Air pollution in large cities has been a major and a serious environmental problem all over the w... more Air pollution in large cities has been a major and a serious environmental problem all over the world; hence, many countries initiated air quality management systems to monitor and control pollution rates around big cities. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man's health, natural life and agriculture; thus leading to major loss on the nation's economy. Government in industrialized countries deployed many regulations to apply restrictions on emission limits thus reduce the levels of pollution in air and enforce the international standards for air quality levels. The main objective of this study is to develop a non-parametric Artificial Neural Network (ANN) models to predict both the Particulate Matters (PM10) and Total Suspended Particles (TSP) in Salt, Jordan. A data set collected around Al-Fuhais cement plant over one-year period (2006)(2007) by eight monitoring stations were used in our study. The proposed ANN models considered the meteorological parameters: Temperature (Temp), Relative Humidity (RH), Wind Speed (WS) as inputs. We developed two Artificial Neural Network based AutoRegressive with eXternal (ANNARX) Input models to provide high performance modeling for the PM10 and the TSP air pollution parameters. Experimental results show that ANNARX can provide good modeling results using a limited number of measurements.
ABSTRACT Various algorithms have been investigated for solving different optimization problems. S... more ABSTRACT Various algorithms have been investigated for solving different optimization problems. Some of them are capable of producing high quality solutions. However, most of them employ acceptance criterion needs parameter tuning. This work proposes an adaptive acceptance criterion (AGD) that inspired from the Great Deluge (GD) algorithm idea and not rely on parameter tuning. GD always accepts the current solution if the solution quality is better than the best solution found or better or equal to the (acceptance criterion) level. During the search process, the level is updated by a constant (decreasing rate). However, the decreasing rate in GD relies on a defined estimated quality, which poses a fixed decreasing rate not rely on the improvement process. AGD differs from GD algorithm in the acceptance criterion, where the level in AGD updated by a decreasing rate relies on the current solution and does not employ any parameter tuning. AGD tested over six medical clustering datasets. In order to evaluate the effectiveness of AGD, a comparison performed to compare the performance of AGD, GD and other algorithms drawn from the scientific literature. Results show that AGD produces better quality solution than GD and other approaches.
Air pollution in large cities has been a major and a serious environmental problem all over the w... more Air pollution in large cities has been a major and a serious environmental problem all over the world; hence, many countries initiated air quality management systems to monitor and control pollution rates around big cities. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man's health, natural life and agriculture; thus leading to major loss on the nation's economy. Government in industrialized countries deployed many regulations to apply restrictions on emission limits thus reduce the levels of pollution in air and enforce the international standards for air quality levels. The main objective of this study is to develop a non-parametric Artificial Neural Network (ANN) models to predict both the Particulate Matters (PM10) and Total Suspended Particles (TSP) in Salt, Jordan. A data set collected around Al-Fuhais cement plant over one-year period (2006)(2007) by eight monitoring stations were used in our study. The proposed ANN models considered the meteorological parameters: Temperature (Temp), Relative Humidity (RH), Wind Speed (WS) as inputs. We developed two Artificial Neural Network based AutoRegressive with eXternal (ANNARX) Input models to provide high performance modeling for the PM10 and the TSP air pollution parameters. Experimental results show that ANNARX can provide good modeling results using a limited number of measurements.
AbstractConstructing university course timetable is a very difficult task where a set of events ... more AbstractConstructing university course timetable is a very difficult task where a set of events has to be scheduled in timeslots and located in suitable rooms. The objective of course timetabling problem is to satisfy the hard constraints and minimize the violation of soft constraints. In ...
Proceeding of the 4th International Multi-conferences on Computer Science and Information Technology (CSIT 2006), Apr 1, 2006
ABSTRACT Tuning the parameters of complex industrial processes to provide an outstanding product ... more ABSTRACT Tuning the parameters of complex industrial processes to provide an outstanding product with minimum losses represents a challenge for optimization theory. Many algorithms were proposed in the past to handle such type of optimization problems. ...
Finding a good university timetabling system is not a simple task for a higher educational organi... more Finding a good university timetabling system is not a simple task for a higher educational organisation. As a result, many approaches to generating sufficiently good solutions have been introduced. This is mainly due to the high complexity within the search landscape; moreover, each educational organisation has its own rules and specifications. In this paper, a Tabu-based memetic algorithm that hybridises a genetic algorithm with a Tabu Search algorithm is proposed as an improved algorithm for university timetabling problems.
Abstract In this paper, we present an automated university course timetabling system for Zarqa Un... more Abstract In this paper, we present an automated university course timetabling system for Zarqa University. In general, timetabling problem is based on assigns lectures to proper numbers of timeslots and rooms. The main goal is to maximize the overall quality of the timetable, while taking various constraints into account. The students are allowed to select courses from a course pool, which gives feedback information to the administrator about the favorite courses for the forthcoming semester.
In this work, a simulation of fish swarm intelligence has been applied on the course timetabling ... more In this work, a simulation of fish swarm intelligence has been applied on the course timetabling problem. The proposed algorithm simulates the movements of the fish when searching for food inside a body of water (refer as a search space). The search space is classified based on the visual scope of fishes into three categories which are crowded, not crowded and empty areas. Each fish represents a solution in the solution population.
