Saeed Mohsen

A Hybrid Architecture of CNN–Swin-T Integrated with Attention Mechanism and Explainable AI for Alzheimer’s Disease Classification

Saeed Mohsen, Saada Khadragy, Norah M. Alnaim, Noorah Albehaijan et autres

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis to improve patient outcomes. In this paper, an attention-enhanced hybrid deep learning (DL) framework is proposed that combines Convolutional Neural Network (CNN) and Swin Transformer (Swin-T) architectures for …

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A deep super-resolution GAN framework integrating ResNet-18 and CNN for enhanced Alzheimer’s disease diagnosis

Saeed Mohsen, Mohammed M. Abo-Zahhad, M. Abdel-Aziz

Medical imaging techniques, such as computerized tomography (CT), magnetic resonance imaging (MRI), and ultrasound, provide critical insights for precise disease diagnosis and detection. Despite their importance, these imaging methods are often expensive to produce and store due to their substantial data size …

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Efficient Deep Learning Models Integrated with a Smart Web Application for Classifying Heart Diseases Based on ECG Signals

Saeed Mohsen, A. K. Ibrahim, Osama Farouk Hassan, Norah M. Alnaim et autres

Recent advancements in the accuracy of deep learning (DL) hold significant promise for improving the classification of heart patients. Nevertheless, continued refinement is essential to achieve even greater levels of precision in DL techniques. This paper proposes three efficient DL models: Swin …

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A Hybrid of ResNext101_32x8d and Swin Transformer Networks with XAI for Alzheimer’s Disease Detection

Saeed Mohsen, Amr Yousef, M. Abdel-Aziz

Medical images obtained from advanced imaging devices play a crucial role in supporting disease diagnosis and detection. Nevertheless, acquiring such images is often costly and storage-intensive, and it is time-consuming to diagnose individuals. The use of artificial intelligence (AI)-based automated diagnostic systems …

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Design and Implementation of a Compact 20-Port Wideband MIMO Antenna Array for 5G/6G Indoor Base Stations

Ibrahim Zewail, Saeed Mohsen, M. Mokhtar Zayed

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Correction: A novel adaptive IEEE 802.11ah MAC protocol using machine learning for data collision reduction in smart cities IoT networks

Amin S. Ibrahim, Ahmed M. Abbas, Saeed Mohsen

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Efficient LSTM and RF Models for Network Intrusion Detection

Saeed Mohsen, Ahmed M. Abbas, M. Mokhtar Zayed, Sarah Magdy Oraby et autres

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A novel adaptive IEEE 802.11ah MAC protocol using machine learning for data collision reduction in smart cities IoT networks

Amin S. Ibrahim, Ahmed M. Abbas, Saeed Mohsen

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Deep LearnIng and Machine Learning for Brain Tumor Detection: A Review, Challenges, and Future Directions

Saeed Mohsen, Mohamed H. Abdel-Aziz

Abstract Brain tumor diagnosis and classification remain critical challenges in modern healthcare. Recent advancements in artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), have significantly enhanced medical image analysis, enabling automated and accurate detection of brain tumors. This paper …

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AI-IoT based smart agriculture pivot for plant diseases detection and treatment

Amin S. Ibrahim, Saeed Mohsen, Ibrahim Mohamed Selim, Roobaea Alroobaea et autres

There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases …

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A novel intelligent healthcare system based on efficient deep learning models for brain stroke prediction

Saeed Mohsen, Ahmed Ali Gomaa

Abstract Smart hospitals should equip patients with wearable devices capable of monitoring and predicting brain strokes using deep learning (DL) techniques. The latest advancements in the accuracy of DL have the potential to make a substantial impact in addressing the issues associated …

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Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images

Ahmed A. Emran, Basel Mounir El Saghir, Saeed Mohsen

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). …

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