Papers by mohamed maher ben ismail
Local Ensemble Approach for Meningioma Tumor Firmness Detection in MRI Images
Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing
Determining if a Meningioma, which is a primary tumor of the brain, is firm or not guides patient... more Determining if a Meningioma, which is a primary tumor of the brain, is firm or not guides patient counseling and the operative strategy to be adopted. In this paper we propose a novel approach for automatic firmness detection for Meningioma tumor. The proposed framework relies on a possibilistic based ensemble method to classify MRI image instances as firm or non-firm cases. The proposed algorithm minimizes some objective function which combines context identification and multi-algorithm ensemble criteria. The optimization is intended to learn contexts as compact clusters in subspaces of the visual feature space via possibilistic clustering and feature discrimination. The yields optimal ensemble parameters for each context. The experiments were conducted and assessed using a real dataset. The obtained performance proved that the proposed approach outperforms relevant state of the art methods.
Applied Sciences
In this paper, we propose two novel Adaptive Neural Network Approaches (ANNAs), which are intende... more In this paper, we propose two novel Adaptive Neural Network Approaches (ANNAs), which are intended to automatically learn the optimal network depth. In particular, the proposed class-independent and class-dependent ANNAs address two main challenges faced by typical deep learning paradigms. Namely, they overcome the problems of setting the optimal network depth and improving the model interpretability. Specifically, ANNA approaches simultaneously train the network model, learn the network depth in an unsupervised manner, and assign fuzzy relevance weights to each network layer to better decipher the model behavior. In addition, two novel cost functions were designed in order to optimize the layer fuzzy relevance weights along with the model hyper-parameters. The proposed ANNA approaches were assessed using standard benchmarking datasets and performance measures. The experiments proved their effectiveness compared to typical deep learning approaches, which rely on empirical tuning and...
Sensors, 2020
Nowadays, Internet of Things (IoT) technology has various network applications and has attracted ... more Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botne...
2010 20th International Conference on Pattern Recognition, 2010
We propose a novel possibilistic clustering algorithm based on robust modelling of the Generalize... more We propose a novel possibilistic clustering algorithm based on robust modelling of the Generalized Dirichlet (GD) finite mixture. The algorithm generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership represents the degree of "typicality" and is used to indentify and discard noise points. The algorithm minimizes one objective function to optimize GD mixture parameters and possibilistic membership values. This optimization is done iteratively by dynamically updating the Dirichlet mixture parameters and the membership values in each iteration. We compare the performance of the proposed algorithm with an EM based approach. We show that the possibilistic approach is more robust.
Dar-jen chang, and Dr. Roman V. Yampolskiy, for their continual guidance and support. I would lik... more Dar-jen chang, and Dr. Roman V. Yampolskiy, for their continual guidance and support. I would like to thank Ouiem Bchir, who as a good wife, was always willing to help and give her best suggestions. Many thanks to all researchers in the Multimedia Research Lab for their help. I would like to express my gratitude to my parents for their love, guidance, and understanding.
1Semi-Supervised Relational Fuzzy clustering with Local Distance Measure Learning
All in-text references underlined in blue are linked to publications on ResearchGate, letting you... more All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS, 2020
Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering proc... more Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semisupervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and confirmed the outperformance of our clustering approach.
Journal of X-Ray Science and Technology, 2020
Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a ... more Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.
In this paper, a child monitoring system is proposed to watch and track children while playing. I... more In this paper, a child monitoring system is proposed to watch and track children while playing. In particular, the system detects the moving child using a background subtraction approach which relies on adaptive nonparametric Gaussian mixture modelling. Then, Kalman Filter is adopted for the tracking task. If the child oversteps the specified area or does something he is not supposed to do, the proposed system notifies the parents and convey a message to the child. The experimental results proved the effectiveness of the proposed system with an average F-score of 0.87 with a standard deviation of 0.07.
A Deep Learning Approach for Brain Tumor Firmness Detection Using YOLOv4
2022 45th International Conference on Telecommunications and Signal Processing (TSP)
Automatic fall detection using region-based convolutional neural network
International Journal of Injury Control and Safety Promotion
International Journal of Advanced Computer Science and Applications
In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicia... more In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicians in the early detection of Alzheimer's Disease (AD) and ensure an effective diagnosis. The proposed framework is designed to be fully-automated upon the capture of the brain structure using Magnetic Resonance Imaging (MRI) scanners. The Voxel-Based Morphometry (VBM) analysis is a key element in the proposed detection process as it is intended to investigate the Gray Matter (GM) tissues in the captured MRI images. In other words, the feature extraction phase consists in encoding the voxel properties in the MRI images into numerical vectors. The resulting feature vectors are then fed into a Neighborhood Component Analysis and Feature Selection (NCFS) algorithm coupled with K-Nearest Neighbor (KNN) algorithm in order to learn a classification model able to recognize AD cases. The feature selection based on NCFS algorithm improved the overall classification performance.
