shabana habib - Academia.edu (original) (raw)
Papers by shabana habib
Electronics, 2021
Crowd counting is an active research area within scene analysis. Over the last 20 years, research... more Crowd counting is an active research area within scene analysis. Over the last 20 years, researchers proposed various algorithms for crowd counting in real-time scenarios due to many applications in disaster management systems, public events, safety monitoring, and so on. In our paper, we proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image. Our proposed framework was based on semantic scene segmentation using an optimized convolutional neural network. The framework successfully highlighted the foreground and suppressed the background part. The framework encoded the high-density maps through a guided attention mechanism system. We obtained crowd counting through integrating the density maps. Our proposed algorithm classified the crowd counting in each image into groups to adapt the variations occurring in crowd counting. Our algorithm overcame the scale variations of a crowded image through multi-scale features extracted from the images....
IEEE Access
Brain MRI classification is one of the key areas of research. The classification of brain MRI can... more Brain MRI classification is one of the key areas of research. The classification of brain MRI can help radiologists in different brain disease diagnostics without invasive measures. Brain MRI classification is a difficult task due to the variance and complexity of brain diseases. We have proposed a novel and efficient binary classification model for brain MRI images. The proposed model includes discrete wavelet transform (DWT) used for features extraction, statistical features for diminishing the number of features, and a blended artificial neural network for brain MRI classification. Brain MRI classification with less features is a challenging task. In this paper, we have proposed a novel technique for statical features calculation of approximate RGB images obtained from DWT. We have also proposed a new blended artificial neural network to improve classification accuracy. The proposed technique is compared with other state-of-the-art techniques, and results show that the proposed technique gives better outcomes in terms of accuracy and simplicity. INDEX TERMS Brain MRI, classification, artificial neural network, wavelet, statistical features.
Electronics
Power electronics devices are made from semiconductor switches such as thyristors, MOSFETs, and d... more Power electronics devices are made from semiconductor switches such as thyristors, MOSFETs, and diodes, along with passive elements of inductors, capacitors, and resistors, and integrated circuits. They are heavily used in power processing for applications in computing, communication, medical electronics, appliance control, and as converters in high power DC and AC transmission in what is now called harmonized AC/DC networks. A converter’s operation is described as a periodic sequencing of different modes of operation corresponding to different topologies interfaced to filters made of passive elements. The performance of converters has improved considerably using high switching frequency, which leads to a significant improvement in a power converter’s performance. However, the high dv/dt through a fast-switching transient of the MOSFET is associated with parasitic components generating oscillations and voltage spikes having adverse effects on the operation of complementary switches,...
As websites are developed by organization to share information with their users/visitors, so it i... more As websites are developed by organization to share information with their users/visitors, so it is very important for organization that their websites must have accessibility and usability. There are some parameters against which an organization can check its websites accessibility and usability. It can help organizations to develop an efficient and up to date website as a result they can achieve their goals. To find out the accessibility and usability of websites, in this paper we are using three automated tools Qualidator, Website Grader and Website Analyzer. This paper evaluates ten public universities of KPK, Pakistan and grade the sites according to their scores
The dynamic network topology of Flying Ad hoc Networks (FANETs) leads to challenges during commun... more The dynamic network topology of Flying Ad hoc Networks (FANETs) leads to challenges during communications. This becomes complicated when dealing with multiple transmission paths and relays. Conventional routing protocols proposed fail to address the dynamic issues inherent in FANETs. This work addresses it by diversifying the selection of relay in order to establish the significance of cooperative diversity technique. Inspiration is taken from bio inspired computing which assists in finding solutions to several challenging tasks. The natural behavior of the different species leads to elevated design concepts of protocols. This paper proposes GW-COOP (Gray Wolf Algorithm using Cooperative Diversity Technique) routing protocol for FANETs. This protocol consists of Gray Wolf Optimizer (GWO) that drives gray wolves’ social hierarchy and collaboration technique. First, we opt the design implementation of gray wolves natural posture GWO to handle flying node requirements. Second, we envis...
Vehicles need to be re-advancing by video transmission among vehicles for safety and cooperative ... more Vehicles need to be re-advancing by video transmission among vehicles for safety and cooperative deriving. The video images captured from camera could help the driver to monitor the surroundings as well as transmit the compressed images over vehicular communication network. Video over wireless communication has a lot of potential applications in intelligent transportation systems (ITS).Video streaming utilizes high bandwidth data links to transmit information. The high-bandwidth systems required larger equipment, better line-of-sight, and more complex mechanism for reliable transmit ion over the network. The intended platform for the system described in this study, is to develop a software defined algorithm for automatic video compression and transmission. The proposed algorithm is able to robustly find the left and right boundary of the lane using Hough Transform method and transmit over the network. Therefore the limitations of high-bandwidth equipment become more significant in a...
Computer Systems Science and Engineering
Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2... more Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.
Nowadays, political parties have widely adopted social media for their party promotions and elect... more Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded w...
Computers, Materials & Continua
Vehicle type classification is considered a central part of an intelligent traffic system. In rec... more Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive dataaugmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.
IEEE Access
The importance of image security in the field of medical imaging is challenging. Several research... more The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques. INDEX TERMS Internet of Medical Things, medical image encryption, lightweight encryption.
Emerging Science Journal
Crowd management has attracted serious attention under the prevailing pandemic conditions of COVI... more Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework fo...
