Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

Abstract-Servo motors are important parts of industry automation due to their several advantages such as cost and energy efficiency, simple design, and flexibility. However, the position control of the servo motor is a difficult task because of different factors of external disturbances, nonlinearities, and uncertainties. To tackle these challenges, an adaptive integral sliding mode control (AISMC) is proposed, in which a novel bidirectional adaptive law is constructed to reduce the control chattering. The proposed control has three steps to be designed. Firstly, a full-order integral sliding manifold is designed to improve the servo motor position tracking performance, in which the reaching phase is eliminated to achieve the invariance of

Nuclear shell model is adopted to calculate the electric quadrupole moments for some Calcium isotopes 20Ca (N = 21, 23, 25, and 27) in the fp shell. The wave function is generated using a two body effective interaction fpd6 and fp space model. The one body density matrix elements (OBDM) are calculated for these isotopes using the NuShellX@MSU code. The effect of the core-polarizations was taken through the theory microscopic by taking the set of the effective charges. The results for the quadrupole moments by using Bohr-Mottelson (B-M) effective charges are the best. The behavior of the form factors of some Calcium isotopes was studied by using Bohr-Mottelson (B-M) effective charges.

197 vaginal swabs were collected from women of different ages. (60) Isolates of Candida albicans (30.4%) were obtained, and the other species of Candida represent (18.27%). Bacterial infections showed (41.11%), and infection with Trichomonas vaginalis was (2.03%). Ten isolates of C. albicans were chosen randomly for farther study which include two virulence factors tendency of adhesion wich showed a percentage of (52%) to(32%) , and the ability to produce phospholipaze enzyme and it’s activity which showed (50%) of the isolates have the ability to produce the enzyme in different degrees .

The inelastic longitudinal electron scattering form factors are calculated for the low-lying excited states of 7Li {the first excited state 2121TJ (0.478 MeV) and the second excited state 2127TJ (4.63 MeV)}. The exact value of the center of mass correction in the translation invariant shell model (TISM) has been included and gives good results. A higher 2p-shell configuration enhances the form factors for high q-values and resolves many discrepancies with the experiments. The data are well described when the core polarization (CP) effects are included through effective nucleon charge. The results are compared with other theoretical models.
Keyword: 7Li inelastic electron scattering form factors calculated with exact

Abstract Twelve isolates of bacteria were obtained from samples of different soils and water amended with 100µg/ml of five heavy metals chlorides (i.e: Aluminum Al+2, Iron Fe+2, Lead Pb+2, Mercury Hg+2 and Zinc Zn+2). Four isolates were identified as Bacillus subtilis and B. subtilis (B2) isolate was selected for this study according to their resistance to all five heavy metals chlorides. The ability of B. subtilis (B2) isolate for growing in different concentration of heavy metals chlorides ranging from 200-1200 µg/ml was tested. The highest conc. that B. subtilis (B2) isolate tolerate was 1000 µg/ml for Al+2, Fe+2, Pb+2, and Zn+2and 300 µg/ml for Hg+2 for 24hour. The effect of heavy metals chlorides on bacterial growth for 72 hrs was

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.

We compare two methods Bayesian and . Then the results were compared using MSe criteria.

A simulation had been used to study the empirical behavior for the Logistic model , with  different sample sizes and variances. The results using represent that the Bayesian method is better than the   at small samples sizes.