Parallel processing in knowledge-based diagnostic systems

This paper highlights the use of the parallel processing concept in knowledge-based diagnostic systems. A MIMD machine connected in a cubic mesh fashion using Parlog has been suggested to implement such systems. An algorithm supporting the concurrent execution of multiple conflict set rules of the same production system program is presented. A specific application to communication systems maintenance utilizing these principles has been discussed.     

Neuro-fuzzy based constitutive modeling of undrained response of Leighton Buzzard Sand mixtures

This study aims to develop neuro-fuzzy (NF) based constitutive model for Leighton Buzzard Sand fraction B and Leighton Buzzard Sand fraction E mixtures using experimental data. The experimental database used for NF modeling is based on a laboratory study of saturated mixtures with various mix ratios under a 100 kPa effective stress. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation in a 100 mm diameter triaxial testing apparatus. The input variables in the developed rule based NF models are the Leighton Buzzard Sand fraction E content, and strain, and the outputs are deviatoric stress, pore water pressure generation and undrained Young’s modulus. Experimental results show that Leighton Buzzard Sand fraction B and Leighton Buzzard Sand fraction E mixtures exhibits clay-like behavior due to particle–particle effects with the increase in Leighton Buzzard Sand fraction E content. It is also shown that the performance of capacities of proposed NF models are quite satisfactory.     

A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering

This paper presents a novel image segmentation algorithm based on neutrosophic c-means clustering and indeterminacy filtering method. Firstly, the image is transformed into neutrosophic set domain. Then, a new filter, indeterminacy filter is designed according to the indeterminacy value on the neutrosophic image, and the neighborhood information is utilized to remove the indeterminacy in the spatial neighborhood. Neutrosophic c-means clustering is then used to cluster the pixels into different groups, which has advantages to describe the indeterminacy in the intensity. The indeterminacy filter is employed again to remove the indeterminacy in the intensity. Finally, the segmentation results are obtained according to the refined membership in the clustering after indeterminacy filtering operation. A variety of experiments are performed to evaluate the performance of the proposed method, and a newly published method neutrosophic similarity clustering (NSC) segmentation algorithm is utilized to compare with the proposed method quantitatively. The experimental results show that the proposed algorithm has better performances in quantitatively and qualitatively.

     

Synthesis,characterization, and performance evaluation of PES/EDA‐functionalized TiO2 mixed matrix membranes for CO2/CH4 separation

Poor adhesion between hydrophobic polymers and hydrophilic inorganic fillers is a challenge that encumbers a high separation performance of mixed matrix membrane (MMM). In this study, Titanium(IV) oxide (TiO₂) nanoparticles were functionalized using ethylenediamine (EDA) before embedment in poly(ether sulfone) (PES) polymer matrix. MMMs were synthesized through dry phase inversion technique. Membranes morphology and nanoparticles dispersion was drastically enhanced posterior amine modification indicating an improved adhesion between the polymer and filler particles. Membranes thermal stability was likewise improved as higher degradation temperatures were perceived for PES/EDA–TiO₂ MMMs. Gas separation evaluation for pure carbon dioxide (CO₂) and methane (CH₄) gases revealed a remarkably enhanced separation performance upon amine‐grafting of TiO₂ as EDA‐TiO₂ MMMs exhibited a higher separation performance as compared to MMMs with pristine TiO₂. The highest ideal separation factor achieved was 41.52 with CO₂ permeability of 10.11 Barrer at an optimum loading of 5% wt of EDA‐TiO₂ which is threefold higher as compared to neat PES membrane and approximately twofold higher than MMMs with pristine TiO₂, respectively, at the same filler loading. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45346.     

System‐based strategies for p53 recovery

The authors have proposed a systems theory‐based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations‐based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re‐activation of wild‐type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin. Attractor point analysis is carried out to select the suitable domain of attraction. A two‐loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.Inspec keywords: proteins, tumours, cancer, cellular biophysics, molecular biophysics, molecular configurations, biochemistry, differential equations, closed loop systems, bifurcation, biology computingOther keywords: system‐based strategies, p53 recovery, systems theory‐based novel drug design approach, dynamic system, ordinary differential equations‐based mathematical model, control engineering practices, subsequent controller design, wild‐type p53, p53 revival, oscillatory, control system paradigm, mathematical model, Nutlin effect, attractor point analysis, domain‐of‐attraction, two‐loop negative feedback control strategy, cellular concentration, pharmacokinetic effects, cancerous cells, bifurcation analysis, p53 oscillation, anomalous cell     

A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

In this study, we propose a robust technique based on invariant moments – adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.     

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.     

Optimization of atrazine removal from synthetic groundwater by electrooxidation process using titanium dioxide and graphite electrodes

ABSTRACT

Atrazine is used in agriculture and is known for its high toxicity. It therefore poses a risk to surface and ground waters, and human life. In this study, an electrochemical method was optimized for atrazine removal from SGW using TiO₂ and graphite electrodes. A comparison between one factor at a time period optimization and Box-Behnken design (BBD) optimization using RSM was carried out to select the optimum conditions. The results show excellent atrazine removal efficiency (99.70%) and close optimum conditions for both applied methods (pH 7 and 7.4; current 2.5 and 2 A and time 14 and 12.12 min, respectively).     

Determining students’ level of page viewing in intelligent tutorial systems with artificial neural network

The concept of level of page viewing (LPV) refers to the extent to which a student actively revises the pages that he or she has to study in tutorial systems. In the present study, an artificial neural network (ANN) model, which is composed of 5 inputs, 20 and 30 neurons, 2 hidden layers, and 1 output, was designed to determine the students’ LPV. After this network was trained, it was integrated into a web-based prototype teaching system, which was developed by ASP.net C# programming language. Additionally, Decision Tree method is tried to determine students’ LPV. However, this method gave wrong results according to expected LPV values. In this system, the student first studies the pages uploaded by the teacher onto the system. After studying all the pages within the scope of a topic, the student can go to the test page for evaluation purposes. LPVs of a student who wants to navigate to the test page are calculated by an ANN module added to the system. On the condition that one or more of the LPV’s are not up to the desired level, the student is not allowed to take the test and is informed of the pages with missing LPV’s so that he can re-study these pages. This prototype system developed based on ANN to determine students’ LPV is essential for intelligent tutorial systems, geared to provide intelligent assistance and guidance. The system can track the pages which the students did not study sufficiently and thus direct them to relevant pages. How much activity the students perform on each page to study is observed before they actually take the test, and the areas which should be further revised are determined much in advance.