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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是471-480 订阅

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Prediction of Plunger Lift Dynamics Using a Bidirectional Long Short-Term Memory Neural Network with an Innovative Forecasting Strategy 3

Prediction of Plunger Lift Dynamics Using a Bidirectional Lo...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Zhong, Zikang Wang, Haoyu Li, Nan Zhu, Haiwen Zhu, Jianjun Wang, Jianli Southeast University Nanjing China Research Institute of Exploration & Development PetroChina Beijing China China University of Petroleum Beijing China

ISBN: (纸本)9798350380989

Plunger lift is commonly applied to both conventional and unconventional gas wells to address issues such as liquid loading, which can result in liquid accumulation and even cease production. By leveraging machine learning (ML) algorithms, well surveillance data can be utilized to predict the condition of gas wells utilizing plunger lifts, facilitating decisionmaking in gas well deliquification. In this paper, a recurrent neural network (RNN) based on long short-term memory (LSTM) for production process data analytics was employed to capture the temporal dynamic behaviors of time-series data. Our findings indicate that the bidirectional LSTM combined with the encoder-decoder architecture (BILSTM-AE), multiforecast average bidirectional LSTM (ABILSTM), and noisesmoothed bidirectional LSTM (SBILSTM) achieved root mean square error (RMSE) values of 1.20%, 1.09%, and 0.95%, along with R-squared (R2) scores of 95.49%, 96.97%, and 96.38%, respectively. A further statistical comparison revealed that the ABILSTM model demonstrated superior performance compared to the other models. With high-precision minute-level forecasting, this study has the potential to significantly improve production forecasting, operational adjustments, and hazard prevention in practical processes. © 2024 IEEE.

关键词： Liquid loading machine learning Plunger lift Recurrent neural networks Time series forecasting

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EvauleBlock: Evaluating Answers in E-learning Applications with Enhanced Security and Intelligence through DRL and Blockchain Integration 3

EvauleBlock: Evaluating Answers in E-Learning Applications w...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Parthasarathy, R. Kiruthiga Devi, M. Karthick, V. Kumar, P. Mahendran, Rakesh Kumar Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Dr. Mgr Educational and Research Institute Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Evaluating the candidate answers during online examination by a human examiner is wearisome task. Along with that, security concerns also associated in the E-learning environment in terms of false data injection and modification attacks respectively. The state of the art work, either focus on designing intelligent and robust question answering model using machine learning (ML)/Deep learning (DL) algorithm, or designing a secure framework for E-learning environment using blockchain. To neutralize that issue, we design a secure and intelligent model for E-learning environment using Deep Reinforcement learning (DRL) and blockchain methodology respectively named EvauleBlock. The EvauleBlock composed of four major processes such as data assortment, data pre-processing, intelligent answer evaluation, and blockchain storage. In data assortment stage, the training of the proposed DRL algorithm named Multi Agent Deep Deterministic Policy Gradient (MADDPG) is taken place by crawling the related question answering data from the various websites. Followed by, we have performed pre-processing for the candidate written answers using adequate pre-processing steps. Once the answers are pre-processed, it is then provided to the MADDPG algorithm which employs four agents for validating the candidate answers by checking the semantic, syntactic, keyword, and context similarity scoring respectively. At last, the overall scores are securely stored in the blockchain using Proof of Work (PoW) consensus algorithm. The implementation of this work is carried out using python 3.11.0 tool and performance of the proposed model are validated and compared with adequate performance metrics. The results are promising when compared to the conventional models. © 2024 IEEE.

关键词： Answer Evaluation Blockchain Deep Reinforcement learning E-learning Question Answering system

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YouTube Video Summarizer in Regional Language 3

YouTube Video Summarizer in Regional Language

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Jadhav, Ranjana Damre, Prajwal Hire, Atharva Gosavi, Priti Deshmukh, Sanskruti Vishwakarma Institiute of Technology Department of Multidisciplinary Engineering Pune India Vishwakarma Institiute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350396157

The idea and features of the proposed You Tube video summarizer tool created especially for regional languages is presented in this research study. Generally, it is a challenging task for viewers, who prefer regional languages to access and understand videos that are primarily available in a different language in YouTube. The proposed You Tube video summarizer in regional language attempts to overcome this challenge by offering a service to summarize videos in regional languages, making them easier for viewers who might not understand the original language of the video to access and comprehend. The developed program analyses and extracts important information from the video's audio or subtitles using natural language processing (NLP) and machine learning algorithms. This YouTube video summarizer in local languages has a wide range of potential uses. It can improve non-English speakers' access to information, improve video browsing, and promote effective content consumption in localized language groups. © 2024 IEEE.

关键词： machine learning

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Enhanced Loan Approval Prediction System using Ensemble machine learning Techniques 3

Enhanced Loan Approval Prediction System using Ensemble Mach...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Sridhar, K. Department of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India MSc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331532420

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%. © 2024 IEEE.

关键词： Support vector machines

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AMIMC: Based on Improved Meta-learning Contrast Network with Attention Mechanism for Few Shot Text Classification 3

AMIMC: Based on Improved Meta-Learning Contrast Network with...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Li, Senyan Dong, Ye Qin, Xizhong Xinjiang University Xinjiang Signal Detection and Processing Key Laboratory Urumqi830049 China

ISBN: (纸本)9798331506582

Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-task sampling strategies, and the rigidity of convolution operations in traditional Text CNNs. To address this, we propose the Attention Mechanism-based Improved Meta-learning Contrast Network (AMIMC), which enhances intra-class aggregation, increases inter-class separation and improves embedding quality. Additionally, Double Dynamic Similar Sampling (DDSS) generates more challenging meta-tasks, and the attention mechanism enhances the flexibility of Text CNNs, significantly boosting accuracy on five few-shot text classification datasets. © 2024 IEEE.

