Despite the declining COVID-19 cases, global healthcare systems still face significant challenges... more Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detec...
Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective tre... more Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants’ cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convo...
Selecting the appropriate undergraduate program is a critical decision for students. Many element... more Selecting the appropriate undergraduate program is a critical decision for students. Many elements influence this choice for secondary students, including financial, social, demographic, and cultural factors. If a student makes a poor choice, it will have implications for their academic life as well as their professional life. These implications may include having to change their major, which will cause a delay in their graduation, having a low grade-point average (GPA) in their chosen major, which will cause difficulties in finding a job, or even dropping out of university. In this paper, various supervised machine learning techniques, including Decision Tree, Random Forest, and Support Vector Machine, were investigated to predict undergraduate majors. The input features were related to the student’s academic history and the job market. We were able to recommend the program that guarantees both a high academic degree and employment, depending on previous data and experience, for Ma...
Crying is the only means of communication for a newborn baby with its surrounding environment, bu... more Crying is the only means of communication for a newborn baby with its surrounding environment, but it also provides significant information about the newborn’s health, emotions, and needs. The cries of newborn babies have long been known as a biomarker for the diagnosis of pathologies. However, to the best of our knowledge, exploring the discrimination of two pathology groups by means of cry signals is unprecedented. Therefore, this study aimed to identify septic newborns with Neonatal Respiratory Distress Syndrome (RDS) by employing the Machine Learning (ML) methods of Multilayer Perceptron (MLP) and Support Vector Machine (SVM). Furthermore, the cry signal was analyzed from the following two different perspectives: 1) the musical perspective by studying the spectral feature set of Harmonic Ratio (HR), and 2) the speech processing perspective using the short-term feature set of Gammatone Frequency Cepstral Coefficients (GFCCs). In order to assess the role of employing features from...
Journal of engineering sciences and information technology
This study has investigated the improvement of communication between students, academic and admin... more This study has investigated the improvement of communication between students, academic and administrative departments at Palestinian universities, Palestine Ahliya University (PAU) in particular, by proposing a smartphone application. In particular, the study aimed to measure the level of administrative and academic communication provided to students and staff by the current e-services (portal and email) and traditional paper method at PAU, and thus identify their most prominent administrative and academic needs. In this paper, the descriptive-analytical method was used and a questionnaire was distributed to academics and head of administrative departments who have direct contact with university students. A random sample of the university students of different levels was selected at PAU as a case study. More precisely, 221 questionnaires were distributed, 169 were retrieved and analyzed using Google Drive. The results showed a real weakness in the communication level between the ac...
Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority... more Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressivel...
Mağallaẗ al-ʿulūm al-handasiyyaẗ wa-al-tiknūlūğiyā al-maʿlūmāt, Dec 30, 2019
Obviously, digital technology offers simplified solutions to solve or mitigate problems in genera... more Obviously, digital technology offers simplified solutions to solve or mitigate problems in general. In the academic sector in particular, the field training is one of the core courses that students must enroll during the third and fourth academic year, where the students have manually to select a relevant organization or institution based on their specialization. The academic staff and hosting institutions do not supervise the trainees as required due to lack of communication between them, wrong selection of the hosting institutions by students in some cases, limited following-up the trainees, thus leading to uncertainty in the number of training hours and reports required by students. These problems can be addressed or mitigated by proposing an electronic training system improving the communication between supervisors of field training, host institutions, and trainees and thus solving most of the mentioned problems. Based on this e-training system, the training unit at the university could therefore follow-up the trainees and thus improve the communication and cooperation with the training institutions. It will also definitely contribute in improving the training task itself for the students. In this proposed paper, the importance of the proposed system was presented. Other related systems were mentioned and used as references in the analysis stage. In the analytical stage, data was collected using 3 different questionnaires developed for students, supervising staff, and for the training institutions. Consequently, obstacles and problems faced these entities were extracted and mentioned. Finally, a preliminary design was proposed in this paper to develop and implement an electronic training system at Palestine Ahliya University.
Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be p... more Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (...
I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS whi... more I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS which is associated to Paris Sud and Sorbonne Universities for their supports and help. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.
