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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
H. Zayed, Hala
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Computer Science & IT Engineering

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Real-time indoor tracking for augmented reality using computer vision technique Shewail, Ashraf Saad; H. Zayed, Hala; A. M. Elsayed, Neven
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1845-1857

Abstract

In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces
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. 
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. A. M. Elsayed, Neven Azer, Marina F. El Abady, Naglaa Shewail, Ashraf Saad Taha, Mohamed