Maggie Mashaly - Academia.edu (original) (raw)

Papers by Maggie Mashaly

Research paper thumbnail of Internet of Wearable Medical Things for COVID-19 Diagnostics

Research paper thumbnail of Re-Configurable Parallel Feed-Forward Neural Network Implementation Using Fpga

Research paper thumbnail of A deep learning-based adaptive receiver for full-duplex systems

AEU - International Journal of Electronics and Communications

Research paper thumbnail of High Performance Design of Traffic-Flow Prediction Using Artificial Neural Network

2022 International Conference on Microelectronics (ICM)

Research paper thumbnail of Anomaly-Based Intrusion Detection System using One-Dimensional Convolutional Neural Network

Procedia Computer Science

Research paper thumbnail of A Mixed-Signal Neuron Circuit Implementation using 40nm Technology

2022 International Conference on Microelectronics (ICM)

Research paper thumbnail of Color Image Encryption Through Chaos and KAA Map

IEEE Access

The unprecedented growth in production and exchange of multimedia over unsecured channels is over... more The unprecedented growth in production and exchange of multimedia over unsecured channels is overwhelming mathematicians, scientists and engineers to realize secure and efficient cryptographic algorithms. In this paper, a color image encryption algorithm combining the KAA map with multiple chaotic maps is proposed. The proposed algorithm makes full use of Shannon's ideas of security, such that image encryption is carried out through bit confusion and diffusion. Confusion is carried out through employing 2 encryption keys. The first key is generated from the 2D Logistic Sine map and a Linear Congruential Generator, while the second key is generated from the Tent map and the Bernoulli map. Diffusion is attained through the use of the KAA map. An elaborate mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential and brute-force attacks. Moreover, the proposed image encryption algorithm is also shown to successfully pass all the tests of the NIST SP 800 suite. INDEX TERMS Chaos theory, image encryption, KAA map.

Research paper thumbnail of Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning

The Journal of Supercomputing

Network intrusion detection systems (NIDS) are the most common tool used to detect malicious atta... more Network intrusion detection systems (NIDS) are the most common tool used to detect malicious attacks on a network. They help prevent the ever-increasing different attacks and provide better security for the network. NIDS are classified into signature-based and anomaly-based detection. The most common type of NIDS is the anomaly-based NIDS which is based on machine learning models and is able to detect attacks with high accuracy. However, in recent years, NIDS has achieved even better results in detecting already known and novel attacks with the adoption of deep learning models. Benchmark datasets in intrusion detection try to simulate real-network traffic by including more normal traffic samples than the attack samples. This causes the training data to be imbalanced and causes difficulties in detecting certain types of attacks for the NIDS. In this paper, a data resampling technique is proposed based on Adaptive Synthetic (ADASYN) and Tomek Links algorithms in combination with diffe...

Research paper thumbnail of Employing Genetic Algorithm and Discrete Event Simulation for Flexible Job-Shop Scheduling Problem

2021 International Conference on Computational Science and Computational Intelligence (CSCI)

Research paper thumbnail of Intelligent Two-Dimensional Resource Allocation and Re-Use in Cloud Radio Access Networks

Research paper thumbnail of An Online Reinforcement Learning Approach for Solving the Dynamic Flexible Job-Shop Scheduling Problem for Multiple Products and Constraints

2021 International Conference on Computational Science and Computational Intelligence (CSCI)

Research paper thumbnail of Digital Twinning for Closed-Loop Control of a Three-Wheeled Omnidirectional Mobile Robot

Research paper thumbnail of Omnidirectional-Wheel Conveyor Path Planning and Sorting using Reinforcement Learning Algorithms

Research paper thumbnail of Efficient Sequence Generation for Hardware Verification Using Machine Learning

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), 2021

Research paper thumbnail of Automatic energy efficiency management of data center resources by load-dependent server activation and sleep modes

Ad Hoc Networks, 2015

ABSTRACT The operation of large Data Centers (DC) with thousands of servers is very costly in ter... more ABSTRACT The operation of large Data Centers (DC) with thousands of servers is very costly in terms of energy consumption and cooling requirements. Currently, major efforts can be observed for server virtualization and consolidation to approach a proportionality between computation amount and energy consumption. In this contribution, a generalized model is presented which allows an automatic server consolidation by a load-dependent control of server activations using multi-parallel hysteresis thresholds, cold and hot server stand-by, and Dynamic Voltage and Frequency Scaling (DVFS). For the energy-efficiency and performance analysis, a multi-server queuing model is defined which is controlled by a Finite State Machine (FSM). The parameters of the queuing model are defined such that Service Level Agreements (SLA, e.g. as mean or percentiles of response times) are guaranteed except for overload conditions. The queuing model can be exactly analyzed under Markovian process assumptions from which all relevant quality of service (QoS) and energy efficiency (EE) metrics are derived. Numerical results are provided which demonstrate the applicability of the proposed model for the DC management, in particular to theoretically quantify the tradeoff between the conflicting aims of EE and QoS.