Jordanian Journal of Computers and Information Technology, 2020
One of the best ways of communication between deaf people and hearing people is based on sign lan... more One of the best ways of communication between deaf people and hearing people is based on sign language or so-called hand gestures. In the Arab society, only deaf people and specialists could deal with Arabic sign language, which makes the deaf community narrow and thus communicating with normal people difficult. In addition to that, studying the problem of Arabic sign language recognition (ArSLR) has been paid attention recently, which emphasizes the necessity of investigating other approaches for such a problem. This paper proposes a novel ArSLR scheme based on an unsupervised deep learning algorithm, a deep belief network (DBN) coupled with a direct use of tiny images, which has been used to recognize and classify Arabic alphabetical letters. The use of deep learning contributed to extracting the most important features that are sparsely represented and played an important role in simplifying the overall recognition task. In total, around 6,000 samples of the 28 Arabic alphabetic signs have been used after resizing and normalization for feature extraction. The classification process was investigated using a softmax regression and achieved an overall accuracy of 83.32%, showing high reliability of the DBN-based Arabic alphabetical character recognition model. This model also achieved a sensitivity and a specificity of 70.5% and 96.2%, respectively.
Global journal of computer science and technology, 2017
This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann ... more This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann Machines and a direct use of tiny images. Restricted Boltzmann Machines are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in deep architecture (Deep Belief Networks) leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. After appropriate coding, a softmax regression in the feature space must be sufficient to recognize a hand sign according to the input image. To our knowledge, this is the first attempt that tiny images feature extraction using deep architecture is a simpler alternative approach for Arabic sign language recognition that deserves to be considered and investigated.
This paper proposes an alternative approach for the problem of Arabic handwritten character recog... more This paper proposes an alternative approach for the problem of Arabic handwritten character recognition. The proposed model is based on Deep Belief Networks (DBNs) which are unsupervised machine learning methods. A greedy layer-wise fashion based on Restricted Boltzmann Machines and contrastive divergence learning algorithm will be used to train such model. Previous studies have shown that DBNs are capable to extract a set of sparse features, which can be used to code the initial data in an efficient way. The assumption is that such representation must improve the linear separation among the different classes and thus a simple classification algorithm, like softmax regression, should be sufficient to achieve accurate recognition rates. The literature reviewed showed that this alternative approach has not been considered yet in the context of Arabic character recognition, which deserves to be investigated and evaluate its performance for such problem.
Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, 2012
This paper presents a novel approach for robot semantic place recognition (SPR) based on Restrict... more This paper presents a novel approach for robot semantic place recognition (SPR) based on Restricted Boltzmann Machines (RBMs) and a direct use of tiny images. RBMs are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in a deep architecture leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. In this article, we show that SPR can thus be achieved using tiny images instead of conventional Bag-of-Words (BoW) methods. After appropriate coding, a softmax regression in the feature space suffices to compute the probability to be in a given place according to the input image.
International Journal of Signal Processing Systems, 2016
Image processing such as Traffic Sign Recognition (TSR) plays a key role in Intelligent Transport... more Image processing such as Traffic Sign Recognition (TSR) plays a key role in Intelligent Transportation Systems particularly in Traffic Sign Recognition (TSR) which aims at increasing driver safety. Several studies have proposed TSR systems based on different image processing and machine learning algorithms. However, the efficiency of the proposed TSR algorithms requires improvement to enable real-time alerts on onboard devices which have limited computational power. Further improvement on accuracy of TSR is also required mainly in unstable weather conditions or when multiple signs exist on one pillar. This research proposes an improved model for Automatic TSR (ATSR) consisting of improved Connected Components Labeling and color histogram. Firstly, images are captured in real-world conditions in Palestine. The region of interests is detected using the improved Connected Components Labeling algorithm combined with Vectorization to reduce computational time. Secondly, the local features of detected images are computed using the color histogram. The results of the research show that combining the Connected Components Labeling with Vectorization reduces the computational time. Also, the Connected Components Labeling algorithm followed by histogram increase the accuracy of recognition. The low computational cost and the accuracy of the model enable us to use the model on smart phones for accurately recognizing traffic signs and alerting drivers in real time.
We propose an artifact classification scheme based on a combined deep and convolutional neural ne... more We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed met...
I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS whi... more I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS which is associated to Paris Sud and Sorbonne Universities for their supports and help. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.
Despite the declining COVID-19 cases, global healthcare systems still face significant challenges... more Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detec...
Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective tre... more Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants’ cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convo...
Selecting the appropriate undergraduate program is a critical decision for students. Many element... more Selecting the appropriate undergraduate program is a critical decision for students. Many elements influence this choice for secondary students, including financial, social, demographic, and cultural factors. If a student makes a poor choice, it will have implications for their academic life as well as their professional life. These implications may include having to change their major, which will cause a delay in their graduation, having a low grade-point average (GPA) in their chosen major, which will cause difficulties in finding a job, or even dropping out of university. In this paper, various supervised machine learning techniques, including Decision Tree, Random Forest, and Support Vector Machine, were investigated to predict undergraduate majors. The input features were related to the student’s academic history and the job market. We were able to recommend the program that guarantees both a high academic degree and employment, depending on previous data and experience, for Ma...
Crying is the only means of communication for a newborn baby with its surrounding environment, bu... more Crying is the only means of communication for a newborn baby with its surrounding environment, but it also provides significant information about the newborn’s health, emotions, and needs. The cries of newborn babies have long been known as a biomarker for the diagnosis of pathologies. However, to the best of our knowledge, exploring the discrimination of two pathology groups by means of cry signals is unprecedented. Therefore, this study aimed to identify septic newborns with Neonatal Respiratory Distress Syndrome (RDS) by employing the Machine Learning (ML) methods of Multilayer Perceptron (MLP) and Support Vector Machine (SVM). Furthermore, the cry signal was analyzed from the following two different perspectives: 1) the musical perspective by studying the spectral feature set of Harmonic Ratio (HR), and 2) the speech processing perspective using the short-term feature set of Gammatone Frequency Cepstral Coefficients (GFCCs). In order to assess the role of employing features from...
Journal of engineering sciences and information technology
This study has investigated the improvement of communication between students, academic and admin... more This study has investigated the improvement of communication between students, academic and administrative departments at Palestinian universities, Palestine Ahliya University (PAU) in particular, by proposing a smartphone application. In particular, the study aimed to measure the level of administrative and academic communication provided to students and staff by the current e-services (portal and email) and traditional paper method at PAU, and thus identify their most prominent administrative and academic needs. In this paper, the descriptive-analytical method was used and a questionnaire was distributed to academics and head of administrative departments who have direct contact with university students. A random sample of the university students of different levels was selected at PAU as a case study. More precisely, 221 questionnaires were distributed, 169 were retrieved and analyzed using Google Drive. The results showed a real weakness in the communication level between the ac...
Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority... more Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressivel...
Mağallaẗ al-ʿulūm al-handasiyyaẗ wa-al-tiknūlūğiyā al-maʿlūmāt, Dec 30, 2019
Obviously, digital technology offers simplified solutions to solve or mitigate problems in genera... more Obviously, digital technology offers simplified solutions to solve or mitigate problems in general. In the academic sector in particular, the field training is one of the core courses that students must enroll during the third and fourth academic year, where the students have manually to select a relevant organization or institution based on their specialization. The academic staff and hosting institutions do not supervise the trainees as required due to lack of communication between them, wrong selection of the hosting institutions by students in some cases, limited following-up the trainees, thus leading to uncertainty in the number of training hours and reports required by students. These problems can be addressed or mitigated by proposing an electronic training system improving the communication between supervisors of field training, host institutions, and trainees and thus solving most of the mentioned problems. Based on this e-training system, the training unit at the university could therefore follow-up the trainees and thus improve the communication and cooperation with the training institutions. It will also definitely contribute in improving the training task itself for the students. In this proposed paper, the importance of the proposed system was presented. Other related systems were mentioned and used as references in the analysis stage. In the analytical stage, data was collected using 3 different questionnaires developed for students, supervising staff, and for the training institutions. Consequently, obstacles and problems faced these entities were extracted and mentioned. Finally, a preliminary design was proposed in this paper to develop and implement an electronic training system at Palestine Ahliya University.
Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be p... more Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (...
I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS whi... more I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS which is associated to Paris Sud and Sorbonne Universities for their supports and help. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.