Electronics, 2021
Crowd counting is an active research area within scene analysis. Over the last 20 years, research... more Crowd counting is an active research area within scene analysis. Over the last 20 years, researchers proposed various algorithms for crowd counting in real-time scenarios due to many applications in disaster management systems, public events, safety monitoring, and so on. In our paper, we proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image. Our proposed framework was based on semantic scene segmentation using an optimized convolutional neural network. The framework successfully highlighted the foreground and suppressed the background part. The framework encoded the high-density maps through a guided attention mechanism system. We obtained crowd counting through integrating the density maps. Our proposed algorithm classified the crowd counting in each image into groups to adapt the variations occurring in crowd counting. Our algorithm overcame the scale variations of a crowded image through multi-scale features extracted from the images....
IEEE Access
Brain MRI classification is one of the key areas of research. The classification of brain MRI can... more Brain MRI classification is one of the key areas of research. The classification of brain MRI can help radiologists in different brain disease diagnostics without invasive measures. Brain MRI classification is a difficult task due to the variance and complexity of brain diseases. We have proposed a novel and efficient binary classification model for brain MRI images. The proposed model includes discrete wavelet transform (DWT) used for features extraction, statistical features for diminishing the number of features, and a blended artificial neural network for brain MRI classification. Brain MRI classification with less features is a challenging task. In this paper, we have proposed a novel technique for statical features calculation of approximate RGB images obtained from DWT. We have also proposed a new blended artificial neural network to improve classification accuracy. The proposed technique is compared with other state-of-the-art techniques, and results show that the proposed technique gives better outcomes in terms of accuracy and simplicity. INDEX TERMS Brain MRI, classification, artificial neural network, wavelet, statistical features.
Electronics
Power electronics devices are made from semiconductor switches such as thyristors, MOSFETs, and d... more Power electronics devices are made from semiconductor switches such as thyristors, MOSFETs, and diodes, along with passive elements of inductors, capacitors, and resistors, and integrated circuits. They are heavily used in power processing for applications in computing, communication, medical electronics, appliance control, and as converters in high power DC and AC transmission in what is now called harmonized AC/DC networks. A converter’s operation is described as a periodic sequencing of different modes of operation corresponding to different topologies interfaced to filters made of passive elements. The performance of converters has improved considerably using high switching frequency, which leads to a significant improvement in a power converter’s performance. However, the high dv/dt through a fast-switching transient of the MOSFET is associated with parasitic components generating oscillations and voltage spikes having adverse effects on the operation of complementary switches,...
As websites are developed by organization to share information with their users/visitors, so it i... more As websites are developed by organization to share information with their users/visitors, so it is very important for organization that their websites must have accessibility and usability. There are some parameters against which an organization can check its websites accessibility and usability. It can help organizations to develop an efficient and up to date website as a result they can achieve their goals. To find out the accessibility and usability of websites, in this paper we are using three automated tools Qualidator, Website Grader and Website Analyzer. This paper evaluates ten public universities of KPK, Pakistan and grade the sites according to their scores
The dynamic network topology of Flying Ad hoc Networks (FANETs) leads to challenges during commun... more The dynamic network topology of Flying Ad hoc Networks (FANETs) leads to challenges during communications. This becomes complicated when dealing with multiple transmission paths and relays. Conventional routing protocols proposed fail to address the dynamic issues inherent in FANETs. This work addresses it by diversifying the selection of relay in order to establish the significance of cooperative diversity technique. Inspiration is taken from bio inspired computing which assists in finding solutions to several challenging tasks. The natural behavior of the different species leads to elevated design concepts of protocols. This paper proposes GW-COOP (Gray Wolf Algorithm using Cooperative Diversity Technique) routing protocol for FANETs. This protocol consists of Gray Wolf Optimizer (GWO) that drives gray wolves’ social hierarchy and collaboration technique. First, we opt the design implementation of gray wolves natural posture GWO to handle flying node requirements. Second, we envis...
Vehicles need to be re-advancing by video transmission among vehicles for safety and cooperative ... more Vehicles need to be re-advancing by video transmission among vehicles for safety and cooperative deriving. The video images captured from camera could help the driver to monitor the surroundings as well as transmit the compressed images over vehicular communication network. Video over wireless communication has a lot of potential applications in intelligent transportation systems (ITS).Video streaming utilizes high bandwidth data links to transmit information. The high-bandwidth systems required larger equipment, better line-of-sight, and more complex mechanism for reliable transmit ion over the network. The intended platform for the system described in this study, is to develop a software defined algorithm for automatic video compression and transmission. The proposed algorithm is able to robustly find the left and right boundary of the lane using Hough Transform method and transmit over the network. Therefore the limitations of high-bandwidth equipment become more significant in a...
Computer Systems Science and Engineering
Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2... more Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.
Nowadays, political parties have widely adopted social media for their party promotions and elect... more Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded w...
Computers, Materials & Continua
Vehicle type classification is considered a central part of an intelligent traffic system. In rec... more Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive dataaugmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.
IEEE Access
The importance of image security in the field of medical imaging is challenging. Several research... more The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques. INDEX TERMS Internet of Medical Things, medical image encryption, lightweight encryption.
Emerging Science Journal
Crowd management has attracted serious attention under the prevailing pandemic conditions of COVI... more Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework fo...