Development of a Mobile Application for Interaction between Patients and Doctors in Rural Populations

Alazzam, Malik Bader; Alassery, Fawaz; Almulihi, Ahmed

doi:10.1155/2021/5006151

Cited by 37 publications

(35 citation statements)

References 12 publications

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“…Alazzam et al' study [ 1 ] is an example. Control and hybrid three-dimensional synchronization (HPS) procedures are for a unique hyperchaotic system.…”

Section: Introductionmentioning

confidence: 99%

Applying Dynamic Systems to Social Media by Using Controlling Stability

Hamad

Thivagar

Alazzam

et al. 2022

Computational Intelligence and Neuroscience

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This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When lim t ⟶ ∞ Y − α X = 0 , where Y and X are the state vectors of the drive and response systems, respectively, and Wan ( α = ∓ 1)), the two systems’ hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach.

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“…Alazzam et al' study [ 1 ] is an example. Control and hybrid three-dimensional synchronization (HPS) procedures are for a unique hyperchaotic system.…”

Section: Introductionmentioning

confidence: 99%

Applying Dynamic Systems to Social Media by Using Controlling Stability

Hamad

Thivagar

Alazzam

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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“…One of the limitations that arose during the project was creating the work environment and the lack of memory of the equipment used and the incompatibility of libraries, but thanks to Google Collaboratory, it was possible to solve it. In addition, it allows us to share the code with other researchers [17][18][19][20][21][22].…”

Section: Web Applicationmentioning

confidence: 99%

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric

Alshamrani

Asiri

et al. 2022

BioMed Research International

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Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X-rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model’s performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep-learning models can be created to assist health professionals in the early detection of pneumonia.

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“…The SS-GAN-3D is ready as often as it should be and keeps making new 3D generative and discriminative associations until it runs out. General misfortune esteem meets to an ideal edge by consolidating virtual perception pictures from novel perceptions with the genuine unique perception [18].…”

Section: Algorithm Flowmentioning

confidence: 99%

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Wang

2022

Wireless Communications and Mobile Computing

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The developing intricacy and extent of building projects, as well as the way that construction schedule management is still mostly done by hand, has resulted in low efficiency in construction schedule management, resulting in cost overruns and legal disputes as a result of schedule delays in many projects. Existing 3D reconstruction algorithms frequently result in large there may be holes, distortions, or hazy regions in remade 3D models, but AI-based 3D remaking strategies regularly reestablish straightforward isolated parts and portray them as 3D boxes. Accordingly, these algorithmic systems are generally not sufficient for certifiable use. The primary objective of this paper is to apply the creation ill-disposed network strategy to 3D recreation works on the nature of the underlying 3D recreation model via preparing a generative ill-disposed network model to a joined state. As solo examples, just recently noticed 2D pictures are required, with no dependence on earlier information on the 3D hidden shape or reference insights. Test results show that this algorithmic structure outflanks present status of-the-workmanship 3D reproduction approaches on an average 3D recreation test set. On the common 3D remaking test set, exploratory outcomes uncover that this algorithmic structure beats the current cutting edge 3D reproduction techniques.

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Development of a Mobile Application for Interaction between Patients and Doctors in Rural Populations

Cited by 37 publications

References 12 publications

Applying Dynamic Systems to Social Media by Using Controlling Stability

Applying Dynamic Systems to Social Media by Using Controlling Stability

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

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