17 results on '"Elhadad, Ahmed"'
Search Results
2. High-capacity data hiding for medical images based on the mask-RCNN model
- Author
-
Saidi, Hadjer, Tibermacine, Okba, and Elhadad, Ahmed
- Published
- 2024
- Full Text
- View/download PDF
3. Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach
- Author
-
Elhadad, Ahmed, Jamjoom, Mona, and Abulkasim, Hussein
- Published
- 2024
- Full Text
- View/download PDF
4. A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare
- Author
-
Taloba, Ahmed I., Elhadad, Ahmed, Rayan, Alanazi, Abd El-Aziz, Rasha M., Salem, Mostafa, Alzahrani, Ahmad A., Alharithi, Fahd S., and Park, Choonkil
- Published
- 2023
- Full Text
- View/download PDF
5. A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts
- Author
-
Elhadad, Ahmed, Ghareeb, A, and Abbas, Safia
- Published
- 2021
- Full Text
- View/download PDF
6. Robust 3D object watermarking scheme using shape features for copyright protection.
- Author
-
M. Alhammad, Sarah, Ahmed, Nada, Abbas, Safia, Abulkasim, Hussein, and Elhadad, Ahmed
- Subjects
COPYRIGHT ,DIGITAL watermarking ,GRAYSCALE model ,IMAGE registration ,DISCRETE wavelet transforms ,WATERMARKS ,VISUAL cryptography - Abstract
This article utilizes the discrete wavelet transformation to introduce an advanced 3D object watermarking model depending on the characteristics of the object's vertices. The model entails two different phases: integration and extraction. In the integration phase, a novel technique is proposed, which embeds the secret grayscale image three times using both the encrypted pixels and the vertices' coefficients of the original 3D object. In the extraction phase, the secret image is randomly extracted and recaptured using the inverse phase of the integration technique. Four common 3D objects (Stanford bunny, horse, cat figurine, and angel), with different faces and different vertices, are used in this model as a dataset. The performance of the proposed technique is evaluated using different metrics to show its superiority in terms of execution time and imperceptibility. The results demonstrated that the proposed method achieved high imperceptibility and transparency with low distortion. Moreover, the extracted secret grayscale image perfectly matched the original watermark with a structural similarity index of 1 for all testing models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. An Efficient Indoor Localization Based on Deep Attention Learning Model.
- Author
-
Abozeid, Amr, Taloba, Ahmed I., El-Aziz, Rasha M. Abd, Alwaghid, Alhanoof Faiz, Salem, Mostafa, and Elhadad, Ahmed
- Subjects
INDOOR positioning systems ,WIRELESS localization ,HEALTH care industry ,ARTIFICIAL intelligence ,DEEP learning - Abstract
Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization's accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale PyramidModule (SPM). It not only enhances accuracy but also ensures the model's real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25° for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03° for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data.
- Author
-
AlEisa, Hussah N., Hamad, Safwat, and Elhadad, Ahmed
- Subjects
ERROR correction (Information theory) ,GENOME size ,ALGORITHMS ,GENETIC testing ,HUMAN genome ,CROP development ,NUCLEOTIDE sequencing - Abstract
In the mid-1970s, the first-generation sequencing technique (Sanger) was created. It used Advanced BioSystems sequencing devices and Beckman's GeXP genetic testing technology. The second-generation sequencing (2GS) technique arrived just several years after the first human genome was published in 2003. 2GS devices are very quicker than Sanger sequencing equipment, with considerably cheaper manufacturing costs and far higher throughput in the form of short reads. The third-generation sequencing (3GS) method, initially introduced in 2005, offers further reduced manufacturing costs and higher throughput. Even though sequencing technique has result generations, it is error-prone due to a large number of reads. The study of this massive amount of data will aid in the decoding of life secrets, the detection of infections, the development of improved crops, and the improvement of life quality, among other things. This is a challenging task, which is complicated not just by a large number of reads and by the occurrence of sequencing mistakes. As a result, error correction is a crucial duty in data processing; it entails identifying and correcting read errors. Various k-spectrum-based error correction algorithms' performance can be influenced by a variety of characteristics like coverage depth, read length, and genome size, as demonstrated in this work. As a result, time and effort must be put into selecting acceptable approaches for error correction of certain NGS data. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