ACCENTS Transactions on Image Processing and Computer Vision
In recent years, handwritten character recognition has become an active research field. In partic... more In recent years, handwritten character recognition has become an active research field. In particular, digitalization has triggered the interest of researchers from various computing disciplines to address several handwriting related challenges. Despite these efforts, there are still opportunities for the development and improvement of the recognition of the handwritten Arabic letters. In this paper, we designed and developed a deep ensemble architecture in which ResNet-18 architecture is exploited to model and classify character images. Specifically, we adapted ResNet-18 by adding a dropout layer after all convolutional layer and integrated it in multiple ensemble models to automatically recognize isolated handwritten Arabic characters. A standard Arabic Handwritten Character Dataset (AHCD) was used in the experiments to train and assess all the proposed models. Satisfactory results were obtained using all models. The best-attained accuracy was 98.30% using a typical ResNet-18 mode...
ACCENTS Transactions on Image Processing and Computer Vision
Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases a... more Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia cl...
Computer Science and Information Technologies
Recently, deep learning has been coupled with notice- able advances in Natural Language Processin... more Recently, deep learning has been coupled with notice- able advances in Natural Language Processing related research. In this work, we propose a general framework to detect verbal offense in social networks comments. We introduce a partitional CNN-LSTM architecture in order to automatically recognize ver- bal offense patterns in social network comments. Specifically, we use a partitional CNN along with a LSTM model to map the social network comments into two predefined classes. In particular, rather than considering a whole document/comments as input as performed using typical CNN, we partition the comments into parts in order to capture and weight the locally relevant information in each partition. The resulting local information is then sequentially exploited across partitions using LSTM for verbal offense detection. The combination of the partitional CNN and LSTM yields the integration of the local within comments information and the long distance correlation across comments. ...
Image-based smoke detection using feature mapping and discrimination
Soft Computing
Journal of Computer Science
As the number of fall incidents among elderly people and patients are continuously growing, resea... more As the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems. In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as "Fall" or "Non-Fall" based on a model learned using labeled training frames. Despite the promising reported results, existing fall detection systems exhibit noticeable room for improvement. Learner fusion which builds multiple models and aggregates their respective decisions is an alternative that would improve the fall detection performance. In this paper, an image-based fall detection system that captures the visual property and the spatial position of the human body using the Histogram of Oriented Gradient from the video frames is proposed. Then, the extracted features are used to train three classification models. Namely, the Naïve Bayes, the K-Nearest Neighbors and the Support Vector Machine algorithms are adopted. Next, the majority vote is used to aggregate the decisions of the individual learners. The proposed system was assessed using a standard dataset and yielded promising results. Standard performance measures along with the statistical significance t-test were used to prove that the fall detection system based on majority vote fusion outperforms the individual classifier based approaches.
Empirical investigation of multiple query content-based image retrieval
International Journal of Applied Pattern Recognition
Journal of Imaging
We propose a novel multiple query retrieval approach, named weight-learner, which relies on visua... more We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
In this paper, the proposed PCMRM (possibilistic based cross-media relevance model) annotates ima... more In this paper, the proposed PCMRM (possibilistic based cross-media relevance model) annotates images based on their visual contents. PCMRM framework relies on unsupervised learning to group the visually similar image regions into homogeneous clusters, along with the cross-media relevance model (CMRM) that is used to estimate the joint distribution of textual keywords and images. Besides, the unsupervised learning task exploits the robustness to noise of a possibilistic clustering algorithm, and generates membership degrees that represent the typicality of image regions with respect to the obtained clusters. To validate and assess the proposed system, we used the standard Corel dataset. PCMRM produced promising results. The reported performance measures proved that the proposed automatic image annotation approach outperforms similar state of the art solutions. This attainment is mainly attributed to the exploitation of the possibilistic membership produced by the clustering algorithm which allowed accurate learning of the association between annotating labels and the visual content of the image regions.
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Papers by mohamed maher ben ismail