In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

Background: Clubfoot, or talipes equinovarus, is a congenital deformity that consist of; supination and adduction of the forefoot and midfoot; equinus of hindfoot and varus. It was found that more than 100,000 babies are born each year with congenital clubfoot

Objectives: The purpose of this study was to investigate the complications of  ponseti method for treatment of children with idiopathic club foot.

Subjects and Methods: 50 children with 74 clubfeet were managed by Ponseti method from May 2019 to July 2020 in Al-Wasity teaching hospital with primary correction of the deformity followed sometimes by elongation of Achilles tendon then the pati

One of the troublesome duties in chemical industrial units is determining the instantaneous drop size distribution, which is created between two immiscible liquids within such units. In this work a complete system for measuring instantaneous droplet size is constructed. It consists of laser detection system (1mW He-Ne laser), drop generation system (turbine mixer unit), and microphotography system. Two immiscible liquids, water and kerosene were mixed together with different low volume fractions (0.0025, 0.02) of kerosene (as a dispersed phase) in water (as a continuous phase). The experiments were carried out at different rotational speed (1180- 2090 r.p.m) of the turbine mixer. The Sauter mean diameter of the drops was determined by la

       Drought is one of the most natural hazards that may harm human life and property under different weather and environmental conditions. This study used remote sensing data to monitor agricultural and meteorological drought in Babel Governorate. Drought maps were drawn using Landsat 8 images based on Normalized Difference Vegetation Index (NDVI) for 2015, 2018, and 2021. The meteorological drought was assessed using a standardized precipitation index (SPI 12) for the same years. The results showed that the SPI-12 indicated near-normal drought types in 2015 and 2018, whereas SPI values showed a lower value in 2021. Two drought categories were identified which were moderate drought and severe drought. The NDVI results showed tha

In this study, a novel application of lab-scale dual chambered air-cathode microbial fuel cell (MFC) has been developed for simultaneous bio-treatment of real pharmaceutical wastewater and renewable electricity generation. The microbial fuel cell (MFC) was provided with zeolite-packed anodic compartment and a cation exchange membrane (CEM) to separate the anode and cathode. The performance of the proposed MFC was evaluated in terms of COD removal and power generation based on the activity of the bacterial consortium in the biofilm mobilized on zeolite bearer. The MFC was fueled with real pharmaceutical wastewater having an initial COD concentration equal to 800 mg/L and inoculated with anaerobic aged sludge. Results demo

The use of a communication network in the closed loop control systems has many advantages such as remotely controlling equipment, low cost, easy to maintenance, efficient information transmission, etc. However, the Networked Control System (NCS) has many drawbacks, such as network-induce end-to-end time delay and packet loss, which lead to significant degradation in controller performance and may result in instability. Aiming at solving performance degradation in NCS, this paper propose to take the advantages and strength of the conventional Proportional-Integral-Derivative (PID), Fuzzy Logic (FL), and Gain Scheduling (GS) fundamentals to design a Fuzzy-PID like-Gain Scheduling (F-PID-GS) control technique, which has been proved to be ef

In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinearities, exterior disturbances, and time delay in which the observer gains are dynamically adjusted based on the estimation error to enhance est

      Despite the availability of information technology banking features and benefits of the banking sector, they involve many risks and challenges and put in the face of the administrative authorities and regulatory institutions in the banking system, organizational matters and control sensitive and bear direct responsibility for conducting independent assessments of their regulatory and information and determine the degree of its durability and its ability to confront problems imposed by the technical challenges and technological .
And the success of the administrative authorities and regulatory institutions in achieving its objectives in the management of risks and threats oversight resulting from the act

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor