Computing fuzzy rough approximations in large scale information systems

Rough set theory is a popular and powerful machine learning tool. It is especially suitable for dealing with information systems that exhibit inconsistencies, i.e. objects that have the same values for the conditional attributes but a different value for the decision attribute. In line with the emerging granular computing paradigm, rough set theory groups objects together based on the indiscernibility of their attribute values. Fuzzy rough set theory extends rough set theory to data with continuous attributes, and detects degrees of inconsistency in the data. Key to this is turning the indiscernibility relation into a gradual relation, acknowledging that objects can be similar to a certain extent. In very large datasets with millions of objects, computing the gradual indiscernibility relation (or in other words, the soft granules) is very demanding, both in terms of runtime and in terms of memory. It is however required for the computation of the lower and upper approximations of concepts in the fuzzy rough set analysis pipeline. Current non-distributed implementations in R are limited by memory capacity. For example, we found that a state of the art non-distributed implementation in R could not handle 30,000 rows and 10 attributes on a node with 62GB of memory. This is clearly insufficient to scale fuzzy rough set analysis to massive datasets. In this paper we present a parallel and distributed solution based on Message Passing Interface (MPI) to compute fuzzy rough approximations in very large information systems. Our results show that our parallel approach scales with problem size to information systems with millions of objects. To the best of our knowledge, no other parallel and distributed solutions have been proposed so far in the literature for this problem

Rough Set Theory (RST) Data Analysis with R and its Application on Studying Relative Significances of Self-Regulated Learning (SRL) Strategies of Gifted Students

Abstract Rough set theory (RST) was proposed by Zdzistaw Pawlak (Pawlak,1982) as a methodology for data analysis using the notion of discernibility of objects based on their attribute values. The main advantage of using RST approach is that it does not need additional assumptions – like data distribution in statistical analysis. Besides, it provides efficient algorithms and tools for finding hidden patterns. Despite its advantages, the adoption of RST in educational research is still limited, which may be due to limited quality software available for RST. Recently, the RoughSets package in R statistical system has been developed (Riza et al., 2014), providing various utilities of the RST methods. In the paper, we will first describe the basic RST concepts and steps of data analysis under RST. We will then apply RST to a study, which aimed to determine the relative significance of various SRL strategies used by student participants, so as to illustrate the steps of data analysis using the RoughSets R package. From the illustration, it is expected that more researchers in the field of education will be encouraged to try RST methods in their own studies. Keywords: rough set theory, discernibility, RoughSets R package, online learning, self-regulated learning strategie

Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets

We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters

Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability

COVID-19 is an unprecedented health crisis causing a great deal of stress and mental health challenges in populations in Canada. Recently, research is emerging highlighting the potential of cannabinoids’ beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of medicinal cannabis since COVID-19 was declared a pandemic. Furthermore, evidence points to a correlation between mental health and sleep patterns. The objective of this research is threefold: i) to assess the relationship of the clinical delivery of cannabinoid medicine, by utilizing machine learning, to anxiety, depression and sleep scores; ii) to discover patterns based on patient features such as specific cannabis recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (GAD7, PHQ9 and PSQI) scores over a period of time (including during the COVID timeline); and iii) to predict whether new patients could potentially experience either an increase or decrease in clinical assessment tool scores. The dataset for this thesis was derived from patient visits to Ekosi Health Centres in Manitoba, Canada and Ontario, Canada from January, 2019 to April, 2021. Extensive pre-processing and feature engineering was performed. To determine the outcome of a patients treatment, a class feature (Worse, Better, or No Change) indicative of their progress or lack thereof due to the treatment received was introduced. Three well-known supervised machine learning models (tree-based, rule-based and nearest neighbour) were trained on the patient dataset. In addition, seven rough and rough-fuzzy hybrid methods were also trained on the same dataset. All experiments were conducted using a 10-fold CV method. Sensitivity and specificity measures were higher in all classes with rough and rough-fuzzy hybrid methods. The highest accuracy of 99.15% was obtained using the rule-based rough-set learning method.Ekosi Health Center, MitacsMaster of Science in Applied Computer Scienc

Fuzzy Rule-based Classification Systems for the Gender Prediction from Handwriting

