Tamer Ahmed Farrag | Taif University, Kingdom of Saudi Arabia (original) (raw)

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Papers by Tamer Ahmed Farrag

Research paper thumbnail of An improved parallel processing-based strawberry optimization algorithm for drone placement

Telecommunication Systems

It is challenging to place drones in the best possible locations to monitor all sensor targets wh... more It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. Strawberry optimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method, it has never been used to solve problems involving optimal drone placement. SBA is preferred for optimizing drone placement in this study due to its promising results for nonlinear, mixed, and multimodal problems. Based on the references listed below, no study has investigated the need to develop a parallelized strategy version. Several studies have been conducted on the use of drones for coverage. However, no optimization algorithms have been evaluated regarding time complexity or execution time. Despite what has been said thus far, no study has looked into the significance of a systematic framework for assessing drone coverage technique...

Research paper thumbnail of Drones Coverage Point Placement Dataset

Research paper thumbnail of Adaptive Dynamic Meta-heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults

IEEE Access

Detection of transformer faults avoids the transformer's undesirable loss from service and ensure... more Detection of transformer faults avoids the transformer's undesirable loss from service and ensures utility service continuity. Diagnosis of transformer faults is determined using dissolved gas analysis (DGA). Several traditional DGA techniques, such as IEC code 60599, Rogers' ratio method, Dornenburg method, Key gas method, and Duval triangle method, but these DGA techniques suffer from poor diagnosis transformer faults. Therefore, more research was used to diagnose transformer fault and diagnostic accuracy by combining traditional DGA techniques with artificial intelligence and optimization techniques. In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques' parameters that were used to enhance the transformer diagnostic accuracy. The results showed that the proposed AD-PRS-Guided WOA provides high diagnostic accuracy of transformer faults as 97.1%, which is higher than other DGA techniques in the literature. The statistical analysis based on different tests, including ANOVA and Wilcoxon's rank-sum, confirms the algorithm's accuracy.

Research paper thumbnail of Optimized Deep Stacked Long Short-Term Memory Network for Long-Term Load Forecasting

IEEE Access

Long-term load forecasting (LTLF) is an essential process for strategical planning of the future ... more Long-term load forecasting (LTLF) is an essential process for strategical planning of the future needed extension in the power systems of any country. Besides, deep learning has become the heart of the machine learning paradigm, which is wildly used nowadays in many fields, and it also has become the current revolution in Artificial Intelligence (AI). In this paper, an optimized deep learning model based on Stacked Long Short-Term Memory Network (SLSTMN) is proposed. The architecture of the model is optimized to get the best configuration using Genetic Algorithm (GA). In addition, the hyperparameters of the model network are optimized using many deep learning techniques. During the optimization process, hundreds of model configurations are tested. The accuracy of this model is compared with many deep learning models and is compared against the related work in the field of LTLF. The dataset of the South Australia State (SA) power system is used to test the compared models. This data includes maximum daily load, daily maximum temperature, daily minimum temperature, weekday, the month, and holidays for 12 years from 2005 to 2016. SLSTMN achieves excellent accuracy and the lowest error value (almost 1%) when compared with other models on the same dataset and with related work models on different datasets.

Research paper thumbnail of Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

IEEE Access

Proposing a new precise price forecasting method is still a challenging task as electricity price... more Proposing a new precise price forecasting method is still a challenging task as electricity price signals generally exhibit various complex features. In this paper, a new approach for electricity price forecasting called hybrid local general regression neural network, and harmony search algorithm (LGRNN-HSA) is proposed. The proposed LGRNN-HSA is developed by combining the coordinate delay (CD) method, local forecasting paradigm, general regression neural network (GRNN) and harmony search algorithm (HSA). The CD is employed in the proposed method to reconstruct the time series dataset. Then the local forecasting paradigm is utilized with GRNN to predict the future price based on the nearest neighbours only so that the shortcomings in global forecasting methods can be overcome. To enhance the forecasting accuracy, the HSA is employed to optimize not only the parameters of local forecasting paradigm but also the smooth parameter which has a great effect on the accuracy of GRNN. In LGRNN-HSA, a different smooth parameter for every training point is utilized instead of utilizing a constant value in GRNN. To verify the forecasting accuracy of the LGRNN-HSA, a real-world price and demand dataset is employed. The simulation results prove that the LGRNN-HSA significantly improved forecasting accuracy compared to other methods.

