Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students... more Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students/employees face several challenges navigating and communicating with their superiors and colleagues. Mobile applications for people with Autism Spectrum Disorder (ASD apps for short) have been increasingly being adapted to help autistic people manage their conditions and daily activities. User feedback analysis is an effective method that can be used to improve ASD apps’ services. In this article, we investigate the usage of ASD apps to improve the quality of life for autistic students/employees based on user feedback analysis. For this purpose, we analyze user reviews suggested on highly ranked ASD apps for college students, and workers. A total of 97,051 reviews have been collected from 13 ASD apps available on Google Play and Apple App stores. The collected reviews have been classified into negative, positive, and neutral opinions. This analysis has been performed using machine learni...
Heart disease detection is the need of the hour as it not only deteriorated adults but children a... more Heart disease detection is the need of the hour as it not only deteriorated adults but children are also showing symptoms of it all over the world. It can occur to a person having an improper diet, high cholesterol level, smoking habits, addiction to alcohol or drugs and even occurs to a diabetic patient. Various approaches are there in various fields, say in Machine Learning, Soft Computing, Data Mining are there. This paper aims to provide a survey of several research papers comprising of the above techniques on determining the heart diseases. This paper gives the perspective for the researchers for future work.
Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that affects human co... more Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that affects human communication and behavior. ASD is associated with significant healthcare costs for diagnosis as well as for treatment. Disease diagnosis using deep learning model has become a wide research area. This paper proposes a deep classifier model for ASD prediction. The evaluation of the proposed model is performed over three datasets involving child, adolescent, and adult provided by ASDTest database. The obtained results showed that deep classifier model provides better results than other common machine learning classification techniques, with an accuracy of 99.50 %, 99.23 % and 99.42% for respectively adult, adolescent, and child datasets. Practical experiments conducted over these datasets report encouraging performances which are competitive to other existing ASD prediction models.
Abstract The risk of death incurred by breast cancer is rising exponentially, especially among wo... more Abstract The risk of death incurred by breast cancer is rising exponentially, especially among women. The early breast cancer diagnosis before it metastasizes helps medical staff controlling this disease, which decreases the risk of death. This made early breast cancer detection a crucial problem. Different imaging modalities offer complementary information concerning the same lesion helps to increase the performance of thcy fusing several modalities. This paper proposes a computerized features classification of breast cancer lesions through both the Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and Digital Mammographic images (MGs). This study aims to investigate a Multimodal Fusion-based Computer-Aided Diagnosis (CAD) system, called MF-CAD, based on multivariate analysis of different modalities, for breast cancer mass detection. In this paper, firstly a new local feature descriptor is proposed in feature extraction, namely, the Gradient Local Information Pattern (GLIP), where we consider the gradient magnitude and orientation as well as the local differences as local binary features for DCE-MRI (or MGs) modality. Secondly, the fusion scheme is conducted using the Canonical Correlation Analysis (CCA) to highlight the intrinsic relation between these modalities. Finally, for comparative purposes, several selected machine learning classifiers (K-Nearest Neighbors, Support Vector Machine, Random forests, Artificial Neural Networks and Radial Basis Function Neural Network (RBFNN)) are used to distinguish between mass and No-mass breast images.Evaluation experiments of the diagnostic performances of our MF-CAD system are conducted over private datasets that contain both MG and DCE-MRI images acquired from 286 patients, which are “Breast DCE-MRI”, “Breast-MG” and “Breast Multimodal” datasets. Experimental results of the proposed MF-CAD system achieved an Area Under the ROC Curve (AUC) value of 99.10% using RBFNN classifier, while for each single modality alone, the best AUC values of 97.20% and 93.50% are obtained respectively for MG and DCE-MRI modalities using random forest classifier. A comparative study with recent existing approaches shows the competitive performances of the proposed approach.
