Aliaa Youssif | Helwan University (original) (raw)

Papers by Aliaa Youssif

American Journal of Applied Sciences, 2018

Some research in Semantic video retrieval is concerned with predicting the temporal existence of ... more Some research in Semantic video retrieval is concerned with predicting the temporal existence of certain concepts. Most of the used methods in those studies depend on rules defined by experts and use ground-truth annotation. The Ground-truth annotation is time consuming and labour intensive. Additionally, it involves a limited number of annotated concepts, and a limited number of annotated shots. Video concepts have interrelated relations, so the extracted temporal rules from ground-truth annotation are often inaccurate and incomplete. However concept detections scores are a large high-dimensional continuous valued dataset, and generated automatically. Temporal association rules algorithms are efficient methods in revealing temporal relations, but they have some limitations when applied on high-dimensional and continuous-valued data. These constraints have led to a lack of research used temporal association rules. So, we propose a novel framework to encode the high-dimensional continuousvalued concept detection scores data into a single stream of characters without loss of important information and to predict a temporal shot behavior by generating temporal association rules.

Journal of Computer Science, 2017

This paper provides a novel approach for the problem of detecting the yellowish lesions in the ey... more This paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image was partitioned into five disjoint clusters based on this information via the unsupervised kmeans algorithm. Among the clustered images, the one having the brightest average intensity was chosen to be the best cluster containing all the bright yellowish pixels. Using this cluster, a threshold value was estimated via statistic-based metrics and subsequently applied to remove any non-bright clustered pixels and preserve only the relatively bright ones within the image. Finally, the optic disc was eliminated from the thresholded image, leaving out only the bright abnormalities. This approach was evaluated over a total of 1419 images retrieved from three heterogeneous datasets: DIARETDB0, DIARETDB1 and MESSIDOR. The proposed segmentation algorithm was fullyautomated, non-customized, simple and straightforward, regardless of the heterogeneity of the datasets. The proposed system correctly detected the bright abnormalities achieving an average sensitivity and specificity of 85.08% and 56.77%, respectively.

Journal of Computer and Communications, 2018

There is a tremendous growth of digital data due to the stunning progress of digital devices whic... more There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. So, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, video-on-demand broadcasting. In this paper, the different approaches of video retrieval are clearly and briefly categorized. Moreover, the different methods which try to bridge the semantic gap in video retrieval are discussed in more details.

Wireless Sensor Network, 2016

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms ... more Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms of memory, computing, bandwidth, and battery lifetime. Multimedia transmission over WSN requires certain QoS guarantees such as huge amount of bandwidth, strict delay and lower loss ratio that makes transmitting multimedia is a complicated task. However, adopting cross-layer approach in WMSNs improves quality of service of WSN under different environmental conditions. In this work, an energy efficient and QoS aware framework for transmitting multimedia content over WSN (EQWSN) is presented, where packet, queue and path scheduling were introduced. It adapts the application layer parameter of video encoder to current wireless channel state, and drops less important packets in case of network congestion according to packet type. Finally, the path scheduling differentiates packets types/priority and route them through different paths with different QoS considering network lifetime. Simulation results show that the new scheme EQWSN transmits video quality with QoS guarantees in addition to prolonging network lifetime.

International Journal of Advanced Computer Science and Applications, 2012

The topic of automatic recognition of facial expressions deduce a lot of researchers in the late ... more The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance). Expressions recognition is performed by using radial basis function (RBF) based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness) in addition to the natural.The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

In recent years, terrorism, accidents, violence thefts have increased, leads to install surveilla... more In recent years, terrorism, accidents, violence thefts have increased, leads to install surveillance cameras everywhere in order to identify the people who have done the bad deeds. In most cases, the images are not clear to identify and know the people, so we have developed a system that helps to improve the image level that identify people. To obtain a latent image from a blurred image, effective regularizations are required. In paper, we propose a Contrast-limited adaptive histogram equalization Algorithm and dark channel of blurred images with Downsampling and gradient prior to improving blur kernel estimation (L0 regularized intensity.) and finally use filter to remove Noise. Experimental results demonstrate that the proposed method can better handle various complex face poses, as compared with state-of-the-art approaches.