9. Fog Computing Service in the Healthcare Monitoring System for Managing the Real-Time Notification.
- Author
-
Elhadad, Ahmed, Alanazi, Fulayjan, Taloba, Ahmed I., and Abozeid, Amr
- Subjects
HEART rate monitors ,COMPUTER systems ,HEALTH care industry ,MEDICAL care ,BLOOD pressure ,COMPUTING platforms ,CELL phones - Abstract
A new computing paradigm that has been growing in computing systems is fog computing. In the healthcare industry, Internet of Things (IoT) driven fog computing is being developed to speed up the services for the general public and save billions of lives. This new computing platform, based on the fog computing paradigm, may reduce latency when transmitting and communicating signals with faraway servers, allowing medical services to be delivered more quickly in both spatial and temporal dimensions. One of the necessary qualities of computing systems that can enable the completion of healthcare operations is latency reduction. Fog computing can provide reduced latency when compared to cloud computing due to the use of only low-end computers, mobile phones, and personal devices in fog computing. In this paper, a new framework for healthcare monitoring for managing real-time notification based on fog computing has been proposed. The proposed system monitors the patient's body temperature, heart rate, and blood pressure values obtained from the sensors that are embedded into a wearable device and notifies the doctors or caregivers in real time if there occur any contradictions in the normal threshold value using the machine learning algorithms. The notification can also be set for the patients to alert them about the periodical medications or diet to be maintained by the patients. The cloud layer stores the big data into the cloud for future references for the hospitals and the researchers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
10. An Effective Data Science Technique for IoT-Assisted Healthcare Monitoring System with a Rapid Adoption of Cloud Computing.
- Author
-
M Abd El-Aziz, Rasha, Alanazi, Rayan, R Shahin, Osama, Elhadad, Ahmed, Abozeid, Amr, I Taloba, Ahmed, and Alshalabi, Riyad
- Subjects
CLOUD computing ,DATA science ,SCIENTIFIC computing ,FEATURE selection ,CLOUD storage ,EXTRACTION techniques ,ELECTRONIC data processing - Abstract
Patients are required to be observed and treated continually in some emergency situations. However, due to time constraints, visiting the hospital to execute such tasks is challenging. This can be achieved using a remote healthcare monitoring system. The proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoT sensors are employed, which collect real healthcare data. These data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. This algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. Next, the optimum feature selection technique for extraction and selection of features is illustrated. A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. The proposed system's performance is finally examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery.
- Author
-
Abozeid, Amr, Alanazi, Rayan, Elhadad, Ahmed, Taloba, Ahmed I., and Abd El-Aziz, Rasha M.
- Subjects
OLIVE ,DEEP learning ,REMOTE-sensing images ,COMPUTER vision ,CULTURAL values ,COUNTING - Abstract
Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
12. Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems.
- Author
-
Taloba, Ahmed I., Alanazi, Rayan, Shahin, Osama R., Elhadad, Ahmed, Abozeid, Amr, and Abd El-Aziz, Rasha M.
- Subjects
ARRHYTHMIA ,INTEROCEPTION ,HUMAN mechanics ,CARDIAC arrest ,ELECTROCARDIOGRAPHY ,AUTOREGRESSIVE models ,ALGORITHMS - Abstract
Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals' autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
13. A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright.
- Author
-
Al-Saadi, Hanan S., Ghareeb, A., and Elhadad, Ahmed
- Subjects
DIGITAL watermarking ,DISCRETE cosine transforms ,EUCLIDEAN distance ,PIXELS - Abstract
In this paper, we propose a novel model for 3D object watermarking. The proposed method is based on the properties of the discrete cosine transform (DCT) of the 3D object vertices to embed a secret grayscale image three times. The watermarking process takes place by using the vertices coefficients and the encrypted image pixels. Moreover, the extraction process is totally blind based on the reverse steps of the embedding process to recover the secret grayscale image. Various performance aspects of the method are measured and compared between the original 3D object and the watermarked one using Euclidean distance, Manhattan distance, cosine distance, and correlation distance. The obtained results show that the proposed model provides better performances in terms of execution time and invisibility. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
14. Rapid and Blind Watermarking Approach of the 3D Objects Using QR Code Images for Securing Copyright.
- Author
-
Al-Saadi, Hanan S., Elhadad, Ahmed, and Ghareeb, A.
- Subjects
- *
TWO-dimensional bar codes , *COSINE function , *DIGITAL watermarking , *CARTESIAN coordinates , *EUCLIDEAN distance , *DIGITAL media , *TRANSPARENCY (Optics) - Abstract
Watermarking techniques in a wide range of digital media was utilized as a host cover to hide or embed a piece of information message in such a way that it is invisible to a human observer. This study aims to develop an enhanced rapid and blind method for producing a watermarked 3D object using QR code images with high imperceptibility and transparency. The proposed method is based on the spatial domain, and it starts with converting the 3D object triangles from the three-dimensional Cartesian coordinate system to the two-dimensional coordinates domain using the corresponding transformation matrix. Then, it applies a direct modification on the third vertex point of each triangle. Each triangle's coordinates in the 3D object can be used to embed one pixel from the QR code image. In the extraction process, the QR code pixels can be successfully extracted without the need for the original image. The imperceptibly and the transparency performances of the proposed watermarking algorithm were evaluated using Euclidean distance, Manhattan distance, cosine distance, and the correlation distance values. The proposed method was tested under various filtering attacks, such as rotation, scaling, and translation. The proposed watermarking method improved the robustness and visibility of extracting the QR code image. The results reveal that the proposed watermarking method yields watermarked 3D objects with excellent execution time, imperceptibility, and robustness to common filtering attacks. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
