Modelling email traffic workloads with RNN and LSTM models

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic

Advances in Natural Language Question Answering: A Review

Question Answering has recently received high attention from artificial intelligence communities due to the advancements in learning technologies. Early question answering models used rule-based approaches and moved to the statistical approach to address the vastly available information. However, statistical approaches are shown to underperform in handling the dynamic nature and the variation of language. Therefore, learning models have shown the capability of handling the dynamic nature and variations in language. Many deep learning methods have been introduced to question answering. Most of the deep learning approaches have shown to achieve higher results compared to machine learning and statistical methods. The dynamic nature of language has profited from the nonlinear learning in deep learning. This has created prominent success and a spike in work on question answering. This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges

Antagonistic Effect of Eight Sri Lankan Isolates of Pseudomonas fluorescens on, Meloidogyne incognita in Tomato, Lycopersicon esculentum

The study was conducted to determine the efficacy of Pseudomonas fluorescens isolates collected from eight locations in the Central Province of Sri Lanka against Meloidogyne incognita in tomato. Isolates were tested under laboratory conditions to determine the efficacy on egg hatchability and mortality of second stage juveniles. A planthouse experiment was conducted using potted tomato plants to determine the potential of P. fluorescens isolates and effective application technique. All tested isolates have significantly inhibited egg hatchability and increased the juvenile mortality after 72 hours. P. fluorescens isolate from Kangkung field in Pallekelle (PK) and tomato field in Udispattuwa (UT I) recorded 95% and 95.5% inhibition of egg hatchability after 72 hours. P. fluorescens isolates collected from tomato fields in Bopane (BT II) and Udispattuwa (UT II) and from Kangkung field in Pallekelle recorded the higher mortality of second stage juveniles 93%, 87% and 83.3% respectively. The highest reduction in the root knots (96.8%, 96.3%), egg masses (98.5%, 98.2%) and lower root galling index (1 and 1) were recorded in tomato plants treated as soil drench with UT II and PK isolates respectively.The root dipping technique gave higher reduction in the number of root knots (47.4%), egg masses (44.9%) and lower root galling index (3.75) were recorded from BT II, UT II and tomato fields in Nugethenna (NT) isolates respectively. UT II and PK found to be the most effective isolates and most effective application technique determined as soil drenching ten days after transplanting under plant house conditions

Association between age at disease onset of anti-neutrophil cytoplasmic antibody-associated vasculitis and clinical presentation and short-term outcomes

Objectives: ANCA-associated vasculitis (AAV) can affect all age groups. We aimed to show that differences in disease presentation and 6 month outcome between younger- A nd older-onset patients are still incompletely understood. Methods: We included patients enrolled in the Diagnostic and Classification Criteria for Primary Systemic Vasculitis (DCVAS) study between October 2010 and January 2017 with a diagnosis of AAV. We divided the population according to age at diagnosis: <65 years or ≥65 years. We adjusted associations for the type of AAV and the type of ANCA (anti-MPO, anti-PR3 or negative). Results: A total of 1338 patients with AAV were included: 66% had disease onset at <65 years of age [female 50%; mean age 48.4 years (s.d. 12.6)] and 34% had disease onset at ≥65 years [female 54%; mean age 73.6 years (s.d. 6)]. ANCA (MPO) positivity was more frequent in the older group (48% vs 27%; P = 0.001). Younger patients had higher rates of musculoskeletal, cutaneous and ENT manifestations compared with older patients. Systemic, neurologic,cardiovascular involvement and worsening renal function were more frequent in the older-onset group. Damage accrual, measured with the Vasculitis Damage Index (VDI), was significantly higher in older patients, 12% of whom had a 6 month VDI ≥5, compared with 7% of younger patients (P = 0.01). Older age was an independent risk factor for early death within 6 months from diagnosis [hazard ratio 2.06 (95% CI 1.07, 3.97); P = 0.03]. Conclusion: Within 6 months of diagnosis of AAV, patients >65 years of age display a different pattern of organ involvement and an increased risk of significant damage and mortality compared with younger patients

A genome-wide association study identifies risk alleles in plasminogen and P4HA2 associated with giant cell arteritis

Giant cell arteritis (GCA) is the most common form of vasculitis in individuals older than 50 years in Western countries. To shed light onto the genetic background influencing susceptibility for GCA, we performed a genome-wide association screening in a well-powered study cohort. After imputation, 1,844,133 genetic variants were analysed in 2,134 cases and 9,125 unaffected controls from ten independent populations of European ancestry. Our data confirmed HLA class II as the strongest associated region (independent signals: rs9268905, P = 1.94E-54, per-allele OR = 1.79; and rs9275592, P = 1.14E-40, OR = 2.08). Additionally, PLG and P4HA2 were identified as GCA risk genes at the genome-wide level of significance (rs4252134, P = 1.23E-10, OR = 1.28; and rs128738, P = 4.60E-09, OR = 1.32, respectively). Interestingly, we observed that the association peaks overlapped with different regulatory elements related to cell types and tissues involved in the pathophysiology of GCA. PLG and P4HA2 are involved in vascular remodelling and angiogenesis, suggesting a high relevance of these processes for the pathogenic mechanisms underlying this type of vasculitis

Estimating overdiagnosis in giant cell arteritis diagnostic pathways using genetic data: genetic association study

Objectives GCA can be confirmed by temporal artery biopsy (TAB) but false negatives can occur. GCA may be overdiagnosed in TAB-negative cases, or if neither TAB nor imaging is done. We used HLA genetic association of TAB-positive GCA as an ‘unbiased umpire’ test to estimate historic overdiagnosis of GCA. Methods Patients diagnosed with GCA between 1990 and 2014 were genotyped. During this era, vascular imaging alone was rarely used to diagnose GCA. HLA region variants were jointly imputed from genome-wide genotypic data of cases and controls. Per-allele frequencies across all HLA variants with P < 1.0 × 10−5 were compared with population control data to estimate overdiagnosis rates in cases without a positive TAB. Results Genetic data from 663 GCA patients were compared with data from 2619 population controls. TAB-negative GCA (n = 147) and GCA without TAB result (n = 160) had variant frequencies intermediate between TAB-positive GCA (n = 356) and population controls. For example, the allele frequency of HLA-DRB1*04 was 32% for TAB-positive GCA, 29% for GCA without TAB result, 27% for TAB-negative GCA and 20% in population controls. Making several strong assumptions, we estimated that around two-thirds of TAB-negative cases and one-third of cases without TAB result may have been overdiagnosed. From these data, TAB sensitivity is estimated as 88%. Conclusions Conservatively assuming 95% specificity, TAB has a negative likelihood ratio of around 0.12. Our method for utilizing standard genotyping data as an ‘unbiased umpire’ might be used as a way of comparing the accuracy of different diagnostic pathways

The fuzzy misclassification analysis with deep neural network for handling class noise problem

Most of the real world data is embedded with noise, and noise can negatively affect the classification learning models which are used to analyse data. Therefore, noisy data should be handled in order to avoid any negative effect on the learning algorithm used to build the analysis model. Deep learning algorithm has shown to outperform general classification algorithms. However, it has undermined by noisy data. This paper proposes a Fuzzy misclassification the analysis with deep neural networks (FAD) to handle the noise in classification ion data. By combining the fuzzy misclassification analysis with the deep neural network, it can improve the classification confidence by better handling the noisy data. The FAD has tested on Ionosphere, Pima, German and Yeast3 datasets by randomly adding 40% of noise to the data. The FAD has shown to consistently provide good results when compared to other noise removal techniques. FAD has outperformed CMTF-SVM by an average of 3.88% in the testing datasets