Abstract. This article describes the origin and significant associated with the development of th... more Abstract. This article describes the origin and significant associated with the development of the Hybrid Optimization Genetic Algorithms (HOGA) Toolbox with Matlab as they relate to global optimization. HOGA provide three approaches to handle Genetic Algorithms penalty functions for solving complex optimization problems. These approaches include 1) Joines and Houck Method; 2) the nearest feasible crossover point; 3) the hybrid Sequential Quadratic Programming (SQP) crossover.
A hybrid fish swarm algorithm has been proposed to solve exam timetabling problems where the move... more A hybrid fish swarm algorithm has been proposed to solve exam timetabling problems where the movement of the fish is simulated when searching for food inside water (refer as a search space). The search space is categorised into three categories which are crowded, not crowded and empty areas. The movement of fish (where the fish represents the solution) is determined based on a Nelder-Mead simplex search algorithm. The quality of the solution is enhanced using a great deluge algorithm or a steepest descent algorithm.
Abstract Hyper-heuristics can be defined as search method for selecting or generating heuristics ... more Abstract Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving attribute reduction problems.
Abstract Despite the excellent applicability of kernel methods, there seems to be no systematic w... more Abstract Despite the excellent applicability of kernel methods, there seems to be no systematic way of choosing appropriate kernel functions or the optimum parameters. Therefore, the performance of support vector machines (SVMs) cannot be easily optimized. To address this problem, a general procedure is suggested to produce nonparametric and efficient kernels. This is achieved by finding an empirical and theoretical connection between positive semidefinite matrices and certain metric space properties.
Sheta, AE and Turabieh, H. and Vasant, Pandian (2007) Two New Selection Mechanisms for Genetic Al... more Sheta, AE and Turabieh, H. and Vasant, Pandian (2007) Two New Selection Mechanisms for Genetic Algorithms to Handle NLP Problems. In: Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference, April 21-22, 2007., DePaul University, Chicago, IL, USA.. ... UTP Institutional Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.
Curriculum-based course timetabling deals with the weekly assignment of a set of lectures for uni... more Curriculum-based course timetabling deals with the weekly assignment of a set of lectures for university courses to specific timeslots and rooms, where conflicts between courses are set according to curricula published by the university and not on the basis of enrolment data. The curriculum-based course timetabling is considered as the third track in the 2nd International timetabling competition (ITC2007).
Air pollution in large cities has been a major and a serious environmental problem all over the w... more Air pollution in large cities has been a major and a serious environmental problem all over the world; hence, many countries initiated air quality management systems to monitor and control pollution rates around big cities. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man's health, natural life and agriculture; thus leading to major loss on the nation's economy. Government in industrialized countries deployed many regulations to apply restrictions on emission limits thus reduce the levels of pollution in air and enforce the international standards for air quality levels. The main objective of this study is to develop a non-parametric Artificial Neural Network (ANN) models to predict both the Particulate Matters (PM10) and Total Suspended Particles (TSP) in Salt, Jordan. A data set collected around Al-Fuhais cement plant over one-year period (2006)(2007) by eight monitoring stations were used in our study. The proposed ANN models considered the meteorological parameters: Temperature (Temp), Relative Humidity (RH), Wind Speed (WS) as inputs. We developed two Artificial Neural Network based AutoRegressive with eXternal (ANNARX) Input models to provide high performance modeling for the PM10 and the TSP air pollution parameters. Experimental results show that ANNARX can provide good modeling results using a limited number of measurements.
ABSTRACT Various algorithms have been investigated for solving different optimization problems. S... more ABSTRACT Various algorithms have been investigated for solving different optimization problems. Some of them are capable of producing high quality solutions. However, most of them employ acceptance criterion needs parameter tuning. This work proposes an adaptive acceptance criterion (AGD) that inspired from the Great Deluge (GD) algorithm idea and not rely on parameter tuning. GD always accepts the current solution if the solution quality is better than the best solution found or better or equal to the (acceptance criterion) level. During the search process, the level is updated by a constant (decreasing rate). However, the decreasing rate in GD relies on a defined estimated quality, which poses a fixed decreasing rate not rely on the improvement process. AGD differs from GD algorithm in the acceptance criterion, where the level in AGD updated by a decreasing rate relies on the current solution and does not employ any parameter tuning. AGD tested over six medical clustering datasets. In order to evaluate the effectiveness of AGD, a comparison performed to compare the performance of AGD, GD and other algorithms drawn from the scientific literature. Results show that AGD produces better quality solution than GD and other approaches.
