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Full-Text Articles in Computer Sciences
Development Of An Electronic Nose For Olfactory System Modelling Using Artificial Neural Network, Proceso L. Fernandez Jr, Mary Anne Sy Roa
Development Of An Electronic Nose For Olfactory System Modelling Using Artificial Neural Network, Proceso L. Fernandez Jr, Mary Anne Sy Roa
Department of Information Systems & Computer Science Faculty Publications
Electronic nose (e-nose) devices have received considerable attention in the field of sensor technology because of their many potential uses such as in identification of toxic wastes, monitoring air quality, examining odors in infected wounds and in inspection of food. Notwithstanding the vast amount of literature on the usage of e-noses for specific purposes, the technology originally and ultimately aims to mimic the capability of mammals to discriminate odors from all sorts of objects. This study demonstrates the theoretical and practical feasibility of designing an e-nose towards general odor classification. A multi-sensor array hardware unit was carefully constructed for data …
Artificial Neural Network (Ann) In A Small Dataset To Determine Neutrality In The Pronunciation Of English As A Foreign Language In Filipino Call Center Agents, Proceso L. Fernandez Jr, Rey Benjamin M. Baquirin
Artificial Neural Network (Ann) In A Small Dataset To Determine Neutrality In The Pronunciation Of English As A Foreign Language In Filipino Call Center Agents, Proceso L. Fernandez Jr, Rey Benjamin M. Baquirin
Department of Information Systems & Computer Science Faculty Publications
Artificial Neural Networks (ANNs) have continued to be efficient models in solving classification problems. In this paper, we explore the use of an ANN with a small dataset to accurately classify whether Filipino call center agents’ pronunciations are neutral or not based on their employer’s standards. Isolated utterances of the ten most commonly used words in the call center were recorded from eleven agents creating a dataset of 110 utterances. Two learning specialists were consulted to establish ground truths and Cohen’s Kappa was computed as 0.82, validating the reliability of the dataset. The first thirteen Mel-Frequency Cepstral Coefficients (MFCCs) were …