2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)最新文献_第2页

Power Distribution System Optimization Based on Improved Free Search Algorithm 基于改进自由搜索算法的配电系统优化

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690562

Thi-Kien Dao, Trong-The Nguyen, Quynh-Nga Nguyen, Truong-Giang Ngo

引用次数: 0

Partial Least Squares Structural Equation Modeling Algorithm for Electronic Wallet 电子钱包的偏最小二乘结构方程建模算法

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690548

N. Ngan, B. Khoi

引用次数: 0

Stochastic Linear Programming Approach for Portfolio Optimization Problem 组合优化问题的随机线性规划方法

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690552

Dao Minh Hoang, T. N. Thang, Nguyen Danh Tu, Nguyen Viet Hoang

引用次数: 2

Effects of uncertainty in laser energy on temperature evolutions in directed energy deposition process using machine learning-based stochastic approach 基于机器学习的随机方法研究激光能量不确定性对定向能沉积过程温度演化的影响

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690557

T. Pham, X. Tran, A. Habraken

引用次数: 0

Enhanced Text Classification Methods to Improve the Performance of the Various Text Mining Processes using Rapid Miner 增强文本分类方法以提高使用快速Miner的各种文本挖掘过程的性能

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690551

Shitanshu Jain, S. Jain, Santosh K. Vishwakarma

引用次数: 2

A Hybrid Fault Detection Framework for Mobile Computing System 移动计算系统的混合故障检测框架

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690536

N. Malhotra, M. Bala

引用次数: 0

Predicting ocean-wave conditions using buoy data supplied to a hybrid RNN-LSTM neural network and machine learning models 使用提供给混合RNN-LSTM神经网络和机器学习模型的浮标数据预测海浪状况

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690528

Gulustan Dogan, Meghan Ford, Scott James

{"title":"Predicting ocean-wave conditions using buoy data supplied to a hybrid RNN-LSTM neural network and machine learning models","authors":"Gulustan Dogan, Meghan Ford, Scott James","doi":"10.1109/ICMLANT53170.2021.9690528","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690528","url":null,"abstract":"The ability to accurately predict ocean-wave conditions is paramount for many maritime activities. A framework comprising a bi-directional recurrent neural network (RNN) with long-short term memory (LSTM) cells (RNN-LSTM) was developed for timely and accurate predictions of ocean-wave conditions. For comparison, we employed a suite of non-deep learning data-driven techniques to predict significant wave height. To obtain precise predictions, models need accurate and consistent data. The dataset was an aggregation of condition data taken from wave-observing floats (buoys) that recorded readings in increments of 20 and 30 minutes. The buoy measured wave height, wave period, sea surface temperature, and other attributes off the coast of Wilmington, NC. These features were used in various approaches to predict future conditions. Using an RNN-LSTM deep learning methodology, wave conditions were predicted with an average validation accuracy of 81–83%. The Gradient Boosting machine learning model predicted significant wave height with an RMSE of 0.237. This approach has global application as it can be deployed using any sufficient data set collected from wave-monitoring buoys.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115121376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Propose Method for the Design of a Transtibial Prosthesis using Generative CAD/FEM Analysis and Additive Manufacturing 提出了一种基于生成式CAD/FEM分析和增材制造的跨胫假体设计方法

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690535

Jose Luis Ordoñez Avila, Marcial Gustavo Ordoñez Avila, Douglas Adalberto Aguilar, M. Cardona

{"title":"Propose Method for the Design of a Transtibial Prosthesis using Generative CAD/FEM Analysis and Additive Manufacturing","authors":"Jose Luis Ordoñez Avila, Marcial Gustavo Ordoñez Avila, Douglas Adalberto Aguilar, M. Cardona","doi":"10.1109/ICMLANT53170.2021.9690535","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690535","url":null,"abstract":"Prosthesis designers must deal with the constant cost problems for manufacturing a prosthesis for one person. A prosthesis is subjected to the body weight of people, especially when the person leans on the prosthesis. All the weight of the person falls on the limb that is attached to the prosthesis. Therefore, it is necessary to perform the static analysis of each piece to confirm that none of these will have break points and that mechanical displacements do not negatively influence the prosthesis. The aim of this project is the proposal of a method for the design and manufacture of prostheses based on six steps, among which digital design and additive manufacturing stand out. For this, a five-step method was used, starting with digital parameterization, generative design, finite element analysis, fabrication, and recycling of the prosthesis. Digital parameterization is the use of the scanner to create a mesh with which to design the coupling of the prosthesis. With the generative design, the measurements of the prosthesis were optimized, which were based on the scan of a stump and can support the weight of 225 lb. The h method to analyze the loads based on finite elements adapted the mesh to generate the best solution and guarantee the functioning of the prosthesis. The shapes developed by generative design were manufactured with additive manufacturing using ABS with a 100% fill. By using this method, it is guaranteed that the product can be recycled and use the raw material to generate new prostheses.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122735062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Night club recommendation system based on decision trees 基于决策树的夜总会推荐系统

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690547

Claudia N. Sánchez, José-Carlos Delgado-Gómez, Pablo Ramírez-España, Luis García-Zermeño, Samantha Licea, J. Domínguez-Soberanes

{"title":"Night club recommendation system based on decision trees","authors":"Claudia N. Sánchez, José-Carlos Delgado-Gómez, Pablo Ramírez-España, Luis García-Zermeño, Samantha Licea, J. Domínguez-Soberanes","doi":"10.1109/ICMLANT53170.2021.9690547","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690547","url":null,"abstract":"The following document presents the use of a recommendation system designed for nightclubs in Aguascalientes City, Mexico, which offers customized services according to different profiles based on user’s information such as age, gender, and preferred type of music. The advantage of this system is that it improves users’ experience. A preference questionnaire was applied to 75 young people in order to collect their preferences. One decision tree per nightclub was modeled to predict the user’s evaluation based on their profile. Based on the data collected, the most important features for choosing a nightclub are age and type of music that the customer likes, being reggeaton, banda, and electronic the most preferred ones. The absolute error average of predictions was 0.96 using a scale of 4 points. Besides, data from INEGI and the Kernel Density Estimation technique were used to locate the nightclubs. It was found that nightclubs are located mainly in two points in the city: north and downtown.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132201135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Automatic Speaker Identification System Using Shuffled MFCC Features 利用shuffle MFCC特性的鲁棒自动扬声器识别系统

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690530

Mahdi Barhoush, Ahmed Hallawa, A. Schmeink

引用次数: 5