Expert Systems with Applications: X最新文献_第2页

Mutual information inspired feature selection using kernel canonical correlation analysis 基于核典型相关分析的互信息激励特征选择

Expert Systems with Applications: X Pub Date : 2019-11-01 DOI: 10.1016/j.eswax.2019.100014

Wang Yan , Cang Shuang , Yu Hongnian

引用次数: 21

Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method 股票市场预测采用萤火虫算法与进化框架优化特征约简的OSELM方法

Expert Systems with Applications: X Pub Date : 2019-11-01 DOI: 10.1016/j.eswax.2019.100016

Smruti Rekha Das , Debahuti Mishra , Minakhi Rout

{"title":"Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method","authors":"Smruti Rekha Das , Debahuti Mishra , Minakhi Rout","doi":"10.1016/j.eswax.2019.100016","DOIUrl":"https://doi.org/10.1016/j.eswax.2019.100016","url":null,"abstract":"<div><p>Forecasting future trends of the stock market using the historical data is the exigent demand in the field of academia as well as business. This work has explored the feature optimization capacity of firefly with an evolutionary framework considering the biochemical and social aspects of Firefly algorithm, along with the selection procedure of objective value in evolutionary notion. The performance of the proposed model is evaluated using four different stock market datasets, such as BSE Sensex, NSE Sensex, S&P 500 index and FTSE index. The datasets are regenerated using the proper mathematical formulation of the fundamental part belonging to technical analysis, such as technical indicators and statistical measures. The feature reduction through transformation is carried out on the enhanced dataset before employing the experimented dataset to the prediction models such as Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN). For feature reduction, both statistical and optimized based feature reduction strategies are considered, where Principal Component Analysis (PCA) and Factor Analysis (FA) are examined for statistical based feature reduction and Firefly Optimization (FO), Genetic Algorithm (GA) and Firefly algorithm with evolutionary framework are well thought out for optimized feature reduction techniques. An empirical comparison is established among the experimented prediction models considering all the feature reduction techniques for the time horizon of 1 day, 3 days, 5 days, 7 days, 5 days and 30 days in advance, applying on all the datasets used in this study. From the simulation result, it can be clearly figured out that firefly with evolutionary framework optimized feature reduction applying to OSELM prediction model outperformed over the rest experimented models.</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"4 ","pages":"Article 100016"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92087583","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: 49

A geometric and fractional entropy-based method for family photo classification 基于几何分数熵的全家福分类方法

Expert Systems with Applications: X Pub Date : 2019-09-01 DOI: 10.1016/j.eswax.2019.100008

Maryam Asadzadeh Kaljahi , Palaiahnakote Shivakumara , Tianping Hu , Hamid A. Jalab , Rabha W. Ibrahim , Michael Blumenstein , Lu Tong , Mohamad Nizam Bin Ayub

{"title":"A geometric and fractional entropy-based method for family photo classification","authors":"Maryam Asadzadeh Kaljahi , Palaiahnakote Shivakumara , Tianping Hu , Hamid A. Jalab , Rabha W. Ibrahim , Michael Blumenstein , Lu Tong , Mohamad Nizam Bin Ayub","doi":"10.1016/j.eswax.2019.100008","DOIUrl":"https://doi.org/10.1016/j.eswax.2019.100008","url":null,"abstract":"<div><p>Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate.</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"3 ","pages":"Article 100008"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92261578","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: 7

Mining Twitter data for causal links between tweets and real-world outcomes 挖掘Twitter数据，寻找tweet与现实世界结果之间的因果关系

Expert Systems with Applications: X Pub Date : 2019-09-01 DOI: 10.1016/j.eswax.2019.100007

Sunghoon Lim , Conrad S. Tucker

{"title":"Mining Twitter data for causal links between tweets and real-world outcomes","authors":"Sunghoon Lim , Conrad S. Tucker","doi":"10.1016/j.eswax.2019.100007","DOIUrl":"10.1016/j.eswax.2019.100007","url":null,"abstract":"<div><p>The authors present an expert and intelligent system that (1) identifies influential term groups having causal relationships with real-world enterprise outcomes from Twitter data and (2) quantifies the appropriate time lags between identified influential term groups and enterprise outcomes. Existing expert and intelligent systems, which are defined as computer systems that imitate the ability of human decision making, could enable computers to identify the spread of Twitter users’ enterprise-related feedback automatically. However, existing expert and intelligent systems have limitations on automatically identifying the causal effects on enterprise outcomes. Identifying the causal effects on enterprise outcomes is important, because Twitter users’ feedback toward enterprise decisions may have real-world implications. The proposed expert and intelligent system can support decision makers’ decisions considering the real-world effects of identified Twitter users’ feedback on enterprise outcomes. In particular, (1) a co-occurrence network analysis model is exploited to discover term candidates for generating influential term groups that are combinations of enterprise-related terms, which potentially influence enterprise outcomes. (2) Time series models and (3) a Granger causality analysis model are then employed to identify influential term groups having causal relationships with enterprise outcomes with the appropriate time lags. Case studies involving a real-world internet video streaming and disc rental provider as well as an airline company are used to test the validity of the proposed expert and intelligent system for both predicting enterprise outcomes in a long period and predicting the effects of specific events on enterprise outcomes in a short period.</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"3 ","pages":"Article 100007"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116080666","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: 14

