Negm Shawky - Academia.edu (original) (raw)

Papers by Negm Shawky

Journal of the ACS Advances in Computer Science (Print), May 31, 2023

International journal of engineering research and technology, 2016

The data mining is applied to discover the knowledge from information system. Classification is o... more The data mining is applied to discover the knowledge from information system. Classification is one of the tools which are used for data mining. Ensemble procedures verified to be superior to the single classification method for vast datasets. Hence, this paper presents an experimental study to investigate the quality of fusion methods for combining classifiers in an ensemble. Also, comparing between single classifiers and Ensemble Classifiers using majority voting with respect to accuracy in discovering breast cancer over four breast cancer datasets. We present a combination between classifiers to get the best subset of classifiers for each data set separately. By applying confusion matrix accuracy and 10-fold cross validation method. Also, we present a comparison among the three open source data mining tools named KNIME, ORANGE and TANAGRA. Analysis the performance of different classification algorithms shows that using Ensemble Classifiers Techniques improved the accuracy in thre...

Handbook of Research on Machine Learning Innovations and Trends

Data Mining is a field that interconnects areas from computer science, trying to discover knowled... more Data Mining is a field that interconnects areas from computer science, trying to discover knowledge from databases in order to simplify the decision making. Classification is a Data Mining chore that learns from a set of instances in order to precisely classify the target class for new instances. Open source Data Mining tools can be used to make classification. This paper compares four tools: KNIME, Orange, Tanagra and Weka. Our goal is to discover the most precise tool and technique for breast cancer classifications. The experimental results show that some tools achieve better results more than others. Also, using fusion classification task verified to be better than the single classification task over the four datasets have been used. Also, we present a comparison between using complete datasets by substituting missing feature values and incomplete ones. The experimental results show that some datasets have better accuracy when using complete datasets.

International Journal of Security and Privacy in Pervasive Computing, 2020

GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via... more GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via a ground control station can be processed for detecting and tracking estimate target position. Tracking drones based on GPS has had some issues with missed received information or received information with an error that can lead to lost tracking. The proposed algorithm, Markov chain Monte Carlo based particle filter (MCMC-PF) can be used to overcome these issues of error in received information with keeping tracks and provides continuous tracking with a higher accuracy. This is suitable for real time applications that deal with GPS receiver devices with low efficiency during tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithms of the Kalman filter (KF).

International Journal of Advanced Computer Science and Applications, 2011

Improving data association technique in dense clutter environment for multi-target tracking used ... more Improving data association technique in dense clutter environment for multi-target tracking used in Markov chain Monte Carlo based particle filter (MCMC-PF) are discussed in this paper. A new method named Viterbi filtered gate Markov chain Monte Carlo VFG-MCMC is introduced to avoid track swap and to overcome the issue of losing track to highly maneuvering targets in the presence of more background clutter and false signals. An adaptive search based on Viterbi algorithm is then used to detect the valid filtered data point in each target gate. The detected valid point for each target is applied to the estimation algorithm of MCMC-PF during calculating the sampling weights. This proposed method makes the MCMC interacts only with the valid target that is candidate from the filtered gate and no more calculations are considered for invalid targets. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm MCMC-PF.

In multi-target tracking system, data association and tracking filter are two basic parts of trac... more In multi-target tracking system, data association and tracking filter are two basic parts of tracking objects. The choosing of data association technique to associate the track to the true target in noisy received measurements is an important key to overcome the issues of the tracking process. Many data association algorithms have been developed to be the most powerful techniques for these issues, but still there are disadvantages in their restricting assumptions, complexity and in the resulting performance. For these reasons, some of data association algorithms that are widely used have been studied. These algorithms have some issues during tracking in dense clutter environment, tracking a highly maneuvering targets and swapping in the presence of more background clutter and false signal. So, these algorithms have been updated to overcome the issues, improve the performance, decrease the burden of the computational cost, decrease the probability of error and to give the targets the...

ijcsi.org

In multi-target tracking system (MTT) Improving data association process in the presence of sever... more In multi-target tracking system (MTT) Improving data association process in the presence of severe clutter are discussed in this paper. New technique in dense clutter environment based on filtering gate method applied to conventional approaches as joint probabilistic data association filter (JPDAF) is introduced to overcome the issue that the data association algorithm begins to fail due to the increase in background clutter and false signals. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for multi-target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.

International Journal of Computer …

In this paper, a new method, named optimum innovation data association (OI-DA), is proposed to gi... more In this paper, a new method, named optimum innovation data association (OI-DA), is proposed to give the nearest neighbor data association the ability to track maneuvering multi-target in dense clutter environment. Using the measurements of two successive scan ...

