Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy

© 2018 IEEE. In this paper, advanced particle swarm optimization Algorithm (APSO) with improved velocity updated strategy is presented. The algorithm incorporates an improved velocity update equation so that the particles will reach the optimum point quickly and convergence is much faster than the standard PSO (SPSO) and other improved PSOs in the literature. Five benchmark functions have been selected to evaluate the efficiency of the proposed algorithm. The simulation results demonstrate that the proposed technique has remarkably improved in terms of convergence and solution quality

A modified particle swarm optimization algorithm used for feature selection of UCI biomedical data sets

© 2019 IEEE. In biomedical and health care applications, classification examination is extensively used to help diagnose health problems, decision making and enhance standards of patient care. Feature selection is a significant data pre-processing method in classification problems. Training of the data is achieved by using a subset dataset from the UCI biomedical database. If the training dataset comprises inappropriate features, classification analysis resulted in inaccurate and incomprehensible results. In data mining, feature subset selection is data pre-processing phase that is of enormous importance. In this paper, for selecting a minimum number of features K-Nearest Neighbour (KNN) classifier is presented with a modified particle swarm optimization (MPSO) to obtain good classification precision. The proposed method is applied to three UCI medical data sets and is compared with other feature selection approach available in the literature. Results demonstrate that the feature subset recognized by the presented MPSO with KNN neighbor classifiers give better results and accuracy as compared to the other techniques

A hybrid advanced PSO-neural network system

© 2019 IEEE. In this paper, a combination of Advanced Particle Swarm Optimization (APSO) and Neural Network are presented to compensate the drawbacks of both the techniques and utilize the strong attributes to form a hybrid system called Hybrid Advance Particle Swarm Optimization-Neural Network System (HAPSONNS). APSO is used for the training of the neural network. In the initial phases of the search, PSO has swift convergence for global optimum, but later it suffers from slow convergence around the global optimum position. On the contrary, the gradient method attains prior to convergence around the global optimum point, therefore, attaining better accuracy in terms of convergence. This paper elucidates the usage of APSO applied to feedforward neural network to improve the classification accuracy of the network and also decreases the network training time

COMPARISON OF XANTHINE OXIDASE INHIBITORS IN GOUTY PATIENTS WITH HYPERURICEMIA

Objective: Febuxostat is more effective/superior to Allopurinol in reducing the serum uric acid (SUA) level in the treatment of hyperuricemic withgout.Methods: This randomized control study included 200 hyperuricemic patients with gout, at Multi-center study including Outdoor Departments ofMedicine from four different hospitals of Lahore, Pakistan. Patients age range 18-50 years diagnosis with hyperuricemia and gout, SUA >8 mg/dlwere included while severe renal impairment and alanine aminotransferase and aspartate aminotransferase patients were excluded from the study.Results: About 200 patients treated with hyperuricemic with gout were randomly divided into four groups (50%) patients were in each groupreceived different treatment. Out of 200 patients, 118 (59%) were male and 82 (41%) were female with mean age 42.37±9.47 years. Among theFebuxostat group, patients' success rate of lowering SUA level was found to be 32 (64%) as compared to Allopurinol 16 (32%). Drug compliance wassimilar among treatment groups, i.e. Allopurinol and Febuxostat while the trend toward drug compliance in Allopurinol + Vitamin C and Febuxostat +Vitamin C groups showed similar in number.Conclusion: Febuxostat is safe and effective to Allopurinol for the treatment of hyperuricemia with gout as the Febuxostat has a significant associationwith lowering SUA concentration <6 mg/dl. It is concluded that although Febuxostat is safe and effect alone in gouty patients, but it has somehow alittle effect with Vitamin C especially in patients who are feeble.Keywords: Febuxostat, Allopurinol, Serum uric acid.Â

A novel hybrid gravitational search particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a member of the swarm computational family and widely used for solving nonlinear optimization problems. But, it tends to suffer from premature stagnation, trapped in the local minimum and loses exploration capability as the iteration progresses. On the contrary, Gravitational Search Algorithm (GSA) is proficient for searching global optimum, however, its drawback is its slow searching speed in the final phase. To overcome these problems in this paper a novel Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO) is presented. The key concept behind the proposed method is to merge the local search ability of GSA with the capability for social thinking (gbest) of PSO. To examine the effectiveness of these methods in solving the abovementioned issues of slow convergence rate and trapping in local minima five standard and some modern CEC benchmark functions are used to ensure the efficacy of the presented method. Additionally, a DNA sequence problem is also solved to confirm the proficiency of the proposed method. Different parameters such as Hairpin, Continuity, H-measure, and Similarity are employed as objective functions. A hierarchal approach was used to solve this multi-objective problem where a single objective function is first obtained through a weighted sum method and the results were then empirically validated. The proposed algorithm has demonstrated an extraordinary performance per solution stability and convergence

