Walid Gomaa - Academia.edu (original) (raw)
Papers by Walid Gomaa
Artificial Intelligence, 2008
In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the... more In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the twentieth century, namely, \<شادية> (Shadia). Lyrics analysis provides a deep insight into the artist's career evolution, her interactions with the surrounding environment including the social, political, and economic conditions. In order to perform such analysis we had to collect and compile the lyrics of Shadia accompanied with the necessary metadata into an organized and structured form. The data are preprocessed by removing stop words and doing some normalization operations over the songs prose. We did not perform any lemmatization or stemming as the original form of the tokens convey much more information than the source words.We performed a lexical analysis in order to study both the lexical density and diversity over the course of Shadia career life. We have as well studied the most significant words, idioms, and terms played in the songs using tools such as word clouds...
Studies in computational intelligence, Aug 29, 2021
Several intelligent transportation systems focus on studying the various driver behaviors for num... more Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems.
Advances in intelligent systems and computing, Mar 17, 2019
In recent years, automatic human activity recognition has drawn much attention. On one hand, this... more In recent years, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware, which resulted in the tremendous explosion of activity data. On the other hand there are urgent growing and pressing demands from many application areas such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver’s behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper, we introduce a detailed survey on multiple human activity recognition (HAR) systems which use portable inertial sensors (Accelerometer, Magnetometer, and Gyro), where the sensor’s produced temporal signals are used for modeling and recognition of different human activities based on various machine learning techniques.
Intelligent Service Robotics, Feb 15, 2018
In this paper, a novel framework which enables humanoid robots to learn new skills from demonstra... more In this paper, a novel framework which enables humanoid robots to learn new skills from demonstration is proposed. The proposed framework makes use of real-time human motion imitation module as a demonstration interface for providing the desired motion to the learning module in an efficient and user-friendly way. This interface overcomes many problems of the currently used interfaces like direct motion recording, kinesthetic teaching, and immersive teleoperation. This method gives the human demonstrator the ability to control almost all body parts of the humanoid robot in real time (including hand shape and orientation which are essential to perform object grasping). The humanoid robot is controlled remotely and without using any sophisticated haptic devices, where it depends only on an inexpensive Kinect sensor and two additional force sensors. To the best of our knowledge, this is the first time for Kinect sensor to be used in estimating hand shape and orientation for object grasping within the field of real-time human motion imitation. Then, the observed motions are projected onto a latent space using Gaussian process latent variable model to extract the relevant features. These relevant features are then used to train regression models through the variational heteroscedastic Gaussian process regression algorithm which is proved to be a very accurate and very fast regression algorithm. Our proposed framework is validated using different activities concerned with both human upper and lower body parts and object grasping also. Keywords Imitation learning • Humanoid robot • Gaussian process latent variable model (GPLVM) • Variational heteroscedastic Gaussian process regression (VHGPR) • Kinect sensor • NAO robot • Grasping B Reda Elbasiony
Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics
Artificial Neural Networks and Machine Learning – ICANN 2018, 2018
In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB ... more In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB image based on a deep convolutional neural network (CNN). The proposed method depends on reducing the huge search space of the continuous-valued 3D human poses by discretizing and approximating these continuous poses into many discrete key-poses. These key-poses constitute more restricted search space and then can be considered as multiple-class candidates of 3D human poses.
Lecture Notes in Computer Science, 2018
In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB ... more In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB image based on a deep convolutional neural network (CNN). The proposed method depends on reducing the huge search space of the continuous-valued 3D human poses by discretizing and approximating these continuous poses into many discrete key-poses. These key-poses constitute more restricted search space and then can be considered as multiple-class candidates of 3D human poses.
