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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是611-620 订阅

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machine learning Model for Applicability of Hybrid learning in Practical Laboratory 2

Machine Learning Model for Applicability of Hybrid Learning ...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Verma, Chaman Department of Media and Educational Informatics Eötvös Loránd University Faculty of Informatics Budapest Hungary

Significant changes have been observed from 2019 in the COVID period as study styles shifted from traditional to hybrid learning. This paper predicted the applicability of the hybrid learning mode of education in the programming labs for the informatics faculty. It explored novel vital features that predicted the applicability of hybrid learning in the practical labs of the informatics domain. For this, the Classification and Regression Tree (CART) identified the relevance of hybrid education with an accuracy of 71.7%. The confusion matrix of the CART model has 67.3% correctness (sensitivity) for "Yes", and 76% correctness (specificity) for "No" during validation. The false negative rate (type II error) is 32.7%, and the false positive rate (type I error) is 24%. The AUC value of the CART model is 0.68. Some obstacles were identified, such as isolation from the natural study environment, reduced quality of interaction with other faculty, and turning off the mic or camera during labs. Also, it was found that the university initiated and provided support to create less stress and ease of communication. Adopting the Hyrbid learning mode is unsuitable for handling group tasks or projects in the laboratory. Also, the hybrid learning mode proved a vital mode of study in practical labs as online learning with a safe environment. © 2024 Elsevier B.V.. All rights reserved.

关键词： machine learning

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Automated Log Classification Using Deep learning

Automated Log Classification Using Deep Learning

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Ramachandran, Shekar Agrahari, Rupali Mudgal, Priyanka Bhilwaria, Harshita Long, Garth Kumar, Arisha Intel Technologies Pvt. Ltd. Bangalore India Intel Technologies Pvt. Ltd. United States

System logs are a rich source of information. The complexity and amount of data contained in log files is increasing rapidly, especially with the development of cloud computing in clustered environments. Log analysis is effective to find the root cause of an issue. Because of the huge volume of unstructured logs, manual inspection becomes difficult and time consuming. Hence, there is a need to automate the process of log analysis. Current state-of-the-art deep learning-based techniques are trained on generic datasets and do not perform well on specific system logs. The proposed invention is focused to predict errors from large system specific logs by using manual vectorization technique called "LogWord2Vec"and combining it with data-cleaning, augmentation, embedding, and deep learning method together. Unlike other solutions, our method does not require a large dataset, and works well on medium to small sized datasets. The proposals described in this disclosure help in improving both the accuracy and the efficiency of log parsing systems. We have used combination of Convolution neural network (CNN) layer and long short-term memory (LSTM) layer to build the model and obtained accuracy of 99.19% with 0.81% of misclassification. © 2023 The Authors. Published by Elsevier B.V.

关键词： Multilayer neural networks

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A Deep study of data science related problems, application and machine learning algorithms utilized in data science

A Deep study of Data science related problems, application a...

引用

2022 international conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Ranjani, J. Kalaichelvi, V.K.G. Anbalagan, Swathi Niranjan Kumar, S. Sudarsan, Murari Reddy Sri Sai Ram Engineering College Chennai India

ISBN: (纸本)9781665479950

data Science study utilized for gathering information as data, taking out data from other systems, accumulating information, signifying and safeguarding data collected are used by organizations for marketing purposes and in high-tech implementations. Name of data science denotes a combination of databases and software engineering as well as a number of types of qualitative, quantitative entities and non-mathematical entities are furthermore necessary. data science and its importance, components in data science included, life cycle of data science, Application of data science, various algorithms in machine learning are used with data science and implementation of machine learning algorithm in real life cases are also described. Finally, wound up with how to decode an obstacle in data Science using machine learning algorithms. © 2022 IEEE.

关键词： data warehouses

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Predicting Autism Spectrum Disorder (ASD) meltdown using Fuzzy Semi-Supervised learning with NNRW

Predicting Autism Spectrum Disorder (ASD) meltdown using Fuz...