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017
In this paper, we investigate an efficient palmprint texture modeling method that incorporates a ... more In this paper, we investigate an efficient palmprint texture modeling method that incorporates a robust analysis based on fusing multiple information. In fact, a single descriptor alone may not achieve a high accuracy in palmprint biometric system. Hence, we propose the fusion of various information features extracted by the different descriptors, such as the fractal and the Multi-fractal techniques which produce a robustness to face the numerous challenging and variation of palmprint in unconstrained environments. To increase the performance of palmprint biometric systems, information fusion is proposed as a key phase in multi-characteristic systems. The obtained information can be combined at different levels, i.e., at the feature level, the score level or the decision level. Nevertheless, the feature level fusion is considered more effective than both the matching score and the classifier decision levels, thanks to a feature vector set which contains more and richer information about the input palmprint image. In order to improve the discriminating texture information, our proposed method extracts the fractal dimension features from the preprocessed palmprint images and fuses them with the Multi-fractal dimension features using the Canonical Correlation Analysis (CCA) incorporating the Linear Discriminant Analysis (LDA) in order to reduce the feature dimensionality for each feature set. To demonstrate the feasibility and effectiveness of our proposed method, we performed the experimental results on two benchmark datasets. These results outperform other well-known state of the art methods and produce promising recognition rates by achieving 96.02% for PolyU-Palmprint database and 97.00% for CASIA-Palmprint database.
Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the les... more Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the lesions based on mammographic images is playing an essential role to assist experts. A novel Computer-Aided Diagnosis (CADx) scheme of breast lesion classification is proposed in this paper based on an optimized combination of texture and shape features using machine and deep learning algorithms for mass classification as benign-malignant namely C(M-ZMs)*. The main advantage of using Zernike moments for shape feature extraction is their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our case. We implemented for texture feature extraction the Monogenic-Local Binary Pattern taking the advantage of lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. Therefore, we used Zernike moments for shape feature extraction due to their scale, translation, and ...
International Journal of Digital Crime and Forensics, 2020
The security of people requires a beefy guarantee in our society, particularly, with the spread o... more The security of people requires a beefy guarantee in our society, particularly, with the spread of terrorism throughout the world. In this context, palmprint identification based on texture analysis is amongst the pattern recognition applications to recognize people. In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlati...
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
This paper presents a new method to recognize the person's identity through their palmprints.... more This paper presents a new method to recognize the person's identity through their palmprints. Palmprint recognition is among the most reliable physiological characteristics that can be used especially in forensic applications thanks to its simplicity and its ease of use, its user friendliness and high identification reliability. Accordingly, it has gained great popularity within the pattern recognition field over the past three decades. In this paper, we suggest a new approach for personal identification based on palmprint features extracted using the various methods of fractal theory. These methods have been broadly applied in image processing fields to estimate the fractal dimensions of an image as an important parameter for the analysis of objects of irregular shapes of the texture image. The novelty of this approach is two-fold. On the one hand, we apply the Box counting (BC), the Mass Radius (MS) and the Cumulative Intersection (CumInt) methods to extract the palmprint texture information. On the other hand, the combination of efficient information from the three descriptors has been presented in order to make identification system more efficient and achieve better performances. Then, we explore such texture information features by using classical machine learning techniques: the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM) and the Multiclass Random Forest classification algorithms. The results of the experiments conducted on two large datasets show that our proposed method gives better recognition rates of about 96.35% for CASIA-Palmprint dataset and 95.98% for IITD-Touchless-Palmprint dataset. These results obtained are compared to other well-known state-of-the-art approaches.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
Recently, personal identification, which is based on the palmprint texture features analysis, has... more Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI) of the hand. Secondly, we combine multiple descriptors to extract the palmprint texture information, which are Gray-Level Co-occurrence Matrix (GLCM) and the Gabor filters using feature level fusion. These descriptors have been broadly applied in various tasks, specifically in the image processing domain to analyze the image texture. Then, we apply the generalized discriminant analysis (GDA) to reduce the length of the feature vectors and their redundancies. Finally, we classify these final resulting features by developing the SVM method which supports several kernel functions to reach a best recognition rate. We have conducted extensive experiments on the “CASIA-Palmprint” and “PolyU-palmprint” datasets. The obtained results of the proposed approach provide promising results compared to other well-known state-of-the-art approaches.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
As a machine learning algorithms, deep learning algorithms developed in recent years, have been s... more As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this study, a deep learning model based on Support Vector Machine (SVM) named Deep SVM (DSVM) is represented. We applied the dropout technique on the Deep SVM (DSVM). It is worth noting that this model has an inherent capacity to choose data points crucial to classify good generalization capacities. The deep SVM is built by a stack of SVMs permitting to extracting/learning automatically features from the raw images and to realize classification, too. We chose and tested the Multi-class Support Vector Machine with an RBF kernel, as non-linear discriminative features for classification, on Handwritten Arabic Characters Database (HACDB). Further to these advantages, our model is safeguarded against over-fitting because of strong performance of dropout. Simulation outcomes prove the efficiency of the suggested model.
Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students... more Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students/employees face several challenges navigating and communicating with their superiors and colleagues. Mobile applications for people with Autism Spectrum Disorder (ASD apps for short) have been increasingly being adapted to help autistic people manage their conditions and daily activities. User feedback analysis is an effective method that can be used to improve ASD apps’ services. In this article, we investigate the usage of ASD apps to improve the quality of life for autistic students/employees based on user feedback analysis. For this purpose, we analyze user reviews suggested on highly ranked ASD apps for college students, and workers. A total of 97,051 reviews have been collected from 13 ASD apps available on Google Play and Apple App stores. The collected reviews have been classified into negative, positive, and neutral opinions. This analysis has been performed using machine learni...
Heart disease detection is the need of the hour as it not only deteriorated adults but children a... more Heart disease detection is the need of the hour as it not only deteriorated adults but children are also showing symptoms of it all over the world. It can occur to a person having an improper diet, high cholesterol level, smoking habits, addiction to alcohol or drugs and even occurs to a diabetic patient. Various approaches are there in various fields, say in Machine Learning, Soft Computing, Data Mining are there. This paper aims to provide a survey of several research papers comprising of the above techniques on determining the heart diseases. This paper gives the perspective for the researchers for future work.
Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that affects human co... more Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that affects human communication and behavior. ASD is associated with significant healthcare costs for diagnosis as well as for treatment. Disease diagnosis using deep learning model has become a wide research area. This paper proposes a deep classifier model for ASD prediction. The evaluation of the proposed model is performed over three datasets involving child, adolescent, and adult provided by ASDTest database. The obtained results showed that deep classifier model provides better results than other common machine learning classification techniques, with an accuracy of 99.50 %, 99.23 % and 99.42% for respectively adult, adolescent, and child datasets. Practical experiments conducted over these datasets report encouraging performances which are competitive to other existing ASD prediction models.
Abstract The risk of death incurred by breast cancer is rising exponentially, especially among wo... more Abstract The risk of death incurred by breast cancer is rising exponentially, especially among women. The early breast cancer diagnosis before it metastasizes helps medical staff controlling this disease, which decreases the risk of death. This made early breast cancer detection a crucial problem. Different imaging modalities offer complementary information concerning the same lesion helps to increase the performance of thcy fusing several modalities. This paper proposes a computerized features classification of breast cancer lesions through both the Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and Digital Mammographic images (MGs). This study aims to investigate a Multimodal Fusion-based Computer-Aided Diagnosis (CAD) system, called MF-CAD, based on multivariate analysis of different modalities, for breast cancer mass detection. In this paper, firstly a new local feature descriptor is proposed in feature extraction, namely, the Gradient Local Information Pattern (GLIP), where we consider the gradient magnitude and orientation as well as the local differences as local binary features for DCE-MRI (or MGs) modality. Secondly, the fusion scheme is conducted using the Canonical Correlation Analysis (CCA) to highlight the intrinsic relation between these modalities. Finally, for comparative purposes, several selected machine learning classifiers (K-Nearest Neighbors, Support Vector Machine, Random forests, Artificial Neural Networks and Radial Basis Function Neural Network (RBFNN)) are used to distinguish between mass and No-mass breast images.Evaluation experiments of the diagnostic performances of our MF-CAD system are conducted over private datasets that contain both MG and DCE-MRI images acquired from 286 patients, which are “Breast DCE-MRI”, “Breast-MG” and “Breast Multimodal” datasets. Experimental results of the proposed MF-CAD system achieved an Area Under the ROC Curve (AUC) value of 99.10% using RBFNN classifier, while for each single modality alone, the best AUC values of 97.20% and 93.50% are obtained respectively for MG and DCE-MRI modalities using random forest classifier. A comparative study with recent existing approaches shows the competitive performances of the proposed approach.
