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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Development and application of Network Data Mining in English Corpus Software Design 4

Development and Application of Network Data Mining in Englis...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Xu, Wei Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798350333558

With the development of technology and the progress of the times, the existence of data information has penetrated into various industries in society along with network and computer technology. Today, online office and decision-making are commonplace. How to use network data mining technology in the design of English corpus software to accurately find valuable data and information. this article first explores big data, then explains some of the changes that big data has brought to the field of corpus design, and then explores the important achievements and breakthroughs that can be achieved by combining big data methods in corpus design. the questionnaire survey method was used to investigate and statistically analyze the data mining methods used in the corpus. From the survey results, it can be seen that there are more staff using the PageRank and Apriori algorithms, as well as the C.45 algorithm, while the proportion of staff using K-means is relatively small, indicating that these three algorithms have higher efficiency and wider usage. therefore, data mining based on big data can significantly improve the efficiency of corpus design. © 2023 IEEE.

关键词： Software design

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Hybrid machine learning Method for Product Sales Forecasting in E-Commerce 4

Hybrid Machine Learning Method for Product Sales Forecasting...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, Ravi Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350300888

the term 'e-commerce' refers to the practice of buying and selling products using the internet. E-commerce was created so that consumers wouldn't have to leave the comfort of their homes to purchase something. So, they can buy it online and have it delivered to their house in a few days. Online retailing has recently developed in a favorable environment because of the robust backing of national legislation. this year's pandemic has made the e-commerce sector's contribution to the growth of the country's economy more noticeable. the quantity and effectiveness of e-commerce networks and e-commerce businesses are growing under such circumstances. A network must've been able to better match user demands and perform admirably in all areas of collaboration and administration if it hopes to keep its competitive edge. Accurately predicting the sales volume of e-commerce networks at this time is crucial. this article presents a novel hybrid machine learning (ML) algorithm to accurately predict revenue growth for an online store. A hybrid cat-XGBoost (HC-XG) algorithm is suggested to predict the sale. the main goal of the proposed work is to forecast e-commerce sales, thus i the final selling value is included in the solution since it has a significant impact on sales in extremely competitive and price-sensitive contexts like e-commerce. We next continue with the development of the suggested solution and evaluate it for accuracy, error, and overall efficiency, and compare it to some competitor solutions. thus, the proposed approach is compared with other existing models for sales forecasting and demonstrate that it provides the best overall performance in product sales forecasting. © 2023 IEEE.

关键词： Sales

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13th international conference on Image Processing and Communications, IP and C 2023, 13th international conference on computer Recognition Systems, CORES 2023

13th International Conference on Image Processing and Commun...

引用

13th international conference on Image Processing and Communications, IP and C 2023, 13th international conference on computer Recognition Systems, CORES 2023

ISBN: (纸本)9783031416293

the proceedings contain 23 papers. the special focus in this conference is on Image Processing and Communications, international conference on computer Recognition Systems. the topics include: Coregistration of Selected Sequences in MRI Imaging Based on Edge Analysis of Image Difference;actinic Keratosis Prediction Based on Deep learning Methods;a New Assessment of Convolutional Neural Networks for Texture Directionality Detection;knowledge Distillation with Relative Representations for Image Representation learning;An Approach for CT Image Conversion Using Filtering Based on Quaternion Mathematics;exploring Text-Guided Synthetic Distribution Shifts for Robust Image Classification;Color Tracking application Using AI-Based Docker Container Scheduling in Fog Computing;air Pollution Monitoring and Information Distribution System;Detection of Conspiracy theories from Tweets Using NLI-Based Zero Shot Text Classification Models;hollow n-grams Vectorizer for Natural Language Processing Problems;battery Management System for Polish Hybrid Residential Photovoltaic Power Plants;document Annotation Tool for News Content Analysis;dynamic Time Warping Technique Applied to the User’s Intent Recognition for Myoelectric-Based Control of Upper Limb Prosthesis;A Hybrid Long Short-Term Memory Network Based on Wind and Rain Sensitive and Its application to PM2.5 Prediction;a Non-deep Approach to Classifying Movie Genres Based on Multimodal Data;incremental Extreme learning machine for Binary Data Stream Classification;Interpreting Intrusions - the Role of Explainability in AI-Based Intrusion Detection Systems;real-Time Deformation of three-Dimensional Volumetric Models Using Mesh Models;Hyperparameters Optimization Using GridSearchCV Method for TinyML Models;analysis of the Possibility to Employ Relationship Between the Problem Complexity and the Classification Quality as Model Optimization Proxy;deep learning for Pit Detection in Cherries.

