Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.

Some Methods to Maximize Extraction of Scientific Knowledge from Parallel Group Randomized Trials

The amount of scientific knowledge from randomized parallel group trials have been improved by the CONSORT Guideline, but important intelligence with important clinical implications remains to be extracted. This may though be obtained if the conventional statistical significance testing is supplied by 1) Addition of an unbiased and reproducible quantification of the magnitude or size of the clinical significance/importance of a difference in treatment outcome;2) Addition of a quantification of the credulity of statements on any possible effect size and finally;3) Addition of a quantification of the risk of committing an error when the null hypothesis is either accepted or rejected. These matters are crucial to proper conversion of trial results into good usage in every-day clinical practice and may produce immediate therapeutic consequence in quite opposite direction to the usual ones. In our drug eluting stent trial “SORT OUT II”, the implementation of our suggestions would have led to immediate cessation of use of the paclitaxel-eluting stent, which the usual Consort like reporting did not lead to. Consequently harm to subsequent patients treated by this stent might have been avoided. Our suggestions are also useful in cancer treatment trials and in fact generally so in most randomized trial. Therefore increased scientific knowledge with immediate and potentially altered clinical consequence may be the result if hypothesis testing is made complete and the corresponding adjustments are added to the CONSORT Guideline—first of all— for the potential benefit of future patients.

Stochastic Chaos of Exponential Oscillons and Pulsons

An exact three-dimensional solution for stochastic chaos of I wave groups of M random internal waves governed by the Navier-Stokes equations is developed. The Helmholtz decomposition is used to expand the Dirichlet problem for the Navier-Stokes equations into the Archimedean, Stokes, and Navier problems. The exact solution is obtained with the help of the method of decomposition in invariant structures. Differential algebra is constructed for six families of random invariant structures: random scalar kinematic structures, time-complementary random scalar kinematic structures, random vector kinematic structures, time-complementary random vector kinematic structures, random scalar dynamic structures, and random vector dynamic structures. Tedious computations are performed using the experimental and theoretical programming in Maple. The random scalar and vector kinematic structures and the time-complementary random scalar and vector kinematic structures are applied to solve the Stokes problem. The random scalar and vector dynamic structures are employed to expand scalar and vector variables of the Navier problem. Potentialization of the Navier field becomes available since vortex forces, which are expressed via the vector potentials of the Helmholtz decomposition, counterbalance each other. On the contrary, potential forces, which are described by the scalar potentials of the Helmholtz decomposition, superimpose to generate the gradient of a dynamic random pressure. Various constituents of the kinetic energy are ascribed to diverse interactions of random, three-dimensional, nonlinear, internal waves with a two-fold topology, which are termed random exponential oscillons and pulsons. Quantization of the kinetic energy of stochastic chaos is developed in terms of wave structures of random elementary oscillons, random elementary pulsons, random internal, diagonal, and external elementary oscillons, random wave pulsons, random internal, diagonal, and external wave oscillons, random group pulsons, random internal, diagonal, and external group oscillons, a random energy pulson, random internal, diagonal, and external energy oscillons, and a random cumulative energy pulson.

Observations of Freak Waves in Random Wave Field in 2D Experimental Wave Flume

Long time series of wave field are experimentally simulated by JONSWAP spectra with random phases in a 2D wave flume. Statistic properties of wave surface, such as significant wave height, skewness and kurtosis, are analyzed, and the freak wave occurrence probability and its relations with Benjamin-Feir index （BFI） are also investigated. The results show that the skewness and the kurtosis are significantly dependent on the wave steepness, and the kurtosis increases along the flume when BFI is large. The freak waves are observed in random wave groups. They occur more frequently than expected, especially for the wave groups with large BFI.

Clinical Observation of Zhuang Medicine Yangxue Xiaozheng Decoction in Treating the Combined Endometriosis of Dampness and Blood Stasis in Guangxi

Objective:To explore the clinical effects of Zhuang Medicine Yangxue Xiaozheng Decoction in treating the combined Ems of dampness and blood stasis in Guangxi;Methods:100 patients with endometriosis treated in Lili Clinic of Famous Doctor,Guangxi International Zhuang Medicine Hospital from Mach 2016 to May 2017 were chosen as the research object.According to the random grouping method,patients were randomly divided into the treatment group(The Zhuang Medicine Yangxue Xiaozheng Decoction(ZYF)Group)and the control group(Chinese patent medicine SanJieZhenTongJiaoNang(SJZT)group with 50 cases in each group.After treatment,the TCM syndrome score,changes in pelvic mass size,hepatocyte growth factor(HGP)levels,and clinical effects before and after treatment were evaluated.Results:After two courses of treatment,the total effective rate of patients in the ZYF group was 88%,which were significantly better than 70%of the SJZT group.The difference was statistically significant(P<0.05).Conclusion:With a significant effect on patients with the combined Ems of dampness and blood stasis in Guangxi,Zhuang Medicine Yangxue Xiaozheng Decoction can improve the uterine cavity mass and reduce serum HGP level.

Large-Scale Expensive Optimization with a Switching Strategy

Some optimization problems in scientific research,such as the robustness optimization for the Internet of Things and the neural architecture search,are large-scale in decision space and expensive for objective evaluation.In order to get a good solution in a limited budget for the large-scale expensive optimization,a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems.A surrogate model is then trained for each sub-problem using different strategies to select training data adaptively.After that,a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem optimization.Furthermore,an escape mechanism is proposed to keep the diversity of the population.The performance of the method is evaluated on CEC’2013 benchmark functions.Experimental results show that the algorithm has better performance in solving expensive large-scale optimization problems.