Soft Computing Letters最新文献

Analysis of French phonetic idiosyncrasies for accent recognition 法语语音特征对口音识别的分析

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2021.100018

Pierre Berjon , Avishek Nag , Soumyabrata Dev

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引用次数: 6

Bus journey simulation to develop public transport predictive algorithms 公交行程模拟，开发公共交通预测算法

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2021.100029

Thilo Reich , Marcin Budka , David Hulbert

{"title":"Bus journey simulation to develop public transport predictive algorithms","authors":"Thilo Reich , Marcin Budka , David Hulbert","doi":"10.1016/j.socl.2021.100029","DOIUrl":"10.1016/j.socl.2021.100029","url":null,"abstract":"<div><p>Encouraging the use of public transport is essential to combat congestion and pollution in an urban environment. To achieve this, the reliability of arrival time prediction should be improved as this is one area of improvement frequently requested by passengers. The development of accurate predictive algorithms requires good quality data, which is often not available. Here we demonstrate a method to synthesise data using a reference curve approach derived from very limited real world data without reliable ground truth. This approach allows the controlled introduction of artefacts and noise to simulate their impact on prediction accuracy. To illustrate these impacts, a recurrent neural network next-step prediction is used to compare different scenarios in two different UK cities. The results show that a realistic data synthesis is possible, allowing for controlled testing of predictive algorithms. It also highlights the importance of reliable data transmission to gain such data from real world sources. Our main contribution is the demonstration of a synthetic data generator for public transport data, which can be used to compensate for low data quality. We further show that this data generator can be used to develop and enhance predictive algorithms in the context of urban bus networks if high-quality data is limited, by mixing synthetic and real data.</p></div>","PeriodicalId":101169,"journal":{"name":"Soft Computing Letters","volume":"3 ","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666222121000174/pdfft?md5=ac9f2e119a87da7041d08bee1f99991e&pid=1-s2.0-S2666222121000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84949895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust fuzzy factorization machine with noise clustering-based membership function estimation 基于噪声聚类隶属函数估计的鲁棒模糊分解机

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2021.100024

Katsuhiro Honda, Keita Hoshii, Seiki Ubukata, Akira Notsu

{"title":"Robust fuzzy factorization machine with noise clustering-based membership function estimation","authors":"Katsuhiro Honda, Keita Hoshii, Seiki Ubukata, Akira Notsu","doi":"10.1016/j.socl.2021.100024","DOIUrl":"10.1016/j.socl.2021.100024","url":null,"abstract":"<div><p>Factorization machine (FM) is a promising model-based algorithm for collaborative filtering (CF), but can bring inferior performances if datasets include users having low confidence. In this paper, a robust FM model is proposed by introducing the noise clustering-based noise rejection mechanism into Fuzzy FM, which utilizes fuzzy memberships of users for considering the responsibility of each user in FM modeling. By automatically updating fuzzy memberships with user-wise criteria of prediction errors, the FM model is better fitted to reliable users and is expected to improve the generalization ability for predicting the preference degrees of unknown items. The characteristics of the proposed method are demonstrated through numerical experiments with MovieLens movie evaluation data such that the prediction ability for not only the training ratings but also the test ratings of reliable users can be improved by carefully tuning the noise sensitivity weight.</p></div>","PeriodicalId":101169,"journal":{"name":"Soft Computing Letters","volume":"3 ","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666222121000137/pdfft?md5=db9ce1979cb69520fcfc8a1b31b5917b&pid=1-s2.0-S2666222121000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85771419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Foreign exchange currency rate prediction using a GRU-LSTM hybrid network 基于GRU-LSTM混合网络的外汇汇率预测

