Kamel Rahouma - Academia.edu (original) (raw)

Papers by Kamel Rahouma

Research paper thumbnail of A deep learning-based system for accurate diagnosis of pelvic bone tumors

Bulletin of Electrical Engineering and Informatics, Jun 1, 2024

Research paper thumbnail of Knee osteoarthritis automatic detection using U-Net

IAES International Journal of Artificial Intelligence, Jun 1, 2024

Research paper thumbnail of DESIGN OF A SIMPLE Monitoring SYSTEM FOR WATER DISTRIBUTION STATIONS AND COMPANIES

Journal of Advanced Engineering Trends (JAET)/Journal of Advanced Engineering Trends (JAET), 2024

Research paper thumbnail of Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification

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.

Research paper thumbnail of Soil Morphology Based on Deep Learning, Polynomial Learning and Gabor Teager-Kaiser Energy Operators

Studies in big data, Dec 15, 2020

Research paper thumbnail of Design and Implementation of Light Fidelity Communication Systems: A Recent Study

Research paper thumbnail of TCP/IP Network Layers and Their Protocols (A Survey)

Lecture notes in networks and systems, 2020

Research paper thumbnail of Automatic knee anterior cruciate ligament torn diagnosis using CNN-XGBoost

International Journal of Medical Engineering and Informatics, 2022

Research paper thumbnail of Hardware Implementation of Effective Framework for the Trade-off between Security and QoS in Wireless Sensor Networks

Microprocessors and Microsystems, Sep 1, 2022

Research paper thumbnail of Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots

Research paper thumbnail of Location Estimation of Multiple Emitting RF Sources Using Supervised Machine Learning Technique

International journal of engineering trends and technology, Oct 31, 2022

Research paper thumbnail of Design and Implementation of a Face Recognition System Based on API mobile vision and Normalized Features of Still Images

Procedia Computer Science, 2021

Research paper thumbnail of Applying Deep Learning for Automatic Segmentation of Pelvic Bone Tumors

Research paper thumbnail of Efficient Tradeoff between Throughput and Energy Efficiency of Massive-MIMO Technique for Satellite Communication applications

2023 20th Learning and Technology Conference (L&T)

Research paper thumbnail of Lung Cancer Diagnosis Based on Chan-Vese Active Contour and Polynomial Neural Network

Procedia Computer Science

Research paper thumbnail of The Impact of COVID-19 on the Cybersecurity in Civil Aviation: Review and Analysis

2022 International Telecommunications Conference (ITC-Egypt)

Research paper thumbnail of Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Computers, Materials & Continua

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.

Research paper thumbnail of Hybrid Cryptography for Cloud Security: Methodologies and Designs

Digital Transformation Technology, 2021

Research paper thumbnail of Applying Smart Security Model to Protect the Client Services from the Threats of the Optical Network

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...

Research paper thumbnail of Bone osteosarcoma tumor classification

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.

Research paper thumbnail of A deep learning-based system for accurate diagnosis of pelvic bone tumors

Bulletin of Electrical Engineering and Informatics, Jun 1, 2024

Research paper thumbnail of Knee osteoarthritis automatic detection using U-Net

IAES International Journal of Artificial Intelligence, Jun 1, 2024

Research paper thumbnail of DESIGN OF A SIMPLE Monitoring SYSTEM FOR WATER DISTRIBUTION STATIONS AND COMPANIES

Journal of Advanced Engineering Trends (JAET)/Journal of Advanced Engineering Trends (JAET), 2024

Research paper thumbnail of Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification

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.

Research paper thumbnail of Soil Morphology Based on Deep Learning, Polynomial Learning and Gabor Teager-Kaiser Energy Operators

Studies in big data, Dec 15, 2020

Research paper thumbnail of Design and Implementation of Light Fidelity Communication Systems: A Recent Study

Research paper thumbnail of TCP/IP Network Layers and Their Protocols (A Survey)

Lecture notes in networks and systems, 2020

Research paper thumbnail of Automatic knee anterior cruciate ligament torn diagnosis using CNN-XGBoost

International Journal of Medical Engineering and Informatics, 2022

Research paper thumbnail of Hardware Implementation of Effective Framework for the Trade-off between Security and QoS in Wireless Sensor Networks

Microprocessors and Microsystems, Sep 1, 2022

Research paper thumbnail of Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots

Research paper thumbnail of Location Estimation of Multiple Emitting RF Sources Using Supervised Machine Learning Technique

International journal of engineering trends and technology, Oct 31, 2022

Research paper thumbnail of Design and Implementation of a Face Recognition System Based on API mobile vision and Normalized Features of Still Images

Procedia Computer Science, 2021

Research paper thumbnail of Applying Deep Learning for Automatic Segmentation of Pelvic Bone Tumors

Research paper thumbnail of Efficient Tradeoff between Throughput and Energy Efficiency of Massive-MIMO Technique for Satellite Communication applications

2023 20th Learning and Technology Conference (L&T)

Research paper thumbnail of Lung Cancer Diagnosis Based on Chan-Vese Active Contour and Polynomial Neural Network

Procedia Computer Science

Research paper thumbnail of The Impact of COVID-19 on the Cybersecurity in Civil Aviation: Review and Analysis

2022 International Telecommunications Conference (ITC-Egypt)

Research paper thumbnail of Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Computers, Materials & Continua

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.

Research paper thumbnail of Hybrid Cryptography for Cloud Security: Methodologies and Designs

Digital Transformation Technology, 2021

Research paper thumbnail of Applying Smart Security Model to Protect the Client Services from the Threats of the Optical Network

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...

Research paper thumbnail of Bone osteosarcoma tumor classification

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.