WITHDRAWN: Extracting actionable knowledge from social networks with node attributes 撤回:从具有节点属性的社交网络中提取可操作的知识

Expert Systems with Applications: X Pub Date : 2019-09-01 DOI: 10.1016/j.eswax.2019.100013

Nasrin Kalanat, Eynollah Khanjari

引用次数: 1

Robust machine layout design under dynamic environment: Dynamic customer demand and machine maintenance 动态环境下稳健的机器布局设计:动态的客户需求和机器维护

Expert Systems with Applications: X Pub Date : 2019-09-01 DOI: 10.1016/j.eswax.2019.100015

Srisatja Vitayasak , Pupong Pongcharoen , Christian Hicks

{"title":"Robust machine layout design under dynamic environment: Dynamic customer demand and machine maintenance","authors":"Srisatja Vitayasak , Pupong Pongcharoen , Christian Hicks","doi":"10.1016/j.eswax.2019.100015","DOIUrl":"10.1016/j.eswax.2019.100015","url":null,"abstract":"<div><p>The layout of manufacturing facilities has a large impact on manufacturing performance. The layout design process produces a block plan that shows the relative positioning of resources that can be developed into a detailed layout drawing. The total material handling distance is commonly used for measuring material flow. Manufacturing systems are subject to external and internal uncertainties including demand and machine breakdowns. Uncertainty and the rerouting of material flows have an impact on the material handling distance. No previous research has integrated robust machine layout design through multiple periods of dynamic demand with machine maintenance planning. This paper presents a robust machine layout design tool that minimises the material flow distance using a Genetic Algorithm (GA), taking into account demand uncertainty and machine maintenance. Experiments were conducted using eleven benchmark datasets that considered three scenarios: preventive maintenance (PM), corrective maintenance (CM) and both PM and CM. The results were analysed statistically. The effect of several maintenance scenarios including the ratio of the number of machines with period-based PM (PPM) to the number with production quantity-based PM (QPM), the percentage of machines with CM (%CM), and a combination of PMM/QPM ratios and %CM on material flow distance were examined. The results show that designing robust layouts considering maintenance resulted in shorter material flow distances. The distance was decreased by 30.91%, 9.8%, and 20.7% for the PM, CM, and both PM/CM scenarios, respectively. The PPM/QPM ratios, %CM, and a combination of PPM/QPM and %CM had significantly resulted in the material flow distance on almost all datasets.</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"3 ","pages":"Article 100015"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129924555","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: 13

WITHDRAWN: A combined genetic algorithm and inverse data envelopment analysis model for target setting in mergers 摘要:一种结合遗传算法和反数据包络分析的并购目标设定模型

Expert Systems with Applications: X Pub Date : 2019-07-12 DOI: 10.1016/J.ESWAX.2019.100012

F. Guijarro, M. Martínez-Gómez, Delimiro Visbal-Cadavid

引用次数: 1

International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization 基于多粒度模糊时间序列和粒子群优化的国际粗糙度指数预测

Expert Systems with Applications: X Pub Date : 2019-07-01 DOI: 10.1016/j.eswax.2019.100006

Wei Li , Ju Huyan , Liyang Xiao , Susan Tighe , Lili Pei

{"title":"International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization","authors":"Wei Li , Ju Huyan , Liyang Xiao , Susan Tighe , Lili Pei","doi":"10.1016/j.eswax.2019.100006","DOIUrl":"10.1016/j.eswax.2019.100006","url":null,"abstract":"<div><p>The effective prediction of pavement performance trends can help in achieving the cost-effective management of pavements over their service life. The international roughness index (IRI) is a widely used pavement performance index, which can be considered as a time-dependent variable in terms of scientific modeling. This research aims to develop an innovative IRI prediction model based on fuzzy-trend time-series forecasting and particle swarm optimization (PSO) techniques. Raw datasets extracted from the Long-Term Pavement Performance database are used for model training, testing, and performance assessment. First, IRI values are divided into different granular spaces, which are considered as the principal factor and subfactors. In addition, the multifactor interval division method is proposed according to the principle of the automatic clustering technique. Next, a second-order fuzzy-trend model and fuzzy-trend relationship classification method are proposed to predict the fuzzy-trend of each factor. Then, the fuzzy-trend states for multiple granular spaces are generated while giving full consideration to various uncertainties. Finally, the PSO technique is used to optimize the performance model while carrying out future IRI forecasting. Comparative experiments are performed using more than 20,000 data items from different regions to verify the effectiveness of the proposed method. The experimental results indicate that the proposed method outperforms other approaches including the polynomial fitting, autoregressive integrated moving average, and backpropagation neural network methods in terms of the root mean squared error (0.191) and relative error (6.37%).</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"2 ","pages":"Article 100006"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127493874","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: 18