引用

2022 international conference on Innovations in Science, engineering and Technology, ICISET 2022

作者： Karim, Sara Akter, Nazina Patwary, Muhammed J.A. International Islamic University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781665483971

Autism Spectrum Condition (ASD) is a notable psychological disorder that affects a human's ability to communicate socially. The need of early diagnosis prompted researchers' attention to the usage of various machine learning-based approaches. A number of supervised and unsupervised learning methods from the disciplines of machine learning were used to enhance the precision of the Autism prediction system. In supervised learning systems, only labeled data is utilized to build a classification model, but gathering enough classified data is time-consuming and usually involves field specialist's assistance. However, in various real-world circumstances, unlabeled samples are easily available. By integrating large volumes of labeled and unlabeled data to produce a stronger classification model, Semi-Supervised learning (SSL) solves this problem better than all other machine learning methods. The study proposes a novel fuzziness-based semi-supervised learning strategy for ASD meltdown prediction that employs mislabeled data in conjunction with training labeled data to increase the model's reliability. The prediction performance utilizing this method on the dataset demonstrated significant advancements to increasing the classifier's results in comparison to other classification methods such as Fuzzy MinMax Classifier, Naive bayes, Fuzzy data Mining, ZeroR, Random tree, and others. © 2022 IEEE.

关键词： Fuzzy systems

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Linking Entities across Relations and Graphs 38

Linking Entities across Relations and Graphs

引用

38th IEEE international conference on data engineering (ICDE)

作者： Fan, Wenfei Geng, Liang Jin, Ruochun Lu, Ping Tugay, Resul Yu, Wenyuan Univ Edinburgh Edinburgh Midlothian Scotland Shenzhen Inst Comp Sci Shenzhen Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

This paper proposes a notion of parametric simulation to link entities across a relational database D and a graph G. Taking functions and thresholds for measuring vertex closeness, path associations and important properties as parameters, parametric simulation identifies tuples t in D and vertices v in G that refer to the same real-world entity, based on topological and semantic matching. We develop machine learning methods to learn the parameter functions and thresholds. We show that parametric simulation is in quadratic-time, by providing such an algorithm. Putting these together, we develop HER, a parallel system to check whether (t, v) makes a match, find all vertex matches of t in G, and compute all matches across D and G, all in quadratic-time. Using real-life and synthetic data, we empirically verify that HER is accurate with F-measure of 0.94 on average, and is able to scale with database D and graph G.

关键词： machine learning algorithms Scalability conferences Semantics Relational databases machine learning data engineering

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Research of data-driven Iterative Optimization Scheme for machine learning Models 5

Research of Data-driven Iterative Optimization Scheme for Ma...

引用

5th international conference on Computing and Big data, ICCBD 2022

作者： Yang, Jinsen Zou, Zhijian Xiu, Jiapeng Wang, Peinian Yang, Yiting Beijing University of Posts and Telecommunications Software Systems and Engineering Center Beijing China Beijing Qiyuan Capital Market Development Service Co. Ltd. Beijing China Law School Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665457163

With the development of big data technology providing massive data information for machine learning, more researchers are focusing on data methods in big data technology, by constructing better data set to improve machine learning model's performance. This research based on the data-driven and human-machine collaboration methods in big data technology, continuously build data set to optimize machine learning models. We take the growth and risk assessment model of technology companies as an example, design and implement a data-driven machine learning model pipeline. The experiments show that the pipeline can effectively and continuously optimize these machine learning models, and ensure the stability of machine learning application. © 2022 IEEE.

关键词： Big data

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Advanced Pattern Detection and Trend Forecasting in European Carbon Markets Using machine learning Algorithms

Advanced Pattern Detection and Trend Forecasting in European...

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2024 IEEE international conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Hosseini, Seyed Ali Niccolai, Alessandro Lorenzo, Marco Casamatta, Fabio Grimaccia, Francesco Politecnico di Milano Department of Energy Milan Italy Bu Generazione e Trading A2A S.p.A. Milan Italy

ISBN: (纸本)9798350389838

This paper investigates the detection and prediction of patterns in European Carbon Emission price trends using machine learning on high-frequency tick-by-tick data, resampled at 5-minute intervals. After data preprocessing to manage outliers and ensure data integrity, trends in close prices were identified through pivot points and percentage change thresholds. Five machine learning models, including, Linear Regression, Decision Tree Regressor, K-Nearest Neighbors (KNN) Regressor, Random Forest Regressor, and Gradient Boosting Regressor were trained and evaluated for pattern prediction and classification tasks. The Gradient Boosting Regressor emerged as the top performer, achieving the lowest test Mean Squared Error (MSE) and the highest R2 score, outperforming KNN and Decision Tree models by approximately 20% in MSE. It also achieved the highest F1 Score in pattern classification, underscoring its superior accuracy and reliability in analyzing complex financial data. This study offers valuable insights for future financial data analysis, presenting robust methods for predicting and classifying price trends. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of data Driven Model for Proton Exchange Membrane Fuel Cell Using machine learning Approaches 3

Development of Data Driven Model for Proton Exchange Membran...