Research paper thumbnail of Load balancing in cloud-based content delivery networks using adaptive server activation/deactivation

2012 International Conference on Engineering and Technology (ICET), 2012

Content delivery networks have been widely used for many years providing service for millions of ... more Content delivery networks have been widely used for many years providing service for millions of users. Lately, many of these networks are migrating to the cloud for its numerous advantages such as lower costs, increased performance, availability and flexibility. This work introduces a new approach towards load balancing in cloud-based content delivery networks. By applying adaptive server activations/deactivations at each data center in the cloud, overloaded data centers can move the extra load to lightly loaded ones without affecting the performance of any of the data centers or violating service level agreements (SLA) of users.. In addition to load balancing this method allows better resource management by adapting the number of active resources inside the data center to the offered load.

Research paper thumbnail of Modeling and simulation of energy-efficient cloud data centers

2014 International Conference on Engineering and Technology (ICET), 2014

The rapid development of Internet has given birth to a new technology called cloud computing. Clo... more The rapid development of Internet has given birth to a new technology called cloud computing. Cloud computing is attractive to business owners as it provides several features, such as no up-front investment, lowering operating cost, highly scalable, reducing business risks and lowering maintenance expenses. However, cloud computing is still in its infancy, as many crucial problems need to be addressed. Energy saving is one of these problems; where efficient power management can help lower the cost of operating a large data center hosting massive computing resources. This paper studies a new proposed model that can reduce the system energy consumption by setting hystereses thresholds of activating and deactivating servers. The model is implemented and simulated using OPNET Modeler and Simulator to evaluate its performance metrics. Simulations have shown the effect of the model on reducing server activation/deactivation rates which consequently reduces the energy consumption. Moreover, simulations proved that the model is a generic model that works for non-Markovian assumptions.

Research paper thumbnail of A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

Applied Sciences

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production s... more In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when...

Research paper thumbnail of A Novel Sigmoid Function Approximation Suitable for Neural Networks on FPGA

2019 15th International Computer Engineering Conference (ICENCO), 2019

Artificial Neural Networks (ANN) is invading a lot of practical applications in our life nowadays... more Artificial Neural Networks (ANN) is invading a lot of practical applications in our life nowadays. One of the main blocks of ANN is the activation function block, which is based on the sigmoid function. The hardware implementation of sigmoid function is a challenging task; hence some approximation techniques were previously developed. In this paper, a novel sigmoid approximation technique is proposed and compared with previous techniques, on both simulation and hardware design levels. They are applied in a neural network application, where the proposed technique showed high accuracy compared to the original sigmoid function. Moreover, the different techniques are implemented on Virtex 7 FPGA using IEEE 754 Floating Point representation to achieve high precision, where the proposed approximation consumed the least hardware area utilization compared to previous works for clock frequency of 358.166 MHZ.

Research paper thumbnail of Load Balancing in Distributed Cloud Data Center Configurations: Performance and Energy-Efficiency

Proceedings of the Eighth International Conference on Future Energy Systems, 2017

In this contribution two cloud server clusters are considered which process virtualized user serv... more In this contribution two cloud server clusters are considered which process virtualized user service requests defined as Virtual Machines (VM) operated under the Hypervisor control. Load Balancing (LB) is applied to avoid temporary overloads and to enforce negotiated Service Level Agreements (SLA) defined by means and percentiles of processing delays. Two novel LB strategies are defined through which the two server clusters perfform job processing cooperatively through mutual job overflows by a "Local Server System First" (LSSF) and through a "Shortest Response Time First" (SRTF) strategy, respectively. The cooperation operation is performed by VM migration at the instant of VM scheduling by the Hypervisor. Both LB models are defined by queuing systems which are analyzed by the method of Markov-Chains. Energy efficiency has been analyzed by the authors through server consolidation, server sleep modes, and through Dynamic Voltage and Frequency Scaling (DVFS), c.f....