The handwriting is an object that can describe information about the author implicitly. For example, it is able to predict the gender. Recently, the gender prediction based on handwriting becomes an interesting research. Even in 2013, an competition for prediction gender from handwriting has been held by Kaggle. However, the accuracies of current approaches are relatively low. So, in this study, we attempt to implement Fuzzy Rule-Based Classification Systems (FRBCSs) for gender predictions from handwriting. Three stages are conducted to achieve the objective, as follows: defining some features based on Graphology Techniques (e.g., pressure, height, and margin on writing), collecting real datasets, processing on digital images (i.e., image segmentation, projection profiles, and margin calculation, etc.), and implementing FRBCSs. The implemented algorithm based on FRBCSs in this research is Chi’s Algorithm, which is a method based on Fuzzy Logic for classification tasks. Moreover, some experiments and analysis, involving 75 respondents consisting of 36 males and 39 females, have been done to validate the proposed model. From the simulations, the classification rate obtained is 76%. Besides improving the accuracy rate, the proposed model can provide an understandable model by utilizing fuzzy rule-based systems

DEVELOPMENT OF R PACKAGE AND EXPERIMENTAL ANALYSIS ON PREDICTION OF THE CO2 COMPRESSIBILITY FACTOR USING GRADIENT DESCENT

Nowadays, many variants of gradient descent (i.e., the methods included in machine learning for regression) have been proposed. Moreover, these algorithms have been widely used to deal with real-world problems. However, the implementations of these algorithms into a software library are few. Therefore, we focused on building a package written in R that includes eleven algorithms based on gradient descent, as follows: Mini-Batch Gradient Descent (MBGD), Stochastic Gradient Descent (SGD), Stochastic Average Gradient Descent (SAGD), Momentum Gradient Descent (MGD), Accelerated Gradient Descent (AGD), Adagrad, Adadelta, RMSprop and Adam. Additionally, experimental analysis on prediction of the CO2 compressibility factor were also conducted. The results show that the accuracy and computational cost are reasonable, which are 0.0085 and 0.142 second for the average of root mean square root and simulation time

Detection of HIV by using Rough Set and Homotopy Analysis Method

The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV & AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV & AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST & HAM over other soft computing techniques and Mathematical Modelling as both RST and Homotopy Analysis (HAM) because RST can handle vague and imprecise data efficiently, and HAM is a suitable technique for finding analytical solutions. We have used the chi-squared test to validate our claim.&nbsp