The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022)
2023 International Joint Conference on Neural Networks (IJCNN)
SSRN Electronic Journal, 2019
2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)
Accidents fatality is generally dependent on the time an emergency service is dispatched to the a... more Accidents fatality is generally dependent on the time an emergency service is dispatched to the accident scene. Decreasing this time requires fast and accurate accident detection and notification systems. Therefore, existing well-established systems rely on rugged devices, with specialised hardware and accurate sensors to allow for in-vehicle detection and notification. Smartphones have been considered as an alternative mainly due to their much lower cost. In this paper, we show that reliable accident detection can be achieved using main stream smartphone sensors (e.g., accelerometer and gyroscope). The method relies on detecting the accident pulse through using Fourier transform and a random forest classifier. The method also utilises a moving window to incorporate time; and is simple enough to allow for during-accident detection, not requiring the mobile to survive the accident. We have validated the model using the Ollie car-like robot micro accident-testbed and the gold standard in accident simulation, LS-DYNA, achieving a true positive rate of about 96% and true negative rate of 99%.
Social Science Research Network, 2019
EPJ Quantum Technology
Current quantum processing technology is generally noisy with a limited number of qubits, stressi... more Current quantum processing technology is generally noisy with a limited number of qubits, stressing the importance of quantum state fidelity estimation. The complexity of this problem is mainly due to not only accounting for single gates and readout errors but also for interactions among which. Existing methods generally rely on either reconstructing the given circuit state, ideal state, and computing the distance of which; or forcing the system to be on a specific state. Both rely on conducting circuit measurements, in which computational efficiency is traded off with obtained fidelity details, requiring an exponential number of experiments for full information. This paper poses the question: Is the mapping between a given quantum circuit and its state fidelity learnable? If learnable, this would be a step towards an alternative approach that relies on machine learning, providing much more efficient computation. To answer this question, we propose three deep learning models for 1-,...
Accidents fatality is generally dependent on the time an emergency service is dispatched to the a... more Accidents fatality is generally dependent on the time an emergency service is dispatched to the accident scene. Decreasing this time requires fast and accurate accident detection and notification systems. Therefore, existing well-established systems rely on rugged devices, with specialised hardware and accurate sensors to allow for in-vehicle detection and notification. Smartphones have been considered as an alternative mainly due to their much lower cost. In this paper, we show that reliable accident detection can be achieved using main stream smartphone sensors (e.g., accelerometer and gyroscope). The method relies on detecting the accident pulse through using Fourier transform and a random forest classifier. The method also utilises a moving window to incorporate time; and is simple enough to allow for during-accident detection, not requiring the mobile to survive the accident. We have validated the model using the Ollie car-like robot micro accident-testbed and the gold standard in accident simulation, LS-DYNA, achieving a true positive rate of about 96% and true negative rate of 99%.
2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)
Traffic fatalities are increasing in developing countries where there are few investments in road... more Traffic fatalities are increasing in developing countries where there are few investments in road safety. Culture and road conditions also affect driving habits. Therefore, automatic detection and reporting of driver behavior to concerned entities can potentially save lives. In particular, we analyze a driving maneuvers dataset collected from one environment (country) but tested in another environment with aggressive driving habits and irregular road conditions. We also develop an on-edge system with fast response time to serve users on a large scale. Specifically, we propose an approach for detecting aggressive and normal events using random forest classifier. We utilize the accelerometer and gyroscope smartphone readings to classify driving maneuvers events to five types (aggressive acceleration, suddenly break, aggressive turn right, aggressive turn left, and normal). We achieved an accuracy of only 63.4% by training our model on an available dataset collected from a foreigner environment and tested on our environment. The lowest precision value was 54% while the lowest recall was 42%. However, we achieved an accuracy of 98.4% when augmenting an available dataset with data collected with our application. The lowest precision value was 98% while the lowest recall was 90%. From the results, it is shown that the available datasets do not generalize well to different driving habits and road conditions. Finally, an implementation of the random forest model using OpenCV on an Android platform is analyzed.
Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics, 2020
Gender recognition has been adopted recently by researchers due to its benefits in many applicati... more Gender recognition has been adopted recently by researchers due to its benefits in many applications such as recommendation systems and health care. The rise of using smart phones in everyday life made it very easy to have sensors like accelerometer and gyroscope in phones and other wearable devices. Here, we propose a robust method for gender recognition based on data from Inertial Measurement Unit (IMU) sensors. We explore the use of wavelet transform to extract features from the accelerometer and gyroscope signals along side with proper classifiers. Furthermore, we introduce our own collected dataset (EJUST-GINR-1) which contains samples from smart watches and IMU sensors placed at eight different parts of the human body. We investigate which sensor placements on the body best distinguish between males and females during the activity of walking. The results prove that wavelet transform can be used as a reliable feature extractor for gender recognition with high accuracy and less computations than other methods. In addition, sensors placed on the legs and waist perform better in recognizing the gender during walking than other sensors.
In this paper, we assess the scoring, ranking, docking, and screening powers of deep learning and... more In this paper, we assess the scoring, ranking, docking, and screening powers of deep learning and random forest scoring functions. For the scoring power, the DL RF scoring function (arithmetic mean between DL and RF scores) achieves Pearson's correlation coefficient between the predicted and experimentally measured binding affinities of 0.799 versus 0.758 of the RF scoring function. For the ranking power, the DL scoring function ranks the ligands bound to fixed target protein with accuracy 54% for the high-level ranking (correctly ranking the three ligands bound to the same target protein in a cluster) and with accuracy 78% for the low-level ranking (correctly ranking the best ligand only in the cluster) while the RF scoring function achieves (46% and 62%) respectively. For the docking power, the DL RF scoring function has a success rate when the three best-scored ligand binding poses are considered within 2Å root-mean-square-deviation from the native pose of 36.0% versus 30.2% of the RF scoring function. For the screening power, the DL scoring function has an average enrichment factor and success rate at the top 1% level of (2.69 and 6.45%) respectively versus (1.61 and 4.84%) respectively of the RF scoring function.
Artificial Intelligence, 2008
In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the... more In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the twentieth century, namely, \<شادية> (Shadia). Lyrics analysis provides a deep insight into the artist's career evolution, her interactions with the surrounding environment including the social, political, and economic conditions. In order to perform such analysis we had to collect and compile the lyrics of Shadia accompanied with the necessary metadata into an organized and structured form. The data are preprocessed by removing stop words and doing some normalization operations over the songs prose. We did not perform any lemmatization or stemming as the original form of the tokens convey much more information than the source words.We performed a lexical analysis in order to study both the lexical density and diversity over the course of Shadia career life. We have as well studied the most significant words, idioms, and terms played in the songs using tools such as word clouds...
Studies in computational intelligence, Aug 29, 2021
Several intelligent transportation systems focus on studying the various driver behaviors for num... more Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems.
Advances in intelligent systems and computing, Mar 17, 2019
In recent years, automatic human activity recognition has drawn much attention. On one hand, this... more In recent years, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware, which resulted in the tremendous explosion of activity data. On the other hand there are urgent growing and pressing demands from many application areas such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver’s behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper, we introduce a detailed survey on multiple human activity recognition (HAR) systems which use portable inertial sensors (Accelerometer, Magnetometer, and Gyro), where the sensor’s produced temporal signals are used for modeling and recognition of different human activities based on various machine learning techniques.
