Wyniki wyszukiwania - BazTech

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Development of cost estimation models based on ANN ensembles and the SVM method

Juszczyk Michał

Civil and Environmental Engineering Reports

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2020

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Vol. 30, no. 3

48--67

EN

Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.

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Diagnosis of malignant melanoma by neural network ensemble-based system utilising hand-crafted skin lesion features

Grochowski Michał, Mikołajczyk Agnieszka, Kwasigroch Arkadiusz

Metrology and Measurement Systems

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2019

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Vol. 26, nr 1

65--80

EN

Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with asingle neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.

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Prognozowanie zapotrzebowania mocy w KSE z horyzontem dobowym przy zastosowaniu zespołu sieci neuronowych

Ciechulski T., Osowski S.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 9

108--112

PL

Artykuł przedstawia neuronową metodę prognozowania 24-godzinnego zapotrzebowania na moc elektryczną w Krajowym Systemie Elektroenergetycznym w Polsce z wyprzedzeniem dobowym. W predykcji zastosowano zespół sieci neuronowych złożony z RBF, MLP i SVM. Wskazania każdego członka zespołu podlegają integracji tworząc końcową prognozę zapotrzebowani na moc dla kolejnych 24 godzin następnego dnia. Metoda została przetestowana na danych rzeczywistych Krajowego Systemu Elektroenergetycznego wykazując znaczną przewagę nad pojedynczymi rozwiązaniami neuronowymi.

EN

The paper shows the neural method applied for 24-hour load forecasting for the next day in National Power System in Poland. Three neural networks: RBF, MLP and SVM arranged in an ensemble were used for predicting hourly load demands in the power system. Their individual predictions have been integrated into final forecast. The method was tested on the real data of Polish Power System, proving its effectiveness and significant improvement in accuracy.