software system is employed to assess the performance of the LSPR biosensor chip, encompassing sensitivity, figure of merit, resolution, and other crucial metrics. This study aims to rectify existing limitations in spectrometer software systems, offering more efficient and versatile methodologies and algorithms for spectral analysis.

关键词： Spectral analysis Sensitivity Figure of merit LSPR biosensor Integrated software

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1st international conference on Optimization and data Science in Industrial Engineering, ODSIE 2023

1st International Conference on Optimization and Data Scienc...

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1st international conference on Optimization and data Science in Industrial Engineering, ODSIE 2023

ISBN: (纸本)9783031814570

The proceedings contain 35 papers. The special focus in this conference is on Optimization and data Science in Industrial Engineering. The topics include: A Framework for Simulating the Optimal Allocation of Shared E-Scooters;Performance Enhancement of Two-Way DF Relayed Cooperative NOMA Vehicular Network with Outdated CSI and Imperfect SIC;integration of Artificial Intelligence for the Analysis and Monitoring of Projects Within Companies;multi-criteria Inventory Classification with Machine Learning algorithms in the Manufacturing Industry;analyzing Results of Business Process Automation with Machine Learning Methods;predictive Analysis of Surface Defects in Engineering Structures Using Machine Learning Technologies;enhancing Autonomous Industrial Navigation: Deep Reinforcement Learning for Obstacle Avoidance in Challenging Environments;Fast and Accurate Right-Hand Detection Based on YOLOv8 from the Egocentric Vision dataset;Fine-Tuning of 3D Hand Pose Estimation on HOI4D dataset by Convolutional Neural Networks;prediction of Ethereum Prices Based on Blockchain Information in an Industrial Finance System Using Machine Learning Techniques;email Classification of Text data Using Machine Learning and Natural Language processing Technique;the Fake News Detection Model Explanation and Infrastructure Aspects;An Advanced Approach to COVID-19 Detection Using Deep Learning and X-ray Imaging;exploring Factors that Affect the User Intention to Take Covid Vaccine Dose;unsupervised Incremental-Decremental Attribute Learning Healthcare Application Based Feature Selection;on Solving the Physicians Scheduling Problem at an Emergency Department: A Case Study from Canada;Temporal Emotional and Thematic Progression (TETP): A Novel Analysis of Mental Health Discussions on Social Platforms.

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1st international conference on Optimization and data Science in Industrial Engineering, ODSIE 2023

1st International Conference on Optimization and Data Scienc...

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1st international conference on Optimization and data Science in Industrial Engineering, ODSIE 2023

ISBN: (纸本)9783031814549

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Analyzing Human Behavior in Digitized Society: Insights from Wordle data

Analyzing Human Behavior in Digitized Society: Insights from...

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algorithms, computing and data processing (acdp), international conference on

作者： Yitong Hu Yizhe Wang Zixuan Zhu Beijing University of Posts and Telecommunications Beijing China

This paper focuses on analyzing the human behavior of Wordle players using available player data. While there have been many studies discussing the mechanism, strategy, and cultural influence of Wordle, research on the relationship between player data and human group behavior is still lacking. The authors conducted a time series analysis of the number of daily Wordle players and developed a MIMO XGBoost model to predict the distribution of player attempts. The model demonstrates high confidence through a test set, and the authors analyze the predicted data to provide insights for game designers and sociologists.

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Curriculum Reform of Sampling Techniques Based on Machine Learning algorithms: A Panel data Analysis

Curriculum Reform of Sampling Techniques Based on Machine Le...

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algorithms, computing and data processing (acdp), international conference on

作者： Wang Gao Linlin Zhang Qian Cao Research Center for Finance and Enterprise Innovation Hebei University of Economics and Business Shijiazhuang China School of Finance Hebei University of Economics and Business Shijiazhuang China School of Information Technology Hebei University of Economics and Business Shijiazhuang China Institute of Science and Technology Information Beijing Academy of Science and Technology Beijing China

Integrating machine learning algorithms into sampling techniques and survey courses is a crucial step in educational reform in these fields. With the rapid growth of data-driven decision-making, the use of machine learning algorithms has become increasingly important in various industries and disciplines. By incorporating machine learning algorithms into sampling techniques and survey courses, students will be able to learn how to apply these algorithms to solve real-world sampling problems. This study validates the significant positive impact of machine learning algorithms on sampling technique courses through a panel data probit model. This will provide data support for educational instruction.

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Mini-batching with Fused Training and Testing for data Streams processing on the Edge 21

Mini-batching with Fused Training and Testing for Data Strea...

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21st ACM international conference on computing Frontiers (CF)

作者： Luna, Reginaldo Cassales, Guilherme Pfahringer, Bernhard Bifet, Albert Gomes, Heitor Murilo Senger, Hermes Univ Fed Sao Carlos Sao Carlos Brazil Univ Waikato Hamilton New Zealand Victoria Univ Wellington Wellington New Zealand

ISBN: (纸本)9798400705977

Edge computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.

关键词： computing methodologies Parallel computing methodologies Parallel algorithms Shared memory algorithms

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