Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

A refined approach for forecasting based on neutrosophic time series

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%

Kapılı tekrarlayan hücreler tabanlı bulanık zaman serileri tahminleme modeli

Time series forecasting and prediction are utilized in various industries, such as e-commerce, stock markets, wind power, and energy demand forecasting. An accurate forecast in these applications is an essential and challenging task because of the complexity and uncertainty of time series. Nowadays, deep learning methods are popular in time series forecasting and show better performance than classical methods. However, in the literature, only some studies use deep learning methods in fuzzy time series (FTS) forecasting. In this study, we propose a novel FTS forecasting model based upon the hybridization of Recurrent Neural Networks with FTS to deal with the complexity and uncertainty of these series. The proposed model utilizes Gated Recurrent Unit (GRU) to make predictions using a combination of membership values and past values from original time series data as model input and produce real forecast value. Moreover, the proposed model can handle first-order fuzzy relations and high-order ones. In experiments, we have compared our model results with state-of-art methods by using two real-world datasets; The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Nikkei Stock Average. The results indicate that our model outperforms or performs similarly to other methods. The proposed model is validated using the Covid-19 active case dataset and BIST100 Index dataset and performs better than Long Short-term Memory (LSTM) networks.Zaman serisi tahminleme hava durumu, iş dünyası, satış verileri ve enerji tüketimi tahminleme gibi bir çok alanda uygulama alanına sahiptir. Bu alanlarda tahminleme yaparken kesin sonuçlar elde etmek çok önemlidir ama aynı zamanda zaman serilerinin karmaşık ve de belirsizlik içeren veriler olması nedeniyle çok zordur. Günümüzde, derin öğrenme metotları bu alanda klasik metotlara göre daha iyi sonuçlar vermektedir. Fakat literatürde bulanık zaman serileri tahminleme konusunda çok az çalışma vardır. Bu çalışmada, zaman serilerindeki karmaşıklığın ve belirsizliğin doğurduğu problemleri yok etmek için Yinelemeli sinir Ağları ile bulanık zaman serilerini bir arada kullanan bir model ortaya konumuştur. Bu çalışmada, Kapılı Tekrarlayan Hücreler kullanarak geçmiş veriler ile bulanık verilerin üyelik değerleri birleştirilerek tahminleme değeri hesaplanmıştır. Ayrıca, bu çalışmadaki model ilk seviye bulanık ilişkileri ele alabildiği gibi, çoklu seviye bulanık ilişkileri de kapsamaktadır. Testlerde literatürde var olan çalışmalar ilgili model ile iki açık veri seti ile karşılaştırılmış olup bahsi geçen modelin daha iyi veya benzer sonuçlar verdiği gözlemlenmiştir. Ayrıca model Covid-19 ve BIST100 borsa verileri kullanılarak da test edilmiş ve Uzun-Kısa Süreli Bellek modellerinden daha iyi sonuç vermiştir

R-Norm Information Measure with Applications in Multi Criteria Decision Making Technique under Intuitionistic Fuzzy Set Environment

The main aim of this research article is to define a new information measure for quantifying fuzziness in the intuitionistic fuzzy set environment. For this purpose, we present R-norm intuitionistic fuzzy measure that quantifies the amount of vagueness or fuzziness of a particular fuzzy set. We prove that this measure is a valid measure of intuitionistic fuzzy entropy by making it satisfy essential properties. Also, some mathematical properties are used to check the validation of the measure. In the end, a practical example of decision-making is illustrated in terms of Multi Criteria Decision Making problem that presents the application of the proposed measure

A systematic literature review of soft set theory

[EN] Soft set theory, initially introduced through the seminal article ‘‘Soft set theory—First results’’ in 1999, has gained considerable attention in the field of mathematical modeling and decision-making. Despite its growing prominence, a comprehensive survey of soft set theory, encompassing its foundational concepts, developments, and applications, is notably absent in the existing literature. We aim to bridge this gap. This survey delves into the basic elements of the theory, including the notion of a soft set, the operations on soft sets, and their semantic interpretations. It describes various generalizations and modifications of soft set theory, such as N-soft sets, fuzzy soft sets, and bipolar soft sets, highlighting their specific characteristics. Furthermore, this work outlines the fundamentals of various extensions of mathematical structures from the perspective of soft set theory. Particularly, we present basic results of soft topology and other algebraic structures such as soft algebras and sigma-algebras. This article examines a selection of notable applications of soft set theory in different fields, including medicine and economics, underscoring its versatile nature. The survey concludes with a discussion on the challenges and future directions in soft set theory, emphasizing the need for further research to enhance its theoretical foundations and broaden its practical applications. Overall, this survey of soft set theory serves as a valuable resource for practitioners, researchers, and students interested in understanding and utilizing this flexible mathematical framework for tackling uncertainty in decision-making processes