A biochemical and histopathologic study showing protection and treatment of gentamicin-induced nephrotoxicity in rabbits using vitamin c

Gentamicin and vitamin C have been proposed as nephrotoxic and antioxidant, respectively. This study involved biochemical and histopathologic investigation showing protection and treatment of gentamicin-induced nephrotoxicity in rabbits using vitamin C for 26 days hypothesizing that whether vitamin C would inhibit or decrease the raised serum urea and creatinine levels. This study was conducted on 25 healthy male albino rabbits (average weight 1.5±0.2 kg), classified into 5groups: group A, B, C, D and E for nephrocurative (study-I) and  nephroprotective (study-II) studies. Control group of rabbits (group A) received only the vehicle of gentamicin ampoule. In study-I, gentamicin sulphate (GS 80 mg/kg, i.m.) was administered to group B and C rabbits for ten days, then group C rabbits received vitamin C 250 mg/Kg for remaining 16 days. Group D and E received GS 80 mg/kg and GS 80 mg/kg i.m.-vitamin C 250 mg/kg orally, respectively during whole period (26 days) of study-II. After 26 days, various biochemical parameters, i.e. serum creatinine, blood urea nitrogen (BUN), and serum antioxidant activity, and histopathologic investigations were made. Nephrotoxicity was observed in rabbit groups B, C and D as evident from significant (p<0.05) high levels of serum creatinine and BUN and low serum antioxidantlevels as compared to the levels of control group. Decrease in the levels of serum creatinine and BUN along with the increase in serum antioxidant activity was observed after vitamin C treatment in group C. While, renal-protective role of vitamin C was seen in group E as compared to the control. In conclusion, Gentamicin induced nephrotoxicity can be  attenuated or treated using vitamin C

Nexus Between Demographic Change and Elderly Care Need in the Gulf Cooperation Council (GCC) Countries: Some Policy Implications.

Population ageing is a phenomenon affecting the whole world. The countries that make up the Gulf Cooperation Council (GCC) are no exception but transitions in population ageing are still in the early stages of the process. With current demographic dividends experienced by the GCC and the rest of the Middle-East, the pace of population ageing will be faster than that experienced by many European countries. The purpose of this paper is to explore the population ageing experience of different GCC countries while situating this within a context of social policies that still at the very early stages of acknowledging such change. We utilise data from sources such as the United Nations and the World Bank, complemented by policy analysis of current age-related social security measures in the GCC. Given the importance of the family aged care system in the region, we consider the implications of changes in family structures, living conditions, and care needs for the elderly. The findings confirm the declining trend in fertility combined with increased life expectancy in all the six GCC countries. However, they highlight that social policy measures focused on the older generations and their care needs are still relatively at the early stages of each country's policy agenda. The implications of such changes are serious in term of both the demand for and supply of care. Policy-makers need to adapt cohesive social policy strategies that strengthen the complementing relationships between the state, family and wider community as stakeholders in the provision of aged care

Molecular dynamic simulation on temperature evolution of SiC under directional microwave radiation

Silicon carbide (SiC) is widely used as the substrate for high power electronic devices as well as susceptors for microwave (MW) heating. The dynamics of microwave interaction with SiC is not fully understood, especially at the material boundaries. In this paper, we used the molecular dynamics simulation method to study the temperature evolution during the microwave absorption of SiC under various amplitudes and frequencies of the microwave electric field. Directional MW heating of a SiC crystal slab bounded by surfaces along [100] crystallographic direction shows significantly faster melting when the field is applied parallel to the surface compared to when applied perpendicular

Hall current, viscous and Joule heating effects on steady radiative 2-D magneto-power-law polymer dynamics from an exponentially stretching sheet with power-law slip velocity : a numerical study

A mathematical model is developed for 2-D laminar, incompressible, electrically conducting non-Newtonian (Power-law) fluid boundary layer flow along an exponentially stretching sheet with power-law slip velocity conditions in the presence of Hall currents, transverse magnetic field and radiative flux. The secondary flow has been induced with appliance of Hall current. The distinguish features of Joule heating and viscous dissipation are included in the model since they are known to arise in thermal magnetic polymeric processing. Rosseland’s diffusion model is employed for radiation heat transfer. The non-linear partial differential equations describing the flow (mass, primary momentum, secondary momentum and energy conservation) are transformed into non-linear ordinary differential equations by employing local similarity transformations. The non-dimensional nonlinear formulated set of equations is numerically evaluated with famous shooting algorithm by using MATLAB software. The validation of simulated numerical results has been completed with generalized differential quadrature (GDQ). Extensive visualization of primary and secondary velocities and temperature distributions for the effects of the emerging parameters is presented for both pseudo-plastic fluids (n=0.8) and dilatant fluids (n=1.2). The study is relevant to the manufacturing transport phenomena in electro-conductive polymers (ECPs)