Article Per Year (5 Year)
p-Index From 2021 - 2026
0.835
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Taha, Mohamed
Unknown Affiliation
Computer Science & IT Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Source printer identification using convolutional neural network and transfer learning approach F. El Abady, Naglaa; H. Zayed, Hala; Taha, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp948-960

Abstract

In recent years, Source printer identification has become increasingly important for detecting forged documents. A printer's distinguishing feature is its fingerprints. Each printer has a unique collection of fingerprints on every printed page. A model for identifying the source printer and classifying the questioned document into one of the printer classes is provided by source printer identification. A paper proposes a new approach that trains three different approaches on the dataset to choose the more accurate model for determining the printer's source. In the first, some pre-trained models are used as feature extractors, and support vector machine (SVM) is used to classify the generated features. In the second, we construct a two-dimensional convolutional neural network (2D-CNN) to address the source printer identification (SPI) problem. Instead of SoftMax, 2D-CNN is employed for feature extractors and SVM as a classifier. This approach obtains 93.75% 98.5% accuracy for 2D-CNN-SVM in the experiments. The SVM classifier enhanced the 2D-CNN accuracy by roughly 5% over the initial configuration. Finally, we adjusted 13 already-pre-trained CNN architectures using the dataset. Among the 13 pre-trained CNN models, DarkNet-19 has the greatest accuracy of 99.2 %. On the same dataset, the suggested approaches achieve well in terms of classification accuracy than the other recently released algorithms. 
3D visualization diagnostics for lung cancer detection M. Mahmoud, Rana; Elgendy, Mostafa; Taha, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4630-4641

Abstract

Lung cancer is a leading cause of cancer deaths worldwide with an estimated 2 million new cases and 1·76 million deaths yearly. Early detection can improve survival, and CT scans are a precise imaging technique to diagnose lung cancer. However, analyzing hundreds of 2D CT slices is challenging and can cause false alarms. 3D visualization of lung nodules can aid clinicians in detection and diagnosis. The MobileNet model integrates multi-view and multi-scale nodule features using depthwise separable convolutional layers. These layers split standard convolutions into depthwise and pointwise convolutions to reduce computational cost. Finally, the 3D pulmonary nodular models were created using a ray-casting volume rendering approach. Compared to other state-of-the-art deep neural networks, this factorization enables MobileNet to achieve a much lower computational cost while maintaining a decent degree of accuracy. The proposed approach was tested on an LIDC dataset of 986 nodules. Experiment findings reveal that MobileNet provides exceptional segmentation performance on the LIDC dataset, with an accuracy of 93.3%. The study demonstrates that the MobileNet detects and segments lung nodules somewhat better than other older technologies. As a result, the proposed system proposes an automated 3D lung cancer tumor visualization.
Character N-gram model for toxicity prediction Shehab, Eman; Nayel, Hamada; Taha, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4380-4387

Abstract

Molecular toxicity prediction is a crucial step in the drug discovery process. It has a direct relationship with human health and medical destiny. Accurately assessing a molecule’s toxicity can aid in the weeding out of low-quality compounds early in the drug discovery phase, avoiding depletion later in the drug development process. Computational models have been used automatically for molecular toxicity prediction. In this paper, a machine learning-based model has been proposed. TF/IDF representation scheme has been used for N-gram and integrated with simplified molecular-input line-entry system (SMILES). Multiple machine learning classifiers such as logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), AdaBoost, multi-layer perceptron (MLP), and stochastic gradient descent (SGD) classifiers have been implemented. A wide range of N-gram models have been implemented and trigram reported the best results. RF and SVM achieved 85% and 84% accuracy respectively. Comparable to state-of-the-art models, our results are acceptable as we used minimum available resources.
Deep learning approach for forensic facial reconstruction depends on unidentified skull M. Mohammed, Doaa; Elgendy, Mostafa; Taha, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3858-3868

Abstract

Facial reconstruction, or facial approximation, is an essential problem in a criminal investigation involving reconstructing a victim's face from his skull to determine the victim's identification at a crime scene. Facial approximation plays a crucial part when there is a lack of clues with investigators. Investigators utilize facial approximation to guess the victims' identities. This research attempted to use computer-aided face reconstruction rather than traditional approaches. Traditional methods of face reconstruction include the use of clay or gypsum. Traditional procedures necessitate forensic professionals to rebuild the victim's face. This research uses the convolution neural network skull part with sift (CNNSPS) model is employed to reconstruct facial features from a skull image utilizing public datasets CelebAMask-HQ and MUG500+. The proposed algorithm was tested on unidentified skull databases, and celebrity faces were used. The genuine datasets are not available, which is the key issue in this research.
Multi platforms fake accounts detection based on federated learning Azer, Marina; H. Zayed, Hala; A. Gadallah, Mahmoud E.; Taha, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3837-3848

Abstract

Identifying and mitigating fake profiles is an urgent issue during the age of widespread integration with social media platforms. this study addresses the challenge of fake profile detection on major social platforms-Facebook, Instagram, and X (Twitter). Employing a two-sided approach, it compares stacking model of machine learning algorithms with the federated learning. The research extends to four datasets, two Instagram datasets, one X dataset, and one Facebook dataset, reporting impressive accuracy metrics. Federated learning stands out for it is effectiveness in fake profile detection, prioritizing user data privacy. Results reveal Instagram fake/real dataset achieves 96% accuracy while Instagram human/bot dataset reaches 95% accuracy with federated learning. using the stacking model X’s fake/real dataset achieves 99.4% accuracy, and Facebook fake/real dataset reaches 99.8% accuracy using the same model. The study underscores the pivotal role of data privacy, positioning federated learning as an ethical choice. It compares the time efficiency of stacking and federated learning, with the former providing good performance in less time and the latter emphasizing data privacy but consuming more time. Results are benchmarked against related works, showcasing superior performance. The study contributes significantly to fake profile detection, offering adaptable solutions and insights.
Co-Authors A. Gadallah, Mahmoud E. Azer, Marina Elgendy, Mostafa F. El Abady, Naglaa H. Zayed, Hala M. Mahmoud, Rana M. Mohammed, Doaa Nayel, Hamada Shehab, Eman