This work proposes an efficient spatiotemporal compact descriptor for action representation from ... more This work proposes an efficient spatiotemporal compact descriptor for action representation from depth map sequences. The feature descriptor is intended to resolve the problems of distinguishing different posture shapes with temporal order. The proposed work is composed of three phases. In the first phase, a depth sequence is partitioned into three non-overlapping temporal depth parts, which are utilized to produce three depth motion maps (DMMs) to capture the shape and motion cues leading to a multi-temporal DMMs representation. In the following phase, the Histogram of Oriented Gradients (HOG) is adopted from DMMs. Time-frequency statically features then extracted from DMM-HOG descriptor and concatenated in order to feed L2-CRC in the last phase. Comprehensive experiments on the known datasets clarify how the proposed approach exceeds action recognition related approaches. Experimental results achieved 97.93% and 95.97% for MSR Action3D, MSR Gesture3D respectively.

This paper introduces a real-time framework for human action recognition by Microsoft sensor for ... more This paper introduces a real-time framework for human action recognition by Microsoft sensor for Windows. The framework detects human skeleton joints and extracts human from the depth map that received from Kinect. The proposed algorithm for action segmentation used to represent action. Key-frame extraction algorithm is applied to obtain dominant frames that increase the robustness of the system. Depth Motion Maps are generated by projecting depth image maps onto orthogonal Cartesian planes and accumulate them through extracted key-frames from video sequences. Also, a compact action representation is explored which can capture the global action from the front, side and top perspectives. Support Vector Machine (SVM) is utilized to classify action. Our proposed framework achieve 94.63% on MSR-action dataset and 95% in real-time direction.

IEEE Sensors Journal, Nov 1, 2016

Wireless multimedia sensor networks (WMSNs), is an ad hoc network of wirelessly connected sensor ... more Wireless multimedia sensor networks (WMSNs), is an ad hoc network of wirelessly connected sensor nodes that allow retrieving video and audio streams, still images, and scalar sensor data but such sensors are limited in energy, memory, communication, and computational power. Multimedia transmission over wireless sensor network (WSN) is a challenging task due to quality-of-service(QoS) guarantees such as huge amount of bandwidth, strict delay, and lower loss ratio. Recently, cross-layer approach adopted by WMSNs shows a promising approach that improves quality of multimedia transmitted over WSNs under different wireless conditions. In this paper, an energy aware and adaptive cross layer scheme to transmit multimedia content over WSNs is presented. It provides packet, queue, and path scheduling, so that it selects optimal video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type through an adaptive priority video queue so that less important packets are dropped in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority are routed through different paths with different QoSs considering network lifetime. Simulation results show that new scheme transmits video over WSNs efficiently and meets QoS requirements and uses energy wisely to prolongs network lifetime.

As technology continues to increase the various formats in which medical images are created, tran... more As technology continues to increase the various formats in which medical images are created, transmitted, and analyzed, it has become more necessary to restrict the different ways in which this data is stored and formatted between the conflicting modalities. There is a significant increase in the use of medical images in clinical medicine, disease research, and education. While the literature lists several successful systems for contentbased image retrieval and image management methods, they have been unable to make significant inroads in routine medical informatics. This paper presents a new approach to image retrieval based on color, texture, and shape by using pyramid structure wavelet. The major advantage of such an approach is that little human intervention is required. However, most of these systems only allow a user to query using a complete image with multiple regions and are unable to retrieve similar looking images based on a single region. Experimental results of the query system on different test image databases are given. This paper introduces a comparative study between color, texture, shape and the pyramid structure wavelet technique and generates the receiving operating characteristic curve (ROC) to assess the results. The area under the curve when use color is 0.58, when use shape is 0.68, when use texture 0.74 and when use the wavelet technique is 0.8.