Research paper thumbnail of Internet of Wearable Medical Things for COVID-19 Diagnostics

Research paper thumbnail of Re-Configurable Parallel Feed-Forward Neural Network Implementation Using Fpga

Research paper thumbnail of A deep learning-based adaptive receiver for full-duplex systems

AEU - International Journal of Electronics and Communications

Research paper thumbnail of High Performance Design of Traffic-Flow Prediction Using Artificial Neural Network

2022 International Conference on Microelectronics (ICM)

Research paper thumbnail of Anomaly-Based Intrusion Detection System using One-Dimensional Convolutional Neural Network

Procedia Computer Science

Research paper thumbnail of A Mixed-Signal Neuron Circuit Implementation using 40nm Technology

2022 International Conference on Microelectronics (ICM)

Research paper thumbnail of Color Image Encryption Through Chaos and KAA Map

IEEE Access

The unprecedented growth in production and exchange of multimedia over unsecured channels is over... more The unprecedented growth in production and exchange of multimedia over unsecured channels is overwhelming mathematicians, scientists and engineers to realize secure and efficient cryptographic algorithms. In this paper, a color image encryption algorithm combining the KAA map with multiple chaotic maps is proposed. The proposed algorithm makes full use of Shannon's ideas of security, such that image encryption is carried out through bit confusion and diffusion. Confusion is carried out through employing 2 encryption keys. The first key is generated from the 2D Logistic Sine map and a Linear Congruential Generator, while the second key is generated from the Tent map and the Bernoulli map. Diffusion is attained through the use of the KAA map. An elaborate mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential and brute-force attacks. Moreover, the proposed image encryption algorithm is also shown to successfully pass all the tests of the NIST SP 800 suite. INDEX TERMS Chaos theory, image encryption, KAA map.

Research paper thumbnail of Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning

The Journal of Supercomputing

Network intrusion detection systems (NIDS) are the most common tool used to detect malicious atta... more Network intrusion detection systems (NIDS) are the most common tool used to detect malicious attacks on a network. They help prevent the ever-increasing different attacks and provide better security for the network. NIDS are classified into signature-based and anomaly-based detection. The most common type of NIDS is the anomaly-based NIDS which is based on machine learning models and is able to detect attacks with high accuracy. However, in recent years, NIDS has achieved even better results in detecting already known and novel attacks with the adoption of deep learning models. Benchmark datasets in intrusion detection try to simulate real-network traffic by including more normal traffic samples than the attack samples. This causes the training data to be imbalanced and causes difficulties in detecting certain types of attacks for the NIDS. In this paper, a data resampling technique is proposed based on Adaptive Synthetic (ADASYN) and Tomek Links algorithms in combination with diffe...

Research paper thumbnail of Employing Genetic Algorithm and Discrete Event Simulation for Flexible Job-Shop Scheduling Problem

2021 International Conference on Computational Science and Computational Intelligence (CSCI)

Research paper thumbnail of Intelligent Two-Dimensional Resource Allocation and Re-Use in Cloud Radio Access Networks

Research paper thumbnail of An Online Reinforcement Learning Approach for Solving the Dynamic Flexible Job-Shop Scheduling Problem for Multiple Products and Constraints

2021 International Conference on Computational Science and Computational Intelligence (CSCI)

Research paper thumbnail of Digital Twinning for Closed-Loop Control of a Three-Wheeled Omnidirectional Mobile Robot

Research paper thumbnail of Omnidirectional-Wheel Conveyor Path Planning and Sorting using Reinforcement Learning Algorithms

Research paper thumbnail of Efficient Sequence Generation for Hardware Verification Using Machine Learning

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), 2021

Research paper thumbnail of Automatic energy efficiency management of data center resources by load-dependent server activation and sleep modes

Ad Hoc Networks, 2015

ABSTRACT The operation of large Data Centers (DC) with thousands of servers is very costly in ter... more ABSTRACT The operation of large Data Centers (DC) with thousands of servers is very costly in terms of energy consumption and cooling requirements. Currently, major efforts can be observed for server virtualization and consolidation to approach a proportionality between computation amount and energy consumption. In this contribution, a generalized model is presented which allows an automatic server consolidation by a load-dependent control of server activations using multi-parallel hysteresis thresholds, cold and hot server stand-by, and Dynamic Voltage and Frequency Scaling (DVFS). For the energy-efficiency and performance analysis, a multi-server queuing model is defined which is controlled by a Finite State Machine (FSM). The parameters of the queuing model are defined such that Service Level Agreements (SLA, e.g. as mean or percentiles of response times) are guaranteed except for overload conditions. The queuing model can be exactly analyzed under Markovian process assumptions from which all relevant quality of service (QoS) and energy efficiency (EE) metrics are derived. Numerical results are provided which demonstrate the applicability of the proposed model for the DC management, in particular to theoretically quantify the tradeoff between the conflicting aims of EE and QoS.

