Showing papers by "Ahmed Ghoneim published in 2017"
TL;DR: A facial-expression recognition system to improve the service of the healthcare in a smart city by applying a bandlet transform to a face image to extract sub-bands and producing a feature vector of the face image.
Abstract: Human facial expressions change with different states of health; therefore, a facial-expression recognition system can be beneficial to a healthcare framework. In this paper, a facial-expression recognition system is proposed to improve the service of the healthcare in a smart city. The proposed system applies a bandlet transform to a face image to extract sub-bands. Then, a weighted, center-symmetric local binary pattern is applied to each sub-band block by block. The CS-LBP histograms of the blocks are concatenated to produce a feature vector of the face image. An optional feature-selection technique selects the most dominant features, which are then fed into two classifiers: a Gaussian mixture model and a support vector machine. The scores of these classifiers are fused by weight to produce a confidence score, which is used to make decisions about the facial expression’s type. Several experiments are performed using a large set of data to validate the proposed system. Experimental results show that the proposed system can recognize facial expressions with 99.95% accuracy.
102 citations
TL;DR: A social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks and demonstrates superior localization performance compared with the existing methods is proposed.
Abstract: Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.
72 citations
TL;DR: A novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission and demonstrates a superior video transmission performance compared with the existing methods.
Abstract: Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods.
64 citations
24 Sep 2017
TL;DR: The proposed quantum image encryption algorithm utilizing the quantum controlled-NOT image generated by chaotic logistic map, asymmetric tent map and logistic Chebyshev map to control the XOR operation in the encryption process has high efficiency and security against differential and statistical attacks.
Abstract: In this paper, a novel quantum encryption algorithm for color image is proposed based on multiple discrete chaotic systems. The proposed quantum image encryption algorithm utilize the quantum controlled-NOT image generated by chaotic logistic map, asymmetric tent map and logistic Chebyshev map to control the XOR operation in the encryption process. Experiment results and analysis show that the proposed algorithm has high efficiency and security against differential and statistical attacks.
48 citations
TL;DR: Homoscedasticity and statistical features extraction are introduced in this paper as novelty detection enabling techniques, which help extract the important events in sensor data in real time when used with neural classifiers.
Abstract: The evolution of the Internet of things and the continuing increase in the number of sensors connected to the Internet impose big challenges regarding the management of the resulting deluge of data and network latency. Uploading sensor data over the web does not add value. Therefore, an efficient knowledge extraction technique is badly needed to reduce the amount of data transfer and to help simplify the process of knowledge management. Homoscedasticity and statistical features extraction are introduced in this paper as novelty detection enabling techniques, which help extract the important events in sensor data in real time when used with neural classifiers. Experiments have been conducted on a fog computing platform. System performance has been also evaluated on an occupancy data set and showed promising results.
47 citations
TL;DR: This paper proposes an online algorithm to conduct cost-aware scheduling of EV loads and energy supplies for microgrids based on the Lyapunov optimization technique, which has low computational complexity and only requires limited prediction of price information.
Abstract: With increasing concerns about worldwide environmental conditions and rapid development of renewable energy technologies, microgrids have been regarded as a promising solution to reduce the burden of infrastructure-based power systems. However, due to the intrinsically intermittent features of existing renewable energy, along with random residential behavior patterns, unpredictable plugged-in or unplugged actions of electric vehicles (EVs) and the time-varying price of electricity, it is challenging for microgrid operators to efficiently perform load scheduling and energy management. In this paper, we propose an online algorithm to conduct cost-aware scheduling of EV loads and energy supplies for microgrids. We formulate this problem into a stochastic optimization problem with the objective of minimizing the time-average cost of a microgrid, including the purchase cost of electricity from the main grid, the cost of charging and discharging batteries, renewable harvesting costs, and life-cycle greenhouse-gas emission costs. To solve this problem, the key idea is to exploit the dynamics of the price of electricity to conduct battery charging and discharging operations, renewable energy harvesting, and schedule EV loads properly. Our method is based on the Lyapunov optimization technique, which has low computational complexity and only requires limited prediction of price information. The theoretical analysis of our algorithm confirms that the proposed strategy can achieve optimality with explicit bound. By conducting extensive real-data driven simulations, we demonstrate that our proposed algorithm can achieve much lower cost and be more eco-friendly than other alternative solutions.
42 citations
TL;DR: A buffer-aware streaming approach is proposed to allow users to play multimedia streaming over vehicular 5G networks, in the case of handover between different eNodeBs, to achieve minimum delay and have better quality of service.