International Journal of Sensor Networks and Data Communications, 2015

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors which are limited in terms o... more Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors which are limited in terms of computational, memory, bandwidth, and battery capability. Multimedia transmission over WSN requires certain Qos guarantees such as huge amount of bandwidth, strict delay and lower loss ratio, which makes transmitting multimedia content over it, is a challenging problem. Recently adopting cross-layer design in WMSNs proved to be a promising approach, which improves quality of service of WSN under various operational conditions. In this work, an energy aware framework for transmitting multimedia content over WSN (ECWMSN) is presented, where packet and path scheduling were introduced, so that It adaptably selects optimum video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type to drop less important packets in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority is routed through suitable path with suitable Qos taking into consideration the network lifetime. Simulation results show that ECWMSN optimizes video quality and prolongs network lifetime.

Applied sciences, May 31, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

International Journal of Virology, Dec 15, 2004

Biomedical Signal Processing and Control, Aug 1, 2021

Abstract Image denoising is the technique of removing noise or distortions from an image. During ... more Abstract Image denoising is the technique of removing noise or distortions from an image. During medical image acquisition, random noise is added, which results in a lower contrast in those images. For that, image denoising is a crucial task for medical imaging analysis. In this study, a denoising system using three heterogeneous medical datasets is proposed based on stacked convolutional autoencoder (SCAE) technique. To validate its efficiency, different evaluation metrics are used, such as mean squared error (MSE), Peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), structural similarity index measure (SSIM) and cross correlation (CC). The proposed denoising system gives good results among the medical and microscopic datasets that are used for training. The best average results obtained are 0.0039 for MSE, 24.07 for PSNR, 0.1220 for CNR, 0.85 for SSIM, and 0.6358 for CC. Then, the proposed SCAE denoising system was applied to the LECB 2-D PAGE database for denoising real 2-DGE images. The results of denoising 2-DGE images are evaluated by MSE, spot efficiency, false discovery rate (FDR), and signal-to-noise ratio (SNR). The best average results for 2-DGE images are 0.014 for MSE, 75 spot efficiency, 36.3 for FDR and 18.41 for SNR. The proposed system has enhanced the denoising of 2-DGE images by 0.9% to 17.6% when compared to other techniques.

An integrated methodology is proposed to combine the major areas of protein analysis. It can be c... more An integrated methodology is proposed to combine the major areas of protein analysis. It can be considered as a guideline that can be used to analyze and model a protein. This methodology is applied on one of proteins' viruses for analysis and modeling; non-structural protein 5a (NS5a) of Hepatitis C virus (HCV). Also, SemiBoost-Fold Recognition (SB-FR) algorithm is proposed for predicting protein fold. SB-FR proposes a semi-supervised boosting combination to achieve better multi-class classification model. A famous challengeable dataset (Ding and Dubchak dataset) is used for training and testing this proposed algorithm. Moreover, "TreeTest" testing method is introduced for improving the overall accuracy of SB-FR algorithm with lower computational time.

Wireless Sensor Network, 2016

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms ... more Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms of memory, computing, bandwidth, and battery lifetime. Multimedia transmission over WSN requires certain QoS guarantees such as huge amount of bandwidth, strict delay and lower loss ratio that makes transmitting multimedia is a complicated task. However, adopting cross-layer approach in WMSNs improves quality of service of WSN under different environmental conditions. In this work, an energy efficient and QoS aware framework for transmitting multimedia content over WSN (EQWSN) is presented, where packet, queue and path scheduling were introduced. It adapts the application layer parameter of video encoder to current wireless channel state, and drops less important packets in case of network congestion according to packet type. Finally, the path scheduling differentiates packets types/priority and route them through different paths with different QoS considering network lifetime. Simulation results show that the new scheme EQWSN transmits video quality with QoS guarantees in addition to prolonging network lifetime.

Image Denoising has remained a fundamental problem in the field of image processing. This paper p... more Image Denoising has remained a fundamental problem in the field of image processing. This paper proposes an adaptive threshold method for image denoising based on curvelet transform to estimate noise and remove it from digital images in order to achieve a good performance in this respect. The proposed adaptive threshold method is more efficient in estimate and reduce noise from images which have random, salt & pepper and Gaussian noise. Experimental results show that the proposed method demonstrates an improved denoising performance over related earlier techniques according to increasing of PSNR values of enhanced images by 0.044 at Random,1.05 at salt& pepper and 0.457 at Gaussian noise.

Proceedings of SPIE, Jul 21, 2017

The reconstruction of 3D objects from 2D line drawings is regarded as one of the key topics in th... more The reconstruction of 3D objects from 2D line drawings is regarded as one of the key topics in the field of computer vision. The ongoing research is mainly focusing on the reconstruction of 3D objects that are mapped only from 2D straight lines, and that are symmetric in nature. Commonly, this approach only produces basic and simple shapes that are mostly flat or rather polygonized in nature, which is normally attributed to inability to handle curves. To overcome the above-mentioned limitations, a technique capable of handling non-symmetric drawings that encompass curves is considered. This paper discusses a novel technique that can be used to reconstruct 3D objects containing curved lines. In addition, it highlights an application that has been developed in accordance with the suggested technique that can convert a freehand sketch to a 3D shape using a mobile phone.

European Scientific Journal, ESJ, Oct 31, 2013

One of the most important parts of cryptographic systems is key generation. Researchers, for a lo... more One of the most important parts of cryptographic systems is key generation. Researchers, for a long time period, have been inventing ways to produce tough and repeatable cryptographic keys. Keys that had these features are hard to be memorized and may be stolen or lost. For this purpose using biometric features to generate cryptographic key is the best way. Most previous Researchers focused to extract features and generate key from an individual biometric, but it is hard to be used in multi stages cryptographic systems. Therefore, this approach is enhancing the cryptographic systems by using long and complex cryptographic keys that are hard to be guessed and do not need to be memorized and provide better usage in multi stages cryptographic systems by extracting features from multi biometrics, That provides accuracy 99.83% with time less than using individual biometric by 90%.

Big Data and Cognitive Computing

In recent decades, the crime rate has significantly increased. As a result, the automatic video m... more In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentall...

American Journal of Applied Sciences, 2018

Some research in Semantic video retrieval is concerned with predicting the temporal existence of ... more Some research in Semantic video retrieval is concerned with predicting the temporal existence of certain concepts. Most of the used methods in those studies depend on rules defined by experts and use ground-truth annotation. The Ground-truth annotation is time consuming and labour intensive. Additionally, it involves a limited number of annotated concepts, and a limited number of annotated shots. Video concepts have interrelated relations, so the extracted temporal rules from ground-truth annotation are often inaccurate and incomplete. However concept detections scores are a large high-dimensional continuous valued dataset, and generated automatically. Temporal association rules algorithms are efficient methods in revealing temporal relations, but they have some limitations when applied on high-dimensional and continuous-valued data. These constraints have led to a lack of research used temporal association rules. So, we propose a novel framework to encode the high-dimensional continuousvalued concept detection scores data into a single stream of characters without loss of important information and to predict a temporal shot behavior by generating temporal association rules.

Journal of Computer Science, 2017

This paper provides a novel approach for the problem of detecting the yellowish lesions in the ey... more This paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image was partitioned into five disjoint clusters based on this information via the unsupervised kmeans algorithm. Among the clustered images, the one having the brightest average intensity was chosen to be the best cluster containing all the bright yellowish pixels. Using this cluster, a threshold value was estimated via statistic-based metrics and subsequently applied to remove any non-bright clustered pixels and preserve only the relatively bright ones within the image. Finally, the optic disc was eliminated from the thresholded image, leaving out only the bright abnormalities. This approach was evaluated over a total of 1419 images retrieved from three heterogeneous datasets: DIARETDB0, DIARETDB1 and MESSIDOR. The proposed segmentation algorithm was fullyautomated, non-customized, simple and straightforward, regardless of the heterogeneity of the datasets. The proposed system correctly detected the bright abnormalities achieving an average sensitivity and specificity of 85.08% and 56.77%, respectively.

