The paper for the survey is to recapitulate applications of laser, mathematically and also discusses whether the laser can furnish a similar or ameliorate treatment for traditional care. Studies have shown that the use of lasers in the different fields is an efficacious device to increase efficiency, quality, easiness, cost and convenience in dental therapy. The laser is an influential dental auxiliary to control pollution, wound reparations, the discharge and control of tremolo in the expulsion of hard tissues. Lasers designed specifically for this region are surgical and medical devices at the forefront of technology and are used by a large number of doctors in everyday use. Dental laser uses wavelengths that are absorbed in soft or solid tissues and given exceptional privacy in tissue. Its clinical capabilities are extremely complex, providing very good comfort for the patient. Clinical conditions will characterize the various and diverse applications of dental lasers depending on the power used during exposure and absorption of wavelengths in soft or hard tissues, emphasizing the benefits for each case.

One of the most prevalent problems with big data is that many of the features are irrelevant. Gene selection has been shown to improve the outcomes of many algorithms, but it is a difficult task in microarray data mining because most microarray datasets have only a few hundred records but thousands of variables. This type of dataset increases the chances of discovering incorrect predictions due to chance. Finding the most relevant genes is generally the most difficult part of creating a reliable classification model. Irrelevant and duplicated attributes have a negative impact on categorization algorithms’ accuracy. Many Machine Learning-based Gene Selection methods have been explored in the literature, with the aim of improving dimensionality reduction precision. Gene selection is a technique for extracting the most relevant data from a series of datasets. The classification method, which can be used in machine learning, pattern recognition, and signal processing, will benefit from further developments in the Gene selection technique. The goal of the feature selection is to select the smallest subset of features but carrying as much information about the class as possible. This paper models the gene selection approach as a binary-based optimization algorithm in discrete space, which directs binary dragonfly optimization algorithm «BDA» and verifies it in a chosen fitness function utilizing precision of the dataset’s k-nearest neighbors’ classifier. The experimental results revealed that the proposed algorithm, dubbed MI-BDA, in terms of precision of results as measured by cost of calculations and classification accuracy, it outperforms other algorithms

The Hermite numerical interpolation method used to estimate and predict the radon and radium concentrations, from some of the experimental measurements made on the soil samples of the Bartella region in Iraq. The results obtained from deductible mathematical method were close to real experimental results. Predicted results are in the range of 80.04-4051.59 Bq/m 3 radon concentrations and 0.322-17.276 Bq/Kg radium concentrations corresponding to 200-10000 tracks of alpha particles. The maximum errors were less than 0.289 and 1.172 percent for radon and radium concentrations, respectively. The effects of radium and radon radiations estimated of soil in the studied area depend on its concentrations, a higher number of alpha particle tracks mean high radiation concentration, the high radiation will cause human health damage. The values are below 370 Bq/Kg the recommended radium concentration permissible values, by the Organization ащк Economic Cooperation and Development (OECD), the studied area is safe from hazards on health of radium and radon exposure from the soil.