Automatic text classification using BPLion-neural network and semantic word processing

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.     

Single point incremental forming: state-of-the-art and prospects

Incremental sheet metal forming in general and Single Point Incremental Forming (SPIF) specifically have gone through a period of intensive development with growing attention from research institutes worldwide. The result of these efforts is significant progress in the understanding of the underlying forming mechanisms and opportunities as well as limitations associated with this category of flexible forming processes. Furthermore, creative process design efforts have enhanced the process capabilities and process planning methods. Also, simulation capabilities have evolved substantially. This review paper aims to provide an overview of the body of knowledge with respect to Single Point Incremental Forming. Without claiming to be exhaustive, each section aims for an up-to-date state-of-the-art review with corresponding conclusions on scientific progress and outlook on expected further developments.     

Gender-related differences in susceptibility of A/J mouse to benzo[a]pyrene-induced pulmonary and forestomach tumorigenesis

A commercial patient dose verification system utilizing non-invasive metal oxide semiconductor field effect transistor (MOSFET) dosimeters originally designed for radiotherapy applications has been evaluated for use at diagnostic energy levels. The system features multiple dosimeters that may be used to monitor entrance or exit skin dose and intracavity doses in phantoms in real time. We have characterized both the standard MOSFET dosimeter designed for radiotherapy dose verification and a newly developed "high sensitivity" MOSFET dosimeter designed for lower dose measurements. The sensitivity, linearity, angular response, post-exposure response, and physical characteristics were evaluated. The average sensitivity (free in air, including backscatter) of the radiotherapy MOSFET dosimeters ranged from 3.55 x 10(4) mV per C kg(-1) (9.2 mV R(-1)) to 4.87 x 10(4) mV per C kg(-1) (12.6 mV R(-1)) depending on the energy of the x-ray field. The sensitivity of the "high sensitivity" MOSFET dosimeters ranged from 1.15 x 10(5) mV per C kg(-1) (29.7 mV R(-1)) to 1.38 x 10(5) mV per C kg(-1) (35.7 mV R(-1)) depending on the energy of the x-ray field. The high sensitivity dosimeters demonstrated excellent linearity at high energies (90 and 120 kVp) and acceptable linearity at lower energies (60 kVp). The angular response was significant for free-in-air exposures, as illustrated by the sensitivity differences between the two sides of the dosimeter, but was excellent for measurements within a tissue equivalent cylinder. The post-exposure drift response is a complicated but reproducible function of time. Real-time monitoring requires little if any corrections for the post-exposure drift response. The MOSFET dosimeter system brings some unique capabilities to diagnostic radiology dosimetry including small size, real-time capabilities, nondestructive measurement, good linearity, and a predictable angular response.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Implementing enterprise resource planning systems: organizational performance and the duration of the implementation

This paper examines the impact of the duration of ERP implementation on firm performance both during and after implementation. Organizations choose either an accelerated implementation approach or a traditional (longer) implementation approach. The former approach gives the organization the advantage of speed, but the disadvantage of fitting its processes to that of a packaged (thus, undifferentiated from competitors) ERP. The latter approach allows the organization to redesign strategy and processes, and thus, search for ways to be unique from its competition. The study uses a regression model to capture the changes in various performance measures during and after implementation between firms that implemented the ERP, using the performance measure of a matched group of firms that did not implement an ERP as a benchmark/control sample, on the basis of the duration of the implementation. Financial data from Compustat, and data on start date and end date of ERP implementation between 1990 and 2005 for firms in the Oil and Gas industry was collected from an ERP vendor. Results show that measures such as return on sales improved after implementation. However, measures such as inventory turnover, which reflect operational benefits, improve during implementation. We find that accelerated implementation confers both operational and strategic benefits. This study highlights the strategic consequences of the different choices of implementation.     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

Anonymizing bipartite graph data using safe groupings

Private data often come in the form of associations between entities, such as customers and products bought from a pharmacy, which are naturally represented in the form of a large, sparse bipartite graph. As with tabular data, it is desirable to be able to publish anonymized versions of such data, to allow others to perform ad hoc analysis of aggregate graph properties. However, existing tabular anonymization techniques do not give useful or meaningful results when applied to graphs: small changes or masking of the edge structure can radically change aggregate graph properties. We introduce a new family of anonymizations for bipartite graph data, called (k, ℓ)-groupings. These groupings preserve the underlying graph structure perfectly, and instead anonymize the mapping from entities to nodes of the graph. We identify a class of “safe” (k, ℓ)-groupings that have provable guarantees to resist a variety of attacks, and show how to find such safe groupings. We perform experiments on real bipartite graph data to study the utility of the anonymized version, and the impact of publishing alternate groupings of the same graph data. Our experiments demonstrate that (k, ℓ)-groupings offer strong tradeoffs between privacy and utility.     

Diffusion of fluid confined to nanotube with rectangular cross section

A dynamical model is proposed to study self-diffusion coefficient by confining the fluid in rectangular nanotube. The theoretical model is based on the consideration that the confinement affects the movement at atomic level. The model predicts that the diffusion parallel to walls of channel is different from that of diffusion perpendicular to the walls. Near the walls the dynamics of fluid has been found to slow down to an extent that below a certain value of ratio of width to the diameter of particle, the molecules behave as if these belong to solid. The results are contrasted with the result obtained from the model based on similar considerations for a fluid confined only in one direction. It is found that tendency of freezing near the wall increases due to confinement from second direction. Empirical relation which governs the behavior of diffusion coefficient as function of distance from the confining walls has also been proposed. The effect of confinement is more pronounced for denser fluids than for dilute fluid.