Robotic systems have been evolving since decades and touching almost all aspects of life, either ... more Robotic systems have been evolving since decades and touching almost all aspects of life, either for leisure or critical applications. Most of traditional robotic systems operate in well-defined environments utilizing pre-configured on-board processing units. However, modern and foreseen robotic applications ask for complex processing requirements that exceed the limits of on-board computing power. Cloud computing and the related technologies have high potential to overcome on-board hardware restrictions and can improve the performance efficiency. This research highlights the advancements in robotic systems with focus on cloud robotics as an emerging trend. There exists an extensive amount of effort to leverage the potentials of robotic systems and to handle arising shortcomings. Moreover, there are promising insights for future breed of intelligent, flexible, and autonomous robotic systems in the Internet of Things era.
Stringent business ecosystem raises the demand for novel ways of operation to survive and innovat... more Stringent business ecosystem raises the demand for novel ways of operation to survive and innovate. Such change and improved capabilities ask for reconsidering the role of the university in filling the gaps. This research puts in hand first steps and insights regarding the added value of the university in one of the Middle East countries. The main objective is to foster the economic development of the local community in Aqaba city by establishing a technology-focused center on entrepreneurship. Such center will strive to utilize the various resources in hand in order to make available high quality outcomes concerning the local community development. Expected benefits include providing various services to a wide number of clients. Such services assist the local community to overcome challenges as the scarce employment opportunities and the expanding number of higher education graduates. Keywords: Center on entrepreneurship, Entrepreneurial learning center, Regional innovation ecosyst...
Monitoring and controlling air pollutants have been one of the main environmental concerns so far... more Monitoring and controlling air pollutants have been one of the main environmental concerns so far. Such concerns are highly emphasized and monitored in large cities all over the world by air quality management systems. The various polluting emissions transported by atmospheric air affect the living bodies, including the human’s health, wild life, and plants. Nation wise, air pollution negatively imposes economic effects. Regularity boards, usually governmental, induce actions to reduce air pollution levels in the industrial regions; by limiting certain emission amounts and imposing air quality standards. This paper aims to put in hand a symbolic regression prediction model based on the genetic programming algorithm. The main objective of the prediction model is to predict the Particulate Matters (PM10) near Salt City, Jordan. This study analyzes the recordings of five monitoring stations around Al-Fuhais cement plant between the years 2006 and 2007. The incorporated and measured met...
Ozone layer diminution has been one of the major environmental problems so far. Such problem call... more Ozone layer diminution has been one of the major environmental problems so far. Such problem calls for a reliable monitoring mechanism to aid the strategic long-term remedy. However, it is challenging to develop a reliable prediction model due to the complexity of the relationships among the main attributes involved. Therefore, the causal attributes to the problem require an innovative modeling scheme. In this study we will investigate the application of support vector Regression (SVR) for predicting the surface Ozone concentrations. Several SVR models were developed using different kernel functions. The developed prediction models are based on limited number of input attributes which are atmospheric temperature, relative humidity and Nitrogen-dioxide. Apart from the complexity of the adopted approach, models are evaluated and compared using different measurement criteria. [Faris H., Ghatasheh N., Rodan A., Abu-Faraj M. Predicting Surface Ozone Concentrations using Support Vector Re...
The Dead Sea plays an important role for regional development in tourism, agriculture, and indust... more The Dead Sea plays an important role for regional development in tourism, agriculture, and industry in the middle east. Different studies stated that the water level of the Dead Sea is dropping at an average of 3-5 feet per year. Such studies are mainly environmental, some lack comparisons of their results with actual readings taken from the Dead Sea, based on heuristic predictions, lack rich technical details, consider specific cases and others rely on heterogeneous input data sets. Accordingly there is a need to provide accurate and reliable estimates for future water level and edges span of the Dead Sea as well as proving the accuracy and reliability of the selected approaches. This study presents a comparison between different approaches attempted to find the declining rate and surface area of the Dead Sea. It also presents empirical remarks on edge detection and a color based data extraction approaches in order to overcome the estimation accuracy issues. All the approaches were...
Satellite images provide the researchers with the ability to identify objects (such as roads, lan... more Satellite images provide the researchers with the ability to identify objects (such as roads, land use, elevation, waterways, etc.). Analyzing the satellite images of the Dead Sea in Jordan can help determining the edges of the Dead Sea and its declining rate. The analysis process can be done using evolving algorithms and software. This paper shows the outcome of applying three matlab image-processing functions on the Dead Sea images taken from Google Earth. The three functions are: threshold, edgebased and watershed segmentation. Determining the edge of the Dead Sea using image processing functions can be used to calculate, predict and forecast the declining rate of the Dead Sea. We also present a case study showing our analysis.