Research paper thumbnail of An Intelligent Approach for Discovery and Classification of Semantic Web Services

In the past few years, the web services model achieved many successes and became the most widely ... more In the past few years, the web services model achieved many successes and became the most widely distributed model used for providing services on the web. This widespread use and the huge number of available web services have given an exceptional importance to the discovery process. As one of the main benefits of the SWSs appearance, the discovery process receives more attention from the SWSs researches and projects. The distinct way of data representation in SWSs model raises the level of researchers' aspirations about the services that should be offered by any SWSs discovery mechanism. In this Thesis, we aim to propose and implement a new SWSs discovery mechanism. This mechanism has the ability to provide good results for any user request. Thesis Contributions: a complete SWSs discovery mechanism is introduced. The role of this mechanism starts with the preparatory stage of web services registration, passes through the process of classification and crowns its role by performin...

Research paper thumbnail of Scheduling Mechanism for Mobile Computing

Mobile computing is the new trend in the computing systems. It has recently received increasing a... more Mobile computing is the new trend in the computing systems. It has recently received increasing attention by the research and industry corporations. Because it provides (i) improved scalability by aggregating resources and reducing reliance of traditional devices (ii) cost effectiveness by utilizing already deployed resources and eliminate the need for more infrastructures. The traditional computing systems do not consider the special criteria of the mobile resources (definitely connection stability). The originality of this research is to investigate a dynamic scheduling mechanism, which considers the mobile devices as resources. In the proposed mechanism assigning a given task to the available ranked resource is done dynamically by using two types of schedulers (dedicated and Opportunistic). Improving the performance of these algorithms is achieved by using powerfully source evaluation algorithm, so the overall performance of the mobile computing environment will increase. In the ...

Research paper thumbnail of Ongoing a Discovery Mechanism for SWS based on Functional and non-Functional Properties

Research paper thumbnail of Security Constrained Unit Commitment Based Load and Price Forecasting Using Evolutionary Optimized LSVR

ABSTRACT In this paper, a local predictor approach based on proven powerful regression algorithm ... more ABSTRACT In this paper, a local predictor approach based on proven powerful regression algorithm which is support vector regression (SVR) combined with space reconstruction of time series is introduced. In addition, real value genetic algorithm (GA) has been utilized in the proposed method for optimization of the parameters of the SVR. In the proposed approach, the embedding dimension and the time delay constant for the load and price data are computed firstly, and then the continuous load and price data are used for the phase space reconstruction. Subsequently, the reconstructed data matrix is subject to the local prediction algorithm. Then the forecasted loads and price are fed into IEEE 30 bus test system for security constraint unit commitment to show the reactions of unit commitment to load and price forecasting errors. The proposed model is evaluated using real world dataset. The results show that the proposed method provides a much better performance in comparison with other models employing the same data.

Research paper thumbnail of A Cluster-Based Semantic Web Services Discovery and Classification

International Conference on Advanced Computer Theory and Engineering (ICACTE 2009), 2009

Research paper thumbnail of A Dynamic Ranking Value Guided Scheduling Mechanism (RVGSM) for Mobile Grid

2006 International Conference on Computer Engineering and Systems, 2006

Mobile computing is the new trend in the computing systems. The traditional computing systems don... more Mobile computing is the new trend in the computing systems. The traditional computing systems don't consider the special criteria of the mobile resources (definitely connection stability). The originality of this paper is to investigate a dynamic scheduling mechanism which considers the mobile devices as resources. In the proposed mechanism assigning a given task to the available ranked resource is done dynamically by using two types of schedulers (dedicated and Opportunistic). Improving the performance of these algorithms is achieved by using powerfully source evaluation algorithm, so the overall performance of the mobile computing environment will increase. In the proposed mechanism "self ranking algorithm (SRA)", which distributing some of the functions of the access point among the available attached mobile devices, is employed. The proposed mechanism is experimentally validated for the various distribution forms of the incoming jobs. Experimental results depict that, embedding of SRA within the proposed mechanism has a great impact in improving the system load balancing and increase the system throughput by reducing the required scheduling time.

Research paper thumbnail of Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

IEEE Transactions on Knowledge and Data Engineering, 2000

ABSTRACT Semantic web services (SWSs) represent the most recent and revolutionary technology deve... more ABSTRACT Semantic web services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e. WSDL) using semantic annotations (i.e. OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The "local ontology repository" and "ontology search and standardization engine" are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole

Research paper thumbnail of ASWSC: Automatic semantic web services classifier based on semantic relations

ABSTRACT Semantic web services (SWSs) will be the base of machine-to-machine interaction on the w... more ABSTRACT Semantic web services (SWSs) will be the base of machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. Ongoing to design and implement a new semantic web services (SWSs) discovery mechanism, a new approach is introduced in this paper to classify the SWSs. This approach is based on measuring the semantic relations among the different concepts. Also, it considers the frequency of usage of each concept. To apply this approach, a new automatic SWSs classifier (ASWSC) is introduced. This classifier depends on a proposed ranking algorithm for concepts that considers the previously mentioned basics. Empirical experiments show high percentage of accuracy and precision that remarks the proposed classifier.

Research paper thumbnail of An improved parallel processing-based strawberry optimization algorithm for drone placement

Telecommunication Systems

It is challenging to place drones in the best possible locations to monitor all sensor targets wh... more It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. Strawberry optimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method, it has never been used to solve problems involving optimal drone placement. SBA is preferred for optimizing drone placement in this study due to its promising results for nonlinear, mixed, and multimodal problems. Based on the references listed below, no study has investigated the need to develop a parallelized strategy version. Several studies have been conducted on the use of drones for coverage. However, no optimization algorithms have been evaluated regarding time complexity or execution time. Despite what has been said thus far, no study has looked into the significance of a systematic framework for assessing drone coverage technique...

Research paper thumbnail of Drones Coverage Point Placement Dataset

Research paper thumbnail of Adaptive Dynamic Meta-heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults

IEEE Access

Detection of transformer faults avoids the transformer's undesirable loss from service and ensure... more Detection of transformer faults avoids the transformer's undesirable loss from service and ensures utility service continuity. Diagnosis of transformer faults is determined using dissolved gas analysis (DGA). Several traditional DGA techniques, such as IEC code 60599, Rogers' ratio method, Dornenburg method, Key gas method, and Duval triangle method, but these DGA techniques suffer from poor diagnosis transformer faults. Therefore, more research was used to diagnose transformer fault and diagnostic accuracy by combining traditional DGA techniques with artificial intelligence and optimization techniques. In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques' parameters that were used to enhance the transformer diagnostic accuracy. The results showed that the proposed AD-PRS-Guided WOA provides high diagnostic accuracy of transformer faults as 97.1%, which is higher than other DGA techniques in the literature. The statistical analysis based on different tests, including ANOVA and Wilcoxon's rank-sum, confirms the algorithm's accuracy.

Research paper thumbnail of Optimized Deep Stacked Long Short-Term Memory Network for Long-Term Load Forecasting

IEEE Access

Long-term load forecasting (LTLF) is an essential process for strategical planning of the future ... more Long-term load forecasting (LTLF) is an essential process for strategical planning of the future needed extension in the power systems of any country. Besides, deep learning has become the heart of the machine learning paradigm, which is wildly used nowadays in many fields, and it also has become the current revolution in Artificial Intelligence (AI). In this paper, an optimized deep learning model based on Stacked Long Short-Term Memory Network (SLSTMN) is proposed. The architecture of the model is optimized to get the best configuration using Genetic Algorithm (GA). In addition, the hyperparameters of the model network are optimized using many deep learning techniques. During the optimization process, hundreds of model configurations are tested. The accuracy of this model is compared with many deep learning models and is compared against the related work in the field of LTLF. The dataset of the South Australia State (SA) power system is used to test the compared models. This data includes maximum daily load, daily maximum temperature, daily minimum temperature, weekday, the month, and holidays for 12 years from 2005 to 2016. SLSTMN achieves excellent accuracy and the lowest error value (almost 1%) when compared with other models on the same dataset and with related work models on different datasets.

Research paper thumbnail of Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

IEEE Access

Proposing a new precise price forecasting method is still a challenging task as electricity price... more Proposing a new precise price forecasting method is still a challenging task as electricity price signals generally exhibit various complex features. In this paper, a new approach for electricity price forecasting called hybrid local general regression neural network, and harmony search algorithm (LGRNN-HSA) is proposed. The proposed LGRNN-HSA is developed by combining the coordinate delay (CD) method, local forecasting paradigm, general regression neural network (GRNN) and harmony search algorithm (HSA). The CD is employed in the proposed method to reconstruct the time series dataset. Then the local forecasting paradigm is utilized with GRNN to predict the future price based on the nearest neighbours only so that the shortcomings in global forecasting methods can be overcome. To enhance the forecasting accuracy, the HSA is employed to optimize not only the parameters of local forecasting paradigm but also the smooth parameter which has a great effect on the accuracy of GRNN. In LGRNN-HSA, a different smooth parameter for every training point is utilized instead of utilizing a constant value in GRNN. To verify the forecasting accuracy of the LGRNN-HSA, a real-world price and demand dataset is employed. The simulation results prove that the LGRNN-HSA significantly improved forecasting accuracy compared to other methods.

