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Feature Engineering Techniques to Improve Identification Accuracy for Offline Signature Case-Bases 提高离线签名案例库识别精度的特征工程技术
Int. J. Rough Sets Data Anal. Pub Date : 2021-01-01 DOI: 10.4018/IJRSDA.20210101.OA1
Shisna Sanyal, Anindita Desarkar, Uttam Kumar Das, C. Chaudhuri
{"title":"Feature Engineering Techniques to Improve Identification Accuracy for Offline Signature Case-Bases","authors":"Shisna Sanyal, Anindita Desarkar, Uttam Kumar Das, C. Chaudhuri","doi":"10.4018/IJRSDA.20210101.OA1","DOIUrl":"https://doi.org/10.4018/IJRSDA.20210101.OA1","url":null,"abstract":"Handwritten signatures have been widely acclaimed for personal identification viability in educated human society. But, the astronomical growth of population in recent years warrant developing mechanized systems to remove the tedium and bias associated with manual checking. Here the proposed system, performing identification with Nearest Neighbor matching between offline signature images collected temporally. The raw images and their extracted features are preserved using Case Based Reasoning and Feature Engineering principles. Image patterns are captured through standard global and local features, along with some profitable indigenously developed features. Outlier feature values, on detection, are automatically replaced by their nearest statistically determined limit values. Search space reduction possibilities within the case base are probed on a few selected key features, applying Hierarchical clustering and Dendogram representation. Signature identification accuracy is found promising when compared with other machine learning techniques and a few existing well known approaches.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115837602","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: 12
A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators 基于rnn - lstm的技术指标股票市场预测建模框架
Int. J. Rough Sets Data Anal. Pub Date : 2021-01-01 DOI: 10.4018/ijrsda.288521
S. Mittal, A. Chauhan
{"title":"A RNN-LSTM-Based Predictive Modelling Framework for Stock Market Prediction Using Technical Indicators","authors":"S. Mittal, A. Chauhan","doi":"10.4018/ijrsda.288521","DOIUrl":"https://doi.org/10.4018/ijrsda.288521","url":null,"abstract":"The successful prediction of the stocks’ future price would produce substantial profit to the investor. In this paper, we propose a framework with the help of various technical indicators of the stock market to predict the future prices of the stock using Recurrent Neural Network based Long Short-Term Memory (LSTM) algorithm. The historical transactional data set is amalgamated with the technical indicators to create a more effective input dataset. The historical data is taken from 2010-2019 ten years in total. The dataset is divided into 80% training set and 20% test set. The experiment is carried out in two phases first without the technical indicators and after adding technical indicators. In the experimental setup, it has been observed the LSTM with technical indicators have significantly reduced the error value by 2.42% and improved the overall performance of the system as compared to other machine learning frameworks that are not accounting the effect of technical indicators.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246151","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: 12
A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography 一种利用椭圆曲线加密的超混沌增强图像安全性的新方法
Int. J. Rough Sets Data Anal. Pub Date : 2021-01-01 DOI: 10.4018/ijrsda.288520
M. Ganavi, S. Prabhudeva
{"title":"A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography","authors":"M. Ganavi, S. Prabhudeva","doi":"10.4018/ijrsda.288520","DOIUrl":"https://doi.org/10.4018/ijrsda.288520","url":null,"abstract":"Information security dominate the world. All the time we connect to the internet for social media, banking, and online shopping through various applications. Our priceless data may be hacked by attackers. There is a necessity for a better encryption method to enhance information security. The distinctive features of Elliptic Curve Cryptography (ECC) in particular the key atomity, speedy ciphering and preserving bandwidth captivating its use in multimedia encipher. An encryption method is proposed by incorporating ECC, Secure Hash Algorithm – 256 (SHA-256), Arnold transform, and hyperchaos. Randomly generated salt values are concatenated with each pixel of an image. SHA-256 hash is imposed which produces a hash value of 32-bit, later used to generate the key in ECC. Stronger ciphering is done by applying Arnold’s transformation and hyperchaos thereby achieved more randomness in image. Simulation outcomes and analysis show that the proposed approach provides more confidentiality for color images.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130721389","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
The Effects of Sampling Methods on Machine Learning Models for Predicting Long-term Length of Stay: A Case Study of Rhode Island Hospitals 抽样方法对预测长期住院时间的机器学习模型的影响:罗德岛医院的案例研究
Int. J. Rough Sets Data Anal. Pub Date : 2019-07-01 DOI: 10.4018/ijrsda.2019070103
Son Nguyen, Alicia T. Lamere, A. Olinsky, John T. Quinn