Swarm spreading has been gaining more focus since the more reliance on artificial intelligence in... more Swarm spreading has been gaining more focus since the more reliance on artificial intelligence in solving complex problems. Such importance comes from the various possible applications of the swarm robotics physically and virtually. This work has more focus on proposing an enhanced approach for spreading virtual autonomous swarms over an unknown environment. The main considerations included having less direct communication among the agents, covering the whole environment with as few steps as possible, keeping the agents on the move with less waiting time and the possibility of applying the simulation environment to various problems. The preliminary results are promising as they show interesting outcomes over the setup environment.
Churn represents the problem of losing a customer to another business competitor which leads to s... more Churn represents the problem of losing a customer to another business competitor which leads to serious profit loss. Therefore, many companies investigate different techniques that can predict churn rates and help in designing effective plans for customer retention. In this study we investigate the application of Multilayer Perceptron (MLP) neural networks with back-propagation learning for churn prediction in a telecommunication company. Different MLP topologies with different settings are used to build churn classification models. Moreover, two MLP based approaches are used and compared in order to rank the most influencing factors on churn rates. For the purpose of this research, real data of customers in a major Jordanian telecom company were provided. [Omar Adwan, Hossam Faris, Khalid Jaradat, Osama Harfoushi and Nazeeh Ghatasheh. Predicting Customer Churn in Telecom Industry using MLP Neural Networks: Modeling and Analysis. Life Sci J 2014;11(3):75-81]. (ISSN:1097-8135). http:...
Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum... more Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum, time and agitation. Optimizing the relationship between these attributes has been a challenge in biochemical research field. Machine learning techniques such as Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GAs) were used to solve the lipase activity modeling problem. In this paper, we explore the use of Multigene Symbolic Regression GeneticProgramming to solve the production problem of a solvent, detergent, and thermotolerantlipase using the Newly IsolatedAcinetobacter sp. in submerged and solid-state fermentation. Five attributes will be used to develop a mathematical model for the lipase activities. They are temperature, pH, inoculum, time and agitation. Genetic Programming shows promising results compared to reported results in the literature.
Recently, feature selection task has gained more attention in classification of problems. This ta... more Recently, feature selection task has gained more attention in classification of problems. This task aims to find the most important features in a large search space of potential solutions. Hence, a challenging problem is manifested to find the optimal solution. In this paper, we study a metaheuristic-based approach for feature selection in binary classification problems. The scenario deals with several highly imbalanced datasets. In an attempt to handle the problem of imbalanced data, the common fitness function based on the classification accuracy is replaced with two more effective fitness functions: the area under the ROC curve and the geometric mean. To evaluate the effectiveness of the developed approach, two popular metaheuristic approaches are experimented with the three fitness functions for classifying six imbalanced datasets. The chapter discusses the impact of the used fitness function on the final performance of the proposed methods. The proposed methods demonstrated that some fitness functions like the accuracy rate can mislead the identification process of the relevant features in imbalanced datasets.
2013 Fourth International Conference on e-Learning "Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity", 2013
ABSTRACT Professionals, practitioners, students, and novice users are overwhelmed with the differ... more ABSTRACT Professionals, practitioners, students, and novice users are overwhelmed with the different image file formats. This paper, proposes taxonomy for image file formats after reviewing 82 different image file formats based on the following attributes: raster/vector, compression, number of dimensions represented in the image. Still, these attributes are not mutually exclusive, which leads to more than 20 different classifications shown in the last section of this paper. This research paper is divided into five sections, the first four sections study raster, vector, 3D vector, and stereo formats. The last section shows the findings.
The banking industry has been seeking novel ways to leverage database marketing efficiency. Howev... more The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassif...
ABSTRACT The Dead Sea plays an important role for regional development in tourism, agriculture, a... more ABSTRACT The Dead Sea plays an important role for regional development in tourism, agriculture, and industry in the middle east. Different studies stated that the water level of the Dead Sea is dropping at an average of 3-5 feet per year. Such studies are mainly environmental, some lack comparisons of their results with actual readings taken from the Dead Sea, based on heuristic predictions, lack rich technical details, consider specific cases and others rely on heterogeneous input data sets. Accordingly there is a need to provide accurate and reliable estimates for future water level and edges span of the Dead Sea as well as proving the accuracy and reliability of the selected approaches. This study presents a comparison between different approaches attempted to find the declining rate and surface area of the Dead Sea. It also presents empirical remarks on edge detection and a color based data extraction approaches in order to overcome the estimation accuracy issues. All the approaches were tested on the same data set for credibility. However remote sensing image processing can be used to derive important measurements, it requires careful selection of the approaches and data sets.
