Weather forecasting is the application of science and technology to predict the state of the atmo... more Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) models were used to analyze metrological data sets obtain from the metrological station. The data covers a ten year period (2002-2012) were for the monthly means of minimum and maximum temperature, rainfall, wind run, and relative humidity. The results showed that both models could be applied to weather prediction problems. The performance evaluation of the two models that was carried out showed that the ANFIS model yielded better results than the MLP ANN model with a lower prediction error.
The ever-growing volume of published academic journals and the implicit knowledge that can be der... more The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50 th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
Identifying customers who are more likely to respond to new product offers is an important issue ... more Identifying customers who are more likely to respond to new product offers is an important issue in direct marketing. In direct marketing, data mining has been used extensively to identify potential customers for a new product (target selection). Using historical purchase data, a predictive response model with data mining techniques was developed to predict a probability that a customer in Ebedi Microfinance bank will respond to a promotion or an offer. To achieve this purpose, a predictive response model using customers' historical purchase data was built with data mining techniques. The data were stored in a data warehouse to serve as management decision support system. The response model was built from customers' historic purchases and demographic dataset. Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter and wrapper feature selection techniques were employed in determining inputs to the model. The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services by obtaining a guiding report on the status of their customers which will go a long way in assisting management in saving significant amount of money that could have been spent on wasteful promotional campaigns.
The ever-growing volume of published academic journals and the implicit knowledge that can be der... more The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50 th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
In this paper we proposed a framework for integrating text mining with E-Governance. We suggested... more In this paper we proposed a framework for integrating text mining with E-Governance. We suggested that the users of electronic governance can use the text terms to describe their interest which can be processed for clustering and term extraction. The words thus expressed by users are tracked and subjected to processing wherein it is possible to generate content. We have provided the framework and tested it in a few web sites. We have used the clustering and pre-processing for the content management. The results are encouraging and it is possible to extent such exercises for other text mining processes.
The ever-growing volume of published academic journals and the implicit knowledge that can be der... more The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50 th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
ABSTRACT
Weather forecasting is the application of science and technology to predict the state of... more ABSTRACT Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) models were used to analyze metrological data sets obtain from the metrological station. The data covers a ten year period (2002-2012) were for the monthly means of minimum and maximum temperature, rainfall, wind run, and relative humidity. The results showed that both models could be applied to weather prediction problems. The performance evaluation of the two models that was carried out showed that the ANFIS model yielded better results than the MLP ANN model with a lower prediction error. Keywords: Weather Forecasting, Artificial Neural Networks, Neuro-Fuzzy Inference Systems
Weather forecasting is the application of science and technology to predict the state of the atmo... more Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) models were used to analyze metrological data sets obtain from the metrological station. The data covers a ten year period (2002-2012) were for the monthly means of minimum and maximum temperature, rainfall, wind run, and relative humidity. The results showed that both models could be applied to weather prediction problems. The performance evaluation of the two models that was carried out showed that the ANFIS model yielded better results than the MLP ANN model with a lower prediction error.
The ever-growing volume of published academic journals and the implicit knowledge that can be der... more The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50 th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
Identifying customers who are more likely to respond to new product offers is an important issue ... more Identifying customers who are more likely to respond to new product offers is an important issue in direct marketing. In direct marketing, data mining has been used extensively to identify potential customers for a new product (target selection). Using historical purchase data, a predictive response model with data mining techniques was developed to predict a probability that a customer in Ebedi Microfinance bank will respond to a promotion or an offer. To achieve this purpose, a predictive response model using customers' historical purchase data was built with data mining techniques. The data were stored in a data warehouse to serve as management decision support system. The response model was built from customers' historic purchases and demographic dataset. Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter and wrapper feature selection techniques were employed in determining inputs to the model. The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services by obtaining a guiding report on the status of their customers which will go a long way in assisting management in saving significant amount of money that could have been spent on wasteful promotional campaigns.
The ever-growing volume of published academic journals and the implicit knowledge that can be der... more The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50 th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
In this paper we proposed a framework for integrating text mining with E-Governance. We suggested... more In this paper we proposed a framework for integrating text mining with E-Governance. We suggested that the users of electronic governance can use the text terms to describe their interest which can be processed for clustering and term extraction. The words thus expressed by users are tracked and subjected to processing wherein it is possible to generate content. We have provided the framework and tested it in a few web sites. We have used the clustering and pre-processing for the content management. The results are encouraging and it is possible to extent such exercises for other text mining processes.
The ever-growing volume of published academic journals and the implicit knowledge that can be der... more The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50 th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
ABSTRACT
Weather forecasting is the application of science and technology to predict the state of... more ABSTRACT Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) models were used to analyze metrological data sets obtain from the metrological station. The data covers a ten year period (2002-2012) were for the monthly means of minimum and maximum temperature, rainfall, wind run, and relative humidity. The results showed that both models could be applied to weather prediction problems. The performance evaluation of the two models that was carried out showed that the ANFIS model yielded better results than the MLP ANN model with a lower prediction error. Keywords: Weather Forecasting, Artificial Neural Networks, Neuro-Fuzzy Inference Systems
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Papers by sesan adeyemo
Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at
a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or
present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP)
Artificial Neural Network (ANN) models were used to analyze metrological data sets obtain from the metrological
station. The data covers a ten year period (2002-2012) were for the monthly means of minimum and maximum
temperature, rainfall, wind run, and relative humidity. The results showed that both models could be applied to weather
prediction problems. The performance evaluation of the two models that was carried out showed that the ANFIS model
yielded better results than the MLP ANN model with a lower prediction error.
Keywords: Weather Forecasting, Artificial Neural Networks, Neuro-Fuzzy Inference Systems
Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at
a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or
present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP)
Artificial Neural Network (ANN) models were used to analyze metrological data sets obtain from the metrological
station. The data covers a ten year period (2002-2012) were for the monthly means of minimum and maximum
temperature, rainfall, wind run, and relative humidity. The results showed that both models could be applied to weather
prediction problems. The performance evaluation of the two models that was carried out showed that the ANFIS model
yielded better results than the MLP ANN model with a lower prediction error.
Keywords: Weather Forecasting, Artificial Neural Networks, Neuro-Fuzzy Inference Systems