Journal of Computer and Communications, 2018

There is a tremendous growth of digital data due to the stunning progress of digital devices whic... more There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. So, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, video-on-demand broadcasting. In this paper, the different approaches of video retrieval are clearly and briefly categorized. Moreover, the different methods which try to bridge the semantic gap in video retrieval are discussed in more details.

Wireless Sensor Network, 2016

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms ... more Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms of memory, computing, bandwidth, and battery lifetime. Multimedia transmission over WSN requires certain QoS guarantees such as huge amount of bandwidth, strict delay and lower loss ratio that makes transmitting multimedia is a complicated task. However, adopting cross-layer approach in WMSNs improves quality of service of WSN under different environmental conditions. In this work, an energy efficient and QoS aware framework for transmitting multimedia content over WSN (EQWSN) is presented, where packet, queue and path scheduling were introduced. It adapts the application layer parameter of video encoder to current wireless channel state, and drops less important packets in case of network congestion according to packet type. Finally, the path scheduling differentiates packets types/priority and route them through different paths with different QoS considering network lifetime. Simulation results show that the new scheme EQWSN transmits video quality with QoS guarantees in addition to prolonging network lifetime.

International Journal of Advanced Computer Science and Applications, 2012

The topic of automatic recognition of facial expressions deduce a lot of researchers in the late ... more The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance). Expressions recognition is performed by using radial basis function (RBF) based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness) in addition to the natural.The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

In recent years, terrorism, accidents, violence thefts have increased, leads to install surveilla... more In recent years, terrorism, accidents, violence thefts have increased, leads to install surveillance cameras everywhere in order to identify the people who have done the bad deeds. In most cases, the images are not clear to identify and know the people, so we have developed a system that helps to improve the image level that identify people. To obtain a latent image from a blurred image, effective regularizations are required. In paper, we propose a Contrast-limited adaptive histogram equalization Algorithm and dark channel of blurred images with Downsampling and gradient prior to improving blur kernel estimation (L0 regularized intensity.) and finally use filter to remove Noise. Experimental results demonstrate that the proposed method can better handle various complex face poses, as compared with state-of-the-art approaches.

This work proposes an efficient spatiotemporal compact descriptor for action representation from ... more This work proposes an efficient spatiotemporal compact descriptor for action representation from depth map sequences. The feature descriptor is intended to resolve the problems of distinguishing different posture shapes with temporal order. The proposed work is composed of three phases. In the first phase, a depth sequence is partitioned into three non-overlapping temporal depth parts, which are utilized to produce three depth motion maps (DMMs) to capture the shape and motion cues leading to a multi-temporal DMMs representation. In the following phase, the Histogram of Oriented Gradients (HOG) is adopted from DMMs. Time-frequency statically features then extracted from DMM-HOG descriptor and concatenated in order to feed L2-CRC in the last phase. Comprehensive experiments on the known datasets clarify how the proposed approach exceeds action recognition related approaches. Experimental results achieved 97.93% and 95.97% for MSR Action3D, MSR Gesture3D respectively.

This paper introduces a real-time framework for human action recognition by Microsoft sensor for ... more This paper introduces a real-time framework for human action recognition by Microsoft sensor for Windows. The framework detects human skeleton joints and extracts human from the depth map that received from Kinect. The proposed algorithm for action segmentation used to represent action. Key-frame extraction algorithm is applied to obtain dominant frames that increase the robustness of the system. Depth Motion Maps are generated by projecting depth image maps onto orthogonal Cartesian planes and accumulate them through extracted key-frames from video sequences. Also, a compact action representation is explored which can capture the global action from the front, side and top perspectives. Support Vector Machine (SVM) is utilized to classify action. Our proposed framework achieve 94.63% on MSR-action dataset and 95% in real-time direction.