Mühendislikte yapay zeka ve uygulamaları 2

Kocaeli Üniversitesi1 Parametre Optimizasyonu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.1 Giri¸s 11 1.2 Metasezgisel Yöntem Parametreleri 14 1.3 Parametre Optimizasyonu 15 1.4 F-Race algoritması 16 1.5 Kaynakça 18 2 Kurumsal Kaynak Planlaması . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.1 Giri¸s 25 2.2 Kurumsal Kaynak Planlama Sistemleri 25 2.3 ˙I ¸s Süreçlerinin Modellenmesi 27 2.4 Petri-net Modelleme 29 2.5 BPMN ile Modelleme 30 2.6 BPEL ile Modelleme 32 2.7 EPC ile Modelleme 32 2.8 UML Faaliyet (Activity) Diyagramları 35 2.9 Yapay Zekâ Uygulamalarının˙I ¸sletmelerde Kullanımı 36 2.10 Yapay Zekâ Uygulamalarının Modellenmesi 39 2.11 Sonuç 40 2.12 Kaynakça 41 2.13 Yazarlar Hakkında 44 3 Veri Görselle¸stirme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1 Giri¸s 45 3.2 Veri Görselle¸stirme 46 3.3 Görsel Analitik 48 3.4 ˙I ¸s Zekâsı ile Veri Görselle¸stirme ˙Ili¸skisi 50 3.5 Görselle¸stirme Yazılımları 52 3.6 Tableau ile Örnek Gösterge Paneli Tasarımı 53 3.7 Sonuç 59 3.8 Kaynakça 59 4 PANDAS ile Veri Analizi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.1 Giri¸s 61 4.2 Python Pandas Kütüphanesi Uygulamaları 64 4.2.1 Seriler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2.2 DataFrame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.3 Veri Görselle¸stirme (Matplotlib Kütüphanesi) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3 Kaynakça 81 5 Tensorflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.1 Introduction 83 5.2 Background 84 5.2.1 What is Deep Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.3 Experiment 85 5.3.1 Tensorflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Environment set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.3 Use-case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.3.4 Re-training the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.3.5 Using the Retrained Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.4 Conclusion 90 Acknowledgement 91 5.5 Kaynakça 91 5.6 About the authors 91 6 R ile Kaba Kümeleme ve Uygulamaları . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.1 Giri¸s 93 6.2 Kaba Kümelemede Temel Kavramlar 94 6.3 Bulanık Kaba Küme Teorisi 102 6.4 ‘Roughsets’ Yazılım Paketi 103 6.4.1 Temel Kavramların Uygulanması (BC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.4.2 Eksik Veri Tamamlama Uygulamaları (MV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.4.3 Ayrıkla¸stırma Uygulamaları (D) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 6.4.4 Öznitelik Seçimi Uygulamaları (FS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 6.4.5 Örnek (Veri) Seçimi Uygulamaları (IS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.4.6 Kural Çıkarma Uygulamaları (RI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.4.7 Tahmin/Sınıflandırma Uygulamaları . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.5 Uygulama 108 6.5.1 Gö˘güs Kanseri Te¸shisi Uygulaması . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.5.2 Kaba Kümeleme ˙Ile Kural Tabanlı Sınıflandırıcı . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.5.3 Bulanık Kaba Kümeleme ˙Ile Kural Tabanlı Sınıflandırıcı . . . . . . . . . . . . . . . . . . . 115 6.6 Sonuçlar 118 6.7 Kaynakça 118 6.8 Yazarlar Hakkında 122 7 Lojistik ve Tedarik Zinciri Yönetimi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 7.1 Giri¸s 126 7.2 Lojistik ve Tedarik Zinciri Yönetimi 126 7.3 Endüstri 4.0 127 7.4 Literatür Analizi 129 7.5 Sonuç ve Öneriler 133 7.6 Kaynakça 133 8 TWO Meta-Sezgisel Algoritma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.1 Giri¸s 139 8.1.1 Fonksiyonun türevinin tanımsız veya çok modlu olması durumu . . . . . . . . . . . . 141 8.1.2 Karar de˘gi¸skenlerinin çok olması durumu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 8.2 Meta-Sezgisel Algoritmaların Çalı¸sma Mantı˘gı 142 8.2.1 Yörünge esaslı algoritmalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 8.2.2 Sürü esaslı algoritmalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 8.2.3 Geli¸sim esaslı algoritmalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 8.2.4 Meta-Sezgisel algoritmaların çözüm yakla¸sımı . . . . . . . . . . . . . . . . . . . . . . . . . . 143 8.3 TWO Algoritması 144 8.4 TWO Algoritmasının Uygulaması 147 8.5 Sonuç 147 8 8.6 Kaynakça 149 9 Derin Ö˘grenme Uygulaması . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 9.1 Giri¸s 151 9.2 Derin Ö˘grenme Mimarileri 152 9.2.1 Kıvrımlı (Konvolüsyonel) Sinir A˘gları . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 9.2.2 Derin ˙Inanç A˘gları . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 9.2.3 Derin Oto-Kodlayıcılar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 9.3 Derin Ö˘grenme Uygulama Yapısı 154 9.3.1 Kıvrım (Convolution) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 9.3.2 Bütünle¸stirme (Pooling) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 9.3.3 Erken Durdurma (Early Stopping) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 9.3.4 Dü˘güm Silme (Dropout) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 9.4 Kalite Kontrol Uygulaması 156 9.4.1 Derin Ö˘grenme Modeli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 9.4.2 E˘gitim Süreci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 9.5 Sonuç 162 9.6 Kaynakça 164 10 Python ile Görüntü˙I¸slem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 10.1 Giri¸s 165 10.1.1 Temel Tanımlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 10.2 Python ile Görüntü˙I¸sleme 166 10.2.1 Python Nedir? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 10.2.2 OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 10.3 ˙Imge Bölütleme (Image Segmentatıon) Yöntemleri 167 10.3.1 E¸sikleme (Thresholding) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 10.4 Uygulama 173 10.5 Sonuç 186 10.6 Kaynakça 18