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PT-MVSNet: Overlapping Attention Multi-view Stereo Network with Transformers 4

PT-MVSNet: Overlapping Attention Multi-view Stereo Network w...

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Zhang, Song Li, Lin Qiao, Jiangxuan School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Qinghai Normal University Academy of Plateau Science and Sustainability Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

ISBN: (纸本)9798350326444

In this paper, we propose a new multi-view stereo vision model PT -MVSNet based on multi-view stereo (MVS). Multi-view stereo is a successful reconstruction method that uses multiple images to reconstruct a 3D scene. It has been applied in many practical scenes such as architecture, cultural heritage protection, and map making. MVS still faces a lot of challenges, including inaccurate feature matching, excessive image noise, and overly complex computation. To solve the feature-matching inaccuracy problem, we take the Transformer model as the main structure in the feature-matching and add a patch-based overlap attention module (POLA). In this paper, we proposed PT-MVSNet can solve the image feature extraction problem more effectively. To validate the effectiveness of the model, we conducted experiments on the DTU dataset and evaluated its performance by two evaluation metrics. the experiment results show that our method outperforms the latest methods, whose accuracy and completeness reach 0.386 and 0.271 respectively. © 2023 IEEE.

关键词： Feature extraction

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

4th International Research Conference on Machine Learning, D...

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

ISBN: (纸本)9783031882364

the proceedings contain 9 papers. the special focus in this conference is on machine learning, Deep learning, and Blockchain. the topics include: Development of an Intrusion Detection Model Using a Support Vector machine to Identify Cyber Attacks;vector Quest: A Faster and Better Open-Domain Question-Answering System;deep learning Approach to Indian Sign Language Recognition;false Data Injection Attack Prediction Using Federated Deep learning Approach;block Chain Ballot: A Secure and Transparent E-Voting System;combination of IoT and Block Chain for Safe Data Management;A Prototype for Secure and Efficient Real Estate Transactions Using IPFS and Block Chain Technology.

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machine learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments 56

Machine Learning-Based Power Consumption Prediction for Unma...

引用

56th Annual Hawaii international conference on System Sciences, HICSS 2023

作者： Gatscher, Julian Breitenbach, Johannes Buettner, Ricardo University of Bayreuth Germany University of Bayreuth Fraunhofer FIT Germany

ISBN: (纸本)9780998133164

Unmanned aerial vehicles are becoming integrated into a wide range of modern IoT and CPS environments for various industrial, military, and entertainment applications. With growing estimations for this market in the future, the problem of energy consumption and its prediction is becoming increasingly important for optimal battery-saving, as well as the safety of the application and thus protection of surrounding persons near the drone flight. this paper presents a machine learning-based approach for the prediction of the power consumption of unmanned aerial vehicles at certain times of the flight. Instead of predicting the power consumption in prescribed environments with complex, time-consuming measurement techniques, our approach is fast, easy to implement, and predicts real-world power consumption in five classes, with a balanced accuracy of 66.7 percent. © 2023 IEEE computer Society. All rights reserved.