Research paper thumbnail of An Intelligent Approach for Discovery and Classification of Semantic Web Services

In the past few years, the web services model achieved many successes and became the most widely ... more In the past few years, the web services model achieved many successes and became the most widely distributed model used for providing services on the web. This widespread use and the huge number of available web services have given an exceptional importance to the discovery process. As one of the main benefits of the SWSs appearance, the discovery process receives more attention from the SWSs researches and projects. The distinct way of data representation in SWSs model raises the level of researchers' aspirations about the services that should be offered by any SWSs discovery mechanism. In this Thesis, we aim to propose and implement a new SWSs discovery mechanism. This mechanism has the ability to provide good results for any user request. Thesis Contributions: a complete SWSs discovery mechanism is introduced. The role of this mechanism starts with the preparatory stage of web services registration, passes through the process of classification and crowns its role by performin...

Research paper thumbnail of Scheduling Mechanism for Mobile Computing

Mobile computing is the new trend in the computing systems. It has recently received increasing a... more Mobile computing is the new trend in the computing systems. It has recently received increasing attention by the research and industry corporations. Because it provides (i) improved scalability by aggregating resources and reducing reliance of traditional devices (ii) cost effectiveness by utilizing already deployed resources and eliminate the need for more infrastructures. The traditional computing systems do not consider the special criteria of the mobile resources (definitely connection stability). The originality of this research is to investigate a dynamic scheduling mechanism, which considers the mobile devices as resources. In the proposed mechanism assigning a given task to the available ranked resource is done dynamically by using two types of schedulers (dedicated and Opportunistic). Improving the performance of these algorithms is achieved by using powerfully source evaluation algorithm, so the overall performance of the mobile computing environment will increase. In the ...

Research paper thumbnail of Ongoing a Discovery Mechanism for SWS based on Functional and non-Functional Properties

Research paper thumbnail of Security Constrained Unit Commitment Based Load and Price Forecasting Using Evolutionary Optimized LSVR

ABSTRACT In this paper, a local predictor approach based on proven powerful regression algorithm ... more ABSTRACT In this paper, a local predictor approach based on proven powerful regression algorithm which is support vector regression (SVR) combined with space reconstruction of time series is introduced. In addition, real value genetic algorithm (GA) has been utilized in the proposed method for optimization of the parameters of the SVR. In the proposed approach, the embedding dimension and the time delay constant for the load and price data are computed firstly, and then the continuous load and price data are used for the phase space reconstruction. Subsequently, the reconstructed data matrix is subject to the local prediction algorithm. Then the forecasted loads and price are fed into IEEE 30 bus test system for security constraint unit commitment to show the reactions of unit commitment to load and price forecasting errors. The proposed model is evaluated using real world dataset. The results show that the proposed method provides a much better performance in comparison with other models employing the same data.

Research paper thumbnail of A Cluster-Based Semantic Web Services Discovery and Classification

International Conference on Advanced Computer Theory and Engineering (ICACTE 2009), 2009

Research paper thumbnail of A Dynamic Ranking Value Guided Scheduling Mechanism (RVGSM) for Mobile Grid

2006 International Conference on Computer Engineering and Systems, 2006

Mobile computing is the new trend in the computing systems. The traditional computing systems don... more Mobile computing is the new trend in the computing systems. The traditional computing systems don't consider the special criteria of the mobile resources (definitely connection stability). The originality of this paper is to investigate a dynamic scheduling mechanism which considers the mobile devices as resources. In the proposed mechanism assigning a given task to the available ranked resource is done dynamically by using two types of schedulers (dedicated and Opportunistic). Improving the performance of these algorithms is achieved by using powerfully source evaluation algorithm, so the overall performance of the mobile computing environment will increase. In the proposed mechanism "self ranking algorithm (SRA)", which distributing some of the functions of the access point among the available attached mobile devices, is employed. The proposed mechanism is experimentally validated for the various distribution forms of the incoming jobs. Experimental results depict that, embedding of SRA within the proposed mechanism has a great impact in improving the system load balancing and increase the system throughput by reducing the required scheduling time.

Research paper thumbnail of Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

IEEE Transactions on Knowledge and Data Engineering, 2000

ABSTRACT Semantic web services (SWSs) represent the most recent and revolutionary technology deve... more ABSTRACT Semantic web services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e. WSDL) using semantic annotations (i.e. OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The "local ontology repository" and "ontology search and standardization engine" are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole

Research paper thumbnail of ASWSC: Automatic semantic web services classifier based on semantic relations

ABSTRACT Semantic web services (SWSs) will be the base of machine-to-machine interaction on the w... more ABSTRACT Semantic web services (SWSs) will be the base of machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. Ongoing to design and implement a new semantic web services (SWSs) discovery mechanism, a new approach is introduced in this paper to classify the SWSs. This approach is based on measuring the semantic relations among the different concepts. Also, it considers the frequency of usage of each concept. To apply this approach, a new automatic SWSs classifier (ASWSC) is introduced. This classifier depends on a proposed ranking algorithm for concepts that considers the previously mentioned basics. Empirical experiments show high percentage of accuracy and precision that remarks the proposed classifier.