Introduction: With writing the special code based on Grid Search technique, best method for describing the elements energy spectrum is introduced. This method is suitable for investigation of Atomic, molecular and nuclear Energy spectrum, laser pulses and study of spectrum that obtained by laser interaction with cell and human body.Microraman spectrum of eye lens that fitted by De. Mul and time spectrum of one molecule that excited by 585 nm laser and fitted by Gnu plot software. In a special application, this method is used for 22Na spectrums.Material and Methods: For fitting mathematical function to a set of experimental data, the first step is seeing data plot in every software. Then, an initial mathematical function is guessed and the function parameters change as far as possible for initial fitting. After this step, by using this special code based on Grid Search technique, is the X2 changes between function data and experimental data are compared. If the X2 is high, it will reduce by introducing small change in every parameter of mathematical function. This is done step, by step, so the X2 reaches to the lowest.Results: By this method, we can have the formulation of element energy spectrum. For example, the equation of 22Na energy spectrum’s description with least squares fitted by experiment data is: N=199.0477 +76100EXP (0.00167 (E-508.7539) 2) + 75100EXP (0.00168 (E-509.2428) 2) + 3450.8070 EXP (0.0000831(E-655.356) 2) + 4002.813EXP (0.0001431(E-839.401) 2) + 11000EXP (0.00105 (E-1048.0230) 2) Fitting start with fit parameter X2=1107.6 and finished with X2 =44.16.Conclusion: By using the special code based on Grid Search technique, we try to introduce the best method for describing of the elements energy spectrum.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local Search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local Search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

In this study, free version of MEDLINE database in the web which is PubMed and three Commercial versions which are Ebsco, ISI and First Search were Chosen. These Versions were gathered in five different features including: general information, Searching features, display options, retrieval options and unique features. Then by use of comparative survey they were compared. The results showed that Ebsco has the best user interface features, followed by, PubMed, ISI and First Search. Of course when choosing a database one should have in mind the level of end users, their information needs and the budget of the library.