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New quantum color image watermarking technique (NQCIWT)

Soft Computing 2024年第17-18期28卷 10177-10186页

作者： Aly, Rasha F. Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt

The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive data, the risk of piracy, etc. were the motivation for protecting rights, securing and hiding essential information such as images, audio, etc. Watermarking was a popular security, copyright and privacy approach and one of the problems in modern quantum digital computing that produced more powerful quantum protection techniques than the classical one that used classical techniques such as digital wavelet transformation and Hungarian algorithms. Recent advances in quantum image representation and processing help to manipulate data hidden. This paper proposes a new watermark technique in which a color watermark image is hidden within another color-carrying image after transformation into the quantum domain using the recently completed quantum color image representation technique. Finally, a comparison is made between the proposed method and the most recent watermarking techniques is establish in terms of PSNR (peak signal-to-noise ratio), which shows the high precision of the proposed technique and improves the imperceptibility compared to the related schemes. © The Author(s) 2024.

关键词： Color

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AI-Driven Anomaly Detection Framework for Improving IoT System Reliability

AI-Driven Anomaly Detection Framework for Improving IoT Syst...

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2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024

作者： Salem, Sameh A. Said, Samar A. Nour, Samar M. Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Badr University Cairo Egypt

ISBN: (纸本)9798331529413

Nowadays, Internet of Things (IoT) become progressively a fundamental part of our life. It revolutionizes various industries by enabling seamless connectivity between devices as well as it increases automation and efficiency. However, the reliability of IoT systems is often compromised due to the complexity and scale of these networks. It makes them vulnerable to failures and security breaches. To mitigate this problem, anomaly detection using artificial Intelligence (AI) in IoT can be a promising candidate to help data identifies unusual patterns that could indicate system faults or threats. In this paper, AI-Driven Anomaly Detection Framework for Enhancing IoT Security is proposed. The proposed framework enhances IoT system reliability by reducing downtime, improving security,and ensuring the consistent performance of connected devices. The experimental results demonstrate the efficiency of machine learning techniques and their capabilities with anomalies to enhance detection capability. Finally, the empirical results show that DT and RF outperform the other competitive models for network intrusion detection in the face of anomaly detection in front of the ongoing. ©2024 IEEE.

关键词： Data reliability

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A generic optimization and learning framework for Parkinson disease via speech and handwritten records

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10673-10693页

作者： Yousif, Nada R. Balaha, Hossam Magdy Haikal, Amira Y. El-Gendy, Eman M. Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features;and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches. © 2022, The Author(s).

关键词： Feature extraction

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Smoke Removal Method of Industrial Images Based on Dark Channel Prior Approach and Second-Generation Wavelets

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SN computer Science 2024年第7期5卷 843页

作者： Khmag, Asem Department of Computer Systems Engineering Faculty of Engineering University of Zawia Zawia Libya

In the field of digital image and computer vision, haze and smoke removal (dehazing) is one pf a popular scientific arena where it is being studied by an ample number of computer scientists. However, conventional joint dehazing and noise removal techniques use traditional hand-crafted approaches in order to achieve clean image by apply suitable restoration processes. In this study, a smoke removal technique, which designed according to dark channel, prior DCP is proposed. Theoretically, traditional DCP utilized the atmospheric light in the entire image as a global constant and as a result, in our study, the suggested DCP is further enhanced by using filter of second-generation wavelets (SGWs) and therefore, more powerful model is achieved. Consequently, the SGWs filtering is utilized due to its strength in custom designing for complex domains and irregular sampling in order to improve the solution of atmospheric light. The proposed algorithm is evaluated against several state of the art smoke removal methods. Extensive experimentation on multiple datasets demonstrates that our method exhibits better dehazing performance and generalizability than other image dehazing approaches in terms of peak signal-to-noise ratio PSNR, the structural similarity index SSIM and the average gradient results. Moreover, the execution efficiency of the proposed algorithm can reduce considerably the processing time in comparison with the state-of-the-art- smoke removal techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric light Dark channel prior Dehazing Patch size Second-generation wavelet Smoke removal

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Deep Learning Performance Evaluation Model for Enhancing Network Intrusion Detection systems 6

Deep Learning Performance Evaluation Model for Enhancing Net...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Nour, Samar M. Said, Samar A. Badr University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Helwan University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350378511

Network intrusion detection systems (NIDSs) play an important role in protecting network infrastructure from cyber threats. Traditional NIDS often rely on signature-based or rule-based methods, which can contention to detect incoming strong attacks. Deep learning techniques have emerged as a promising method for improving NIDS due to their ability to learn complex structures and features from raw network traffic data. In this paper, a performance evaluation model for enhancing network intrusion detection is proposed. Deep learning methods including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Deep Neural Networks are investigated in the proposed model. Furthermore, the proposed model discusses the challenges and opportunities associated with the implementation of deep learning-based NIDS. These challenges include scalability, interpretability, and adversarial attacks. The performance of the proposed model is examined across numerous datasets that are real-world. The experimental results prove the efficiency to deep learning techniques and their capabilities with NIDS for enhancing the detection capability. Finally, the empirical results show that CNN outperforms the other competitive models for network intrusion detection in the face of cyber threats and in front of the ongoing. © 2024 IEEE.

