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Volume 11 (2021): Edizione 3 (July 2021)

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Volume 10 (2020): Edizione 4 (October 2020)

Volume 10 (2020): Edizione 3 (July 2020)

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Volume 9 (2019): Edizione 4 (October 2019)

Volume 9 (2019): Edizione 3 (July 2019)

Volume 9 (2019): Edizione 2 (April 2019)

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Volume 8 (2018): Edizione 4 (October 2018)

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Volume 6 (2016): Edizione 4 (October 2016)

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Volume 6 (2016): Edizione 2 (April 2016)

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Volume 5 (2015): Edizione 4 (October 2015)

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Volume 4 (2014): Edizione 4 (October 2014)

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Volume 3 (2013): Edizione 4 (October 2013)

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Volume 3 (2013): Edizione 2 (April 2013)

Volume 3 (2013): Edizione 1 (January 2013)

Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 3 (2013): Edizione 4 (October 2013)

Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

5 Articoli
Accesso libero

A Survey of old and New Results for the Test Error Estimation of a Classifier

Pubblicato online: 30 Dec 2014
Pagine: 229 - 242

Astratto

Abstract

The estimation of the generalization error of a trained classifier by means of a test set is one of the oldest problems in pattern recognition and machine learning. Despite this problem has been addressed for several decades, it seems that the last word has not been written yet, because new proposals continue to appear in the literature. Our objective is to survey and compare old and new techniques, in terms of quality of the estimation, easiness of use, and rigorousness of the approach, so to understand if the new proposals represent an effective improvement on old ones.

Accesso libero

Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

Pubblicato online: 30 Dec 2014
Pagine: 243 - 250

Astratto

Abstract

Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

Accesso libero

B-Tree Algorithm Complexity Analysis to Evaluate the Feasibility of its Application in the University Course Timetabling Problem

Pubblicato online: 30 Dec 2014
Pagine: 251 - 263

Astratto

Abstract

This paper presents a comparative analysis of complexity between the B-TREE and the Binary Search Algorithms, both theoretically and experimentally, to evaluate their efficiency in finding overlap of classes for students and teachers in the University Course Timetabling Problem (UCTP). According to the theory, B-TREE Search complexity is lower than Binary Search. The performed experimental tests showed the B-TREE Search Algorithm is more efficient than Binary Search, but only using a dataset larger than 75 students per classroom.

Accesso libero

A Novel Approach for Automatic Detection and Classification of Suspicious Lesions in Breast Ultrasound Images

Pubblicato online: 30 Dec 2014
Pagine: 265 - 276

Astratto

Abstract

In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.

Accesso libero

Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD

Pubblicato online: 30 Dec 2014
Pagine: 277 - 289

Astratto

Abstract

This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

5 Articoli
Accesso libero

A Survey of old and New Results for the Test Error Estimation of a Classifier

Pubblicato online: 30 Dec 2014
Pagine: 229 - 242

Astratto

Abstract

The estimation of the generalization error of a trained classifier by means of a test set is one of the oldest problems in pattern recognition and machine learning. Despite this problem has been addressed for several decades, it seems that the last word has not been written yet, because new proposals continue to appear in the literature. Our objective is to survey and compare old and new techniques, in terms of quality of the estimation, easiness of use, and rigorousness of the approach, so to understand if the new proposals represent an effective improvement on old ones.

Accesso libero

Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

Pubblicato online: 30 Dec 2014
Pagine: 243 - 250

Astratto

Abstract

Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

Accesso libero

B-Tree Algorithm Complexity Analysis to Evaluate the Feasibility of its Application in the University Course Timetabling Problem

Pubblicato online: 30 Dec 2014
Pagine: 251 - 263

Astratto

Abstract

This paper presents a comparative analysis of complexity between the B-TREE and the Binary Search Algorithms, both theoretically and experimentally, to evaluate their efficiency in finding overlap of classes for students and teachers in the University Course Timetabling Problem (UCTP). According to the theory, B-TREE Search complexity is lower than Binary Search. The performed experimental tests showed the B-TREE Search Algorithm is more efficient than Binary Search, but only using a dataset larger than 75 students per classroom.

Accesso libero

A Novel Approach for Automatic Detection and Classification of Suspicious Lesions in Breast Ultrasound Images

Pubblicato online: 30 Dec 2014
Pagine: 265 - 276

Astratto

Abstract

In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.

Accesso libero

Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD

Pubblicato online: 30 Dec 2014
Pagine: 277 - 289

Astratto

Abstract

This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

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