Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market

With the increasing growth of technology and the emergence of various industries, numerous manufacturers have entered this field. In today's world, sellers and manufacturers find themselves among a vast number of competitors. Therefore, they need to adopt a variety of policies and strategies for their own survival and profitability. Companies should identify their customers’ needs and adopt their own policies based on customers’ purchase behaviors. To this end, attempts have been made to identify the customer choice model since the past decades. These models aim at modeling and predicting customer choice among several brands. Traditional models were of interest for many years and these methods were frequently used with the advent of artificial intelligence and machine learning systems. They could demonstrate very good results. In this study, it has been attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining. Indeed, the new model is to be applied in helping managers with decision-making. Hence, probabilistic neural networks have been combined with artificial bee colony algorithm.  The proposed model was tested in a real market and its efficiency and accuracy were finally compared with those of other models, including neural network trained with back-propagation, probabilistic neural networks, and the neural networks trained with genetic algorithm. The results reveal that the hybrid model shows better performance than the other models.Keywords: Consumer Choice Model, Data Mining, Artificial Intelligence, modeling, predicting, probabilistic neural network, artificial bee colony algorith

An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database

Background: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. Objectives: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). Patients and Methods: A population of 6647 individuals without diabetes, aged � 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. Results: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) � 30 kg/m2, family history of diabetes, wrist circumference > 16.5 cm and waist to height � 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio � 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. Conclusions: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone. © 2015, Research Institute For Endocrine Sciences and Iran Endocrine Society

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: A decade follow-up in a Middle East prospective cohort study

Objective: The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Design: Prospective cohort study. Setting: Tehran Lipid and Glucose Study (TLGS). Methods: A total of 6647 participants (43.4 men) aged >20 years, without T2D at baselines ((1999- 2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. Primary outcome measure: T2D was primary outcome which defined if fasting plasma glucose (FPG) was �7 mmol/L or if the 2h-PCPG was �11.1 mmol/L or if the participant was taking antidiabetic medication. Results: During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78 and 0.75) and (78 and 0.78) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-toheight ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG�4.9 mmol/L and 2h-PCPG�7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG�5.2 mmol/L and WHtR�0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Conclusions: Our study emphasises the utility of DT for exploring interactions between predictor variables

iPSC-based modeling of RAG2 severe combined immunodeficiency reveals multiple T cell developmental arrests

RAG2 severe combined immune deficiency (RAG2-SCID) is a lethal disorder caused by the absence of functional T and B cells due to a differentiation block. Here, we generated induced pluripotent stem cells (iPSCs) from a RAG2-SCID patient to study the nature of the T cell developmental blockade. We observed a strongly reduced capacity to differentiate at every investigated stage of T cell development, from early CD7(-)CD5(-) to CD4(+)CD8(+). The impaired differentiation was accompanied by an increase in CD7(-)CD56(+)CD33(+) natural killer (NK) cell-like cells. T cell receptor D rearrangements were completely absent in RAG2SCID cells, whereas the rare T cell receptor B rearrangements were likely the result of illegitimate rearrangements. Repair of RAG2 restored the capacity to induce T cell receptor rearrangements, normalized T cell development, and corrected the NK cell-like phenotype. In conclusion, we succeeded in generating an iPSC-based RAG2-SCID model, which enabled the identification of previously unrecognized disorder-related T cell developmental roadblocks

iPSC-Based Modeling of RAG2 Severe Combined Immunodeficiency Reveals Multiple T Cell Developmental Arrests

RAG2 severe combined immune deficiency (RAG2-SCID) is a lethal disorder caused by the absence of functional T and B cells due to a differentiation block. Here, we generated induced pluripotent stem cells (iPSCs) from a RAG2-SCID patient to study the nature of the T cell developmental blockade. We observed a strongly reduced capacity to differentiate at every investigated stage of T cell development, from early CD7−CD5− to CD4+CD8+. The impaired differentiation was accompanied by an increase in CD7−CD56+CD33+ natural killer (NK) cell-like cells. T cell receptor D rearrangements were completely absent in RAG2SCID cells, whereas the rare T cell receptor B rearrangements were likely the result of illegitimate rearrangements. Repair of RAG2 restored the capacity to induce T cell receptor rearrangements, normalized T cell development, and corrected the NK cell-like phenotype. In conclusion, we succeeded in generating an iPSC-based RAG2-SCID model, which enabled the identification of previously unrecognized disorder-related T cell developmental roadblocks.In this article, Mikkers