Air pollution in large cities has been a major and a serious environmental problem all over the w... more Air pollution in large cities has been a major and a serious environmental problem all over the world; hence, many countries initiated air quality management systems to monitor and control pollution rates around big cities. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man's health, natural life and agriculture; thus leading to major loss on the nation's economy. Government in industrialized countries deployed many regulations to apply restrictions on emission limits thus reduce the levels of pollution in air and enforce the international standards for air quality levels. The main objective of this study is to develop a non-parametric Artificial Neural Network (ANN) models to predict both the Particulate Matters (PM10) and Total Suspended Particles (TSP) in Salt, Jordan. A data set collected around Al-Fuhais cement plant over one-year period (2006)(2007) by eight monitoring stations were used in our study. The proposed ANN models considered the meteorological parameters: Temperature (Temp), Relative Humidity (RH), Wind Speed (WS) as inputs. We developed two Artificial Neural Network based AutoRegressive with eXternal (ANNARX) Input models to provide high performance modeling for the PM10 and the TSP air pollution parameters. Experimental results show that ANNARX can provide good modeling results using a limited number of measurements.
AbstractConstructing university course timetable is a very difficult task where a set of events ... more AbstractConstructing university course timetable is a very difficult task where a set of events has to be scheduled in timeslots and located in suitable rooms. The objective of course timetabling problem is to satisfy the hard constraints and minimize the violation of soft constraints. In ...
Proceeding of the 4th International Multi-conferences on Computer Science and Information Technology (CSIT 2006), Apr 1, 2006
ABSTRACT Tuning the parameters of complex industrial processes to provide an outstanding product ... more ABSTRACT Tuning the parameters of complex industrial processes to provide an outstanding product with minimum losses represents a challenge for optimization theory. Many algorithms were proposed in the past to handle such type of optimization problems. ...
Finding a good university timetabling system is not a simple task for a higher educational organi... more Finding a good university timetabling system is not a simple task for a higher educational organisation. As a result, many approaches to generating sufficiently good solutions have been introduced. This is mainly due to the high complexity within the search landscape; moreover, each educational organisation has its own rules and specifications. In this paper, a Tabu-based memetic algorithm that hybridises a genetic algorithm with a Tabu Search algorithm is proposed as an improved algorithm for university timetabling problems.
Abstract In this paper, we present an automated university course timetabling system for Zarqa Un... more Abstract In this paper, we present an automated university course timetabling system for Zarqa University. In general, timetabling problem is based on assigns lectures to proper numbers of timeslots and rooms. The main goal is to maximize the overall quality of the timetable, while taking various constraints into account. The students are allowed to select courses from a course pool, which gives feedback information to the administrator about the favorite courses for the forthcoming semester.
In this work, a simulation of fish swarm intelligence has been applied on the course timetabling ... more In this work, a simulation of fish swarm intelligence has been applied on the course timetabling problem. The proposed algorithm simulates the movements of the fish when searching for food inside a body of water (refer as a search space). The search space is classified based on the visual scope of fishes into three categories which are crowded, not crowded and empty areas. Each fish represents a solution in the solution population.
Abstract. This article describes the origin and significant associated with the development of th... more Abstract. This article describes the origin and significant associated with the development of the Hybrid Optimization Genetic Algorithms (HOGA) Toolbox with Matlab as they relate to global optimization. HOGA provide three approaches to handle Genetic Algorithms penalty functions for solving complex optimization problems. These approaches include 1) Joines and Houck Method; 2) the nearest feasible crossover point; 3) the hybrid Sequential Quadratic Programming (SQP) crossover.
A hybrid fish swarm algorithm has been proposed to solve exam timetabling problems where the move... more A hybrid fish swarm algorithm has been proposed to solve exam timetabling problems where the movement of the fish is simulated when searching for food inside water (refer as a search space). The search space is categorised into three categories which are crowded, not crowded and empty areas. The movement of fish (where the fish represents the solution) is determined based on a Nelder-Mead simplex search algorithm. The quality of the solution is enhanced using a great deluge algorithm or a steepest descent algorithm.
Abstract Hyper-heuristics can be defined as search method for selecting or generating heuristics ... more Abstract Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving attribute reduction problems.
Abstract Despite the excellent applicability of kernel methods, there seems to be no systematic w... more Abstract Despite the excellent applicability of kernel methods, there seems to be no systematic way of choosing appropriate kernel functions or the optimum parameters. Therefore, the performance of support vector machines (SVMs) cannot be easily optimized. To address this problem, a general procedure is suggested to produce nonparametric and efficient kernels. This is achieved by finding an empirical and theoretical connection between positive semidefinite matrices and certain metric space properties.
Sheta, AE and Turabieh, H. and Vasant, Pandian (2007) Two New Selection Mechanisms for Genetic Al... more Sheta, AE and Turabieh, H. and Vasant, Pandian (2007) Two New Selection Mechanisms for Genetic Algorithms to Handle NLP Problems. In: Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference, April 21-22, 2007., DePaul University, Chicago, IL, USA.. ... UTP Institutional Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.
Curriculum-based course timetabling deals with the weekly assignment of a set of lectures for uni... more Curriculum-based course timetabling deals with the weekly assignment of a set of lectures for university courses to specific timeslots and rooms, where conflicts between courses are set according to curricula published by the university and not on the basis of enrolment data. The curriculum-based course timetabling is considered as the third track in the 2nd International timetabling competition (ITC2007).
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Papers by Hamza Turabieh