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017
In this paper, we investigate an efficient palmprint texture modeling method that incorporates a ... more In this paper, we investigate an efficient palmprint texture modeling method that incorporates a robust analysis based on fusing multiple information. In fact, a single descriptor alone may not achieve a high accuracy in palmprint biometric system. Hence, we propose the fusion of various information features extracted by the different descriptors, such as the fractal and the Multi-fractal techniques which produce a robustness to face the numerous challenging and variation of palmprint in unconstrained environments. To increase the performance of palmprint biometric systems, information fusion is proposed as a key phase in multi-characteristic systems. The obtained information can be combined at different levels, i.e., at the feature level, the score level or the decision level. Nevertheless, the feature level fusion is considered more effective than both the matching score and the classifier decision levels, thanks to a feature vector set which contains more and richer information about the input palmprint image. In order to improve the discriminating texture information, our proposed method extracts the fractal dimension features from the preprocessed palmprint images and fuses them with the Multi-fractal dimension features using the Canonical Correlation Analysis (CCA) incorporating the Linear Discriminant Analysis (LDA) in order to reduce the feature dimensionality for each feature set. To demonstrate the feasibility and effectiveness of our proposed method, we performed the experimental results on two benchmark datasets. These results outperform other well-known state of the art methods and produce promising recognition rates by achieving 96.02% for PolyU-Palmprint database and 97.00% for CASIA-Palmprint database.
Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the les... more Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the lesions based on mammographic images is playing an essential role to assist experts. A novel Computer-Aided Diagnosis (CADx) scheme of breast lesion classification is proposed in this paper based on an optimized combination of texture and shape features using machine and deep learning algorithms for mass classification as benign-malignant namely C(M-ZMs)*. The main advantage of using Zernike moments for shape feature extraction is their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our case. We implemented for texture feature extraction the Monogenic-Local Binary Pattern taking the advantage of lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. Therefore, we used Zernike moments for shape feature extraction due to their scale, translation, and ...
International Journal of Digital Crime and Forensics, 2020
The security of people requires a beefy guarantee in our society, particularly, with the spread o... more The security of people requires a beefy guarantee in our society, particularly, with the spread of terrorism throughout the world. In this context, palmprint identification based on texture analysis is amongst the pattern recognition applications to recognize people. In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlati...
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
This paper presents a new method to recognize the person's identity through their palmprints.... more This paper presents a new method to recognize the person's identity through their palmprints. Palmprint recognition is among the most reliable physiological characteristics that can be used especially in forensic applications thanks to its simplicity and its ease of use, its user friendliness and high identification reliability. Accordingly, it has gained great popularity within the pattern recognition field over the past three decades. In this paper, we suggest a new approach for personal identification based on palmprint features extracted using the various methods of fractal theory. These methods have been broadly applied in image processing fields to estimate the fractal dimensions of an image as an important parameter for the analysis of objects of irregular shapes of the texture image. The novelty of this approach is two-fold. On the one hand, we apply the Box counting (BC), the Mass Radius (MS) and the Cumulative Intersection (CumInt) methods to extract the palmprint texture information. On the other hand, the combination of efficient information from the three descriptors has been presented in order to make identification system more efficient and achieve better performances. Then, we explore such texture information features by using classical machine learning techniques: the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM) and the Multiclass Random Forest classification algorithms. The results of the experiments conducted on two large datasets show that our proposed method gives better recognition rates of about 96.35% for CASIA-Palmprint dataset and 95.98% for IITD-Touchless-Palmprint dataset. These results obtained are compared to other well-known state-of-the-art approaches.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
Recently, personal identification, which is based on the palmprint texture features analysis, has... more Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI) of the hand. Secondly, we combine multiple descriptors to extract the palmprint texture information, which are Gray-Level Co-occurrence Matrix (GLCM) and the Gabor filters using feature level fusion. These descriptors have been broadly applied in various tasks, specifically in the image processing domain to analyze the image texture. Then, we apply the generalized discriminant analysis (GDA) to reduce the length of the feature vectors and their redundancies. Finally, we classify these final resulting features by developing the SVM method which supports several kernel functions to reach a best recognition rate. We have conducted extensive experiments on the “CASIA-Palmprint” and “PolyU-palmprint” datasets. The obtained results of the proposed approach provide promising results compared to other well-known state-of-the-art approaches.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
As a machine learning algorithms, deep learning algorithms developed in recent years, have been s... more As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this study, a deep learning model based on Support Vector Machine (SVM) named Deep SVM (DSVM) is represented. We applied the dropout technique on the Deep SVM (DSVM). It is worth noting that this model has an inherent capacity to choose data points crucial to classify good generalization capacities. The deep SVM is built by a stack of SVMs permitting to extracting/learning automatically features from the raw images and to realize classification, too. We chose and tested the Multi-class Support Vector Machine with an RBF kernel, as non-linear discriminative features for classification, on Handwritten Arabic Characters Database (HACDB). Further to these advantages, our model is safeguarded against over-fitting because of strong performance of dropout. Simulation outcomes prove the efficiency of the suggested model.
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