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

共 6841 条记录，以下是81-90 订阅

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2024 IEEE 3rd international conference on Computing and machine Intelligence, ICMI 2024 - Proceedings

2024 IEEE 3rd International Conference on Computing and Mach...

引用

3rd IEEE international conference on Computing and machine Intelligence, ICMI 2024

ISBN: (纸本)9798350372977

The proceedings contain 157 papers. The topics discussed include: a self-scaling dynamic blockchain model for IoT;advanced generative ai methods for academic text summarization;data augmentation for entity resolution: a comparative evaluation;mitigation of user-prompt bias in large language models: a natural language processing and deep learning based framework;evaluating the effectiveness of an object detection pipeline to support surveillance of unintended passage;automated scripting for real-time responses to suspicious user actions;highway merging control using multi-agent reinforcement learning;using machine learning to predict student success in undergraduate engineering programs;NeuroAqua: developing an optimized artificial intelligence and Internet of Things-based aquaponics system;and precision fish farming to mitigate pond water quality through IoT.

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Shape Statistical Model-Based Regression for Predicting Anatomical Landmarks 3

Shape Statistical Model-Based Regression for Predicting Anat...

引用

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

作者： Ting, Tong China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350380989

Determining appropriate shapes and establishing corresponding relationships based on 3D models remains a significant challenge. This research employs shape statistical models and direct correspondence between two morphologies via support vector regression. The displacement of sampled points to the standard model, calculated through non-rigid registration, represents the shape state. Constructing shape statistical models based on the deformation fields of sampled points from different models and subsequently mapping high-dimensional cranial features using support vector regression allows for establishing relationships between different models. The study used 77 mandibles and maxillas for the validation. Predicting the mandible using the maxilla resulted in a 2.43mm average error, while predicting the maxilla using the mandible yielded a 1.95mm average error. It offers potential avenues for further research and applications in craniofacial reconstruction. © 2024 IEEE.

关键词： craniofacial reconstruction machine learning mandibles maxillas SVR

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Powering the future: application of machine learning to analyze the global renewable energy consumption 24

Powering the future: application of machine learning to anal...

引用

3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Zou, Zonghao Univ Wisconsin Madison Madison WI 53706 USA

ISBN: (纸本)9798400718212

The global energy consumption landscape has significantly shifted towards renewable sources in recent years. However, despite this positive trend, the overall consumption of fossil fuels has not decreased, indicating challenges for climate change mitigation. To explain this phenomenon, our paper employs several different machine learning algorithms on the World Sustainability dataset to analyze the underlying mechanisms that drive or hinder the adoption of renewable energy across various countries. Utilizing a combination of supervised and unsupervised machine learning techniques, including XGBoost for predictive analysis and K-Means for clustering, we aim to reveal the nuanced factors influencing nations' energy choices. By demystify the complex decision-making process behind adoption of green energy, we hope to provide insights for international sustainability organizations, offering a data-driven foundation to guide their efforts in promoting renewable energy adoption worldwide. Our findings both underscore the intricacies of energy transition dynamics and equip policymakers and stakeholders with actionable intelligence to foster a more sustainable future.

关键词： machine learning renewable energy supervised learning unsupervised learning dimensionality reduction climate change energy transition

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English Teaching Evaluation Algorithm and Modeling based on machine learning and data Mining Technology 3

English Teaching Evaluation Algorithm and Modeling based on ...

引用

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

作者： Sun, Wei Heilongjiang International University Heilongjiang Harbin150025 China

ISBN: (纸本)9798331519032

Traditional English teaching evaluation has problems such as subjectivity, limitations, quantitative bias, and lacks feedback and support. The evaluation algorithm based on data analysis technology provides an accurate, comprehensive, and personalized evaluation method for teachers and students, which can understand their learning needs and ability levels by collecting and analyzing students' learning data. This study adopts a method based on data analysis technology to develop English teaching evaluation algorithms. Firstly, this paper collected students' learning data, including information on language expression, learning behavior, and academic performance. Then, machine learning and data mining techniques are used to analyze and model these data to predict students' English proficiency level and learning needs. The research results indicate that the F1 value range of the algorithm in this paper is 0.85-0.94, and the English teaching evaluation algorithm based on data analysis technology has achieved significant results in evaluating students' English proficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Implementation of Marketing Classification System Based on Artificial Intelligence 3

Design and Implementation of Marketing Classification System...

引用

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

作者： Tian, Shuo Shanghai University Shanghai China

ISBN: (纸本)9798350380989

This paper mainly introduces some machine learning methods used in the field of data mining. The method of data mining is discussed by taking market segmentation algorithm as an example. This paper presents an improved support vector machine method. By judging the similarity between each sampling point and its closest class, the classifier is obtained by training it with support vector machine. A method based on stepwise approximation method is proposed to solve the problem of too large size of KNN training samples. The experimental results show that non-SVM is superior to SVM in classification accuracy and classification speed. In addition, this project will also adopt artificial intelligence technology to build a data analysis system for high-end wine, analyze, compare and match massive information data of high-end wine through data mining, and discover the comparison information and market potential of main ingredients in high-end wine. It is compared with the chemical composition of high-grade red wine, and the relevant marketing treatment of high-grade red wine is obtained. It can help high-grade red wine manufacturers to analyze the composition content and quality of high-grade red wine. © 2024 IEEE.

关键词： artificial intelligence data analysis data mining high-end red wine machine learning marketing classification algorithm

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Lamb wave measurement method for in-plane elastic constants of balanced symmetric laminates based on machine learning 3

Lamb wave measurement method for in-plane elastic constants ...

