Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

Implementation of Random Forest Algorithm in Order to Use Big Data to Improve Real-Time Traffic Monitoring and Safety

Nowadays the active traffic management is enabled for better performance due to the nature of the real-time large data in transportation system. With the advancement of large data, monitoring and improving the traffic safety transformed into necessity in the form of actively and appropriately. Per-formance efficiency and traffic safety are considered as an im-portant element in measuring the performance of the system. Although the productivity can be evaluated in terms of traffic congestion, safety can be obtained through analysis of incidents. Exposure effects have been done to identify the Factors and solutions of traffic congestion and accidents.In this study, the goal is reducing traffic congestion and im-proving the safety with reduced risk of accident in freeways to improve the utilization of the system. Suggested method Man-ages and controls traffic with use of prediction the accidents and congestion traffic in freeways. In fact, the design of the real-time monitoring system accomplished using Big Data on the traffic flow and classified using the algorithm of random-ized forest and analysis of Big Data Defined needs. Output category is extracted with attention to the specified characteristics that is considered necessary and then by Alarms and signboards are announced which are located in different parts of the freeways and roads. All of these processes are evaluated by the Colored Petri Nets using the Cpn Tools tool

A Fuzzy Realistic Mobility Model for Ad hoc Networks

Realistic mobility models can demonstrate more precise evaluation results because their parameters are closer to the reality. In this paper a realistic Fuzzy Mobility Model has been proposed. This model has rules which are changeable depending on nodes and environmental conditions. It seems that this model is more complete than other mobility models.After simulation, it was found out that not only considering nodes movement as being imprecise (fuzzy) has a positive effects on most of ad hoc network parameters, but also, more importantly as they are closer to the real world condition, they can have a more positive effect on the implementation of ad hoc network protocols