{"title":"The Effects of Sampling Methods on Machine Learning Models for Predicting Long-term Length of Stay: A Case Study of Rhode Island Hospitals","authors":"Son Nguyen, Alicia T. Lamere, A. Olinsky, John T. Quinn","doi":"10.4018/ijrsda.2019070103","DOIUrl":"https://doi.org/10.4018/ijrsda.2019070103","url":null,"abstract":"The ability to predict the patients with long-term length of stay (LOS) can aid a hospital's admission management, maintain effective resource utilization and provide a high quality of inpatient care. Hospital discharge data from the Rhode Island Department of Health from the time period between 2010 to 2013 reveals that inpatients with long-term stays, i.e. two weeks or more, costs about six times more than those with short stays while only accounting for 4.7% of the inpatients. With the imbalance in the distribution of long-stay patients and short-stay patients, predicting long-term LOS patients becomes an imbalanced classification problem. Sampling methods—balancing the data before fitting it to a traditional classification model—offer a simple approach to the problem. In this work, the authors propose a new resampling method called RUBIES which provides superior predictive ability when compared to other commonly used sampling techniques.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411847","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: 22
Analyzing Evolution Patterns of Object-Oriented Metrics: A Case Study on Android Software 分析面向对象度量的演化模式:以Android软件为例
Int. J. Rough Sets Data Anal. Pub Date : 2019-07-01 DOI: 10.4018/ijrsda.2019070104
R. Malhotra, Megha Khanna
{"title":"Analyzing Evolution Patterns of Object-Oriented Metrics: A Case Study on Android Software","authors":"R. Malhotra, Megha Khanna","doi":"10.4018/ijrsda.2019070104","DOIUrl":"https://doi.org/10.4018/ijrsda.2019070104","url":null,"abstract":"Software evolution is mandatory to keep it useful and functional. However, the quality of the evolving software may degrade due to improper incorporation of changes. Quality can be monitored by analyzing the trends of software metrics extracted from source code as these metrics represent the structural characteristics of a software such as size, coupling, inheritance etc. An analysis of these metric trends will give insight to software practitioners regarding effects of software evolution on its internal structure. Thus, this study analyzes the trends of 14 object-oriented (OO) metrics in a widely used mobile operating system software, Android. The study groups the OO metrics into four dimensions and analyzes the trends of these metrics over five versions of Android software (4.0.2-4.3.1). The results of the study indicate certain interesting patterns for the evaluated dimensions, which can be helpful to software practitioners for outlining specific maintenance decisions to improve software quality.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116312766","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: 20
Parallel and Distributed Pattern Mining 并行和分布式模式挖掘
Int. J. Rough Sets Data Anal. Pub Date : 2019-07-01 DOI: 10.4018/ijrsda.2019070101
Ishak H. A. Meddah, Nour El Houda Remil
{"title":"Parallel and Distributed Pattern Mining","authors":"Ishak H. A. Meddah, Nour El Houda Remil","doi":"10.4018/ijrsda.2019070101","DOIUrl":"https://doi.org/10.4018/ijrsda.2019070101","url":null,"abstract":"The treatment of large data is difficult and it looks like the arrival of the framework MapReduce is a solution of this problem. This framework can be used to analyze and process vast amounts of data. This happens by distributing the computational work across a cluster of virtual servers running in a cloud or a large set of machines. Process mining provides an important bridge between data mining and business process analysis. Its techniques allow for extracting information from event logs. Generally, there are two steps in process mining, correlation definition or discovery and the inference or composition. First of all, their work mines small patterns from log traces. Those patterns are the representation of the traces execution from a log file of a business process. In this step, the authors use existing techniques. The patterns are represented by finite state automaton or their regular expression; and the final model is the combination of only two types of different patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Second, they compute these patterns in parallel, and then combine those small patterns using the Hadoop framework. They have two steps; the first is the Map Step through which they mine patterns from execution traces, and the second one is the combination of these small patterns as a reduce step. The results show that their approach is scalable, general and precise. It minimizes the execution time by the use of the Hadoop framework.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122613539","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: 16
An Arabic Dialects Dictionary Using Word Embeddings 使用单词嵌入的阿拉伯方言词典
Int. J. Rough Sets Data Anal. Pub Date : 2019-07-01 DOI: 10.4018/ijrsda.2019070102
Chaimae Azroumahli, Yacine El Younoussi, Otman Moussaoui, Youssra Zahidi
