Readiness Assessment of Iran’s Insurance Industry for E-Commerce and E-Insurance Success

This paper attempts to develop a method for readiness assessment of potential electronic commerce (e-commerce) success of Iran’s insurance industry. This method can expand in other industries. Key factors impacting e-commerce are identified from Wells, D. and Thomann, j. (2006) researches. This model is a two-dimension 3*4 matrix. Each dimension consists of variables that influence on insurance industries readiness. These variables on horizontal dimension are: People Readiness, IT Readiness, and Business Readiness and on vertical dimension are: Business Imperative, Executive Sponsorship, Development Method and Business Process Orientation. Each cells of this matrix evaluated by 5 questions. This research’s data are collected via a 60-questions questionnaire-based survey from Iranian insurance companies. At the end of the research, the Iranian insurance companies were strongly recommended by us to invest on e-commerce and e-insurance area

Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4

Perceptual learning, the improvement in perceptual abilities with training, is thought to be mediated by an alteration of neuronal tuning. It remains poorly understood how tuning properties change as training progresses, whether improved stimulus tuning directly links to increased behavioural readout of sensory information, or how population coding mechanisms change with training. Here, we recorded continuously from multiple neuronal clusters in area V4 while macaque monkeys learned a fine contrast categorization task. Training increased neuronal coding abilities by shifting the steepest point of contrast response functions towards the categorization boundary. Population coding accuracy of difficult discriminations resulted largely from an increased information coding of individual channels, particularly for those channels that in early learning had larger ability for easy discriminations, but comparatively small encoding abilities for difficult discriminations. Population coding was also enhanced by specific changes in correlations. Neuronal activity became more indicative of upcoming choices with training

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and systematic bias using a physics-based model is a computationally expensive task. The computational cost of this task is exponentially proportional to the number of model parameters updated using monitoring data. In this paper, a novel model-class selection method is proposed to obtain computationally optimal and identifiable model classes. Unlike traditional sensitivity methods for model-class selection, in the proposed method, model responses at sensor locations are clustered to identify underlying trends in model response datasets. K-means clustering is used to determine relevant clusters in the data. Cluster indices are then used as labels for classification. Support-vector machine classification using forward variable selection with sequential search is used to select model parameters that help classify trends in data. The result of the sequential search is a trade-off curve comparing classification error with number of parameters in the model class. This curve helps select a practical and near-optimal model class. The model-class selection method proposed in this paper is compared with linear regression-based sensitivity analysis using a full-scale bridge. Identification with model classes obtained using both methods for two sensor configurations suggests that the model-based clustering method helps select an identifiable and computationally efficient model class. The minimum remaining fatigue life of the bridge predicted using the updated model classes is 720 years and this represents fatigue-life extension of 10 times compared with design predictions prior to measurements. This approach provides good support for asset managers when they interpret measurement data