Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Various Approaches of Support vector Machines and combined Classifiers in Face Recognition

In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition

Classification of microarray gene expression cancer data by using artificial intelligence methods

Günümüzde bilgisayar teknolojilerinin gelişmesi ile birçok alanda yapılan çalışmaları etkilemiştir. Moleküler biyoloji ve bilgisayar teknolojilerinde meydana gelen gelişmeler biyoinformatik adlı bilimi ortaya çıkarmıştır. Biyoinformatik alanında meydana gelen hızlı gelişmeler, bu alanda çözülmeyi bekleyen birçok probleme çözüm olma yolunda büyük katkılar sağlamıştır. DNA mikroarray gen ekspresyonlarının sınıflandırılması da bu problemlerden birisidir. DNA mikroarray çalışmaları, biyoinformatik alanında kullanılan bir teknolojidir. DNA mikroarray veri analizi, kanser gibi genlerle alakalı hastalıkların teşhisinde çok etkin bir rol oynamaktadır. Hastalık türüne bağlı gen ifadeleri belirlenerek, herhangi bir bireyin hastalıklı gene sahip olup olmadığı büyük bir başarı oranı ile tespit edilebilir. Bireyin sağlıklı olup olmadığının tespiti için, mikroarray gen ekspresyonları üzerinde yüksek performanslı sınıflandırma tekniklerinin kullanılması büyük öneme sahiptir. DNA mikroarray’lerini sınıflandırmak için birçok yöntem bulunmaktadır. Destek Vektör Makinaları, Naive Bayes, k-En yakın Komşu, Karar Ağaçları gibi birçok istatistiksel yöntemler yaygın olarak kullanlmaktadır. Fakat bu yöntemler tek başına kullanıldığında, mikroarray verilerini sınıflandırmada her zaman yüksek başarı oranları vermemektedir. Bu yüzden mikroarray verilerini sınıflandırmada yüksek başarı oranları elde etmek için yapay zekâ tabanlı yöntemlerin de kullanılması yapılan çalışmalarda görülmektedir. Bu çalışmada, bu istatistiksel yöntemlere ek olarak yapay zekâ tabanlı ANFIS gibi bir yöntemi kullanarak daha yüksek başarı oranları elde etmek amaçlanmıştır. İstatistiksel sınıflandırma yöntemleri olarak K-En Yakın Komşuluk, Naive Bayes ve Destek Vektör Makineleri kullanılmıştır. Burada Göğüs ve Merkezi Sinir Sistemi kanseri olmak üzere iki farklı kanser veri seti üzerinde çalışmalar yapılmıştır. Sonuçlardan elde edilen bilgilere göre, genel olarak yapay zekâ tabanlı ANFIS tekniğinin, istatistiksel yöntemlere göre daha başarılı olduğu tespit edilmiştir

Identifying Real Estate Opportunities using Machine Learning

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.Comment: 24 pages, 13 figures, 5 table