关键词： Drones

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UNF-IDT: Automated Irony Detection in English Tweets 22

UNF-IDT: Automated Irony Detection in English Tweets

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22nd IEEE international conference on machine learning and applications, ICMLA 2023

作者： Jaganathan, Guna Sekaran Kilungeja, Grentina Kahanda, Indika School of Computing University of North Florida JacksonvilleFL United States

ISBN: (纸本)9798350345346

the usage of social media platforms has increased tremendously in the past decade. Twitter is one of the most popular platforms for sharing opinions and feelings on various topics, organizations rely on Twitter data to analyze and gather insights for their businesses using Natural Language Processing (NLP) techniques. However, there are various challenges in analyzing such a huge volume of tweets that are in text format. One such challenge is to identify irony in tweets, which has a significant impact on analyzing sentiments. To overcome this challenge, we propose a solution that automatically recognizes the presence of irony in text and classifies the type of irony. the solution consists of two tasks: Task A performs binary classification to annotate whether irony is expressed or not in each tweet, while Task B performs multi-class classification to classify the type of irony expressed in the tweets. We used the dataset from the 'SemEval-2018 Task 3: Irony detection in English tweets' challenge to train and test the proposed solution. the dataset contains 3,817 English tweets for training and 784 English tweets for testing both Tasks A and B. We developed different machine learning models by leveraging traditional classifiers, neural networks, and large language models. Our UNF-IDT (Irony Detector in Text) model developed using BERT (Bidirectional Encoder Representations from Transformers) was able to achieve an F1 score of 0.757 for Task A (Binary Classification) and an F1 score of 0.449 for Task B (Multi-class Classification), retrospectively obtaining 4th and 9th ranks, in the two tasks respectively. © 2023 IEEE.

关键词： BERT Irony Detection machine learning Text Mining Twitter

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Research on the control of overloading of muck vehicles based on morphological feature extraction from the perspective of deep learning 4

Research on the control of overloading of muck vehicles base...

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4th international conference on Image Processing and Intelligent Control, IPIC 2024

作者： Zhu, Shengkai Geng, Yiting Yang, Jiaqi Cao, Yiwen Shandong Xiehe University Shandong Jinan China

ISBN: (纸本)9781510682313

With the acceleration of urbanization, urban demolition, construction, reconstruction and other engineering projects to promote urban waste transportation activities increasingly frequent, but because of overload, overrun, dangerous driving and other non-standard operation problems, endangering social and traffic safety. Traditional police law enforcement has problems such as management and governance leakage, incomplete coverage, and weak law enforcement. In the application of the existing detection methods based on traditional loadometer weighing and road weighing, there are pain points such as high equipment investment, small detection range, and easy damage of equipment. In order to do a better job of muck vehicle management, this paper breaks the traditional muck vehicle management and loadometer weight measurement method, and creatively puts forward no gap dynamic real-time supervision and machine vision based on deep learning. through the accurate judgment of overload and limit of muck vehicle morphological detection, it effectively solves the problem of no blind spot, continuous and less investment in law enforcement. It provides an effective management scheme for the accurate law enforcement and violation remediation of traffic control departments and administrative departments. © 2024 SPIE.

关键词： Urban transportation

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Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile application Traffic 26

Multi-Feature Fusion Based Approach for Classifying Encrypte...

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26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yang, Qingya Fu, Peipei Shi, Junzheng Wang, Bingxu Li, Zhen Xiong, Gang Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350331684

With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN. © 2023 IEEE.

关键词： Classification (of information)

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the application of machine learning in Deception Detection : a study based on the effect of empathy on deception detection 4

The Application of Machine Learning in Deception Detection :...

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4th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Wang, Buxue Shen, Xunbing Jiangxi University of Chinese Medicine Key Laboratory of Psychology of Tcm and Brain Science Jiangxi Administration of Traditional Chinese Medicine 1688 Meiling Avenue Nanchang China

ISBN: (纸本)9798350333947

Accurate deception detection is crucial for national security, judicial interrogation, clinical diagnosis, etc. However, humans have difficulties in deception detection. Shifting the perspective to machine learning to build models for automatic deception detection by feature extraction and feature classification of misleading information such as verbal, nonverbal, or physiological indicators will open up new prospects in the field of deception detection. Many deception detection algorithms have been developed, but machine learning algorithms still need to be continuously optimized to make it possible to achieve fully automated deception detection for more effective applications in national security and interrogation. © 2022 IEEE.

关键词： Feature extraction

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