Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems ELM is based on empirical risk minimization theory and its learning process needs only a single iteration The algorithm avoids multiple iterations and local minimization It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future

Extreme learning machine: algorithm, theory and applications

Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.

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A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.

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Speech Emotion Recognition Using Deep Learning Techniques: A Review

The selection right distance measure is important for most machine learning algorithms. Euclidean distance is a commonly used distance measure in many methods due to simplicity of implementation. However the properties of problem domain are important thing, this selection must be done carefully. For example, most problems use data vectors with real-valued and nominal feature values. Euclidian distance produces reasonable results for real data, whereas it can not be said for nominal data. Hence in this study the new distance measure has been proposed for calculating distance between data vectors with nominal feature value. As the testing K-Means Clustering algorithm and the Mammographic Mass Data form UCI Repository have been used.

A novel distance measure for data vectors with nominal feature values

ISTATISTIKSEL YAKLASIMLA TRAFIK KAZALARINDAKI OLUM VE YARALANMA DURUMLARININ KUSURLU UNSURLARLA ILISKILERININ INCELENMESI Ozet Zaman ilerledikce insanlarin ulasim alanindaki gereksinimleri artmaktadir. Bu gereksinimler yeni teknolojilerle birlikte farkli ulastirma alanlarinda yeni yatirimlari da dogurmustur. Tum bu farkli alanlarin yaninda karayolu tasimaciligi hâlâ tum dunyada popularitesini korumakta ve bununla birlikte karayollarinda seyreden arac sayilarinda da hizli bir artis gorulmektedir. Bu artisla birlikte trafik yogunlugu artmis ve trafikte guvenlik kavrami daha onemli hale gelmistir. Trafik guvenligini tehlikeye atan ya da baska bir deyisle trafik kazalarina sebep olan unsurlar farkli kaynaklarda farkli sekillerde siniflandirilmistir. Bu calismada, Turkiye Istatistik Kurumu (TUIK) tarafindan yapilan trafik kazalari ile ilgili arastirmalarda esas alinan bes kusurlu unsurun kaza sonucu meydana gelen olum ve yaralanma sayilari uzerinde etkileri incelenmistir.  Bu etkiler TUIK verilerine dayanilarak arastirilmis ve sonuclar farkli acilardan degerlendirilmistir. Kaza sonucu meydana gelen hem olu hem de yarali sayisi uzerinde bes unsurun bir arada etkisinin en fazla oldugu gozlemlenmistir. Bunun yaninda yaya kusurunun etkisi olum sayilari uzerinde ihmal edilebilecek duzeyde dusuktur. Arac kusurunun hem olum hem de yarali sayilari uzerinde dogrudan en etkili unsur olmasinin yaninda, ikinci etkili unsur degisim gostermektedir. Olum sayilarinda ikinci kusurlu neden yol kusuru, yarali sayilarinda ise surucu kusuru olmustur. Calismada olu sayisina etki eden unsurlarin incelendigi regresyon modelleri arasinda en yuksek belirleme katsayisi R 2 =0,640, yarali sayisina etki eden unsurlarin incelendigi regresyon modelleri arasinda da en yuksek belirleme katsayisi R 2 =0,736 degerleri elde edilmistir. Anahtar Kelimeler: Istatistik, Dogrusal Regresyon, Trafik Kazalari, Kusurlu Unsurlar INVESTIGATION OF RELATIONSHIP BETWEEN DEATH AND INJURY STATUS AND DEFECTIVE ELEMENTS IN TRAFFIC ACCIDENTS BY STATISTICAL APPROACH Abstract As time progresses, the needs of people in the field of transportation are increasing. These requirements, along with new technologies, have also led to new investments in different transport areas. Next to all these different fields the road transport still maintains its popularity on all over the world and it is seen a rapid increase in the number of vehicles on the roads. With this increase, traffic density has increased and the concept of security in traffic has become more important. Elements that put traffic safety at risk or in other words cause traffic accidents are classified in different forms in different sources. In this study, the effects of five imperfect factors which is uses in the researches relating to traffic accidents conducted by Turkey Statistical Institute (TSI) on the number of deaths and injuries from the traffic accident were examined. These effects were investigated based on TSI data and the results were evaluated from different aspects. It was observed that the combination of the five factors on the number of both dead and injured from the traffic accident was the largest. Besides, the effect of pedestrian flaw on the number of deaths is low level enough to be neglected. Vehicle flaws are the most direct effect on both death and injured numbers; however, the second factor is changing. The second cause in death numbers is the road flaw, while in the injuries number is the driver fault. R 2 =0.640 and R 2 =0.736 have been obtained for best models among the regression models in which the elements affecting the number of deaths and injured were examined, respectively. Keywords: Statistics, Linear Regression, Traffic Accidents, Imperfect Factors

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Humar Kahramanli

Papers

A novel distance measure for data vectors with nominal feature values

İstatistiksel yaklaşımla trafik kazalarındaki ölüm ve yaralanma durumlarının kusurlu unsurlarla ilişkilerinin incelenmesi