Jordanian Journal of Computers and Information Technology, 2020
One of the best ways of communication between deaf people and hearing people is based on sign lan... more One of the best ways of communication between deaf people and hearing people is based on sign language or so-called hand gestures. In the Arab society, only deaf people and specialists could deal with Arabic sign language, which makes the deaf community narrow and thus communicating with normal people difficult. In addition to that, studying the problem of Arabic sign language recognition (ArSLR) has been paid attention recently, which emphasizes the necessity of investigating other approaches for such a problem. This paper proposes a novel ArSLR scheme based on an unsupervised deep learning algorithm, a deep belief network (DBN) coupled with a direct use of tiny images, which has been used to recognize and classify Arabic alphabetical letters. The use of deep learning contributed to extracting the most important features that are sparsely represented and played an important role in simplifying the overall recognition task. In total, around 6,000 samples of the 28 Arabic alphabetic signs have been used after resizing and normalization for feature extraction. The classification process was investigated using a softmax regression and achieved an overall accuracy of 83.32%, showing high reliability of the DBN-based Arabic alphabetical character recognition model. This model also achieved a sensitivity and a specificity of 70.5% and 96.2%, respectively.
Global journal of computer science and technology, 2017
This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann ... more This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann Machines and a direct use of tiny images. Restricted Boltzmann Machines are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in deep architecture (Deep Belief Networks) leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. After appropriate coding, a softmax regression in the feature space must be sufficient to recognize a hand sign according to the input image. To our knowledge, this is the first attempt that tiny images feature extraction using deep architecture is a simpler alternative approach for Arabic sign language recognition that deserves to be considered and investigated.
This paper proposes an alternative approach for the problem of Arabic handwritten character recog... more This paper proposes an alternative approach for the problem of Arabic handwritten character recognition. The proposed model is based on Deep Belief Networks (DBNs) which are unsupervised machine learning methods. A greedy layer-wise fashion based on Restricted Boltzmann Machines and contrastive divergence learning algorithm will be used to train such model. Previous studies have shown that DBNs are capable to extract a set of sparse features, which can be used to code the initial data in an efficient way. The assumption is that such representation must improve the linear separation among the different classes and thus a simple classification algorithm, like softmax regression, should be sufficient to achieve accurate recognition rates. The literature reviewed showed that this alternative approach has not been considered yet in the context of Arabic character recognition, which deserves to be investigated and evaluate its performance for such problem.
Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, 2012
This paper presents a novel approach for robot semantic place recognition (SPR) based on Restrict... more This paper presents a novel approach for robot semantic place recognition (SPR) based on Restricted Boltzmann Machines (RBMs) and a direct use of tiny images. RBMs are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in a deep architecture leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. In this article, we show that SPR can thus be achieved using tiny images instead of conventional Bag-of-Words (BoW) methods. After appropriate coding, a softmax regression in the feature space suffices to compute the probability to be in a given place according to the input image.
International Journal of Signal Processing Systems, 2016
Image processing such as Traffic Sign Recognition (TSR) plays a key role in Intelligent Transport... more Image processing such as Traffic Sign Recognition (TSR) plays a key role in Intelligent Transportation Systems particularly in Traffic Sign Recognition (TSR) which aims at increasing driver safety. Several studies have proposed TSR systems based on different image processing and machine learning algorithms. However, the efficiency of the proposed TSR algorithms requires improvement to enable real-time alerts on onboard devices which have limited computational power. Further improvement on accuracy of TSR is also required mainly in unstable weather conditions or when multiple signs exist on one pillar. This research proposes an improved model for Automatic TSR (ATSR) consisting of improved Connected Components Labeling and color histogram. Firstly, images are captured in real-world conditions in Palestine. The region of interests is detected using the improved Connected Components Labeling algorithm combined with Vectorization to reduce computational time. Secondly, the local features of detected images are computed using the color histogram. The results of the research show that combining the Connected Components Labeling with Vectorization reduces the computational time. Also, the Connected Components Labeling algorithm followed by histogram increase the accuracy of recognition. The low computational cost and the accuracy of the model enable us to use the model on smart phones for accurately recognizing traffic signs and alerting drivers in real time.
We propose an artifact classification scheme based on a combined deep and convolutional neural ne... more We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed met...
I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS whi... more I also thank all administrators, technicians, and secretaries in the laboratory of LIMSI-CNRS which is associated to Paris Sud and Sorbonne Universities for their supports and help. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.
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Papers by Ahmad Hasasneh