This paper presents a novel and unreported approach developed to filter T2-weighetd Magnetic Resonance Imaging (MRI). The MRI data is fitted with a parametric bivariate cubic Lagrange polynomial, which is used as the model function to build the continuum into the discrete samples of the two-dimensional MRI images. On the basis of the aforementioned model function, the Classic-Curvature (CC) and the Signal Resilient to Interpolation (SRI) images are calculated and they are used as filter masks to convolve the two-dimensional MRI images of the pathological human brain. The pathologies are human brain tumors. The result of the convolution provides with filtered T2-weighted MRI images. It is found that filtering with the CC and the SRI provides with reliable and faithful reproduction of the human brain tumors. The validity of filtering the T2-weighted MRI for the quest of supplemental information about the tumors is also found positive.

With increasing use of computers, information and communication technologies, some software technologies products become part of everyday life. Many reports shows that use of desktop and mobile operating systems, search engines, web browsers, web servers and programming languages are increasing rapidly. This paper focuses on forecasting growth pattern of selected software technology product families using market share as indicator. Results of four growth curve methods namely Logistic, Gompertz, Log Logistic and Mono-Molecular are compared using MAD and RMSE error measures. For the period under consideration, majority software product families follow increasing / decreasing growth pattern. Results indicate that industry of respective technology product remain dominated by few providers for year 2025. Monopoly or oligopoly market structure will lead to long increasing period for the top providers.

Recent prognoses about the future of Internet of Things and Internet Services show growing demand for an efficient processing of huge amounts of data within strict time limits. First of all, a real-time data store is necessary to fulfill that requirement. One of the most promising architecture that is able to efficiently store large volumes of data in distributed environment is SDDS (Scalable Distributed Data Structure). In this paper we present SDDS LH*RT, an architecture that is suitable for real-time applications. We assume that deadlines, defining the data validity, are associated with real-time requests. In the data store a real-time scheduling strategy is applied to determine the order of processing the requests. Experimental results shows that our approach significantly improves the storage Quality-of-service in a real-time environment.

Genetic Algorithm (GA) is one of the most popular optimization solutions for scheduling problems and has already been used to implement variety of applications. In this paper, we describe a heavily constrained seat allocation problem experienced during counseling for seat allocation in college/universities based upon the merit of students computed on the basis of an entrance test. Manual process of allocating seats is not just inconvenient but proves expensive in terms of time and money. The application of GA involves using selection, crossover or mutation operators applied to populations of chromosomes. We propose a powerful technique using genetic algorithm (GA) in scheduling as a potential solution to the seat allocation process which has been supported with the help of an illustrative example.

In this paper we explore two paradigms: firstly, paradigmatic representation via the native HAL model including a model enriched by adding word order information using the permutation technique of Sahlgren and al [21], and secondly the syntagmatic representation via a words-by-documents model constructed using the Random Indexing method. We demonstrate that these kinds of word space models which were initially dedicated to extract similarity can also been efficient for extracting relatedness from Arabic corpora. For a given word the proposed models search the related words to it. A result is qualified as a failure when the number of related words given by a model is less than or equal to 4, otherwise it is considered as a success. To decide if a word is related to other one, we get help from an expert of the economic domain and use a glossary1 of the domain. First we begin by a comparison between a native HAL model and term- document model. The simple HAL model records a better result with a success rate of 72.92%. In a second stage, we want to boost the HAL model results by adding word order information via the permutation technique of sahlgren and al [21]. The success rate of the enriched HAL model attempt 79.2 %.

Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-a-Service (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

In Mobile Ad Hoc Network, data delivery is very challenging through single path due to dynamic changes in the network topology. To cope up this issue multipath data delivery is very useful. Recently, many works have been carried out in this domain but few are addressing the queueing effect on multipath scenarios. In this paper, we have designed a network model that based on the existence of multipath between source and destination node and every node behave as M/M/1 queue. In order to do this we generate K (K=1, 2, 3…i) paths are available between each source toward the destination node. The traffic arrivals in each node follow poisson process with arrival rate λ packets/sec. The simulation work of this multipath scenario based on varying mean inter-arrival time. The effect of arrival rate on the performance of multipath network model is analysed and compared. To better understand the effect of arrival rate in application and network layer various QoS metrics are computed. Significant performance of individual node is noticed in the obtained results with various arrival rates.

Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as- URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an in-depth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.