Robotic systems have been evolving since decades and touching almost all aspects of life, either ... more Robotic systems have been evolving since decades and touching almost all aspects of life, either for leisure or critical applications. Most of traditional robotic systems operate in well-defined environments utilizing pre-configured on-board processing units. However, modern and foreseen robotic applications ask for complex processing requirements that exceed the limits of on-board computing power. Cloud computing and the related technologies have high potential to overcome on-board hardware restrictions and can improve the performance efficiency. This research highlights the advancements in robotic systems with focus on cloud robotics as an emerging trend. There exists an extensive amount of effort to leverage the potentials of robotic systems and to handle arising shortcomings. Moreover, there are promising insights for future breed of intelligent, flexible, and autonomous robotic systems in the Internet of Things era.
Stringent business ecosystem raises the demand for novel ways of operation to survive and innovat... more Stringent business ecosystem raises the demand for novel ways of operation to survive and innovate. Such change and improved capabilities ask for reconsidering the role of the university in filling the gaps. This research puts in hand first steps and insights regarding the added value of the university in one of the Middle East countries. The main objective is to foster the economic development of the local community in Aqaba city by establishing a technology-focused center on entrepreneurship. Such center will strive to utilize the various resources in hand in order to make available high quality outcomes concerning the local community development. Expected benefits include providing various services to a wide number of clients. Such services assist the local community to overcome challenges as the scarce employment opportunities and the expanding number of higher education graduates. Keywords: Center on entrepreneurship, Entrepreneurial learning center, Regional innovation ecosyst...
Monitoring and controlling air pollutants have been one of the main environmental concerns so far... more Monitoring and controlling air pollutants have been one of the main environmental concerns so far. Such concerns are highly emphasized and monitored in large cities all over the world by air quality management systems. The various polluting emissions transported by atmospheric air affect the living bodies, including the human’s health, wild life, and plants. Nation wise, air pollution negatively imposes economic effects. Regularity boards, usually governmental, induce actions to reduce air pollution levels in the industrial regions; by limiting certain emission amounts and imposing air quality standards. This paper aims to put in hand a symbolic regression prediction model based on the genetic programming algorithm. The main objective of the prediction model is to predict the Particulate Matters (PM10) near Salt City, Jordan. This study analyzes the recordings of five monitoring stations around Al-Fuhais cement plant between the years 2006 and 2007. The incorporated and measured met...
Ozone layer diminution has been one of the major environmental problems so far. Such problem call... more Ozone layer diminution has been one of the major environmental problems so far. Such problem calls for a reliable monitoring mechanism to aid the strategic long-term remedy. However, it is challenging to develop a reliable prediction model due to the complexity of the relationships among the main attributes involved. Therefore, the causal attributes to the problem require an innovative modeling scheme. In this study we will investigate the application of support vector Regression (SVR) for predicting the surface Ozone concentrations. Several SVR models were developed using different kernel functions. The developed prediction models are based on limited number of input attributes which are atmospheric temperature, relative humidity and Nitrogen-dioxide. Apart from the complexity of the adopted approach, models are evaluated and compared using different measurement criteria. [Faris H., Ghatasheh N., Rodan A., Abu-Faraj M. Predicting Surface Ozone Concentrations using Support Vector Re...
The Dead Sea plays an important role for regional development in tourism, agriculture, and indust... more The Dead Sea plays an important role for regional development in tourism, agriculture, and industry in the middle east. Different studies stated that the water level of the Dead Sea is dropping at an average of 3-5 feet per year. Such studies are mainly environmental, some lack comparisons of their results with actual readings taken from the Dead Sea, based on heuristic predictions, lack rich technical details, consider specific cases and others rely on heterogeneous input data sets. Accordingly there is a need to provide accurate and reliable estimates for future water level and edges span of the Dead Sea as well as proving the accuracy and reliability of the selected approaches. This study presents a comparison between different approaches attempted to find the declining rate and surface area of the Dead Sea. It also presents empirical remarks on edge detection and a color based data extraction approaches in order to overcome the estimation accuracy issues. All the approaches were...
Satellite images provide the researchers with the ability to identify objects (such as roads, lan... more Satellite images provide the researchers with the ability to identify objects (such as roads, land use, elevation, waterways, etc.). Analyzing the satellite images of the Dead Sea in Jordan can help determining the edges of the Dead Sea and its declining rate. The analysis process can be done using evolving algorithms and software. This paper shows the outcome of applying three matlab image-processing functions on the Dead Sea images taken from Google Earth. The three functions are: threshold, edgebased and watershed segmentation. Determining the edge of the Dead Sea using image processing functions can be used to calculate, predict and forecast the declining rate of the Dead Sea. We also present a case study showing our analysis.