关键词： Contrastive learning

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Image Style Migration System based on Bootstrap-Flask 3

Image Style Migration System based on Bootstrap-Flask

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Seng, Bingfeng Wang, Xiaomin Technologyy and Communication Qinghai Provincial Key Laboratory of Media Integration Xining China College of Computer Technology and Applications Qinghai University Xining China

ISBN: (纸本)9798331506582

Aiming at the current problem of single effect and same style for all kinds of style migration, the article develops a style migration system based on VGG-19 model. The system is developed with Flask as the back-end framework and Bootstrap as the front-end framework, combined with a MySQL database, utilizing deep learning technology, using the VGG-19 network model for training, completing the model migration and converting the style image and content image data acquired by JS, realizing the migration of the style of the user-selected image to the specified image, and at the same time, combining with other functions of the system, finally forming a system that integrates multi-user use, knowledge learning, sharing and discussion, as well as online style migration. At the same time, it combines with other functions of the system to form a system platform that integrates multi-user use, knowledge learning, sharing and discussion, online style migration and other functional modules. © 2024 IEEE.

关键词： Deep learning

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data Selection Driven by Item Difficulty: On Investigating data Efficient Practice for Hyperparameter Search 24

Data Selection Driven by Item Difficulty: On Investigating D...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： dos Reis, Gustavo Rodrigues Mos, Adrian Cornax, Mario Cortes Labbe, Cyril NAVER LABS Europe Meylan France Univ Grenoble Alpes CNRS INRIA Grenoble INPLIG Grenoble France

ISBN: (纸本)9798400705915

Foundation Models shift the interest to adapting models instead of creating proprietary models from scratch. Despite this change, performing hyperparameter optimization (HPO) is still needed. Users adapting systems powered by those models on proprietary data should not considerably increase the overall resource footprint with extensive hyperparameter search. Given that this footprint is also proportional to the data used in HPO, we aim to investigate how a user can effectively reduce the amount of data used, leveraging the deep learning model's empirical facility to output the expected correct result for an item in the dataset. In this work, we describe a methodology for accomplishing this data reduction through estimating a measure of an item's difficulty. This method allows keeping only a portion of data that conserves the overall proportions of item difficulty throughout the dataset while helping order them meaningfully. The rationale is derived from results from curriculum learning research as we try to answer if the adapted models could help organize and select subsets of data representative of the whole. Preliminary results of evaluating the method are provided for image recognition and scientific name entity recognition (NER). We observe that the amount of data for HPO can be reduced as far as 60% and still point to the same choice of hyperparameters compared to using the whole training set.

关键词： Image recognition

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Intelligent Tutoring System for English Language Acquisition Based on Computer Algorithms 3

Intelligent Tutoring System for English Language Acquisition...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Li, Xing Hunan University of Medicine NO.492 Jinxi South Road Hecheng District Hunan Huaihua China

ISBN: (纸本)9798331519032

With the continuous deepening of computer application, computer-assisted instruction (CAI) has made remarkable progress in foreign language learning. Foreign language learning mainly relies on memory-based learning methods, and CAI has a unique advantage in assisting memory by providing information in the form of visuals, images, sounds and animations, thus constructing a multidimensional knowledge layer in the brain and enhancing learning efficiency. At present, CAI English learning software mainly focuses on memorizing words and quizzes, but tutoring software specifically for college English level exams is scarce. In this paper, based on computer algorithms, combining collaborative filtering algorithm and convolutional neural network algorithm, an intelligent tutoring system for English language acquisition is designed and realized. The experimental data show that students using the intelligent tutoring system show more significant improvement in English proficiency improvement than traditional learning methods, and the average improvement score of the experimental group is about 6 points higher than that of the control group, further verifying the effectiveness of the system in improving learning efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Recommender Systems with Hidden User Interests and machine learning : A Study of Multi -Label Classifiers 3

Enhancing Recommender Systems with Hidden User Interests and...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Geluvaraj, B. Balamurugan, Keerthe Balamurugan, Ajay Balamurugan, Akshay New Horizon College of Engineering Cse Department Bangalore India

ISBN: (纸本)9798350381931

This research project, represents a significant endeavor in the field of recommender systems. It addresses critical challenges such as the "cold start"problem and the quest for superior recommendation quality. The core objectives encompass optimizing multi-label classifiers to maximize accuracy and efficiency, harnessing latent user interests for heightened personalization, particularly for users with limited explicit data, and expanding data sources for the creation of an all-encompassing user preference model. By uncovering latent user interests, the project aims to usher in a future where recommender systems provide users with recommendations that are not only highly accurate and deeply personalized but also adapt seamlessly to their evolving preferences, thereby elevating the overall quality of their online interactions. The overarching mission is to propel recommender systems into a new era of precision, where users across diverse domains can enjoy recommendations that are not only highly accurate but also deeply personalized, thereby enriching their online experiences. © 2024 IEEE.

关键词： Recommender systems

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Unveiling the Impact of Macroeconomic Policies: A Double machine learning Approach to Analyzing Interest Rate Effects on Financial Markets 3

Unveiling the Impact of Macroeconomic Policies: A Double Mac...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kumar, Anoop Dodda, Suresh Kamuni, Navin Arora, Rajeev Kumar IIT Roorkee India Eudoxia Research Center IT Department United States AI-ML BITS Pilani WILP United States Himalayan University India

ISBN: (纸本)9798350372120

This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines machine learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double machine learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns;for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by - 11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors. © 2024 IEEE.

关键词： Financial markets

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