引用

3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Dhaked, Dheeraj Kumar Kumar, Purushottam Ganguly, Sanjib Iit Guwahati Eee Department Assam Guwahati India

ISBN: (纸本)9798350313703

The world is moving towards a sustainable source of transportation and energy. Proton exchange membrane fuel cells (PEMFCs) are the major key for that. A multi-input data-based predictive model of PEM fuel cell is built using machine learning approach, such as, linear regression approach, support vector machine (SVM), decision tree (DT) method, random forest (RF) approach etc. Since fuel cell deterioration is very sensitive to operating circumstances, the transfer learning approach is used to accurately forecast fuel cell deterioration for varied activities, particularly in the early stages. The results illustrate the effectiveness and repeatability of the intended strategy. The created voltage prediction digital model is cross-validated using fuel cell real-time voltage collected during operation. This article's goal is to create an early-stage fuel cell voltage prediction digital model that can predict deterioration in real-time and is tolerant to various prediction patterns. The developed models are able to predict the voltage, and RF has the more accurate data model among all. © 2024 IEEE.

关键词： Support vector machines

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Early Disease Detection in Plants using CNN 2

Early Disease Detection in Plants using CNN

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Tejaswini Rastogi, Priyanka Dua, Swayam Manikanta Dagar, Vikas Manav Rachna International Institute of Research & Studies Haryana Faridabad India

The Indian economy benefits from early disease detection in plant leaves. According to reports, 10-30% of crops suffer harm from diseases that are not discovered during the curing process. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have paved the way for transformative solutions in disease identification for agriculture. This study addresses the critical issue of early disease detection by harnessing the power of deep learning models, specifically CNNs. Different leaf disease detection technologies are used for different crops. The pre-trained deep learning model is used in this study to identify and categorize leaf diseases. A dataset of tomato, potato, and bell pepper leaf pictures from the plant village repository was employed for the current investigation. The developed model can detect 12 plant diseases in normal leaf tissue. The quantitative assessment of our CNN-based technique reveals an impressive accuracy rate of 86.21%. This notable accuracy underscores the efficacy of our approach in the challenging domain of plant disease detection. Our findings show potential for the larger agricultural environment beyond the immediate quantitative advantages. This technical advance not only paves the way for improved crop output and lower losses, but also brings in a new era of data-driven sustainability in agriculture. The bottom line is that our research "offers a tangible pathway for leveraging AI-powered solutions to address the long-standing challenges of plant disease detection, thereby significantly contributing to the well-being of farmers and the sustenance of the Indian agricultural sector. © 2024 Elsevier B.V.. All rights reserved.

关键词： Crops

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machine learning for Intrusion Detection in Vehicular Ad-hoc Networks (VANETs): A Survey 4

Machine Learning for Intrusion Detection in Vehicular Ad-hoc...

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4th international conference on Emerging Smart Technologies and Applications (eSmarTA)

作者： AL-Khulaidi, Nesmah A. Zahary, Ammar T. Al-shargabi, Asma A. Hazaa, Muneer A. S. Yemen Acad Grad Studies Dept Comp Networks Sanaa Yemen Univ Sci & Technol Fac Comp & IT Dept Comp Sci Sanaa Yemen Qassim Univ Coll Comp Dept Informat Technol Buraydah 51452 Saudi Arabia Thamar Univ Fac Comp & Informat Syst Dept Comp Sci Thamar Yemen

ISBN: (纸本)9798350354140;9798350354133

Intrusion Detection Systems (IDSs) have become a key security problem due to the increasing number of connected automobiles and the sensitive nature of the data transferred in Vehicular Ad-hoc Networks (VANETs). By keeping an eye on network traffic, spotting questionable activity, and putting countermeasures in place to lessen risks, IDSs protect the integrity and security of VANETs. For VANETs, this study explores the state-of-the-art in machine learning-based IDSs, with a particular emphasis on work released in 2020-2022. We provide a thorough analysis of developments in widely used machine learning methods used for VANET intrusion detection throughout this time. This investigation explores certain machine learning methods that have been recently applied to VANET IDSs. The survey ends with a summary of the current issues and an exploration of potential directions for further investigation.

关键词： VANETs attacks intrusion detection system in VANETs machine learning detection

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