Intelligent Service Robotics, Feb 15, 2018
In this paper, a novel framework which enables humanoid robots to learn new skills from demonstra... more In this paper, a novel framework which enables humanoid robots to learn new skills from demonstration is proposed. The proposed framework makes use of real-time human motion imitation module as a demonstration interface for providing the desired motion to the learning module in an efficient and user-friendly way. This interface overcomes many problems of the currently used interfaces like direct motion recording, kinesthetic teaching, and immersive teleoperation. This method gives the human demonstrator the ability to control almost all body parts of the humanoid robot in real time (including hand shape and orientation which are essential to perform object grasping). The humanoid robot is controlled remotely and without using any sophisticated haptic devices, where it depends only on an inexpensive Kinect sensor and two additional force sensors. To the best of our knowledge, this is the first time for Kinect sensor to be used in estimating hand shape and orientation for object grasping within the field of real-time human motion imitation. Then, the observed motions are projected onto a latent space using Gaussian process latent variable model to extract the relevant features. These relevant features are then used to train regression models through the variational heteroscedastic Gaussian process regression algorithm which is proved to be a very accurate and very fast regression algorithm. Our proposed framework is validated using different activities concerned with both human upper and lower body parts and object grasping also. Keywords Imitation learning • Humanoid robot • Gaussian process latent variable model (GPLVM) • Variational heteroscedastic Gaussian process regression (VHGPR) • Kinect sensor • NAO robot • Grasping B Reda Elbasiony
Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics
Artificial Neural Networks and Machine Learning – ICANN 2018, 2018
In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB ... more In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB image based on a deep convolutional neural network (CNN). The proposed method depends on reducing the huge search space of the continuous-valued 3D human poses by discretizing and approximating these continuous poses into many discrete key-poses. These key-poses constitute more restricted search space and then can be considered as multiple-class candidates of 3D human poses.
Lecture Notes in Computer Science, 2018
In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB ... more In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB image based on a deep convolutional neural network (CNN). The proposed method depends on reducing the huge search space of the continuous-valued 3D human poses by discretizing and approximating these continuous poses into many discrete key-poses. These key-poses constitute more restricted search space and then can be considered as multiple-class candidates of 3D human poses.
The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022)
2023 International Joint Conference on Neural Networks (IJCNN)
SSRN Electronic Journal, 2019
2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)
Accidents fatality is generally dependent on the time an emergency service is dispatched to the a... more Accidents fatality is generally dependent on the time an emergency service is dispatched to the accident scene. Decreasing this time requires fast and accurate accident detection and notification systems. Therefore, existing well-established systems rely on rugged devices, with specialised hardware and accurate sensors to allow for in-vehicle detection and notification. Smartphones have been considered as an alternative mainly due to their much lower cost. In this paper, we show that reliable accident detection can be achieved using main stream smartphone sensors (e.g., accelerometer and gyroscope). The method relies on detecting the accident pulse through using Fourier transform and a random forest classifier. The method also utilises a moving window to incorporate time; and is simple enough to allow for during-accident detection, not requiring the mobile to survive the accident. We have validated the model using the Ollie car-like robot micro accident-testbed and the gold standard in accident simulation, LS-DYNA, achieving a true positive rate of about 96% and true negative rate of 99%.
Social Science Research Network, 2019
EPJ Quantum Technology
Current quantum processing technology is generally noisy with a limited number of qubits, stressi... more Current quantum processing technology is generally noisy with a limited number of qubits, stressing the importance of quantum state fidelity estimation. The complexity of this problem is mainly due to not only accounting for single gates and readout errors but also for interactions among which. Existing methods generally rely on either reconstructing the given circuit state, ideal state, and computing the distance of which; or forcing the system to be on a specific state. Both rely on conducting circuit measurements, in which computational efficiency is traded off with obtained fidelity details, requiring an exponential number of experiments for full information. This paper poses the question: Is the mapping between a given quantum circuit and its state fidelity learnable? If learnable, this would be a step towards an alternative approach that relies on machine learning, providing much more efficient computation. To answer this question, we propose three deep learning models for 1-,...