Abstract: With the progress of network technology in recent years, multimedia streaming applications have become increasingly popular. However, it is difficult to achieve quality of service and efficiency for multimedia streaming over vehicular networks because of the high mobility feature. Over the existing network architecture, it is difficult to immediately analyze the status of the entire network, and then establish the rules of allocation and management. However, the novel network architecture, software-defined networking, offers other options for making network management more efficient, especially for the 5G network environment. Hence, a buffer-aware streaming approach is proposed to allow users to play multimedia streaming over vehicular 5G networks, in the case of handover between different eNodeBs, to achieve minimum delay and have better quality of service. According to the user's mobility information, the status of the player buffer, and the current strength of the network signal, the proposed approach can provide the transmission strategy of multimedia streaming to the SDN controller. Finally, the experimental results proved that the proposed approach is able to not only adjust the priority of streaming content segments with the buffer and mobility status of user equipment to effectively retain overall streaming services quality, but also avoid the delay of streaming content transmission for 5G vehicular networks.
40 citations
TL;DR: This work improves on the Artificial Bee Colony Algorithm to make it more suitable for the WSS problem and controls the exploitation and exploration strategies in such a way that encourages exploration at early stages and exploitation at later stages.
Abstract: Web Service Composition aims to select and aggregate many web services to generate a workflow. The workflow contains many tasks and for each task there are many web services to choose from. The challenge is to select the best combination of web services that achieve the user requirements. This problem is called Web Service Selection (WSS). In this work, we improve on the Artificial Bee Colony Algorithm to make it more suitable for the WSS problem. Our proposed enhancement controls the exploitation and exploration strategies in such a way that encourages exploration at early stages and exploitation at later stages. Our experiments indicate that our algorithm finds better solutions and reduces the execution time compared with other algorithms.
23 citations
TL;DR: The authors believe that by depositing pheromone on neighboring nodes, FACO may consider a more diverse population of solutions, which may avoid stagnation, and outperform Ant Colony Optimization (ACO) for the WSC problem, in terms of the quality of solutions.
Abstract: Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set of candidates. The candidates have the same functionality and different non-functional criteria such as Quality of Service (QoS). In this work, the authors propose an ant-inspired algorithm for such problem. They named it Flying Ant Colony Optimization (FACO). Flying ants inject pheromone not only on the nodes on their paths but also on neighboring nodes increasing their chances of being explored in future iterations. The amount of pheromone deposited on these neighboring nodes is inversely proportional to the distance between them and the nodes on the path. The authors believe that by depositing pheromone on neighboring nodes, FACO may consider a more diverse population of solutions, which may avoid stagnation. The empirical experiments show that FACO outperform Ant Colony Optimization (ACO) for the WSC problem, in terms of the quality of solutions but it requires slightly more execution time.
14 citations
TL;DR: This approach employs the collaborative tagging accumulated by huge number of users to improve social media recommendation and can compute the tag score and suggest the tags with the highest weight to the user according to their preferences.
Abstract: Massive amounts of data are available on social websites, therefore finding the suitable item is a challenging issue. According to recent social statistics, we have more than 930 million people are using WhatsApp with more than 340 million active daily users and 955 million people who access Facebook daily with an average daily photo uploads up to 325 million. The approach presented in this paper employs the collaborative tagging accumulated by huge number of users to improve social media recommendation. Our approach has two phases, in the first phase, we compute the tag-item weight model and in the second phase, we compute the user-tag preference model. After that we employ the two models to find the suitable items tailored to the user’s preferences and recommend the items with the highest score. Also our model can compute the tag score and suggest the tags with the highest weight to the user according to their preferences. The experiment results performed on Flicker and MovieLens prove that our approach is capable to improve the social media recommendation.
8 citations
TL;DR: ACD, AC angle width and CCT are significantly increased after uneventful phacoemulsification surgery and Foldable IOL implantation, which have become stabilized after 1 month of surgery with no significant difference in the measures given by AS-OCT, UBM or Pentacam.
Abstract: Background: Cataract extraction affects some of the anterior chamber (AC) parameters like AC depth (ACD) and the width of the AC angle. Different techniques are used to evaluate these effects before and after cataract extraction.Purpose: To evaluate the results of Anterior Segment Optical Coherence Tomography (AS-OCT), Ultrasound Biomicroscopy (UBM) & Pentacam in evaluating the changes of the anterior segment parameters after cataract extraction.Methods: Prospective non-randomized study including 40 cataractous eyes. AS-OCT, UBM and Pentacam were done 1 week before, 1 week and 1 month after phacoemulsification with intraocular lens (IOL) implantation, to measure ACD, AC angle width, and central corneal thickness (CCT).Results: ACD depth, AC angle width and CCT increased significantly 1 week and 1 month after surgery than the preoperative level (p 0.05). No significant difference between the 3 techniques in most measures either pre- or postoperatively.Conclusion: ACD, AC angle width and CCT are significantly increased after uneventful phacoemulsification surgery and Foldable IOL implantation, which have become stabilized after 1 month of surgery with no significant difference in the measures given by AS-OCT, UBM or Pentacam.