Improving data association process by increasing the probability of detecting valid data points (... more Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in this paper. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the e...

Journal of the ACS Advances in Computer Science (Print), May 31, 2023

International journal of engineering research and technology, 2016

The data mining is applied to discover the knowledge from information system. Classification is o... more The data mining is applied to discover the knowledge from information system. Classification is one of the tools which are used for data mining. Ensemble procedures verified to be superior to the single classification method for vast datasets. Hence, this paper presents an experimental study to investigate the quality of fusion methods for combining classifiers in an ensemble. Also, comparing between single classifiers and Ensemble Classifiers using majority voting with respect to accuracy in discovering breast cancer over four breast cancer datasets. We present a combination between classifiers to get the best subset of classifiers for each data set separately. By applying confusion matrix accuracy and 10-fold cross validation method. Also, we present a comparison among the three open source data mining tools named KNIME, ORANGE and TANAGRA. Analysis the performance of different classification algorithms shows that using Ensemble Classifiers Techniques improved the accuracy in thre...

Handbook of Research on Machine Learning Innovations and Trends

Data Mining is a field that interconnects areas from computer science, trying to discover knowled... more Data Mining is a field that interconnects areas from computer science, trying to discover knowledge from databases in order to simplify the decision making. Classification is a Data Mining chore that learns from a set of instances in order to precisely classify the target class for new instances. Open source Data Mining tools can be used to make classification. This paper compares four tools: KNIME, Orange, Tanagra and Weka. Our goal is to discover the most precise tool and technique for breast cancer classifications. The experimental results show that some tools achieve better results more than others. Also, using fusion classification task verified to be better than the single classification task over the four datasets have been used. Also, we present a comparison between using complete datasets by substituting missing feature values and incomplete ones. The experimental results show that some datasets have better accuracy when using complete datasets.

International Journal of Security and Privacy in Pervasive Computing, 2020

GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via... more GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via a ground control station can be processed for detecting and tracking estimate target position. Tracking drones based on GPS has had some issues with missed received information or received information with an error that can lead to lost tracking. The proposed algorithm, Markov chain Monte Carlo based particle filter (MCMC-PF) can be used to overcome these issues of error in received information with keeping tracks and provides continuous tracking with a higher accuracy. This is suitable for real time applications that deal with GPS receiver devices with low efficiency during tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithms of the Kalman filter (KF).

International Journal of Advanced Computer Science and Applications, 2011

Improving data association technique in dense clutter environment for multi-target tracking used ... more Improving data association technique in dense clutter environment for multi-target tracking used in Markov chain Monte Carlo based particle filter (MCMC-PF) are discussed in this paper. A new method named Viterbi filtered gate Markov chain Monte Carlo VFG-MCMC is introduced to avoid track swap and to overcome the issue of losing track to highly maneuvering targets in the presence of more background clutter and false signals. An adaptive search based on Viterbi algorithm is then used to detect the valid filtered data point in each target gate. The detected valid point for each target is applied to the estimation algorithm of MCMC-PF during calculating the sampling weights. This proposed method makes the MCMC interacts only with the valid target that is candidate from the filtered gate and no more calculations are considered for invalid targets. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm MCMC-PF.

In multi-target tracking system, data association and tracking filter are two basic parts of trac... more In multi-target tracking system, data association and tracking filter are two basic parts of tracking objects. The choosing of data association technique to associate the track to the true target in noisy received measurements is an important key to overcome the issues of the tracking process. Many data association algorithms have been developed to be the most powerful techniques for these issues, but still there are disadvantages in their restricting assumptions, complexity and in the resulting performance. For these reasons, some of data association algorithms that are widely used have been studied. These algorithms have some issues during tracking in dense clutter environment, tracking a highly maneuvering targets and swapping in the presence of more background clutter and false signal. So, these algorithms have been updated to overcome the issues, improve the performance, decrease the burden of the computational cost, decrease the probability of error and to give the targets the...

ijcsi.org

In multi-target tracking system (MTT) Improving data association process in the presence of sever... more In multi-target tracking system (MTT) Improving data association process in the presence of severe clutter are discussed in this paper. New technique in dense clutter environment based on filtering gate method applied to conventional approaches as joint probabilistic data association filter (JPDAF) is introduced to overcome the issue that the data association algorithm begins to fail due to the increase in background clutter and false signals. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for multi-target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.

International Journal of Computer …

In this paper, a new method, named optimum innovation data association (OI-DA), is proposed to gi... more In this paper, a new method, named optimum innovation data association (OI-DA), is proposed to give the nearest neighbor data association the ability to track maneuvering multi-target in dense clutter environment. Using the measurements of two successive scan ...

Improving data association process by increasing the probability of detecting valid data points (... more Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in this paper. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the e...