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2020.100009

M.S. Islam , E. Hossain

{"title":"Foreign exchange currency rate prediction using a GRU-LSTM hybrid network","authors":"M.S. Islam , E. Hossain","doi":"10.1016/j.socl.2020.100009","DOIUrl":"10.1016/j.socl.2020.100009","url":null,"abstract":"<div><p>The foreign exchange (FOREX) market is one of the biggest financial markets in the world. More than 5.1 trillion dollars are traded each day in the FOREX market by banks, retail traders, corporations, and individuals. Due to complex, volatile, and high fluctuation, it is quite difficult to guess the price ahead of the actual time. Traders and investors continuously look for new methods to outperform the market and to earn a higher profit. Therefore, researchers around the world are continuously coming up with new forecasting models to successfully predict the nature of this unsettled market. This paper presents a new model that combines two powerful neural networks used for time series prediction: Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM), for predicting the future closing prices of FOREX currencies. The first layer of our proposed model is the GRU layer with 20 hidden neurons and the second layer is the LSTM layer with 256 hidden neurons. We have applied our model on four major currency pairs: EUR/USD, GBP/USD, USD/CAD, and USD/CHF. The prediction is done for 10 minutes timeframe using the data from January 1, 2017 to December 31, 2018, and 30 minutes timeframe using the data from January 1, 2019 to June 30, 2020 as a proof-of-concept. The performance of the model is validated using MSE, RMSE, MAE, and <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> score. Moreover, we have compared the performance of our model against a standalone LSTM model, a standalone GRU model and simple moving average (SMA) based statistical model where the proposed hybrid GRU-LSTM model outperforms all models for 10-mins timeframe and for 30-mins timeframe provides the best result for GBP/USD and USD/CAD currency pairs in terms of MSE, RMSE, and MAE performance metrics. But in terms of <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> score, our system outperforms all compared models and thus proves itself as the least risky model among all.</p></div>","PeriodicalId":101169,"journal":{"name":"Soft Computing Letters","volume":"3 ","pages":"Article 100009"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.socl.2020.100009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"101161223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 55

Ranking of Pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems using R-method 基于r法的多目标和多目标优化问题pareto最优解排序与最优解选取

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2021.100015

R.V. Rao , R.J. Lakshmi

{"title":"Ranking of Pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems using R-method","authors":"R.V. Rao , R.J. Lakshmi","doi":"10.1016/j.socl.2021.100015","DOIUrl":"10.1016/j.socl.2021.100015","url":null,"abstract":"<div><p>This paper presents a new multi-attribute decision-making (MADM) method, named as R-method, for ranking of Pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems. The compromise among the optimization objectives is different for each Pareto-optimal solution and, hence, the solution that has the best compromise among the objectives can be considered as the best solution. The proposed R-method is used to identify such best compromise solution. The method ranks the objectives based on their importance for the given optimization problem and ranks the alternative solutions (i.e. Pareto-optimal solutions) based on their data corresponding to the objectives. The ranks assigned to the objectives and the ranks assigned to the alternative solutions with respect to each of the objectives are converted to appropriate weights and the final composite scores of the alternative solutions are computed using these weights. The final ranking of alternative solutions is done based on the composite scores. The steps of the proposed method are described along with a pseudocode. Three examples are considered to demonstrate and validate the proposed method. The first example contains 4-objectives and 50 alternative solutions, the second example contains 6-objectives and 30 alternative solutions, and the third example contains 3-objectives and 25 alternative solutions. The results of the proposed method are compared with those of the other widely used MADM methods for the three examples considered. Also, the proposed method is compared with four well-known ranking methods to demonstrate its rationality in assigning weights to the ranks of the objectives and the alternative solutions. The proposed method is comparatively easier, more logical, and can be used for choosing the best compromise solution in multi- and many-objective optimization problems.</p></div>","PeriodicalId":101169,"journal":{"name":"Soft Computing Letters","volume":"3 ","pages":"Article 100015"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.socl.2021.100015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"105498496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

Using data complexity measures and an evolutionary cultural algorithm for gene selection in microarray data 利用数据复杂性度量和进化文化算法在微阵列数据中进行基因选择

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2020.100007

Saeed Sarbazi-Azad, Mohammad Saniee Abadeh, Mohammad Erfan Mowlaei

引用次数: 9

A modified fuzzy approach to project team selection 项目团队选择的改进模糊方法

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2021.100012

Sunny Joseph Kalayathankal , Joseph Varghese Kureethara , Samayan Narayanamoorthy

引用次数: 7

A new auditory algorithm in stock market prediction on oil and gas sector in Nigerian stock exchange 一种新的听觉算法在尼日利亚证券交易所油气板块股票市场预测中的应用

Soft Computing Letters Pub Date : 2021-12-01 DOI: 10.1016/j.socl.2021.100013

David O. Oyewola , Asabe Ibrahim , Joshua.A. Kwanamu , Emmanuel Gbenga Dada