Usage of class dependency based feature selection and fuzzy weighted pre-processing methods on classification of macular disease

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and decision tree classifier as decision making. The proposed system consists of three parts. First, we have reduced to 9 features number of features of macular disease dataset that has 63 features using class dependency based feature selection, which is first developed by ours. Second, the macular disease dataset that has 9 features is weighted by using fuzzy weighted pre-processing. And finally, decision tree classifier was applied to PERG signals to distinguish between healthy eye and diseased eye (macula diseases). The employed class dependency based feature selection, fuzzy weighted pre-processing and decision tree classifier have reached to 96.22%, 96.27% and 96.30% classification accuracies using 5–10–15-fold cross-validation, respectively. The results confirmed that the medical decision making system based on the class dependency based feature selection, fuzzy weighted pre-processing and decision tree classifier has potential in detecting the macular disease. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.     

Titanium-containing bioactive phosphate glasses

The use of biomaterials has revolutionized the biomedical field and has received substantial attention in the last two decades. Among the various types of biomaterials, phosphate glasses have generated great interest on account of their remarkable bioactivity and favourable physical properties for various biomedical applications relating to both hard and soft tissue regeneration. This review paper focuses mainly on the development of titanium-containing phosphate-based glasses and presents an overview of the structural and physical properties. The effect of titanium incorporation on the glassy network is to introduce favourable properties. The biocompatibility of these glasses is described along with recent developments in processing methodologies, and the potential of Ti-containing phosphate-based glasses as a bone substitute material is explored.     

Statistically monotonic and statistically bounded sequences of fuzzy numbers

We introduce the statistical monotonicity and boundedness of a sequence of fuzzy numbers. We also derive the analogue of monotone convergence theorem and prove the decomposition theorems for this type of sequences.     

The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.     

Improving the discrimination power and weights dispersion in the data envelopment analysis

Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its popularity, DEA has some drawbacks such as unrealistic input–output weights and lack of discrimination among efficient DMUs. In this study, two new models based on a multi-criteria data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using goal programming (GP). These goal programming data envelopment analysis models, GPDEA-CCR and GPDEA-BCC, also improve the discrimination power of DEA.     

Osteogenic proteins (bone sialoprotein and bone morphogenetic protein-7) and dental pulp mineralization

Bone sialoprotein (BSP) cross-linked to collagen/gelatin was implanted in the pulp of rats upper molars. Comparison was carried out with a sham group (non implanted), with a group of rats receiving the carrier alone, and a group of molars where the perforated pulps were capped with calcium hydroxide. The cavities were occluded with a glass-ionomer cement (GIC). After 8, 14 and 30 days respectively the rats were killed by intracardiac perfusion of the fixative and processed for light microscopy. Dentin and predentin debris pushed into the pulp during the preparation enhanced self-repair processes, with large pulp remnants. The carrier alone induced slight inflammation, and calcium hydroxide the formation of a reparative dentin bridge. BSP stimulated the recruitment of cells which produced an homogeneous atubular dentin-like structure, filling after one month the mesial third of the crown pulp. Osteogenic protein (OP-1) used in the same experimental conditions induced the formation of osteodentin in the coronal pulp and the radicular part of the pulp was totally filled by a mineralized material. The differences reported here suggest two possible different therapeutic approaches with the two osteogenic proteins, BSP inducing pulp mineralization in the crown part, and OP-1 occluding the root part of the pulp.     

Knowledge networks in new product development projects: A transactive memory perspective

Even though an individual's knowledge network is known to contribute to the effectiveness and efficiency of his or her work in groups, the way that network building occurs has not been carefully investigated. In our study, activities of new product development teams were analyzed to determine the antecedents and consequences on the transactive memory systems, the moderating affect of task complexity was also considered. We examined 69 new product development projects and found that team stability, team member familiarity, and interpersonal trust had a positive impact on the transactive memory system and also had a positive influence on team learning, speed-to-market, and new product success. Further, we found that the impact of the transactive memory system on team learning, speed-to-market, and new product success was higher when there was a higher task complexity. Theoretical and managerial implications of the study findings are discussed.     

Pervaporation Separation of Methanol-Carbontetrachloride Binary Mixtures Using Low Density Polyethylene Membranes

In this study, the pervaporation behaviour of azeotrope-forming methanol-carbontetrachloride binary mixture at different compositions was investigated at 30°C and 45°C through LDPE membranes with two different melt flow indices for two different thicknesses. Experimental fluxes and selectivities for methanol and carbontetrachloride were determined. Theoretical calculations for fluxes based on a solution-diffusion model as developed by Yeom and Huang using Fujita′s free volume approach and Flory-Huggins thermodynamics showed deviations from the experimental results. Experimental fluxes were decomposed into sorption and diffusion terms to discuss the effects of experimental conditions.     

A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks

Network selection mechanisms have a significant role in guaranteeing the QoS for users in a heterogeneous wireless networks environment. These mechanisms allow the selection of an optimal wireless network to satisfy the needs of users. Users are provided with the opportunity to select from multiple connectivity opportunities available all over various wireless networks. Furthermore, the network operators themselves can execute active selection strategies that facilitate proper decision making, in which user preferences are considered. This study proposes a new noncooperative competing game‐theoretic model and strategy space based on user preference. This model can solve network selection problems and capture the inter‐linkages of decisions taken by various networks. A generalized simple additive weighting method is incorporated into the framework of noncooperative game theory. In addition, the utility function is employed to assess the usefulness of the system. Simulation results and analysis illustrate the efficacy of the suggested model in attaining optimum network utility for heterogeneous wireless networks while optimizing user satisfaction. Copyright © 2014 John Wiley & Sons, Ltd.     

Production scheduling with uncertain supply: a new solution to the open pit mining problem

The annual production scheduling of open pit mines determines an optimal sequence for annually extracting the mineralized material from the ground. The objective of the optimization process is usually to maximize the total Net Present Value (NPV) of the operation. Production scheduling is typically a Mixed Integer Programming (MIP) type problem containing uncertainty in the geologic input data and economic parameters involved. Major uncertainty affecting optimization is uncertainty in the mineralized materials (resource) available in the ground which constitutes an uncertain supply for mine production scheduling. A new optimization model is developed herein based on two-stage Stochastic Integer Programming (SIP) to integrate uncertain supply to optimization; past optimization methods assume certainty in the supply from the mineral resource. As input, the SIP model utilizes a set of multiple, stochastically simulated scenarios of the mineralized materials in the ground. This set of multiple, equally probable scenarios describes the uncertainty in the mineral resource available in the ground, and allows the proposed model to generate a single optimum production schedule. The method is applied for optimizing the annual production scheduling at a gold mine in Australia and benchmarked against a traditional scheduling method using the traditional single “average type” assessment of the mineral resource in the ground. In the case study presented herein, the schedule generated using the proposed SIP model resulted in approximately 10% higher NPV than the schedule derived from the traditional approach.