A review of machine learning algorithms for identification and classification of non-functional requirements 非功能需求识别和分类的机器学习算法综述

Expert Systems with Applications: X Pub Date : 2019-04-01 DOI: 10.1016/j.eswax.2019.100001

Manal Binkhonain, Liping Zhao

{"title":"A review of machine learning algorithms for identification and classification of non-functional requirements","authors":"Manal Binkhonain, Liping Zhao","doi":"10.1016/j.eswax.2019.100001","DOIUrl":"https://doi.org/10.1016/j.eswax.2019.100001","url":null,"abstract":"<div><h3>Context</h3><p>Recent developments in requirements engineering (RE) methods have seen a surge in using machine-learning (ML) algorithms to solve some difficult RE problems. One such problem is identification and classification of non-functional requirements (NFRs) in requirements documents. ML-based approaches to this problem have shown to produce promising results, better than those produced by traditional natural language processing (NLP) approaches. Yet, a systematic understanding of these ML approaches is still lacking.</p></div><div><h3>Method</h3><p>This article reports on a systematic review of 24 ML-based approaches for identifying and classifying NFRs. Directed by three research questions, this article aims to understand what ML algorithms are used in these approaches, how these algorithms work and how they are evaluated.</p></div><div><h3>Results</h3><p>(1) 16 different ML algorithms are found in these approaches; of which supervised learning algorithms are most popular. (2) All 24 approaches have followed a standard process in identifying and classifying NFRs. (3) Precision and recall are the most used matrices to measure the performance of these approaches.</p></div><div><h3>Finding</h3><p>The review finds that while ML-based approaches have the potential in the classification and identification of NFRs, they face some open challenges that will affect their performance and practical application.</p></div><div><h3>Impact</h3><p>The review calls for the close collaboration between RE and ML researchers, to address open challenges facing the development of real-world ML systems.</p></div><div><h3>Significance</h3><p>The use of ML in RE opens up exciting opportunities to develop novel expert and intelligent systems to support RE tasks and processes. This implies that RE is being transformed into an application of modern expert systems.</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136991732","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

A novel multi-module neural network system for imbalanced heartbeats classification 一种新的多模块神经网络系统用于不平衡心跳分类

Expert Systems with Applications: X Pub Date : 2019-04-01 DOI: 10.1016/j.eswax.2019.100003

Jiang Jing, Zhang Huaifeng, Pi Dechang, Dai Chenglong

{"title":"A novel multi-module neural network system for imbalanced heartbeats classification","authors":"Jiang Jing, Zhang Huaifeng, Pi Dechang, Dai Chenglong","doi":"10.1016/j.eswax.2019.100003","DOIUrl":"10.1016/j.eswax.2019.100003","url":null,"abstract":"<div><p>In this paper, a novel multi-module neural network system named MMNNS is proposed to solve the imbalance problem in electrocardiogram (ECG) heartbeats classification. Four submodules are designed to construct the system: preprocessing, imbalance problem processing, feature extraction and classification. Imbalance problem processing module mainly introduces three methods: BLSM, CTFM and 2PT, which are proposed from three aspects of resampling, data feature and algorithm respectively. BLSM is used to synthesize virtual samples linearly around the minority samples. CTFM consists of DAE-based feature extraction part and QRS-based feature selection part, in which selected features and complete features are applied to determine the heartbeat class simultaneously. The processed data are fed into a convolutional neural network (CNN) by applying 2PT to train and fine-tune. MMNNS is trained on MIT-BIH Arrhythmia Database following AAMI standard, using intra-patient and inter-patient scheme, especially the latter which is strongly recommended. The comparisons with several state-of-the-art methods using standard criteria on three datasets demonstrate the superiority of MMNNS for improving detection of heartbeats and addressing imbalance in ECG heartbeats classification.</p></div>","PeriodicalId":36838,"journal":{"name":"Expert Systems with Applications: X","volume":"1 ","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.eswax.2019.100003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115986257","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: 49