Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques

Random Prism: An Alternative to Random Forests.

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting

Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer

The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist. This data is collected with the intention to be utilized by a deep learning-based method which requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available

Mobile Agent based Market Basket Analysis on Cloud

This paper describes the design and development of a location-based mobile shopping application for bakery product shops. Whole application is deployed on cloud. The three-tier architecture consists of, front-end, middle-ware and back-end. The front-end level is a location-based mobile shopping application for android mobile devices, for purchasing bakery products of nearby places. Front-end level also displays association among the purchased products. The middle-ware level provides a web service to generate JSON (JavaScript Object Notation) output from the relational database. It exchanges information and data between mobile application and servers in cloud. The back-end level provides the Apache Tomcat Web server and MySQL database. The application also uses the Google Cloud Messaging for generating and sending notification of orders to shopkeeper.Comment: 6 pages, 7 figure

Enhancement of the superconducting gap by nesting in CaKFe4As4 - a new high temperature superconductor

We use high resolution angle resolved photoemission spectroscopy and density functional theory with experimentally obtained crystal structure parameters to study the electronic properties of CaKFe4As4. In contrast to related CaFe2As2 compounds, CaKFe4As4 has high Tc of 35K at stochiometric composition. This presents unique opportunity to study properties of high temperature superconductivity of iron arsenic superconductors in absence of doping or substitution. The Fermi surface consists of three hole pockets at $\Gamma$ and two electron pockets at the $M$ point. We find that the values of the superconducting gap are nearly isotropic, but significantly different for each of the FS sheets. Most importantly we find that the overall momentum dependence of the gap magnitudes plotted across the entire Brillouin zone displays a strong deviation from the simple cos(kx)cos(ky) functional form of the gap function, proposed in the scenario of the Cooper-pairing driven by a short range antiferromagnetic exchange interaction. Instead, the maximum value of the gap is observed for FS sheets that are closest to the ideal nesting condition in contrast to the previous observations in some other ferropnictides. These results provide strong support for the multiband character of superconductivity in CaKFe4As4, in which Cooper pairing forms on the electron and the hole bands interacting via dominant interband repulsive interaction, enhanced by FS nesting}.Comment: 5 pages, 4 figure

Big Data and Changing Concepts of the Human

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human