Temporal Modeling of Link Characteristic in Mobile Ad hoc Network

Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence is very suitable for temporary communication links in an emergency situation. This flexibility, however, is achieved at a price of communication uncertainty induced due to frequent topology changes. In this article, we have tried to identify the system dynamics using the proven concepts of time series modeling. Here, we have analyzed variation of link utilization between any two particular nodes over a fixed area for differentmobility patterns under different routing algorithm. We have considered four different mobility models – (i) Gauss-Markov mobility model, (ii) Manhattan Grid Mobility model and (iii) Random Way Point mobility model and (iv) Reference Point Group mobility model. The routing protocols under which, we carried out our experiments are (i) Ad hoc On demand Distance Vector routing (AODV), (ii) Destination Sequenced Distance Vector routing (DSDV) and (iii) Dynamic Source Routing (DSR). The value of link load between two particular nodes behaves as a random variable for any mobility pattern under a routing algorithm. The pattern of link load for every combination of mobility model and for every routing protocol can be well modeled as an autoregressive model of order p i.e. AR(p). The order of p is estimated and it is found that most of them are of order 1 only

Deep learning to filter SMS spam

The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-term memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved

Authenticity of Geo-Location and Place Name in Tweets

The place name and geo-coordinates of tweets are supposed to represent the possible location of the user at the time of posting that tweet. However, our analysis over a large collection of tweets indicates that these fields may not give the correct location of the user at the time of posting that tweet. Our investigation reveals that the tweets posted through third party applications such as Instagram or Swarmapp contain the geo-coordinate of the user specified location, not his current location. Any place name can be entered by a user to be displayed on a tweet. It may not be same as his/her exact location. Our analysis revealed that around 12% of tweets contains place names which are different from their real location. The findings of this research can be used as caution while designing location-based services using social media

Modeling Sub-Band Information Through Discrete Wavelet Transform to Improve Intelligibility Assessment of Dysarthric Speech

The speech signal within a sub-band varies at a fine level depending on the type, and level of dysarthria. The Mel-frequency filterbank used in the computation process of cepstral coefficients smoothed out this fine level information in the higher frequency regions due to the larger bandwidth of filters. To capture the sub-band information, in this paper, four-level discrete wavelet transform (DWT) decomposition is firstly performed to decompose the input speech signal into approximation and detail coefficients, respectively, at each level. For a particular input speech signal, five speech signals representing different sub-bands are then reconstructed using inverse DWT (IDWT). The log filterbank energies are computed by analyzing the short-term discrete Fourier transform magnitude spectra of each reconstructed speech using a 30-channel Mel-filterbank. For each analysis frame, the log filterbank energies obtained across all reconstructed speech signals are pooled together, and discrete cosine transform is performed to represent the cepstral feature, here termed as discrete wavelet transform reconstructed (DWTR)- Mel frequency cepstral coefficient (MFCC). The i-vector based dysarthric level assessment system developed on the universal access speech corpus shows that the proposed DTWRMFCC feature outperforms the conventional MFCC and several other cepstral features reported for a similar task. The usages of DWTR- MFCC improve the detection accuracy rate (DAR) of the dysarthric level assessment system in the text and the speaker-independent test case to 60.094 % from 56.646 % MFCC baseline. Further analysis of the confusion matrices shows that confusion among different dysarthric classes is quite different for MFCC and DWTR-MFCC features. Motivated by this observation, a two-stage classification approach employing discriminating power of both kinds of features is proposed to improve the overall performance of the developed dysarthric level assessment system. The two-stage classification scheme further improves the DAR to 65.813 % in the text and speaker- independent test case

An unusual adverse event with the use of intravenous bolus of promethazine (phenergan)

The earlier used sedatives like promethazine, pethidine and pentazocine (fortwin) are not commonly used these days but at times they are used especially in periphery for postoperative sedation and in gynecological surgeries and wards. We hereby report an unusual adverse event associated with the use of intravenous bolus of Promethazine. With this case report we want to highlight that if promethazine is to be used for any purpose it should be given preferably intramuscular and if given intravenously, should be diluted and given slowly in a good running cannula.

Temporal Modeling of Node Mobility in Mobile Ad hoc Network

Ad-hoc network consists of a set of identical nodes that move freely and independently and communicate via wireless links. The most interesting feature of this network is that it does not require any predefined infrastructure or central administration and hence it is very suitable for establishing temporary communication links in emergency situations. This flexibility however is achieved at the price of communication link uncertainties due to frequent topology changes. In this article we describe the system dynamics using the proven concept of time series modeling. Specifically, we analyze variations of the number of neighbor nodes of a particular node over a geographical area and for given total number of nodes assuming different values of (i) the speeds of nodes, (ii) the transmission powers, (iii) sampling periods and (iv) different mobility patterns. We consider three different mobility models: (i) Gaussian mobility model, (ii) random walk mobility model and (iii) random way point mobility model. The number of neighbor nodes of a particular node behaves as a random variable for any mobility pattern. Through our analysis we find that the variation of the number of neibhbor nodes can be well modeled by an autoregressive AR$(p)$ model. The values of $p$ evaluated for different scenarios are found to be in the range between $1$ and $5$. Moreover, we also investigate the relationship between the speed and the time of measurements, and the transmission range of a specific node under various mobility patterns

Nitric Oxide Metabolite Concentration in Cerebrospinal Fluid: Useful as a Prognostic Marker?

Study DesignProspective study.PurposeTo establish the significance of cerebrospinal fluid (CSF) nitric oxide metabolite (NOx) concentration in acute spinal cord injury (SCI) patients to assess the neurological severity and prognosis.Overview of LiteratureQuantitative analysis of specific biomarkers in CSF will assess neurological severity more accurately and permit the formulation of a more precise management plan.MethodsForty SCI patients represented the cases and 20 lower limb injury patients were the controls. NOx concentration in CSF was measured at week 1, 2, and 4 by Griess method. Magnetic resonance imaging (MRI, T2-weighted) done in each case to measure cord edema and neurological severity was assessed using the Frankel classification.ResultsCSF NOx concentration peaked at week 2 and declined to normal by week 4. The concentration remained normal in controls. Mean NOx concentration was directly proportional to the severity of acute SCI as correlated with cord edema seen in MRI and neurological severity assessed.ConclusionsCSF NOx concentration can be considered a specific quantitative biomarker in acute stage of SCI to predict the severity and prognosis of SCI patients

Is this question going to be closed? : Answering question closibility on Stack Exchange

Community question answering sites (CQAs) are often flooded with questions that are never answered. To cope with the problem, experienced users of Stack Exchange are now allowed to mark newly-posted questions as closed if they are of poor quality. Once closed, a question is no longer eligible to receive answers. However, identifying and closing subpar questions takes time. Therefore, the purpose of this paper is to develop a supervised machine learning system that predicts question closibility, the possibility of a newly posted question to be eventually closed. Building on extant research on CQA question quality, the supervised machine learning system uses 17 features that were grouped into four categories, namely, asker features, community features, question content features, and textual features. The performance of the developed system was tested on questions posted on Stack Exchange from 11 randomly chosen topics. The classification performance was generally promising and outperformed the baseline. Most of the measures of precision, recall, F1-score, and AUC were above 0.90 irrespective of the topic of questions. By conceptualizing question closibility, the paper extends previous CQA research on question quality. Unlike previous studies, which were mostly limited to programming-related questions from Stack Overflow, this one empirically tests question closibility on questions from 11 randomly selected topics. The set of features used for classification offers a framework of question closibility that is not only more comprehensive but also more parsimonious compared with prior works