1,096 research outputs found
Integrated approach to detect spam in social media networks using hybrid features
Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques
Acceleration Profiles and Processing Methods for Parabolic Flight
Parabolic flights provide cost-effective, time-limited access to "weightless"
or reduced gravity conditions experienced in space or on planetary surfaces,
e.g. the Moon or Mars. These flights facilitate fundamental research - from
materials science to space biology - and testing/validation activities that
support and complement infrequent and costly access to space. While parabolic
flights have been conducted for decades, reference acceleration profiles and
processing methods are not widely available - yet are critical for assessing
the results of these activities. Here we present a method for collecting,
analyzing, and classifying the altered gravity environments experienced during
a parabolic flight. We validated this method using a commercially available
accelerometer during a Boeing 727-200F flight with parabolas. All data and
analysis code are freely available. Our solution can be easily integrated with
a variety of experimental designs, does not depend upon accelerometer
orientation, and allows for unsupervised and repeatable classification of all
phases of flight, providing a consistent and open-source approach to
quantifying gravito-intertial accelerations (GIA), or levels. As academic,
governmental, and commercial use of space increases, data availability and
validated processing methods will enable better planning, execution, and
analysis of parabolic flight experiments, and thus, facilitate future space
activities.Comment: Correspondence to C.E. Carr ([email protected]). 15 pages, 4 figures, 3
supplemental figures. Code: https://github.com/CarrCE/zerog, Dataset:
https://osf.io/nk2w4
Succinct Indexable Dictionaries with Applications to Encoding -ary Trees, Prefix Sums and Multisets
We consider the {\it indexable dictionary} problem, which consists of storing
a set for some integer , while supporting the
operations of \Rank(x), which returns the number of elements in that are
less than if , and -1 otherwise; and \Select(i) which returns
the -th smallest element in . We give a data structure that supports both
operations in O(1) time on the RAM model and requires bits to store a set of size , where {\cal B}(n,m) = \ceil{\lg
{m \choose n}} is the minimum number of bits required to store any -element
subset from a universe of size . Previous dictionaries taking this space
only supported (yes/no) membership queries in O(1) time. In the cell probe
model we can remove the additive term in the space bound,
answering a question raised by Fich and Miltersen, and Pagh.
We present extensions and applications of our indexable dictionary data
structure, including:
An information-theoretically optimal representation of a -ary cardinal
tree that supports standard operations in constant time,
A representation of a multiset of size from in bits that supports (appropriate generalizations of) \Rank
and \Select operations in constant time, and
A representation of a sequence of non-negative integers summing up to
in bits that supports prefix sum queries in constant
time.Comment: Final version of SODA 2002 paper; supersedes Leicester Tech report
2002/1
Devolution of power, revolution in public health?
No abstract available
ASSESSMENT OF ADVERSE DRUG REACTIONS OCCURRING AT DEPARTMENT OF NEUROLOGY OF A TERTIARY CARE HOSPITAL IN INDIA
Objective: The purpose of this study was to assess the incidence and pattern of adverse drug reactions (ADRs) reported from the department of neurology of a tertiary care hospital in Karnataka, India.Methods: It is a hospital-based prospective, observational study, conducted among the inpatients of all age groups of either sex for a period of 6 months. ADRs were reported by the clinical pharmacists and physicians of this hospital. ADRs obtained were categorized based on its causality, severity, preventability, predictability, and outcomes. Binary logistic regression was carried out to identify the predictors of ADR and Kaplan–Meier analysis was performed for survival analysis.Results: A total of 250 patients were enrolled for the study in which 108 (43%) patients were presented with at least one ADR and a total of 212 ADRs were observed. The highest rate of ADRs was observed with antiepileptics 61 (29.5%). The most commonly reported that ADRs were skin reactions 23 (10.8%). Causality was assessed using three different scales which showed that most of the ADRs were probable. Severity, preventability, and predictability were assessed, of which 125 (59%) ADRs were moderate, 192 (90.6%) ADRs were probably preventable, and 156 (73.6%) ADRs were predictable, respectively. The outcomes showed that 150 (70.1%) patients recovered from the reactions. Predictors such as polypharmacy and duration of stay were found to be significant.Conclusion: The study concluded that the prevalence of ADRs in the department of neurology is high. Thus, early detection and management of ADRs are essential to avoid further complications of the reaction
An effective sensor for tool wear monitoring in face milling : acoustic emmision
Acoustic Emission (AE) has been widely used for monitoring manufacturing
processes particularly those involving metal cutting. Monitoring the
condition of the cutting tool in the machining process is very important since tool
condition will affect the part size, quality and an unexpected tool failure may damage
the tool, work-piece and sometimes the machine tool itself. AE can be effectively
used for tool condition monitoring applications because the emissions from
process changes like tool wear, chip formation i.e. plastic deformation, etc. can
be directly related to the mechanics of the process. Also AE can very effectively
respond to changes like tool fracture, tool chipping, etc. when compared to cutting
force and since the frequency range is much higher than that of machine vibrations
and environmental noises, a relatively uncontaminated signal can be obtained.
AE signal analysis was applied for sensing tool wear in face milling operations.
Cutting tests were carried out on a vertical milling machine. Tests were carried out
for a given cutting condition, using single insert, two inserts (adjacent and opposite)
and three inserts in the cutter. AE signal parameters like ring down count and rms
voltage were measured and were correlated with flank wear values (VB max). The
results of this investigation indicate that AE can be effectively used for monitoring
tool wear in face milling operations.Fundação para a Ciência e a Tecnologia (FCT
System-Level Performance Analysis in 3D Drone Mobile Networks
We present a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. A performance boundary derived by deterministic random (Brownian) motion model over Nakagami-m fading interfering channels is developed. This method allows us to circumvent the extremely complex reality model and obtain the upper and lower performance bounds of actual drone mobile networks. The validity and advantages of the proposed framework are confirmed via extensive Monte-Carlo (MC) simulations. The results reveal several important trends and design guidelines for the practical deployment of drone mobile networks
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