A Wavelet Collocation Method for some Fractional Models

This article presents an effective numerical approach based on the operational matrix of fractional order integration of Haar wavelets for dealing with the fractional models of the mixing and the Newton law of cooling problems. A general procedure of obtaining the fractional integration operational matrix of Haar wavelets which converts the fractional models into a system of algebraic equations is derived so that the computation is very simple and it is much effective than the conventional numerical methods. The reliability and the applicability of the current numerical technique for fractional models are examined by comparing the achieved results with the precise solutions

Prime Coloring of Crossing Number Zero Graphs

In this paper, prime coloring and its chromatic number of some crossing number zero graphs are depicted and its results are vali-dated with few theorems. Prime Coloring is defined as G be a loop less and Without multiple edges with n distinct Vertices on Color class C={c1,c2,c3,…..cn} a bijection ψ:V {c1,c2,c3,…..cn} if for each edge e = cicj ,i≠j , gcd{ ψ (ci), ψ (cj)}=1, ψ (ci) and ψ (cj) receive distinct Colors. The Chromatic number of Prime coloring is minimum cardinality taken by all the Prime colors. It is denoted by η (G)

Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial

Background Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects. Methods FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762. Findings Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months. Interpretation Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function. Funding UK Stroke Association and NIHR Health Technology Assessment Programme

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy

Flowchart of the differential search algorithm.

<p>Flowchart of the differential search algorithm.</p

RMSE for Istanbul Stock Exchange by varying hidden layer neurons.

<p>RMSE for Istanbul Stock Exchange by varying hidden layer neurons.</p

Summary of results obtained for Forest Fires.

<p>Summary of results obtained for Forest Fires.</p

a) Convergence plot b) Successfully predicted samples for Concrete strength.

<p>a) Convergence plot b) Successfully predicted samples for Concrete strength.</p

a) Convergence plot b) Successfully predicted samples for Airfoil self -noise.

<p>a) Convergence plot b) Successfully predicted samples for Airfoil self -noise.</p

a) Convergence plot b) Successfully predicted samples for Boston House Pricing.

<p>a) Convergence plot b) Successfully predicted samples for Boston House Pricing.</p