This paper provides a comparison of processing large traffic data by using decision trees. The ex... more This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.
Computer-aided detection applications have been extensively used to assist physicians in clinical... more Computer-aided detection applications have been extensively used to assist physicians in clinical diagnoses. Extracted information from X-ray, positron emission tomography, and magnetic resonance images enables radiologists and other physicians to identify pathologies, correlate findings with the symptoms, and determine the treatment steps. In this study, we proposed an automatic knowledge extraction methodology from chest X-ray images. The extracted knowledge is obtained from the segmented sections of the images that include pathological findings. We evaluated these segmented images with a) classical machine learning and b) pretrained convolutional neural network (CNN) models. Evaluations were based on areas under the receiver operating characteristic (AUROC) with segmented images using the pretrained CNN and the traditional method models, and they produced the average AUROC scores of 0.96 and 0.52, respectively. Traditional methods yielded lower AUROC scores compared with pretrained CNN methods. However, traditional methods may still be considered as appropriate solutions for disease diagnoses primarily based on their advantages regarding running time and flexibility.
An alternative approach for measuring object-oriented (OO) software quality has been proposed by ... more An alternative approach for measuring object-oriented (OO) software quality has been proposed by applying the Analytic Hierarchy Process (AHP) scheme. This approach is effective in defining the rank of software quality over a number of Java applications based on two sets of metric values representing main OO structural properties. The Metrics for Object-Oriented Software Engineering (MOOSE) and Metrics for Object-Oriented Design (MOOD) that have been describing individual characteristic of OO property now are integrated by means of AHPs pairwise comparison to deliver an estimate quantitative value of OO software design quality. A series of experiments were conducted by applying the approach to students works in Fundamental of Programming class. The results of applying AHP scheme were compared to experts judgments for verification. The result demonstrates the solidity of AHP mechanism for the intended purpose of measuring OO design quality.
This paper proposed a method to build knowledge from one and a half years of UK traffic data sets... more This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream data set, the approach is different from the traditional data sets. The approach of a stream data set is to take several snapshots during the induction to analyze the accuracy progress of the predicted model. We used the tanh activation function to optimize the perceptron rule. Therefore, we are able to reduce the error of the result. The error measurements that we analyzed in this paper are MAE, RMSE, and SMAPE. From 100,000,000 instances of traffic data, the optimized FIMT-DD algorithm method proves to be successful with smaller error value than standard FIMT-DD algorithm. To be able to measure the performance of the error of our optimized FIMT-DD algorithm, we used evaluate prediction sequential to analyze the data. According to the error measurement results of the MAE, RMSE, and SMAPE, tanh(x) activation function has a good influence to decrease the error value and improve the accuracy. The smaller error values did not only happen in a few experiments instances, but it occurs in the majority of the instances evaluation.
This paper provides a comparison of processing large traffic data by using decision trees. The ex... more This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.
This paper proposed a method to build knowledge from one and a half years of UK traffic data sets... more This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream data set, the approach is different from the traditional data sets. The approach of a stream data set is to take several snapshots during the induction to analyze the accuracy progress of the predicted model. We used the tanh activation function to optimize the perceptron rule. Therefore, we are able to reduce the error of the result. The error measurements that we analyzed in this paper are MAE, RMSE, and SMAPE. From 100,000,000 instances of traffic data, the optimized FIMT-DD algorithm method proves to be successful with smaller error value than standard FIMT-DD algorithm. To be able to measure the performance of the error of our optimized FIMT-DD algorithm, we used evaluate prediction sequential to analyze the data. According to the error measurement results of the MAE, RMSE, and SMAPE, tanh(x) activation function has a good influence to decrease the error value and improve the accuracy. The smaller error values did not only happen in a few experiments instances, but it occurs in the majority of the instances evaluation.
Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadv... more Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadvantages. This paper presents an adaptive traffic signal control system using camera as an input sensor that providing real-time traffic data. Principal Component Analysis (PCA) is used to analyze and to classify object on video frame for detecting vehicles. Distributed Constraint Satisfaction Problem (DCSP) method determine
Information extraction using distributed sensors has been widely used to obtain information knowl... more Information extraction using distributed sensors has been widely used to obtain information knowledge from various regions or areas. Vehicle traffic data extraction is one of the ways to gather inf...