IEEE Sensors Journal, Nov 1, 2016

Wireless multimedia sensor networks (WMSNs), is an ad hoc network of wirelessly connected sensor ... more Wireless multimedia sensor networks (WMSNs), is an ad hoc network of wirelessly connected sensor nodes that allow retrieving video and audio streams, still images, and scalar sensor data but such sensors are limited in energy, memory, communication, and computational power. Multimedia transmission over wireless sensor network (WSN) is a challenging task due to quality-of-service(QoS) guarantees such as huge amount of bandwidth, strict delay, and lower loss ratio. Recently, cross-layer approach adopted by WMSNs shows a promising approach that improves quality of multimedia transmitted over WSNs under different wireless conditions. In this paper, an energy aware and adaptive cross layer scheme to transmit multimedia content over WSNs is presented. It provides packet, queue, and path scheduling, so that it selects optimal video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type through an adaptive priority video queue so that less important packets are dropped in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority are routed through different paths with different QoSs considering network lifetime. Simulation results show that new scheme transmits video over WSNs efficiently and meets QoS requirements and uses energy wisely to prolongs network lifetime.

As technology continues to increase the various formats in which medical images are created, tran... more As technology continues to increase the various formats in which medical images are created, transmitted, and analyzed, it has become more necessary to restrict the different ways in which this data is stored and formatted between the conflicting modalities. There is a significant increase in the use of medical images in clinical medicine, disease research, and education. While the literature lists several successful systems for contentbased image retrieval and image management methods, they have been unable to make significant inroads in routine medical informatics. This paper presents a new approach to image retrieval based on color, texture, and shape by using pyramid structure wavelet. The major advantage of such an approach is that little human intervention is required. However, most of these systems only allow a user to query using a complete image with multiple regions and are unable to retrieve similar looking images based on a single region. Experimental results of the query system on different test image databases are given. This paper introduces a comparative study between color, texture, shape and the pyramid structure wavelet technique and generates the receiving operating characteristic curve (ROC) to assess the results. The area under the curve when use color is 0.58, when use shape is 0.68, when use texture 0.74 and when use the wavelet technique is 0.8.

International Journal of Sensor Networks and Data Communications, 2015

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors which are limited in terms o... more Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors which are limited in terms of computational, memory, bandwidth, and battery capability. Multimedia transmission over WSN requires certain Qos guarantees such as huge amount of bandwidth, strict delay and lower loss ratio, which makes transmitting multimedia content over it, is a challenging problem. Recently adopting cross-layer design in WMSNs proved to be a promising approach, which improves quality of service of WSN under various operational conditions. In this work, an energy aware framework for transmitting multimedia content over WSN (ECWMSN) is presented, where packet and path scheduling were introduced, so that It adaptably selects optimum video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type to drop less important packets in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority is routed through suitable path with suitable Qos taking into consideration the network lifetime. Simulation results show that ECWMSN optimizes video quality and prolongs network lifetime.

Applied sciences, May 31, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

International Journal of Virology, Dec 15, 2004

Biomedical Signal Processing and Control, Aug 1, 2021

Abstract Image denoising is the technique of removing noise or distortions from an image. During ... more Abstract Image denoising is the technique of removing noise or distortions from an image. During medical image acquisition, random noise is added, which results in a lower contrast in those images. For that, image denoising is a crucial task for medical imaging analysis. In this study, a denoising system using three heterogeneous medical datasets is proposed based on stacked convolutional autoencoder (SCAE) technique. To validate its efficiency, different evaluation metrics are used, such as mean squared error (MSE), Peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), structural similarity index measure (SSIM) and cross correlation (CC). The proposed denoising system gives good results among the medical and microscopic datasets that are used for training. The best average results obtained are 0.0039 for MSE, 24.07 for PSNR, 0.1220 for CNR, 0.85 for SSIM, and 0.6358 for CC. Then, the proposed SCAE denoising system was applied to the LECB 2-D PAGE database for denoising real 2-DGE images. The results of denoising 2-DGE images are evaluated by MSE, spot efficiency, false discovery rate (FDR), and signal-to-noise ratio (SNR). The best average results for 2-DGE images are 0.014 for MSE, 75 spot efficiency, 36.3 for FDR and 18.41 for SNR. The proposed system has enhanced the denoising of 2-DGE images by 0.9% to 17.6% when compared to other techniques.