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Production Scheduling Requirements to Smart Manufacturing

The production scheduling has attracted a lot of researchers for many years, however most of the approaches are not targeted to deal with real manufacturing environments, and those that are, are very particular for the case study. It is crucial to consider important features related with the factories, such as products and machines characteristics and unexpected disturbances, but also information such as when the parts arrive to the factory and when should be delivered. So, the purpose of this paper is to identify some important characteristics that have been considered independently in a lot of studies and that should be considered together to develop a generic scheduling framework to be used in a real manufacturing environment.authorsversionpublishe

Mutations in CNNM4 Cause Jalili Syndrome, Consisting of Autosomal-Recessive Cone-Rod Dystrophy and Amelogenesis Imperfecta

The combination of recessively inherited cone-rod dystrophy (CRD) and amelogenesis imperfecta (AI) was first reported by Jalili and Smith in 1988 in a family subsequently linked to a locus on chromosome 2q11, and it has since been reported in a second small family. We have identified five further ethnically diverse families cosegregating CRD and AI. Phenotypic characterization of teeth and visual function in the published and new families reveals a consistent syndrome in all seven families, and all link or are consistent with linkage to 2q11, confirming the existence of a genetically homogenous condition that we now propose to call Jalili syndrome. Using a positional-candidate approach, we have identified mutations in the CNNM4 gene, encoding a putative metal transporter, accounting for the condition in all seven families. Nine mutations are described in all, three missense, three terminations, two large deletions, and a single base insertion. We confirmed expression of Cnnm4 in the neural retina and in ameloblasts in the developing tooth, suggesting a hitherto unknown connection between tooth biomineralization and retinal function. The identification of CNNM4 as the causative gene for Jalili syndrome, characterized by syndromic CRD with AI, has the potential to provide new insights into the roles of metal transport in visual function and biomineralization

Study Of Blood Lead Levels, Hemoglobin & Plasma AscorbicAcid In ACarCompanyWelders

Background and Aims: Background and aim: About 30-40% of lead entered the respiratorysystem, is absorbed to the bloodstream. Some studies have demonstrated that cigarette smokingresults in blood-lead levels elevation as well as decreased blood ascorbic acid. This study hasperformed on this issue in lead exposed welder workers.Method:Adescriptive cross-sectional study was performed to evaluate associations of smokinghabit with blood lead, hemoglobin and plasma ascorbic acid levels in 32 welders. All the casesused to work in a car factory in Tehran suburb as welders 8 hours a day. They were divided intothree groups based on blood lead level quartiles.The blood lead levels were determined by 8003 NAIOSH method and blood ascorbic acid levelwas determined by Lowery method. Results were compared (based on mean values) usingindependent sample t-test and analysis of variance (ANOVA) methods.Results: Results indicated that the blood lead levels in those who smoke 7+ cigarettes per day wassignificantly higher than those who smoke <7 cigarettes per day (p<0.05) or no smoking group(p<0.001). The hemoglobin concentrations in 7+ group was significantly lower than of the <7(p<0.01) and no smoking (p<0.05) groups.Conclusion :Smoking hait in population, who occupationally exposed to lead, causes an increaseexposure to lead and, hence, elevation of blood-lead levels as well as inhibition of hemoglobinsynthesis and therefore reduces hemoglobin concentration

Upgrading Fitness in the Production of Garments

This article uses a series of data mining techniques to analyze body types and introduce a new sizing chart in order to produce garments for males. A principle component analysis and hierarchical and non-hierarchical clustering approaches are used to form a new sizing chart. All variables are grouped into two main components with a principle component analysis. Agglomerative hierarchical clustering is used to determine the number of clusters, and then a k-means algorithm is applied to segment the heterogonous population to actually form the clusters. The resultant innovations in designing garments have improved both non-price and price factors, the fittings of garments on all bodies have effectively improved and fabric waste has decreased, so the main goals which include improvement in quality with more comfort and a lower price have been met