Accidents fatality is generally dependent on the time an emergency service is dispatched to the a... more Accidents fatality is generally dependent on the time an emergency service is dispatched to the accident scene. Decreasing this time requires fast and accurate accident detection and notification systems. Therefore, existing well-established systems rely on rugged devices, with specialised hardware and accurate sensors to allow for in-vehicle detection and notification. Smartphones have been considered as an alternative mainly due to their much lower cost. In this paper, we show that reliable accident detection can be achieved using main stream smartphone sensors (e.g., accelerometer and gyroscope). The method relies on detecting the accident pulse through using Fourier transform and a random forest classifier. The method also utilises a moving window to incorporate time; and is simple enough to allow for during-accident detection, not requiring the mobile to survive the accident. We have validated the model using the Ollie car-like robot micro accident-testbed and the gold standard in accident simulation, LS-DYNA, achieving a true positive rate of about 96% and true negative rate of 99%.
2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)
Traffic fatalities are increasing in developing countries where there are few investments in road... more Traffic fatalities are increasing in developing countries where there are few investments in road safety. Culture and road conditions also affect driving habits. Therefore, automatic detection and reporting of driver behavior to concerned entities can potentially save lives. In particular, we analyze a driving maneuvers dataset collected from one environment (country) but tested in another environment with aggressive driving habits and irregular road conditions. We also develop an on-edge system with fast response time to serve users on a large scale. Specifically, we propose an approach for detecting aggressive and normal events using random forest classifier. We utilize the accelerometer and gyroscope smartphone readings to classify driving maneuvers events to five types (aggressive acceleration, suddenly break, aggressive turn right, aggressive turn left, and normal). We achieved an accuracy of only 63.4% by training our model on an available dataset collected from a foreigner environment and tested on our environment. The lowest precision value was 54% while the lowest recall was 42%. However, we achieved an accuracy of 98.4% when augmenting an available dataset with data collected with our application. The lowest precision value was 98% while the lowest recall was 90%. From the results, it is shown that the available datasets do not generalize well to different driving habits and road conditions. Finally, an implementation of the random forest model using OpenCV on an Android platform is analyzed.
Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics, 2020
Gender recognition has been adopted recently by researchers due to its benefits in many applicati... more Gender recognition has been adopted recently by researchers due to its benefits in many applications such as recommendation systems and health care. The rise of using smart phones in everyday life made it very easy to have sensors like accelerometer and gyroscope in phones and other wearable devices. Here, we propose a robust method for gender recognition based on data from Inertial Measurement Unit (IMU) sensors. We explore the use of wavelet transform to extract features from the accelerometer and gyroscope signals along side with proper classifiers. Furthermore, we introduce our own collected dataset (EJUST-GINR-1) which contains samples from smart watches and IMU sensors placed at eight different parts of the human body. We investigate which sensor placements on the body best distinguish between males and females during the activity of walking. The results prove that wavelet transform can be used as a reliable feature extractor for gender recognition with high accuracy and less computations than other methods. In addition, sensors placed on the legs and waist perform better in recognizing the gender during walking than other sensors.
In this paper, we assess the scoring, ranking, docking, and screening powers of deep learning and... more In this paper, we assess the scoring, ranking, docking, and screening powers of deep learning and random forest scoring functions. For the scoring power, the DL RF scoring function (arithmetic mean between DL and RF scores) achieves Pearson's correlation coefficient between the predicted and experimentally measured binding affinities of 0.799 versus 0.758 of the RF scoring function. For the ranking power, the DL scoring function ranks the ligands bound to fixed target protein with accuracy 54% for the high-level ranking (correctly ranking the three ligands bound to the same target protein in a cluster) and with accuracy 78% for the low-level ranking (correctly ranking the best ligand only in the cluster) while the RF scoring function achieves (46% and 62%) respectively. For the docking power, the DL RF scoring function has a success rate when the three best-scored ligand binding poses are considered within 2Å root-mean-square-deviation from the native pose of 36.0% versus 30.2% of the RF scoring function. For the screening power, the DL scoring function has an average enrichment factor and success rate at the top 1% level of (2.69 and 6.45%) respectively versus (1.61 and 4.84%) respectively of the RF scoring function.