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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是261-270 订阅

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Comparative Analysis to identify the best Classifier for Parkinson Prediction 2

Comparative Analysis to identify the best Classifier for Par...

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2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Shamrat, F.M. Javed Mehedi Bhowmik, Shohag Kumar Sultana, Zakia Hossain, Ahbab Amina, Mahdia Thapa, Sittal Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Punjab Agricultural University Department of Plant Breeding and Genetics Ludhiana India

ISBN: (纸本)9781665433686

Parkinson disease has become one of the most common diseases among people over the age of 65. Neurodegenerative disease affects movement, speech and other cognitive abilities. Among patients, the symptoms vary at a different rate, for which diagnosis of the disease sometimes takes years by when treatment is no longer an option. However, using machine learning algorithms to classify the symptoms among patients, it is possible for early detection of the disease. İn this paper, the performance of machine learning algorithms are measures that can detect Parkinson disease. Three different datasets are used for the study. Each dataset goes through various feature selection techniques. machine learning classifiers such as KNN, LDA, NB, LR, SVM, DT, RT, RF and ANN are implemented on the datasets and their performance is measured. It is observed that SVM has a high accuracy rate of prediction over all the feature selection techniques in all the datasets. © 2021 IEEE.

关键词： Neurodegenerative diseases

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machine learning-Based Experimental Design in Chemical Industry: Taking C4 Olefin Preparation from Ethanol as an Example

Machine Learning-Based Experimental Design in Chemical Indus...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： Qianqian Wei Yating Pan

With the acceleration of global carbon-neutralizing process, green chemical industry has attracted more and more attention. Finding appropriate parameters for experiments is important for green chemical reaction. This work focuses on using experiment parameters to predict chemical reaction yield of products based on machine learning methods, which takes C4 olefin preparation from ethanol as an example. To explore how experiment parameters influence the reaction yield of products, figures are plotted to show the correlations between parameters and reaction index. Then the conversion models and selectivity models are constructed based on polynomial regression, decision tree, random forest, Light GBM and neural networks. Different evaluations of the models all indicate that LightGBM performs best to predict the conversion of ethanol, while the selectivity of C4 olefin prefers neural network. Comprehensively we propose a hybrid method to predict yield of C4 olefin which significantly exceeded all single regression models. Furthermore, the sensitivity of optimized conversion/selectivity/yield models are shown in heatmaps that imply the robustness of our models.

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A Review on Malicious Software Detection using machine learning Algorithms 2

A Review on Malicious Software Detection using Machine Learn...

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2nd international conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Tirandasu, Ravi Kumar Prasanth, Y. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9781665428675

Malware recognition is a significant factor in the security of the PC frameworks. Be that as it may, right now used traditional strategies can't detect exact malicious software detection. That is the reason the requirement for machine learning algorithm identification emerges. The motive for this exertion was to decide the best component extraction, highlight portrayal, furthermore, grouping strategies that outcome in the greatest exactness once utilized on the highest point of Cuckoo Sandbox. In particular, KNN, DT, SVM, Innocent Bayes and RF classifiers were assessed. This work presents suggested review techniques for machine learning constructed malicious [30], [31] characterization and identification, rules for its execution. Moreover, the review made be able to valuable as a base for additional exploration in the arena of malicious software investigation with AI techniques. © 2021 IEEE.

关键词： Malware

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Human Gait Analysis Using machine learning: A Review 2

Human Gait Analysis Using Machine Learning: A Review

引用

2nd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Alfayeed, Sk Md Saini, Baljit Singh Lovely Professional University Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665429214

The gait analysis is interpreted to include an overwhelming number of interrelated parameters, which, due to the high volume of data and their relationships and is difficult to implement. The integration of machine learning with biomechanics is a promising approach to simplify the evaluation. The aim of this paper is to educate readers about the key directions to implement the gait analysis with machine learning techniques. The detailed survey is based on review and implementation articles performed by numerous research scholars to detect neurological effects in gait, gait asymmetry, gait disorders, gait events, and gait activities by using supervised machine learning algorithms. This study paper also reveals the effectiveness of ML approaches for condition identification, forecasting recovery time and monitoring for clinical diagnostic instruments. © 2021 IEEE.

关键词： Support vector machines

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machine learning Algorithms for Predicting SARS-CoV-2 (COVID-19) - A Comparative Analysis 2

Machine Learning Algorithms for Predicting SARS-CoV-2 (COVID...

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2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Mary, L. William Albert Antony Raj, S. SRM Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India

ISBN: (纸本)9781665433686

The new public health crisis threatens the entire world. The infectious virus SARS -CoV-2 (COVID-19) is a new virus that has spread rapidly across the world. Coronavirus spreads faster than the early virus, SARS-CoV and MERS-CoV, the first major beta-coronavirus in the human respiratory system. There are approximately 9, 033, 508 reported cases worldwide. CT scans have been used to diagnose suspicious negative and positive cases. To detect infected cases and mortality rates, many epidemiological models are being used. Therefore, machine learning techniques play a vital role in effective prediction. This technology has been used to extract information on large data sets and predictive performance analysis. As a result, a variety of machine learning prediction techniques were employed. This paper aims at determining which Classification method performs a high accuracy rate for the collected data samples of COVID-19 positive cases. The Support Vector machine (SVM) gives 85% of accuracy, K-Nearest Neighbor gives 80% accuracy, and Naive Bayes (NB) text classifier method gives 65% accuracy. © 2021 IEEE.