Research paper thumbnail of Load balancing in cloud-based content delivery networks using adaptive server activation/deactivation

2012 International Conference on Engineering and Technology (ICET), 2012

Content delivery networks have been widely used for many years providing service for millions of ... more Content delivery networks have been widely used for many years providing service for millions of users. Lately, many of these networks are migrating to the cloud for its numerous advantages such as lower costs, increased performance, availability and flexibility. This work introduces a new approach towards load balancing in cloud-based content delivery networks. By applying adaptive server activations/deactivations at each data center in the cloud, overloaded data centers can move the extra load to lightly loaded ones without affecting the performance of any of the data centers or violating service level agreements (SLA) of users.. In addition to load balancing this method allows better resource management by adapting the number of active resources inside the data center to the offered load.

Research paper thumbnail of Modeling and simulation of energy-efficient cloud data centers

2014 International Conference on Engineering and Technology (ICET), 2014

The rapid development of Internet has given birth to a new technology called cloud computing. Clo... more The rapid development of Internet has given birth to a new technology called cloud computing. Cloud computing is attractive to business owners as it provides several features, such as no up-front investment, lowering operating cost, highly scalable, reducing business risks and lowering maintenance expenses. However, cloud computing is still in its infancy, as many crucial problems need to be addressed. Energy saving is one of these problems; where efficient power management can help lower the cost of operating a large data center hosting massive computing resources. This paper studies a new proposed model that can reduce the system energy consumption by setting hystereses thresholds of activating and deactivating servers. The model is implemented and simulated using OPNET Modeler and Simulator to evaluate its performance metrics. Simulations have shown the effect of the model on reducing server activation/deactivation rates which consequently reduces the energy consumption. Moreover, simulations proved that the model is a generic model that works for non-Markovian assumptions.

Research paper thumbnail of A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

Applied Sciences

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production s... more In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when...

Research paper thumbnail of A Novel Sigmoid Function Approximation Suitable for Neural Networks on FPGA

2019 15th International Computer Engineering Conference (ICENCO), 2019

Artificial Neural Networks (ANN) is invading a lot of practical applications in our life nowadays... more Artificial Neural Networks (ANN) is invading a lot of practical applications in our life nowadays. One of the main blocks of ANN is the activation function block, which is based on the sigmoid function. The hardware implementation of sigmoid function is a challenging task; hence some approximation techniques were previously developed. In this paper, a novel sigmoid approximation technique is proposed and compared with previous techniques, on both simulation and hardware design levels. They are applied in a neural network application, where the proposed technique showed high accuracy compared to the original sigmoid function. Moreover, the different techniques are implemented on Virtex 7 FPGA using IEEE 754 Floating Point representation to achieve high precision, where the proposed approximation consumed the least hardware area utilization compared to previous works for clock frequency of 358.166 MHZ.

Research paper thumbnail of Load Balancing in Distributed Cloud Data Center Configurations: Performance and Energy-Efficiency

Proceedings of the Eighth International Conference on Future Energy Systems, 2017

In this contribution two cloud server clusters are considered which process virtualized user serv... more In this contribution two cloud server clusters are considered which process virtualized user service requests defined as Virtual Machines (VM) operated under the Hypervisor control. Load Balancing (LB) is applied to avoid temporary overloads and to enforce negotiated Service Level Agreements (SLA) defined by means and percentiles of processing delays. Two novel LB strategies are defined through which the two server clusters perfform job processing cooperatively through mutual job overflows by a "Local Server System First" (LSSF) and through a "Shortest Response Time First" (SRTF) strategy, respectively. The cooperation operation is performed by VM migration at the instant of VM scheduling by the Hypervisor. Both LB models are defined by queuing systems which are analyzed by the method of Markov-Chains. Energy efficiency has been analyzed by the authors through server consolidation, server sleep modes, and through Dynamic Voltage and Frequency Scaling (DVFS), c.f....