Adaptive link-weight routing protocol using cross-layer communication for MANET

Routing efficiency is one of the challenges offered by Mobile Ad-hoc Networks (MANETs). This paper proposes a novel routing technique called Adaptive Link-Weight (ALW) routing protocol. ALW adaptively selects an optimum route on the basis of available bandwidth, low delay and long route lifetime. The technique adapts a cross-layer framework where the ALW is integrated with application and physical layer. The proposed design allows applications to convey preferences to the ALW protocol to override the default path selection mechanism. The results confirm improvement over AODV in terms of network load, route discovery time and link reliability

Adaptive link-weight routing protocol using cross-layer communication for MANET

Routing efficiency is one of the challenges offered by Mobile Ad-hoc Networks (MANETs). This paper proposes a novel routing technique called Adaptive Link-Weight (ALW) routing protocol. ALW adaptively selects an optimum route on the basis of available bandwidth, low delay and long route lifetime. The technique adapts a cross-layer framework where the ALW is integrated with application and physical layer. The proposed design allows applications to convey preferences to the ALW protocol to override the default path selection mechanism. The results confirm improvement over AODV in terms of network load, route discovery time and link reliability

Estimating the Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers

Electroconvulsive therapy (ECT) is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field

Estimating the Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers

© 2018 IEEE. Electroconvulsive therapy (ECT) is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field

The topography of transmembrane segment six is altered during the catalytic cycle of P-glycoprotein

Structural evidence has demonstrated that P-glycoprotein (P-gp) undergoes considerable conformational changes during catalysis, and these alterations are important in drug interaction. Knowledge of which regions in P-gp undergo conformational alterations will provide vital information to elucidate the locations of drug binding sites and the mechanism of coupling. A number of investigations have implicated transmembrane segment six (TM6) in drug-P-gp interactions, and a cysteine-scanning mutagenesis approach was directed to this segment. Introduction of cysteine residues into TM6 did not disturb basal or drug-stimulated ATPase activity per se. Under basal conditions the hydrophobic probe coumarin maleimide readily labeled all introduced cysteine residues, whereas the hydrophilic fluorescein maleimide only labeled residue Cys-343. The amphiphilic BODIPY-maleimide displayed a more complex labeling profile. The extent of labeling with coumarin maleimide did not vary during the catalytic cycle, whereas fluorescein maleimide labeling of F343C was lost after nucleotide binding or hydrolysis. BODIPY-maleimide labeling was markedly altered during the catalytic cycle and indicated that the adenosine 5'-(beta,gamma-imino)triphosphate-bound and ADP/vanadate-trapped intermediates were conformationally distinct. Our data are reconciled with a recent atomic scale model of P-gp and are consistent with a tilting of TM6 in response to nucleotide binding and ATP hydrolysis

Customer churn prediction in telecommunication industry using data certainty

© 2018 Elsevier Inc. Customer Churn Prediction (CCP) is a challenging activity for decision makers and machine learning community because most of the time, churn and non-churn customers have resembling features. From different experiments on customer churn and related data, it can be seen that a classifier shows different accuracy levels for different zones of a dataset. In such situations, a correlation can easily be observed in the level of classifier\u27s accuracy and certainty of its prediction. If a mechanism can be defined to estimate the classifier\u27s certainty for different zones within the data, then the expected classifier\u27s accuracy can be estimated even before the classification. In this paper, a novel CCP approach is presented based on the above concept of classifier\u27s certainty estimation using distance factor. The dataset is grouped into different zones based on the distance factor which are then divided into two categories as; (i) data with high certainty, and (ii) data with low certainty, for predicting customers exhibiting Churn and Non-churn behavior. Using different state-of-the-art evaluation measures (e.g., accuracy, f-measure, precision and recall) on different publicly available the Telecommunication Industry (TCI) datasets show that (i) the distance factor is strongly co-related with the certainty of the classifier, and (ii) the classifier obtained high accuracy in the zone with greater distance factor\u27s value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor\u27s value (i.e., customer churn and non-churn with low certainty)

Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert

© 2014 IEEE. This article proposes a novel methodology to detect malicious uniform resource locators (URLs) using simulated expert (SE) and knowledge-base system (KBS). The proposed study not only efficiently detects known malicious URLs but also adapts countermeasure against the newly generated malicious URLs. Moreover, this article also explored which lexical features are contributing more in final decision using a factor analysis method, and thus help in avoiding the involvement of human experts. Furthermore, we apply the following state-of-the-art machine learning (ML) algorithms, i.e., naïve Bayes (NB), decision tree (DT), gradient boosted trees (GBT), generalized linear model (GLM), logistic regression (LR), deep learning (DL), and random rest (RF), and evaluate the performance of these algorithms on a large-scale real data set of data-driven Web applications. The experimental results clearly demonstrate the efficiency of NB in the proposed model as NB outperforms when compared to the rest of the aforementioned algorithms in terms of average minimum execution time (i.e., 3 s) and is able to accurately classify the 107 586 URLs with 0.2% error rate and 99.8% accuracy rate