关键词： Coronavirus

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A Comparative Study on Hyperparameter Optimization Methods in Software Vulnerability Prediction 2

A Comparative Study on Hyperparameter Optimization Methods i...

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2nd international conference on Computational Methods in Science and Technology, ICCMST 2021

作者： Bassi, Deepali Singh, Hardeep Guru Nanak Dev University Department of Computer Science Amritsar143005 India

ISBN: (纸本)9781665458665

Hyperparameter optimization (HPO) is the procedure to find the optimal hyperparameters for predictive modelling. In order to build an efficient software vulnerability prediction (SVP) model, effective HPO method is required. Recent studies have explored the area of machine learning for SVP models' construction. Different machine learning algorithms have distinct hyperparameters to be tuned. In this paper, we have compared four HPO methods for six machine learning based SVP models using an open-source public dataset 'Drupal'. The performance of the models is evaluated by the accuracy of the model and the computational time. Experiments are performed using various python libraries such as sklearn, skopt, hyperopt, and tpot. After comparative analysis, we found that bayesian optimization and random search has shown the performance improvement for SVP models with effective computational time. © 2021 IEEE.

关键词： machine learning

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Prototyping machine-learning-Supported Lead Time Prediction Using AutoML 2

Prototyping Machine-Learning-Supported Lead Time Prediction ...

引用

2nd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Bender, Janek Ovtcharova, Jivka FZI Res Ctr Informat Technol Haid & Neu Str 10-14 D-76135 Karlsruhe Germany

Many Small and Medium Enterprises in the domain of Make-To-Order- and Small-Series-Production struggle with accurately predicting lead times of highly customisable orders. This paper investigates an approach using AutoML integrated into existing enterprise systems in order to enable Lead Time Prediction based on machine learning models. This prediction is based on both order data from an ERP system as well as real-time factory state informed by an IIoT platform. We used simulation data to feed the AutoML model generation and developed a lightweight web-based micro service around it to infer lead times of incoming orders during live production. Using industry standards, this microservice can be seamlessly integrated into existing system landscapes. The simplicity of AutoML systems allows for swift (re)training and benchmarking of models but potentially comes at the cost of overall lower model quality. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Production Lead Time Prediction AutoML machine learning

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Active learning for Arabic Text Classification 2

Active Learning for Arabic Text Classification

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2nd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Al-Tamimi, Abdel-Karim Bani-Isaa, Esraa Al-Alami, Ahmed Yarmouk Univeristy Higher Colleges of Technology Cis Department-Computer Eng. Dept Al Ain United Arab Emirates Yarmouk University Computer Engineering Dept Irbid Jordan

ISBN: (纸本)9781665429214

Active learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques. © 2021 IEEE.

关键词： Efficiency

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Mask Detection in Crowded Environment using machine learning 2

Mask Detection in Crowded Environment using Machine Learning

引用

2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Balasundaram, A. Naveen Kumar, M. Kumar Sivaraman, Arun Vincent, Rajiv Rajesh, M. Centre For Cyber Physical Systems School of Computer Science and Engineering Chennai India School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665433686

COVID-19 epidemic has affected many people across the world creating a health crisis around the globe. World Health Organization (WHO) recommends that covering face with mask is one of the best protection to minimize COVID infection. COVID-19 pandemic has forced governments across the planet to impose strict lockdowns to stop the spread of virus. Many health experts says that wearing facemasks while at public places clearly reduces the spread of the virus. A machine learning (ML) based face mask detection with face recognition and alert has been proposed. As part of this work, a face mask detector along with face recognition with CV and ML using Python has been developed. In this system, the input would be a real time input image via laptop webcam. Assuming if anyone is not wearing a mask, an automatic alert is generated and the respective person snapshot is sent to the admin e-mail. © 2021 IEEE.

关键词： COVID-19

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Comparative Analysis of Heterogeneous Ensemble learning using Feature Selection Techniques for Predicting Academic Performance of Students 2

Comparative Analysis of Heterogeneous Ensemble Learning usin...

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2nd international conference on Computational Methods in Science and Technology, ICCMST 2021

作者： Nidhi, Nidhi Kumar, Mukesh Agarwal, Shweta Chandigarh University CSE Punjab India School of Computer Application Lovely Professional University CSE Punjab Phagwara India

ISBN: (纸本)9781665458665

Data stored in digital form is increasing daily, and so its complexity. Processing a massive volume of data needs efficient technology. Data mining and machine learning researchers are focused on finding a suitable algorithm that can find important information after processing that data. In educational data mining, most of the students' records are also stored in digital form. So, the researchers are also trying to find some informative knowledge that can be helpful for the students, teachers, and management to improve their working towards the success of the students and institution also. In predictive modelling, the main challenge is finding the most effective predictive techniques that help achieve an acceptable accuracy level. This article, therefore, proposes a hybrid or heterogeneous approach of Correlation Attribute Evaluation, Ensemble learning like Stacking, Voting and MultiScheme, in conjunction with seven different machine learning algorithms to improve the prediction accuracy up to an acceptable level. Here, k-fold cross-validation was used as a test method to evaluate the predictive performance of the classification algorithms. © 2021 IEEE.

关键词： Students

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