{"title":"An Arabic Dialects Dictionary Using Word Embeddings","authors":"Chaimae Azroumahli, Yacine El Younoussi, Otman Moussaoui, Youssra Zahidi","doi":"10.4018/ijrsda.2019070102","DOIUrl":"https://doi.org/10.4018/ijrsda.2019070102","url":null,"abstract":"The dialectical Arabic and the Modern Standard Arabic lacks sufficient standardized language resources to enable the tasks of Arabic language processing, despite it being an active research area. This work addresses this issue by firstly highlighting the steps and the issues related to building a multi Arabic dialect corpus using web data from blogs and social media platforms (i.e. Facebook, Twitter, etc.). This is to create a vectorized dictionary for the crawled data using the word Embeddings. In other terms, the goal of this article is to build an updated multi-dialect data set, and then, to extract an annotated corpus from it.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124835601","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: 21
Sheaf Representation of an Information System 信息系统的束表示
Int. J. Rough Sets Data Anal. Pub Date : 2019-04-01 DOI: 10.4018/IJRSDA.2019040106
P. V. Sagar, M. Kishore
{"title":"Sheaf Representation of an Information System","authors":"P. V. Sagar, M. Kishore","doi":"10.4018/IJRSDA.2019040106","DOIUrl":"https://doi.org/10.4018/IJRSDA.2019040106","url":null,"abstract":"Ever since Pawlak introduced the concepts of rough sets, it has attracted many researchers and scientists from various fields of science and technology. Particularly for algebraists as it presented a gold mine to explore the algebraic and topological connections with rough set theory. The present article deals with the connections between rough sets and sheaves. The authors studied sheaf representation of an information system in rough set framework and illustrated how it helps information retrieval.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114308934","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: 23
Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm 基于梯度特征提取和克隆选择算法的Odia手写体数字识别
Int. J. Rough Sets Data Anal. Pub Date : 2019-04-01 DOI: 10.4018/IJRSDA.2019040102
Puspalata Pujari, B. Majhi
{"title":"Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm","authors":"Puspalata Pujari, B. Majhi","doi":"10.4018/IJRSDA.2019040102","DOIUrl":"https://doi.org/10.4018/IJRSDA.2019040102","url":null,"abstract":"This article aims to recognize Odia handwritten digits using gradient-based feature extraction techniques and Clonal Selection Algorithm-based (CSA) multilayer artificial neural network (MANN) classifier. For the extraction of features which contribute the most towards recognition from images, are extracted using gradient-based feature extraction techniques. Principal component analysis (PCA) is used for dimensionality reduction of extracted features. A MANN is used as a classifier for classification purposes. The weights of the MANN are adjusted using the CSA to get optimized set of weights. The proposed model is applied on Odia handwritten digits taken from the Indian Statistical Institution (ISI), Calcutta, which consists of four thousand samples. The results obtained from the experiment are compared with a genetic-based multi-layer artificial neural network (GA-MANN) model. The recognition accuracy of the CSA-MANN model is found to be 90.75%.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"325 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114378568","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: 15
Detection of Shotgun Surgery and Message Chain Code Smells using Machine Learning Techniques 使用机器学习技术检测霰弹枪手术和消息链代码气味
Int. J. Rough Sets Data Anal. Pub Date : 2019-04-01 DOI: 10.4018/IJRSDA.2019040103
Thirupathi Guggulothu, S. A. Moiz
{"title":"Detection of Shotgun Surgery and Message Chain Code Smells using Machine Learning Techniques","authors":"Thirupathi Guggulothu, S. A. Moiz","doi":"10.4018/IJRSDA.2019040103","DOIUrl":"https://doi.org/10.4018/IJRSDA.2019040103","url":null,"abstract":"Code smell is an inherent property of software that results in design problems which makes the software hard to extend, understand, and maintain. In the literature, several tools are used to detect code smell that are informally defined or subjective in nature due to varying results of the code smell. To resolve this, machine leaning (ML) techniques are proposed and learn to distinguish the characteristics of smelly and non-smelly code elements (classes or methods). However, the dataset constructed by the ML techniques are based on the tools and manually validated code smell samples. In this article, instead of using tools and manual validation, the authors considered detection rules for identifying the smell then applied unsupervised learning for validation to construct two smell datasets. Then, applied classification algorithms are used on the datasets to detect the code smells. The researchers found that all algorithms have achieved high performance in terms of accuracy, F-measure and area under ROC, yet the tree-based classifiers are performing better than other classifiers.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131219904","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: 25
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