A systematic review of outcome measures used in clinical trials of treatment interventions following traumatic dental injuries

Background: Traumatic dental injuries (TDI) are common, andappropriate treatment will maximize the chances of maintaining teeth infunction while safeguarding their longevity and aesthetics. Subjectively, itappears that outcome measures used in studies investigating TDI arenumerous and diverse. Objectives: To undertake a systematic review ofthe outcomes used in clinical trials of treatment interventions followingTDI. Data sources: The MEDLINE, Cochrane Central Register of Con-trolled Trials, Cochrane Database of Systematic Reviews and EMBASEdatabases were searched up to June 2014. Reference lists of eligible studieswere cross-checked to identify additional studies and strategies to identifygrey literature and ongoing trials were employed. Study selection: Twoauthors independently assessed studies for inclusion and undertook dataextraction. The study designs included were as follows: systematic reviewswith/without meta-analyses, randomized and pseudo-randomized con-trolled trials and controlled clinical trials. There were no language restric-tions. Results: Ten studies confined to two types of TDI were included:avulsion (5) and non-vital immature permanent incisor teeth (5). The out-comes reported predominantly concentrated on injury activity and thephysical consequence of injury. There was little consistency between studiesfor the length of follow up, the time points at which outcomes were evalu-ated or the methods used to measure them. Conclusions: There is signifi-cant heterogeneity in outcomes reported for TDI in the literature. Thesefindings preclude meaningful meta-analysis between studies. Future clinicalstudies need to consider collecting a more consistent and wider range ofoutcomes, which should include one or more from each of the followingdomains: health resources utilisation, adverse effects and quality of life andfamily outcome. There is a clear need for the development of a Core Out-come Set for TDI using robust and established methodology, thus optimiz-ing the value of future research

Customer churn prediction in telecommunication industry using data certainty

Customer Churn Prediction (CCP) is a challenging activity for decision makers and machine learning community because most of the time, churn and non-churn customers have resembling features. From different experiments on customer churn and related data, it can be seen that a classifier shows different accuracy levels for different zones of a dataset. In such situations, a correlation can easily be observed in the level of classifier's accuracy and certainty of its prediction. If a mechanism can be defined to estimate the classifier's certainty for different zones within the data, then the expected classifier's accuracy can be estimated even before the classification. In this paper, a novel CCP approach is presented based on the above concept of classifier's certainty estimation using distance factor. The dataset is grouped into different zones based on the distance factor which are then divided into two categories as; (i) data with high certainty, and (ii) data with low certainty, for predicting customers exhibiting Churn and Non-churn behavior. Using different state-of-the-art evaluation measures (e.g., accuracy, f-measure, precision and recall) on different publicly available the Telecommunication Industry (TCI) datasets show that (i) the distance factor is strongly co-related with the certainty of the classifier, and (ii) the classifier obtained high accuracy in the zone with greater distance factor's value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor's value (i.e., customer churn and non-churn with low certainty)

The association between histamine 2 receptor antagonist use and Clostridium difficile infection: a systematic review and meta-analysis.

Background Clostridium difficile infection (CDI) is a major health problem. Epidemiological evidence suggests that there is an association between acid suppression therapy and development of CDI. Purpose We sought to systematically review the literature that examined the association between histamine 2 receptor antagonists (H2RAs) and CDI. Data source We searched Medline, Current Contents, Embase, ISI Web of Science and Elsevier Scopus from 1990 to 2012 for all analytical studies that examined the association between H2RAs and CDI. Study selection Two authors independently reviewed the studies for eligibility. Data extraction Data about studies characteristics, adjusted effect estimates and quality were extracted. Data synthesis Thirty-five observations from 33 eligible studies that included 201834 participants were analyzed. Studies were performed in 6 countries and nine of them were multicenter. Most studies did not specify the type or duration of H2RAs therapy. The pooled effect estimate was 1.44, 95% CI (1.22–1.7), I2 = 70.5%. This association was consistent across different subgroups (by study design and country) and there was no evidence of publication bias. The pooled effect estimate for high quality studies was 1.39 (1.15–1.68), I2 = 72.3%. Meta-regression analysis of 10 study-level variables did not identify sources of heterogeneity. In a speculative analysis, the number needed to harm (NNH) with H2RAs at 14 days after hospital admission in patients receiving antibiotics or not was 58, 95% CI (37, 115) and 425, 95% CI (267, 848), respectively. For the general population, the NNH at 1 year was 4549, 95% CI (2860, 9097). Conclusion In this rigorous systematic review and meta-analysis, we observed an association between H2RAs and CDI. The absolute risk of CDI associated with H2RAs is highest in hospitalized patients receiving antibiotics