Swarm spreading has been gaining more focus since the more reliance on artificial intelligence in... more Swarm spreading has been gaining more focus since the more reliance on artificial intelligence in solving complex problems. Such importance comes from the various possible applications of the swarm robotics physically and virtually. This work has more focus on proposing an enhanced approach for spreading virtual autonomous swarms over an unknown environment. The main considerations included having less direct communication among the agents, covering the whole environment with as few steps as possible, keeping the agents on the move with less waiting time and the possibility of applying the simulation environment to various problems. The preliminary results are promising as they show interesting outcomes over the setup environment.
Churn represents the problem of losing a customer to another business competitor which leads to s... more Churn represents the problem of losing a customer to another business competitor which leads to serious profit loss. Therefore, many companies investigate different techniques that can predict churn rates and help in designing effective plans for customer retention. In this study we investigate the application of Multilayer Perceptron (MLP) neural networks with back-propagation learning for churn prediction in a telecommunication company. Different MLP topologies with different settings are used to build churn classification models. Moreover, two MLP based approaches are used and compared in order to rank the most influencing factors on churn rates. For the purpose of this research, real data of customers in a major Jordanian telecom company were provided. [Omar Adwan, Hossam Faris, Khalid Jaradat, Osama Harfoushi and Nazeeh Ghatasheh. Predicting Customer Churn in Telecom Industry using MLP Neural Networks: Modeling and Analysis. Life Sci J 2014;11(3):75-81]. (ISSN:1097-8135). http:...
Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum... more Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum, time and agitation. Optimizing the relationship between these attributes has been a challenge in biochemical research field. Machine learning techniques such as Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GAs) were used to solve the lipase activity modeling problem. In this paper, we explore the use of Multigene Symbolic Regression GeneticProgramming to solve the production problem of a solvent, detergent, and thermotolerantlipase using the Newly IsolatedAcinetobacter sp. in submerged and solid-state fermentation. Five attributes will be used to develop a mathematical model for the lipase activities. They are temperature, pH, inoculum, time and agitation. Genetic Programming shows promising results compared to reported results in the literature.
Recently, feature selection task has gained more attention in classification of problems. This ta... more Recently, feature selection task has gained more attention in classification of problems. This task aims to find the most important features in a large search space of potential solutions. Hence, a challenging problem is manifested to find the optimal solution. In this paper, we study a metaheuristic-based approach for feature selection in binary classification problems. The scenario deals with several highly imbalanced datasets. In an attempt to handle the problem of imbalanced data, the common fitness function based on the classification accuracy is replaced with two more effective fitness functions: the area under the ROC curve and the geometric mean. To evaluate the effectiveness of the developed approach, two popular metaheuristic approaches are experimented with the three fitness functions for classifying six imbalanced datasets. The chapter discusses the impact of the used fitness function on the final performance of the proposed methods. The proposed methods demonstrated that some fitness functions like the accuracy rate can mislead the identification process of the relevant features in imbalanced datasets.
2013 Fourth International Conference on e-Learning "Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity", 2013
ABSTRACT Professionals, practitioners, students, and novice users are overwhelmed with the differ... more ABSTRACT Professionals, practitioners, students, and novice users are overwhelmed with the different image file formats. This paper, proposes taxonomy for image file formats after reviewing 82 different image file formats based on the following attributes: raster/vector, compression, number of dimensions represented in the image. Still, these attributes are not mutually exclusive, which leads to more than 20 different classifications shown in the last section of this paper. This research paper is divided into five sections, the first four sections study raster, vector, 3D vector, and stereo formats. The last section shows the findings.
The banking industry has been seeking novel ways to leverage database marketing efficiency. Howev... more The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassif...
ABSTRACT The Dead Sea plays an important role for regional development in tourism, agriculture, a... more ABSTRACT The Dead Sea plays an important role for regional development in tourism, agriculture, and industry in the middle east. Different studies stated that the water level of the Dead Sea is dropping at an average of 3-5 feet per year. Such studies are mainly environmental, some lack comparisons of their results with actual readings taken from the Dead Sea, based on heuristic predictions, lack rich technical details, consider specific cases and others rely on heterogeneous input data sets. Accordingly there is a need to provide accurate and reliable estimates for future water level and edges span of the Dead Sea as well as proving the accuracy and reliability of the selected approaches. This study presents a comparison between different approaches attempted to find the declining rate and surface area of the Dead Sea. It also presents empirical remarks on edge detection and a color based data extraction approaches in order to overcome the estimation accuracy issues. All the approaches were tested on the same data set for credibility. However remote sensing image processing can be used to derive important measurements, it requires careful selection of the approaches and data sets.
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Papers by Nazeeh Ghatasheh