{"title":"Error signal distribution as an indicator of imbalanced data","authors":"D. Furundžić, S. Stankovic, Goran Dimić","doi":"10.1109/NEUREL.2014.7011503","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011503","url":null,"abstract":"This paper defines criteria for assessing the imbalance of datasets for training predictive learning models. The most important criterion for evaluating the imbalance is the distribution of the error signal over the space of local measure of distances between the points of the training set. In this paper is presented the analysis of this indicator for the sets of various distributions, and it has been shown that the most information potential for the case of the identical mapping of data sets from the real domain is incorporated within the data whose internal distribution is uniform.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130756735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Dordevic, Z. Marinković, V. Markovic, O. Pronić-Rančić
{"title":"Extraction of Pospieszalski's noise model parameters of microwave FETs based on ANNs","authors":"V. Dordevic, Z. Marinković, V. Markovic, O. Pronić-Rančić","doi":"10.1109/NEUREL.2014.7011457","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011457","url":null,"abstract":"A new neural approach for extraction of the Pospieszalski's noise model parameters of microwave FETs is presented in this paper. This approach is based on the use of two artificial neural networks. The first network is aimed at calculating the intrinsic noise parameters from the given equivalent circuit parameters, transistor total noise parameters, frequency and ambient temperature. Since the gate noise temperature in the Pospieszalski's noise model is approximately equal to the ambient temperature, only the value of drain noise temperature is to be determined. Therefore, the second network is trained to determine drain noise temperature from the given extracted intrinsic noise parameters, equivalent intrinsic circuit parameters, frequency and ambient temperature. The proposed extracting approach enables avoiding time-consuming optimization procedures in microwave simulators, which are conventionally used for the determination of the noise model parameters. A detailed validation of the proposed approach was done by comparison of the measured transistor noise parameters with those obtained by using the extracted drain noise temperature.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122517008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Electricity load forecasting based on a mixed statistical-neural-computational intelligence approach","authors":"M. Gavrilas, O. Ivanov, G. Gavrilas","doi":"10.1109/NEUREL.2014.7011461","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011461","url":null,"abstract":"This paper presents a medium term load forecasting methodology based on a mixed statistical computational intelligence model. The methodology can be used by any entity (such as transmission and distribution operators, electricity suppliers or energy managers) interested in planning different activities with electricity. The methodology produces daily load profiles forecasts for all the 365 days of the next year. The statistical model predicts annual energy consumption and monthly and daily consumptions based on traditional regression models, while typical load profiles for each day of the week and every week of the year are produced using computational intelligence techniques based on self-organizing models with Kohonen neural networks or a heuristic optimization technique, namely the Gravitational Search Algorithm.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Topic classification in Romanian blogosphere","authors":"A. Vasile, Roxana Rădulescu, I. Pavaloiu","doi":"10.1109/NEUREL.2014.7011480","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011480","url":null,"abstract":"In this paper we analyze the performance of several methods for classification applied to the Romanian blogosphere. Blogs are difficult to categorize by humans and machines alike, because they are written in a changeable style. In the early days of web, directories maintained by humans could not keep up millions the websites; likewise, blog directories cannot keep up with the explosive growth of the blogsphere. This paper investigates the efficacy of using machine learning to categorize blogs written in Romanian language belonging to the Romanian blogosphere. We design a text classification experiment to categorize Romanian blogs into nine topics. The baseline feature is unigrams weighed by TF-IDF. We analyze the corpus, features, and the result data.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129116102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating profitability using a neural classification tool","authors":"D. Năstac, I. Dragan, A. Isaic-Maniu","doi":"10.1109/NEUREL.2014.7011474","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011474","url":null,"abstract":"The analysis of the private sector, mainly composed of micro, small and medium-sized business, briefly known as the SMEs sector, reveals many competitive aspects but also weaknesses, while the economic and social importance of the sector makes it imperative to elaborate specific development and consolidation strategies and policies. In order to establish such strategic directions, based on data on a significant volumes of Romanian SMEs (7902 entries) and on information provided by annual balance sheets, we established connections between results indicators (profit, measured as the rate of commercial profitability) and various causal variables by using a neural classification tool, which was built in accordance to testing and validation requirements in order to obtain consistent results.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129843919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}