Professor of computer science, Ph.D. in communication engineering, Dr of Techn in computer science, M.Sc. in communication engineering, B.Sc. in communication engineering
Bulletin of Electrical Engineering and Informatics, Jun 1, 2024
Cancer of the lungs is considered one of the primary causes of death among patients globally. Ear... more Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.
This paper aims to design an optimizer followed by a Kawahara filter for optimal classification a... more This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.
2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020
The paper discusses the role of the artificial intelligence and the centralized controller in the... more The paper discusses the role of the artificial intelligence and the centralized controller in the protection of the client signals from any wiretapping or loosing over the optical network. The physical layer of the Optical Transport Network (OTN) is the weakest layer in the network as anyone can access the optical cables from any location and states his attack. To overcome this thread a security layer is proposed to be added during the mapping processes of OTN frames. The automatic detection of any intrusion is done by monitoring the variations of the optical signal to noise ratio (OSNR) by using intelligent software-defined networks (SDN). For the first time the paper shows the role of the machine learning (ML) techniques in the multi-failure restorations of the optical transport network, and a new model is introduced by slicing the multi-domains to 3 layers to fit the needs of 5G at the same time all these domains are managed by only one centralized intelligent controller. The res...
Indonesian Journal of Electrical Engineering and Computer Science
Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detec... more Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detection of osteosarcoma tumors increases the likelihood of successful therapy. Manual identification of osteosarcoma requires highly skilled doctors. In this study, we attempt to create a model to automatically diagnose tumors into three categories; non-tumor, viable-tumor, and osteosarcoma tumor. The suggested methodology can help medical professionals identify tumors correctly and quickly. The proposed approach uses the gray level co-occurrence matrix (GLCM) to extract features for feature extraction and three different classifiers for tumor detection. The used classifier are XG-Boost, support vector machine (SVM), and K-nearest neighbors. Finally, ensemble voting is used by combining the predictions from these classifiers. The system achieves 91.8% accuracy.
Bulletin of Electrical Engineering and Informatics, Jun 1, 2024
Cancer of the lungs is considered one of the primary causes of death among patients globally. Ear... more Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.
This paper aims to design an optimizer followed by a Kawahara filter for optimal classification a... more This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.
2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020
The paper discusses the role of the artificial intelligence and the centralized controller in the... more The paper discusses the role of the artificial intelligence and the centralized controller in the protection of the client signals from any wiretapping or loosing over the optical network. The physical layer of the Optical Transport Network (OTN) is the weakest layer in the network as anyone can access the optical cables from any location and states his attack. To overcome this thread a security layer is proposed to be added during the mapping processes of OTN frames. The automatic detection of any intrusion is done by monitoring the variations of the optical signal to noise ratio (OSNR) by using intelligent software-defined networks (SDN). For the first time the paper shows the role of the machine learning (ML) techniques in the multi-failure restorations of the optical transport network, and a new model is introduced by slicing the multi-domains to 3 layers to fit the needs of 5G at the same time all these domains are managed by only one centralized intelligent controller. The res...
Indonesian Journal of Electrical Engineering and Computer Science
Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detec... more Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detection of osteosarcoma tumors increases the likelihood of successful therapy. Manual identification of osteosarcoma requires highly skilled doctors. In this study, we attempt to create a model to automatically diagnose tumors into three categories; non-tumor, viable-tumor, and osteosarcoma tumor. The suggested methodology can help medical professionals identify tumors correctly and quickly. The proposed approach uses the gray level co-occurrence matrix (GLCM) to extract features for feature extraction and three different classifiers for tumor detection. The used classifier are XG-Boost, support vector machine (SVM), and K-nearest neighbors. Finally, ensemble voting is used by combining the predictions from these classifiers. The system achieves 91.8% accuracy.
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Papers by Kamel Rahouma