An integrated methodology is proposed to combine the major areas of protein analysis. It can be c... more An integrated methodology is proposed to combine the major areas of protein analysis. It can be considered as a guideline that can be used to analyze and model a protein. This methodology is applied on one of proteins' viruses for analysis and modeling; non-structural protein 5a (NS5a) of Hepatitis C virus (HCV). Also, SemiBoost-Fold Recognition (SB-FR) algorithm is proposed for predicting protein fold. SB-FR proposes a semi-supervised boosting combination to achieve better multi-class classification model. A famous challengeable dataset (Ding and Dubchak dataset) is used for training and testing this proposed algorithm. Moreover, "TreeTest" testing method is introduced for improving the overall accuracy of SB-FR algorithm with lower computational time.

Wireless Sensor Network, 2016

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms ... more Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms of memory, computing, bandwidth, and battery lifetime. Multimedia transmission over WSN requires certain QoS guarantees such as huge amount of bandwidth, strict delay and lower loss ratio that makes transmitting multimedia is a complicated task. However, adopting cross-layer approach in WMSNs improves quality of service of WSN under different environmental conditions. In this work, an energy efficient and QoS aware framework for transmitting multimedia content over WSN (EQWSN) is presented, where packet, queue and path scheduling were introduced. It adapts the application layer parameter of video encoder to current wireless channel state, and drops less important packets in case of network congestion according to packet type. Finally, the path scheduling differentiates packets types/priority and route them through different paths with different QoS considering network lifetime. Simulation results show that the new scheme EQWSN transmits video quality with QoS guarantees in addition to prolonging network lifetime.

Image Denoising has remained a fundamental problem in the field of image processing. This paper p... more Image Denoising has remained a fundamental problem in the field of image processing. This paper proposes an adaptive threshold method for image denoising based on curvelet transform to estimate noise and remove it from digital images in order to achieve a good performance in this respect. The proposed adaptive threshold method is more efficient in estimate and reduce noise from images which have random, salt & pepper and Gaussian noise. Experimental results show that the proposed method demonstrates an improved denoising performance over related earlier techniques according to increasing of PSNR values of enhanced images by 0.044 at Random,1.05 at salt& pepper and 0.457 at Gaussian noise.

Proceedings of SPIE, Jul 21, 2017

The reconstruction of 3D objects from 2D line drawings is regarded as one of the key topics in th... more The reconstruction of 3D objects from 2D line drawings is regarded as one of the key topics in the field of computer vision. The ongoing research is mainly focusing on the reconstruction of 3D objects that are mapped only from 2D straight lines, and that are symmetric in nature. Commonly, this approach only produces basic and simple shapes that are mostly flat or rather polygonized in nature, which is normally attributed to inability to handle curves. To overcome the above-mentioned limitations, a technique capable of handling non-symmetric drawings that encompass curves is considered. This paper discusses a novel technique that can be used to reconstruct 3D objects containing curved lines. In addition, it highlights an application that has been developed in accordance with the suggested technique that can convert a freehand sketch to a 3D shape using a mobile phone.

European Scientific Journal, ESJ, Oct 31, 2013

One of the most important parts of cryptographic systems is key generation. Researchers, for a lo... more One of the most important parts of cryptographic systems is key generation. Researchers, for a long time period, have been inventing ways to produce tough and repeatable cryptographic keys. Keys that had these features are hard to be memorized and may be stolen or lost. For this purpose using biometric features to generate cryptographic key is the best way. Most previous Researchers focused to extract features and generate key from an individual biometric, but it is hard to be used in multi stages cryptographic systems. Therefore, this approach is enhancing the cryptographic systems by using long and complex cryptographic keys that are hard to be guessed and do not need to be memorized and provide better usage in multi stages cryptographic systems by extracting features from multi biometrics, That provides accuracy 99.83% with time less than using individual biometric by 90%.

Big Data and Cognitive Computing

In recent decades, the crime rate has significantly increased. As a result, the automatic video m... more In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentall...