Background: Colorectal cancer is one of the most common cancers in terms of morbidity and mortality worldwide. A lot of research have been done in this field in Iran and worldwide, which have positive results. The aims of this study were firstly doing a statistical study on colorectal cancer in Mashhad, Iran, and finally predicting the colorectal LOCATION of cancer based on the clinical DATA by using DATA mining science and decision tree model.Materials and Methods: The DATA of 316 patients with colorectal cancer (including 14 features) were extracted from the archive of Imam Reza Hospital, Mashhad. The instrument used in this research was Rapid Miner DATA mining software that would try to be extract the details of the relevant DATA by statistical surveys and then would do initial simulations and the use of classification and decision tree method have predicteion the LOCATION of cancer.Results: Male to female ratio of 56% to 44%, family history of 37%, more young patients, and relatively more distally located cancers (39%) compared with the proximal (35%), and rectum (26%) were the striking findings of this study. The final and most important stage of research models were presented, which was able to predict the LOCATION of the cancerous tumor with 80% accuracy.Conclusion: Similarities with global statistics, such as the ratio of men to women and family history were observed. But there were also differences with global statistics including the Iran’s younger patients and relatively more patients with distal cancers. The efficiency of DATA mining techniques to predict the LOCATION of cancer as well as cost reduction was among the most important results of this study.

The purpose of this paper is optimal LOCATION of distributed generation in electric distribution networks. Load uncertainty and desired voltage range has been modeled using fuzzy DATA theory. The objective function includes loss reduction, improvement of profile index and voltage stability index with their relevant constraints, voltage constraints and transmittable power from the line. Load variation has been shown for three different time durations (peak, off peak and average).PSO technique has been used to optimize the objective function while Max-Min method has been applied to select the answer. Results produced from the proposed model have been provided in 5 different scenarios on a 33 bus system of IEEE.

Finding an optimized place is undeniably a momentous subject in establishing the marketing strategies of a retail store. Based on the existing literature, the process of selecting an optimized LOCATION for a business can be defined as a ranking problem that compares and rates existing or potential sites based on their ability to attract customers. Consequently, this article is concentrated on the evaluation of machine learning ranking methods in ranking existing retail stores based on the DATA derived from LBSNs. Using feature engineering techniques, we defined and calculated a set of features for 239 retail store branches in Tehran, from the venue DATA obtained from the Foursquare API. Additionally, we derived a rank for each store representing store popularity via user-generated DATA from Foursquare, Dunro, and Google Maps. Next, we implemented a number of classification and “ learn-to-rank” algorithms to rate these stores. Finally, by evaluating the prediction precision and ranking precision of the algorithms used, we analyzed the fit and prediction power of all ranking algorithms. The outcomes of this research suggest that most algorithms used are, in fact, reliable methods for ranking retail store sites. Therefore, such algorithms can be used as a technique for retail store site selection, given a list of existing or potential sites for a store. Additionally, our results clearly suggest a superiority in the ranking precision of “ learn-to-rank” algorithms for retail store placement. Out of all algorithms used, with a ranking precision of 0. 854, MART is the most powerful algorithm for ranking retail store sites.