A large part of technological advances, especially in the field of industry, have been focused on the optimization of productive processes. However, the detection of anomalies has turned out to be a great challenge in fields like industry, medicine or stock markets. The present work addresses anomaly detection on a control level plant. We propose the application of different intelligent techniques, which allow to obtain one-class classifiers using real data taken from the correct plant operation. The performance of each classifier (...) is assessed and validated with real created faults, achieving successful overall results. (shrink)

A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis (...) and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification. (shrink)

The travelling salesman problem (TSP) belongs to the class of NP-hard problems, in which an optimal solution to the problem cannot be obtained within a reasonable computational time for large-sized problems. To address TSP, we propose a hybrid algorithm, called GA-TCTIA-LBSA, in which a genetic algorithm (GA), tour construction and tour improvement algorithms (TCTIAs) and a list-based simulated annealing (LBSA) algorithm are used. The TCTIAs are introduced to generate a first population, and after that, a search is continued with the (...) GA. The problem of premature convergence of the GA to local optimum is tackled by a method called social disaster technique. Afterwards, the LBSA is applied to generate a new population based on one of two proposed operators called packing and judgement day. The proposed algorithm is implemented in the MATLAB environment, and its two variants, called GA-TCTIA-LBSA packing and GA-TCTIA-LBSA judgement day, are tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed GA-TCTIA-LBSAs offer promising results, particularly for small-sized instances. (shrink)

Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one’s life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused on (...) a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia. (shrink)

In the past 50 years nurse scheduling has received considerable attention in the research community. There are two cases regarding the nurse scheduling problem : the static and dynamic. Dynamic NSP, is often called nurse rerostering problem, which presents reconstruction or modification of the predetermined roster for the current scheduling horizon. The aim of this paper is to present new hybrid strategy for nurse NRRP problem. The proposed methodology is based on efficient cooperation between fuzzy logic, ordered weighted averaging and (...) variable neighbourhood descent search. Complete system is tested, and experimental results are based on real-world dataset obtained from the Oncology Institute of Vojvodina in Serbia. (shrink)

Headache disorders can be considered as the predominant neurological condition. In the field of neurological diseases, migraine was estimated to cost a total of €27 billion per year for the loss through reduced work productivity in the European Community. Medical data and information in turn provide knowledge based on which physicians make scientific decisions for diagnosis and treatments. It is, therefore, very useful to create diagnostic tools to help physicians make better decisions. This paper is focused on a new hybrid (...) clustering system combining analytic hierarchy process and weighted fuzzy c-means clustering method for diagnosing children with primary headache disorder. The proposed three-stage hybrid diagnosing system is tested on data set collected from hospitalized children in the Clinical Centre of Vojvodina, Novi Sad, Serbia. (shrink)

The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused (...) on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches. (shrink)