Queues with Customer Induced Interruption

In this thesis we have introduced and studied the notion of self interruption of service by customers. Service interruption in queueing systems have been extensively discussed in literature (see, Krishnamoorthy, Pramod and Chakravarthy [38]) for the most recent survey. So far all work reported deal with cases in which service interruptions are generated by sources other than customers. However, there are situations where interruptions are due to the customers rather than the system. Such situations are especially arise at doctors clinic, banks, reservation counter etc. Our attempt is to quantify a few of such problems. Systematically we have proceed from single server queue (in Chapter 2) to multi-server queues (Chapter 3). In Chapte 4, we have studied a very general multiserver queueing model with service interruption and protection of service phases. We also introduced customer interruption in a retrial setup (in Chapter 5). All models (from Chapter 2 to Chapter 4) that were analyzed involve 'non-preemptive priority' for interrupted customers where as in the model discussed in Chapter 5 interruption of service by customers is not encouraged. So the interrupted customers cannot access the server as long as there are primary customers in the system. In Chapter 5 we have obtained an explicit expression for the stability condition of the system. In all models analyzed in this thesis, we have assumed that no more than one interruption is allowed for a customer while in service. Since the models are not analytically tractable, a large number of numerical illustrations were given in each chapter it illustrate the working of the systems. We can extend the models discussed in this thesis to several directions. For example some of the models can be analyzed with both server induced and customer induced interruptions the results for which are not available till date. Another possible extension of work is to the case where there is no bound on the number of interruptions a customer is permitted to have before service completion. More complex is the case where a customer is permitted to have a nite number (K ≥ 2) of We can extend the models discussed in this thesis to several directions.

Investigations on Stochastic Storage Systems with Positive Service Time

In this thesis the queueing-inventory models considered are analyzed as continuous time Markov chains in which we use the tools such as matrix analytic methods. We obtain the steady-state distributions of various queueing-inventory models in product form under the assumption that no customer joins the system when the inventory level is zero. This is despite the strong correlation between the number of customers joining the system and the inventory level during lead time. The resulting quasi-birth-anddeath (QBD) processes are solved explicitly by matrix geometric methods

Comparison of Statistical and Semantic Similarity Techniques for Paraphrase Identification

This paper compares statistical technique of paraphrase identification to semantic technique of paraphrase identification. The statistical techniques used for comparison are word set and word-order based methods where as the semantic technique used is the WordNet similarity matrix method described by Stevenson and Fernando in [3].

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.