This paper provides a comparison of processing large traffic data by using decision trees. The ex... more This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.
Computer-aided detection applications have been extensively used to assist physicians in clinical... more Computer-aided detection applications have been extensively used to assist physicians in clinical diagnoses. Extracted information from X-ray, positron emission tomography, and magnetic resonance images enables radiologists and other physicians to identify pathologies, correlate findings with the symptoms, and determine the treatment steps. In this study, we proposed an automatic knowledge extraction methodology from chest X-ray images. The extracted knowledge is obtained from the segmented sections of the images that include pathological findings. We evaluated these segmented images with a) classical machine learning and b) pretrained convolutional neural network (CNN) models. Evaluations were based on areas under the receiver operating characteristic (AUROC) with segmented images using the pretrained CNN and the traditional method models, and they produced the average AUROC scores of 0.96 and 0.52, respectively. Traditional methods yielded lower AUROC scores compared with pretrained CNN methods. However, traditional methods may still be considered as appropriate solutions for disease diagnoses primarily based on their advantages regarding running time and flexibility.
An alternative approach for measuring object-oriented (OO) software quality has been proposed by ... more An alternative approach for measuring object-oriented (OO) software quality has been proposed by applying the Analytic Hierarchy Process (AHP) scheme. This approach is effective in defining the rank of software quality over a number of Java applications based on two sets of metric values representing main OO structural properties. The Metrics for Object-Oriented Software Engineering (MOOSE) and Metrics for Object-Oriented Design (MOOD) that have been describing individual characteristic of OO property now are integrated by means of AHPs pairwise comparison to deliver an estimate quantitative value of OO software design quality. A series of experiments were conducted by applying the approach to students works in Fundamental of Programming class. The results of applying AHP scheme were compared to experts judgments for verification. The result demonstrates the solidity of AHP mechanism for the intended purpose of measuring OO design quality.
This paper proposed a method to build knowledge from one and a half years of UK traffic data sets... more This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream data set, the approach is different from the traditional data sets. The approach of a stream data set is to take several snapshots during the induction to analyze the accuracy progress of the predicted model. We used the tanh activation function to optimize the perceptron rule. Therefore, we are able to reduce the error of the result. The error measurements that we analyzed in this paper are MAE, RMSE, and SMAPE. From 100,000,000 instances of traffic data, the optimized FIMT-DD algorithm method proves to be successful with smaller error value than standard FIMT-DD algorithm. To be able to measure the performance of the error of our optimized FIMT-DD algorithm, we used evaluate prediction sequential to analyze the data. According to the error measurement results of the MAE, RMSE, and SMAPE, tanh(x) activation function has a good influence to decrease the error value and improve the accuracy. The smaller error values did not only happen in a few experiments instances, but it occurs in the majority of the instances evaluation.
This paper provides a comparison of processing large traffic data by using decision trees. The ex... more This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.
This paper proposed a method to build knowledge from one and a half years of UK traffic data sets... more This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream data set, the approach is different from the traditional data sets. The approach of a stream data set is to take several snapshots during the induction to analyze the accuracy progress of the predicted model. We used the tanh activation function to optimize the perceptron rule. Therefore, we are able to reduce the error of the result. The error measurements that we analyzed in this paper are MAE, RMSE, and SMAPE. From 100,000,000 instances of traffic data, the optimized FIMT-DD algorithm method proves to be successful with smaller error value than standard FIMT-DD algorithm. To be able to measure the performance of the error of our optimized FIMT-DD algorithm, we used evaluate prediction sequential to analyze the data. According to the error measurement results of the MAE, RMSE, and SMAPE, tanh(x) activation function has a good influence to decrease the error value and improve the accuracy. The smaller error values did not only happen in a few experiments instances, but it occurs in the majority of the instances evaluation.
Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadv... more Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadvantages. This paper presents an adaptive traffic signal control system using camera as an input sensor that providing real-time traffic data. Principal Component Analysis (PCA) is used to analyze and to classify object on video frame for detecting vehicles. Distributed Constraint Satisfaction Problem (DCSP) method determine
Information extraction using distributed sensors has been widely used to obtain information knowl... more Information extraction using distributed sensors has been widely used to obtain information knowledge from various regions or areas. Vehicle traffic data extraction is one of the ways to gather inf...
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