Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the s... more Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a preci...
Randomization is a technique used in algorithms as a strategy that uses a random source as part o... more Randomization is a technique used in algorithms as a strategy that uses a random source as part of its logic. It is used in traditional algorithms to reduce time or space complexity. Many efforts have been made to increase the precision of convolutional neural networks (CNN) in various application domains, but less has been done to minimize the computational complexity of this model. In this work, we introduce randomized pooling (RPool) for CNN. RPool has reduced the number of operations of the CNN. Consequently, the computation time of the algorithm is reduced and the accuracy is improved. The MNIST dataset is used to demonstrate the pooling layer (PL) of CNN and compare the results of standard CNN with our proposed RPool for CNN. The simulation results show that as the number of epochs increases, the training and testing time of our proposed RPool decreases while the accuracy increases. We achieved 96.95% accuracy at epoch 10 and 8.85% decrease in training time, which demonstrates...
Mathematical Problems in Engineering, Jul 13, 2022
Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. e... more Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. e traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. e purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. e goal is to alarm and alert the security o cials to take proactive action to prevent violent activities. In deep learning literature, region-based classi ers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classi ers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. e method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by ltering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70-30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. is hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.
The teeth are the most challenging material to work with in the human body. Existing methods for ... more The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental...
Omicron is a covid family virus of COVID-19 and Delta variant. The Omicron (B.1.1.529.) variant o... more Omicron is a covid family virus of COVID-19 and Delta variant. The Omicron (B.1.1.529.) variant of COVID-19 is an extraordinary flow of infections globally and deadly, affecting the masses. The B.1.1.529 variant was first identified to WHO on November 24, 2021, from South Africa. In South Africa, the epidemiological condition has been determined by three different peaks in reported cases, the most recent of which was dominated by the Delta variant. Infections have risen sharply, corresponding with the discovery of the B.1.1.529 variant. The variant contains many mutations, some of which are potentially harmful. Preliminary research suggests that this variant has a higher risk of reinfection than other variants of concern. Nowadays, many scientists worldwide focus on problems that either improve existing methods used in DNA computing or suggest a new manner with a DNA computing approach. Many researchers are working on analyzing several aspects of Omicron from diverse fields. We have...
Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it ... more Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it has been widely used in image processing for image classification, object identification, semantic segmentation etc. It has overcome the constraint of traditional machine learning approaches. There has been a lot of effort done to improve the accuracy of CNN in many application areas, but there has been a lesser amount of work done to reduce the computational complexity of this model. There is a need to improve CNN's complexity problem. Here we have introduced randomized pooling (RANDpool) to CNN. Randomized pooling has reduced the computational complexity cost of CNN. We used MNIST dataset to demonstrate CNN with randomized pooling. This paper used randomization technique to reduce the dimensions of image instead of max, min or average pooling that have very extensive computations.
Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functio... more Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. In this paper, recursive version of the Wiener-Khintchine theorem has been derived by using the sliding DFT approach meant for computation of STFT. The computational complexity of the proposed recursive Wiener-Khintchine algorithm, for a window size of N, is O(N).
Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes... more Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%...
Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the... more Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transform algorithm. The main difference in this algorithm and STFT is that the sliding window (SW) and window for spectral estimation (WSA) are separately defined. WSA is updated and its PSD is computed only when change in statistics is detected in the SW. The computational complexity of the proposed algorithm is found to be lesser than that for standard STFT technique.
— Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation funct... more — Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. In this paper, recursive version of the Wiener-Khintchine theorem has been derived by using the sliding DFT approach meant for computation of STFT. The computational complexity of the proposed recursive Wiener-Khintchine algorithm, for a window size of N, is O(N).
— Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation funct... more — Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. In this paper, recursive version of the Wiener-Khintchine theorem has been derived by using the sliding DFT approach meant for computation of STFT. The computational complexity of the proposed recursive Wiener-Khintchine algorithm, for a window size of N, is O(N).
This work focuses on efficient, joint time-frequency analysis of time series data. Joint time-fre... more This work focuses on efficient, joint time-frequency analysis of time series data. Joint time-frequency analysis is based on the sliding window.There are two major contributions of this thesis. Firstly, we have introduced a notion of “aggregate spectrogram (AS)” which is a unimodal distribution at each time instant.The AS is extremely useful and computationally efficient when we are interested in a few spectral features and not the entire spectrum.Properties/characteristics of the AS have been listed.A parametric method, based on a second order autoregressive model of the signal, for the construction of the AS, has been described. Of all the existing spectral estimation tools, the AS has the least computational complexity.Based on the AS, instantaneous frequency estimation for multicomponent signals with equal amplitudes has been achieved.The AS does not require Goertzel filters in dual tone multi frequency detection applications.The AS finds many potential application.A few example...
A notion of fuzzy wavelets is introduced by using idea of fuzzy transforms. A detailed procedure ... more A notion of fuzzy wavelets is introduced by using idea of fuzzy transforms. A detailed procedure for analysis and synthesis of functions through fuzzy wavelets is presented.
Communications in Computer and Information Science
Abstract. The most widely used methods for time frequency analysis belong to Cohen's class o... more Abstract. The most widely used methods for time frequency analysis belong to Cohen's class of spectrogram estimators. WignerVille distri-bution (WVD) and multiwindow time frequency analysis are the most popular techniques. One serious limitation of Wigner distribution is ...
Abstract. The are various definitions of instantaneous frequency (IF) but most of them have short... more Abstract. The are various definitions of instantaneous frequency (IF) but most of them have short comings. The most popular of these defi-nitions, is the one given by Cohen. To estimate IF accuratly, techniques become more and more complex. This paper introduces the idea of ...
Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the s... more Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a preci...
