The purpose of this study is to investigate the gradual incorporation of deep learning in the dental healthcare system, offering an easy and efficient diagnosis. For that, an electronic search was conducted in the Institute of Electrical and Electronics Engineers (IEEE) Xplore, ScienceDirect, Journal of Dentistry, Health Informatics Journal, and other credible resources. The studies varied with

This paper illustrates a humanlike self-balancing robot's control and sensor fusion with an inherently imbalanced dynamical model. Using a state space model, a two-wheeled self-balancing robot (TWSBR) is modelled as an inverted pendulum. A comparative study of the system performance is conducted, with different control techniques, namely, PD, PID, and Linear Quadratic Regulator (LQR). The system

Introduction: The hyper-catabolic state is a devastating pathophysiological response to severe injury, infection or burns. Nandrolone decanoate (ND) is a potent anabolic steroid have many clinical indications, but not investigated in burn injuries yet. Patients and methods: A prospective randomized control study included 40 burned patients who were treated in Burn unit from burn injuries ranged

Today, automatic food/non-food classification became extremely important for many real-time applications, specifically since the pandemic of the COVID-19 virus. Such that the 'no food policy' now became applied more than ever to help decrease the spread of the COVID-19 virus. Consequently, many studies used deep neural networks for the food/non-food classification task, yet these deep neural

Machine learning (ML) and deep learning (DL) have been broadly used in our daily lives in different ways. Early detection of COVID-19 built on chest Computerized tomography CT empowers suitable management of patients and helps control the spread of the disease. We projected an artificial intelligence (AI) system for rapid COVID-19 detection using analysis of CTs of COVID-19 depending on the AI

Parkinson's disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning

Cross-platform mobile development is a widely used framework due to its nature of building an app using one development life cycle and deploying it to multiple platforms like Android and iOS. Many cross-platform solutions were recently developed to convert from one platform to another using Trans-compilation approach as Trans-Compiler Android to IOS Conversion (TCAIOSC) and Trans-Compiler Based

Rice leaf disease detection is critical for the agriculture sector since rice feeds approximately half of the world's population. Many researchers worked on this subject, and their results varied depending on the methodologies they used. A deep learning classification architecture, known as AlexNet, is used in this paper to detect three common rice leaf diseases: bacterial leaf blight (BLB), brown

the proposed paper aims to design and model an air levitation system, which is a highly nonlinear system because of its fast dynamics and low damping. The system is trained using a Nonlinear Autoregressive model with exogenous input (NARX model). An enhanced height measurement system, modified setup, and several training techniques have been used to overcome the restrictions that the non-linearity

Crime records management is a system that helps police keep records of citizens’ complaints files, investigation evidence and processes. In addition, it helps police keep records of the criminals who have been arrested or who are to be arrested. This paper aims to model the Crime Record Management System (CRMS) using various Unified Modeling Language (UML) diagrams, to demonstrate an explicit