15. A Multicenter retrospective study in Aseer Region Saudi Arabia.
- Author
-
Alqahtani, Mohammed S., Alhazzani, Adel A., Alnaemy, Ibraheem, Alqahtani, Saeed A., Alahmari, Tariq M., Alqarni, Abdulaziz M., Alburaidi, Ibrahim A., Alqahtani, Mohammed A., Alqahtani, Saleh M., Zarbh, Moayad A., wassel, Yasser, Alfaifi, Jaber, and Elhadad, Ahmed
- Subjects
ACQUISITION of data ,CEREBRAL embolism & thrombosis ,VENOUS thrombosis ,TREATMENT effectiveness ,SYMPTOMS - Abstract
Copyright of Neurosciences is the property of Neurosciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
- Full Text
- View/download PDF
16. A Primary Epithelioid Angiosarcoma Arising in a Bilharzial Urinary Bladder: A Reappraisal and Case Report.
- Author
-
Elatreisy A, Hussein MRA, Shalkamy O, Aljubran AM, Aboelnasr M, Safar O, Shah S, Elhadad AYK, Alqahtani S, Ahmad N, Al-Ayafi M, Bosily MJ, and Alhadi A
- Subjects
- Male, Humans, Middle Aged, Urinary Bladder surgery, Urinary Bladder pathology, Hematuria etiology, Endothelial Cells, Hemangiosarcoma surgery, Hemangiosarcoma diagnosis, Hemangiosarcoma pathology, Cystitis
- Abstract
Background: Angiosarcoma (AS) of the urinary bladder is a very rare and aggressive malignancy with a dismal outcome., Case Report: Here, we report a primary epithelioid angiosarcoma (EAS) of the urinary bladder in a forty-nine-year-old male patient who presented with severe hematuria. Cystoscopic examination revealed hemorrhagic ulcerated bladder mucosa but no definite mass lesions. Intractable hematuria raised the initial clinical impression of idiopathic hemorrhagic cystitis. Analysis of the cystoscopic biopsy revealed features of old bilharzial cystitis, markedly atypical epithelioid endothelial cells arranged as primitive anastomosing vascular structures and expressing vascular markers. The diagnosis of EAS was established. The patient developed intractable severe hematuria, and a radical cystoprostatectomy was performed. The patient was started on chemotherapy but suddenly developed widespread distant metastasis (liver, lung, suprarenal glands, and lymph nodes) and succumbed to death two months after the surgery., Conclusion: To the best of these authors' knowledge, we presented the first report of primary EAS arising in a bilharzial bladder. The relevant studies were discussed.
- Published
- 2023
- Full Text
- View/download PDF
17. Clinical and epidemiological profile of cerebral venous thrombosis. A Multicenter retrospective study in Aseer Region Saudi Arabi.
- Author
-
Alqahtani MS, Alhazzani AA, Alnaami I, Alqahtani SA, Alahmari TM, Alqarni AM, Alburaidi IA, Alqahtani MA, Alqahtani SM, Zarbh MA, Wassel Y, Alfaifi J, and Elhadad A
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Retrospective Studies, Risk Factors, Saudi Arabia epidemiology, Young Adult, Sinus Thrombosis, Intracranial diagnosis, Sinus Thrombosis, Intracranial epidemiology, Sinus Thrombosis, Intracranial pathology
- Abstract
Objective: To assess the epidemiological pattern and correlates with the clinical outcome of Cerebral venous thrombosis (CVT) in Abha, Kingdom of Saudi Arabia., Methods: A retrospective record_based cohort design was conducted including all patients admitted with diagnosis of CVT in 2 main tertiary hospitals in Aseer Region between 2015 to the end of 2018. The study hospitals were Aseer Central Hospital and Armed Forces Hospitals Southern Region. The data were collected by structured data sheets, including sociodemographic data. Assessment of known risk factors for CVT, clinical presentation, treatment received, and clinical outcome after treatment were extracted., Results: The study included 119 patients with CVT, whose ages ranged from 15 to 97 years, with a mean age of 35.5-/+14.1 years. Majority of the patients were females (81.5%). Headache was the most presenting (82.4%) symptom, followed by vomiting (30.3%) and a decreased level of consciousness. Thirty_three cases (27.7%) had complications, and recanalization was recorded among 92 cases (94.8%) based on follow up vascular imaging., Conclusion: The study revealed that most of the cases of CVT had favorable clinical outcome and recanalization, especially those who had a shorter duration untildiagnosis. Young females were the most affected group.
- Published
- 2020
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.