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3rd international conference on Materials engineering and Applied Mechanics (ICMEAAE)

作者： Xie, Quan Liu, Yaolu Fang, Jun Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Heterogeneous Mat Mech Chongqing 400044 Peoples R China Sichuan Prov Inst Phys & Chem Measurement & Nonde Chengdu 610100 Sichuan Peoples R China

To achieve in-situ measurement of mechanical properties in large-scale composite structures, this paper introduces a Lamb wave measurement based on machine learning to predict the in-plane engineering elastic constants of balanced symmetric laminates. Firstly, we consider that balanced symmetric laminates are equivalent to orthotropic single-layer plates with nine engineering elastic constants. Secondly, by varying these elastic constants and comparing the dispersion curves at different propagation angles, we conclude that, under low frequency-thickness products, the phase velocity of S-0-mode Lamb waves in orthotropic single-layer plates is dependent on four engineering elastic constants: tensile modulus, in-plane shear modulus, and in-plane Poisson's ratio. Subsequently, leveraging this correlation in dispersion curves, a BP neural network model is established using machine learning techniques. Using the neural network model, the goal of predicting engineering elastic constants using phase velocities is achieved. Finally, the effectiveness of this method is verified through theoretical calculations and numerical simulations.

关键词： Elastic constants

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Deep learning-based Text News Classification using Bi-directional LSTM Model 3

Deep Learning-based Text News Classification using Bi-direct...

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

作者： Anand, Shreyas Prakasam, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The advancement of technology has resulted in increased news consumption through various online platforms and social media channels. Hence, there is a requirement for intelligent systems that may categorize the news that exists on the social media/online platform. Hence, this paper explores creating a news classifier utilizing a Bidirectional Long ShortTerm Memory (Bi-LSTM) deep learning model, which is a type of recurrent neural network (RNN) specifically designed to overcome the challenge of vanishing gradient often faced in deep learning. The machine learning was performed on a Kaggle dataset with over 400,000 records of news stories from various publishers. Natural language processing techniques such as stemming, one-hot encoding, vectorization, and embedding are used to prepare the data for machine learning. The multiclass classification is achieved and the performance of the model is analyzed. © 2024 IEEE.

关键词： Classification (of information)

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Routing Protocol Attack Detection Using machine learning Through Parallel Computing in Wireless Sensor Network 3

Routing Protocol Attack Detection Using Machine Learning Thr...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Kumar, Mithun P. K. Hossain, Al Amin Al Majmaie, Sufian Amsaad, Fathi Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA

ISBN: (纸本)9798350372977;9798350372984

The wireless sensor network is a hot and significant research area nowadays and it can be addressed in almost every sector and environment. A few major challenges are countered in the wireless sensor network such as energy consumption, battery lifetime, Attacks, data Transmission, etc. Generally wireless sensor network produces non-Euclidian sensing data and metadata structures and it is very complex to deal with the structure, especially in order to measure anomalies and disruption in a network. In this paper, we have introduced parallel computing to resolve the heterogeneity of the sensing data and metadata as well. Parallel computing has been applied implicitly for extracting only paramount data from the large scale of data to detect routing layer attacks. The convolution neural network (CNN) has been considered as a machine-learning model and we have enhanced the kernel to optimize the performance of the conventional CNN model to detect network layer attacks in terms of wireless sensor networks. Our investigated method demonstrates better results in detecting anomalies and attacks than the existing methods or techniques.

关键词： Wireless Sensor Networks Network Layer Attack machine learning Parallel Computing

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Prediction of Time Delay in eLoran data Acquisition Systems 3

Prediction of Time Delay in eLoran Data Acquisition Systems

引用

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

作者： Li, Xuyin Software Engineering Institute Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331506582

This study examines the influence of six meteorological factors on eLoran signal propagation delay and develops a predictive model using four machine learning approaches: BP neural networks, random forests, support vector regression, and gradient boosting. Optimization of these models led to significant reductions in root mean square error (RMSE) and mean absolute error (MAE). Results demonstrate that the gradient boosting model offers superior accuracy in predicting propagation delay, satisfying the eLoran system's timing requirement of 100 ns. This research provides valuable theoretical insights and practical guidance for enhancing the eLoran system's application in interference-resistant environments. © 2024 IEEE.

关键词： Prediction models

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Short Transmission Line Fault Identification and Classification Using Supervised machine learning 3

Short Transmission Line Fault Identification and Classificat...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Akther, Lamia Khanam, Tahmida Hossain, Ismail Alam, Shafiul International Islamic University Department of Electrical & Electronic Engineering Chittagong4200 Bangladesh Saudi Arabia Dhahran Saudi Arabia

ISBN: (纸本)9798350388282

This research presents a new approach to detecting and classifying different types of shunt faults in short transmission lines, which is based on wavelet transforms and machine learning. The approach relies on calculating root-mean-square (RMS) values of wavelet-transform coefficients from current signals on all three phases and ground (Phase A, Phase B, Phase C, and Ground) across a variable window length of the RLC branch. Faults are detected and classified using the Daubechies (db4) wavelet, which allows for comprehensive coefficient analysis with specified threshold values at different levels. A Radial Basis Function Neural Network (RBFNN) is the developed by calibrating the input and output data for maximum performance. The proposed wavelet-assisted supervised machine learning for fault identification and classification is tested with simulated power system data. For various simulated faults in short transmission lines, the ratio of energy change, signal energy, and maximum detail coefficients are analyzed. The proposed approach shows excellent performance in detection and classification of faults as compared with other models including KNN, NN, and ELR. © 2024 IEEE.

关键词： Electric lines

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