Randomization is a technique used in algorithms as a strategy that uses a random source as part o... more Randomization is a technique used in algorithms as a strategy that uses a random source as part of its logic. It is used in traditional algorithms to reduce time or space complexity. Many efforts have been made to increase the precision of convolutional neural networks (CNN) in various application domains, but less has been done to minimize the computational complexity of this model. In this work, we introduce randomized pooling (RPool) for CNN. RPool has reduced the number of operations of the CNN. Consequently, the computation time of the algorithm is reduced and the accuracy is improved. The MNIST dataset is used to demonstrate the pooling layer (PL) of CNN and compare the results of standard CNN with our proposed RPool for CNN. The simulation results show that as the number of epochs increases, the training and testing time of our proposed RPool decreases while the accuracy increases. We achieved 96.95% accuracy at epoch 10 and 8.85% decrease in training time, which demonstrates...
Mathematical Problems in Engineering, Jul 13, 2022
Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. e... more Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. e traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. e purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. e goal is to alarm and alert the security o cials to take proactive action to prevent violent activities. In deep learning literature, region-based classi ers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classi ers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. e method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by ltering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70-30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. is hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.
The teeth are the most challenging material to work with in the human body. Existing methods for ... more The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental...
Omicron is a covid family virus of COVID-19 and Delta variant. The Omicron (B.1.1.529.) variant o... more Omicron is a covid family virus of COVID-19 and Delta variant. The Omicron (B.1.1.529.) variant of COVID-19 is an extraordinary flow of infections globally and deadly, affecting the masses. The B.1.1.529 variant was first identified to WHO on November 24, 2021, from South Africa. In South Africa, the epidemiological condition has been determined by three different peaks in reported cases, the most recent of which was dominated by the Delta variant. Infections have risen sharply, corresponding with the discovery of the B.1.1.529 variant. The variant contains many mutations, some of which are potentially harmful. Preliminary research suggests that this variant has a higher risk of reinfection than other variants of concern. Nowadays, many scientists worldwide focus on problems that either improve existing methods used in DNA computing or suggest a new manner with a DNA computing approach. Many researchers are working on analyzing several aspects of Omicron from diverse fields. We have...
Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it ... more Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it has been widely used in image processing for image classification, object identification, semantic segmentation etc. It has overcome the constraint of traditional machine learning approaches. There has been a lot of effort done to improve the accuracy of CNN in many application areas, but there has been a lesser amount of work done to reduce the computational complexity of this model. There is a need to improve CNN's complexity problem. Here we have introduced randomized pooling (RANDpool) to CNN. Randomized pooling has reduced the computational complexity cost of CNN. We used MNIST dataset to demonstrate CNN with randomized pooling. This paper used randomization technique to reduce the dimensions of image instead of max, min or average pooling that have very extensive computations.
Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functio... more Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. In this paper, recursive version of the Wiener-Khintchine theorem has been derived by using the sliding DFT approach meant for computation of STFT. The computational complexity of the proposed recursive Wiener-Khintchine algorithm, for a window size of N, is O(N).
Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes... more Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%...
Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the... more Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transform algorithm. The main difference in this algorithm and STFT is that the sliding window (SW) and window for spectral estimation (WSA) are separately defined. WSA is updated and its PSD is computed only when change in statistics is detected in the SW. The computational complexity of the proposed algorithm is found to be lesser than that for standard STFT technique.
— Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation funct... more — Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. In this paper, recursive version of the Wiener-Khintchine theorem has been derived by using the sliding DFT approach meant for computation of STFT. The computational complexity of the proposed recursive Wiener-Khintchine algorithm, for a window size of N, is O(N).
— Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation funct... more — Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. In this paper, recursive version of the Wiener-Khintchine theorem has been derived by using the sliding DFT approach meant for computation of STFT. The computational complexity of the proposed recursive Wiener-Khintchine algorithm, for a window size of N, is O(N).
This work focuses on efficient, joint time-frequency analysis of time series data. Joint time-fre... more This work focuses on efficient, joint time-frequency analysis of time series data. Joint time-frequency analysis is based on the sliding window.There are two major contributions of this thesis. Firstly, we have introduced a notion of “aggregate spectrogram (AS)” which is a unimodal distribution at each time instant.The AS is extremely useful and computationally efficient when we are interested in a few spectral features and not the entire spectrum.Properties/characteristics of the AS have been listed.A parametric method, based on a second order autoregressive model of the signal, for the construction of the AS, has been described. Of all the existing spectral estimation tools, the AS has the least computational complexity.Based on the AS, instantaneous frequency estimation for multicomponent signals with equal amplitudes has been achieved.The AS does not require Goertzel filters in dual tone multi frequency detection applications.The AS finds many potential application.A few example...
A notion of fuzzy wavelets is introduced by using idea of fuzzy transforms. A detailed procedure ... more A notion of fuzzy wavelets is introduced by using idea of fuzzy transforms. A detailed procedure for analysis and synthesis of functions through fuzzy wavelets is presented.
Communications in Computer and Information Science
Abstract. The most widely used methods for time frequency analysis belong to Cohen's class o... more Abstract. The most widely used methods for time frequency analysis belong to Cohen's class of spectrogram estimators. WignerVille distri-bution (WVD) and multiwindow time frequency analysis are the most popular techniques. One serious limitation of Wigner distribution is ...
Abstract. The are various definitions of instantaneous frequency (IF) but most of them have short... more Abstract. The are various definitions of instantaneous frequency (IF) but most of them have short comings. The most popular of these defi-nitions, is the one given by Cohen. To estimate IF accuratly, techniques become more and more complex. This paper introduces the idea of ...
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Papers by Khalid Aamir