All service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not collected from reliable sources, causing the analysis to be skewed. We introduce a procedure in which the dataset is split into test and training datasets with a specific ratio to overcome this challenge. Process mining will find the traces of actions to streamline the process and aid data mining in producing a more efficient result. The most responsible domain is the healthcare industry. In this study, we used the activity data from the hospital and applied process mining algorithms such as Alpha Miner and fuzzy Miner. Process mining is used to check for conformity in the event log and do performance analysis, and a pattern of accuracy is exhibited. Finally, we used process mining techniques to show the deviation flow and fix the process flow. This study showed that there was a variation in the flow by employing Alpha and fuzzy Miners in the hospital.

Baculovirus expression system has been used successfully to over-express eukaryotic proteins in insect cells. This system uses a very strong viral promoter, AcNPV polyhedrin, for high level protein expression. Here a Baculovirus expression system was used to express the Mouse Gaq protein (mGaq) in SF9 insect cells. The recombinant protein was made at high levels and it was found in the cell membrane where it functions as a signaling molecule.

Background and Objectives: The Ant-Miner algorithm works based on Ant Colony Optimization as a tool for data analysis, and is used to explore classified laws from a set of data. In the current study, two new methods have been proposed for the purpose of optimizing this algorithm. The first method adopted logical negation operation on the records of the produced laws, while the second employed a new pheromone update strategy called “ Generalized exacerbation of quality conflict” . The two proposed methods were executed in Visual studio C#. Net, and 8 public datasets were applied in the test. Each one of these datasets was executed 10 times both in an independent way and combined with others, and the average results were recorded. Methods: In this study, we have proposed two approaches for the earlier method. Using the first method in the construction of rule records, idioms that include the rules can be made in the form of . Compared to the idioms of the early algorithm, these idioms are more compatible while constructing rules with high coverage. The advantage of this generalization is the reduction of the produced rules, which results in greater understandability of the output. During the process of pheromone update in the ordinary ACO algorithms, the amount of the sprayed pheromone is a function of the quality of rules. The objective of the second method is to strengthen the conflict between not-found, weak, good, and superior solutions. This method is a new strategy of pheromone update where ants with high-quality solutions are motivated through increasing the amount of pheromone sprayed on the trail that they have found; conversely, the ants that find weaker solutions are punished through eliminating pheromone from their trails. Results: The optimization of the initial algorithm using the two proposed methods produces a smaller number of rules, but increases the number of construction diagrams and prevents the production of low-quality rules. Conclusion: The results of tests performed on the dataset indicated the enhancement of algorithm efficiency in idioms of fewer tests, increased prediction accuracy of laws, and improved comprehensibility of the produced laws using the proposed methods.

Background and Objectives The tomato leaf Miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), is an important pest of tomato in Iran. Therefore, this study was done to investigate fluctuations in adult male population of this pest using sex pheromone traps in Sirvan County (Ilam Province, western Iran) during a growing season, which lasted before transplanting to the end of growing season. Materials and Methods For conducting this research, 4 tomato farms in Sirvan County were selected and the pest populations were sampled using pheromone traps. The Pearson's correlation coefficient was used to assess the relationship between adult moth density and temperature. For determining optimal sample size, the Green's method was used. Results According to results of the present study, there were seven population peaks at intervals of approximately one month. Flight peak of this pest occurred on May 10, June 14, July 19, August 23, October 4, November 8 ,and  December 6, respectively. The highest population density was recorded on July 19 (397.75 moths) and population density of this pest was declined since late November. Studying the relationship between average number of male moths of T. absoluta captured by pheromone traps and average monthly temperature in a growing season showed that with the increase in temperature, population density of moths was also increased. Results of regression analysis indicated that peaks about abundance of male moth coincided with the previous monthly mean temperatures. Taylor's regression model showed a significant and high correlation between mean and variance regarding density of pest population. Statistically, the aggregation index of tomato leaf Miner was equal to one. In this study, Green's method was used to estimate optimal sample size. The results showed that the required number of sampling units was decreased with the increase in average population density of the pest so that, when average population density of tomato leaf Miner moth in each pheromone trap was one per week, the required number of samples with an accuracy level of 25 and 10% was equal to 1.86 and 11.65, respectively, and when average population density of this pest in each pheromone trap was 10 per week, the required number of samples with an accuracy level of 25 and 10% was equal to 0.37 and 2.32, respectively. Discussion Our results demonstrated that pheromone traps can be used as a suitable sampling method for monitoring population of the tomato leaf Miner. This study provided the data about sampling, population distribution, fluctuations in population of tomato leaf Miner, and the relationship between population density of the pest and temperature. Our findings can greatly contribute to rational management of this pest in the region.

In this study, we will analyze how big data is implemented in TESLA Company, in this case, we will use sales data. With the growth of big data and the need for its use in companies, nowadays big data is everywhere. TESLA is an American automobile and energy storage company founded by engineers Martin Eberhard and Marc Tarpenning in July 2003 under the name Tesla Motors. The company name is a tribute to inventor and electrical engineer Nikola Tesla. Eberhard said that he wanted to build an automobile manufacturer and also a technology company whose core technology is batteries, computer software and proprietary electric motors. As the amount of data that companies must process today continues to increase, companies must keep up with the times by using big data. Big data can be used to move, contain, and access large amounts of unstructured and disparate data in a timely manner. it is good. The method we use is quantitative data. This calculation will use the Rapid Miner software. The result of this study is the data is 2,146 units, total volume from 118,500 to 47,065,000 based on the number of existing sales, and classification results are from 2621300 to 18766300.