关键词： Network intrusion

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Harnessing the Power of AI for Effective Cybersecurity Defense 6

Harnessing the Power of AI for Effective Cybersecurity Defen...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Nour, Samar M. Said, Samar A. Department of Computer and Systems Engineering Faculty of Engineering Badr University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt

ISBN: (纸本)9798350373875

Due to the extreme growth in digital information and data, cybersecurity has become one of the major concerns addressed by recent research, organizations, and governments. However, Traditional security methods are finding it more and more difficult to keep up with the volume and complexity of cybersecurity threats. To mitigate this problem, artificial intelligence (AI) can be a promising candidate that helps with cybersecurity defenses in the face of sophisticated and rising cyber threats. As a result, Artificial intelligence based systems can take advantage of machine learning, natural language processing, and other methods to enhance threat identification, response, and mitigation. This paper provides a blueprint for the state of the art of AI's potential to enhance cyber defense strategies in tlie field of cybersecurity. In addition, it highlights a variety of AI-based cybersecurity strategies that include anomaly detection, behavior analysis, and predictive modeling. Additionally, it explores the drawbacks and limitations of AI in cybersecurity, including data privacy issues and adversarial attacks, and offers suggestions for how to resolve these problems. As a whole, it highlights the significance of using AI to strengthen cybersecurity defenses and offers recommendations for further research and development in this field. © 2024 IEEE.

关键词： Cybersecurity

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Evolutionary optimization framework to train multilayer perceptrons for engineering applications

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Mathematical Biosciences and engineering 2024年第2期21卷 2970-2990页

作者： Al-Hajj, Rami Fouad, Mohamad M. Zeki, Mustafa College of Engineering and Technology American University of the Middle East Kuwait Kuwait Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura35516 Egypt

Training neural networks by using conventional supervised backpropagation algorithms is a challenging task. This is due to significant limitations, such as the risk for local minimum stagnation in the loss landscape of neural networks. That may prevent the network from finding the global minimum of its loss function and therefore slow its convergence speed. Another challenge is the vanishing and exploding gradients that may happen when the gradients of the loss function of the model become either infinitesimally small or unmanageably large during the training. That also hinders the convergence of the neural models. On the other hand, the traditional gradient-based algorithms necessitate the pre-selection of learning parameters such as the learning rates, activation function, batch size, stopping criteria, and others. Recent research has shown the potential of evolutionary optimization algorithms to address most of those challenges in optimizing the overall performance of neural networks. In this research, we introduce and validate an evolutionary optimization framework to train multilayer perceptrons, which are simple feedforward neural networks. The suggested framework uses the recently proposed evolutionary cooperative optimization algorithm, namely, the dynamic group-based cooperative optimizer. The ability of this optimizer to solve a wide range of real optimization problems motivated our research group to benchmark its performance in training multilayer perceptron models. We validated the proposed optimization framework on a set of five datasets for engineering applications, and we compared its performance against the conventional backpropagation algorithm and other commonly used evolutionary optimization algorithms. The simulations showed the competitive performance of the proposed framework for most examined datasets in terms of overall performance and convergence. For three benchmarking datasets, the proposed framework provided increases of 2.7%, 4.83%, and

关键词： Multilayer neural networks

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Enhanced quantum resources via two distant atom-cavity systems under the influence of atomic dissipation

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Communications in Theoretical Physics 2024年第2期76卷 62-73页

作者： M Setodeh Kheirabady M K Tavassoly M Rafeie E Ghasemian Laser and Optics Group Faculty of PhysicsYazd UniversityYazd 89195-741Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data SciencePersian Gulf UniversityBushehrIran

Quantum resources such as entanglement and coherence are the holy grail for modern quantum technologies. Although the unwanted environmental effects tackle quantum information processing tasks, suprisingly these key quantum resources may be protected and even enhanced by the implementation of some special hybrid open quantum systems. Here, we aim to show how a dissipative atom-cavity-system can be accomplished to generate enhanced quantum *** do so, we consider a couple of dissipative cavities, where each one contains two effective two-level atoms interacting with a single-mode cavity field. In practical applications, a classical laser field may be applied to drive each atomic subsystem. After driving the system, a Bell-state measurement is performed on the output of the system to quantify the entanglement and coherence. The obtained results reveal that the remote entanglement and coherence between the atoms existing inside the two distant cavities are not only enhanced, but can be stabilized, even under the action of dissipation. In contrast, the local entanglement between two atoms inside each dissipative cavity attenuates due to the presence of unwanted environmental ***, the local coherence may show the same behavior as the remote ***, the system provides the steady state entanglement in various interaction regimes,particularly in the strong atom-cavity coupling and with relatively large detuning. More interestingly, our numerical analyses demonstrate that the system may show a memory effect due to the fact that the death and revival of the entanglement take place during the interaction. Our proposed model may find potential applications for the implementation of long distance quantum networks. In particular, it facilitates the distribution of quantum resources between the nodes of large-scale quantum networks for secure communication.

关键词： remote entanglement and coherence atomic dissipation steady state entanglement Bell-statemeasurement

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