An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks

Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segment and detect B-cancer disease. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image-processing-and artificial intelligence-based solutions for that task can be of great realistic significance. The presented approach itself scans the whole mammogram and performs filtering, segmentation, features extraction, and detection in a succession mode. The feasibility of the proposed approach was explored on 32 commonly virulent images, and the recognition rate achieved in the detection step is 100 %; further, the approach is able to give reliable results on distorted medical images, since the approach is subjected to a rectification step. Finally, this study is very effectual in decreasing mortality and increasing the quality of treatment of early onset of B-cancer

A.: The effect of using a thesaurus in arabic information retrieval system

Abstract Automatic query expansion methods for English and other languages text retrieval have been studied for a long time. In this research we study the retrieval effectiveness, achieved when we apply a successful automatic query expansion method in Arabic text retrieval based on an automatic thesaurus. Our experiments show that the automatic query expansion method resulted in a notable improvement in Arabic text retrieval using a sample of abstracts of Arabic documents. The study showed that the use of a thesaurus has improved information retrieval system by 10% -20%. The study also shows that the greater the number of documents in the building thesaurus, Thesaurus was more accurate

Crime Classification Algorithm for Mining Crime Hot Spot and Cold Spot

Crime may be a behavior deviating from traditional violation of the norms giving peoples ' losses and harms. Social, psychological, economic and environmental factors are to be thought-about in crime issues. Crime is tried to be explained by numerous theories from totally different sciences. Social and psychological theories contemplate the foundation causes of crime noticing to factors like social disorganization, temperament disorders and inadequate parenting etc. In this work, we discuss the groundwork results of a crime forecasting model developed in association with the Tamil Nadu police department. First part of the research work to collect the crime records from various police departments and rearrange the crime database. The space and time of these crime incidents are implanted in the data. Additionally spatial and temporal characteristics are yielded from the crime data. The second part of the research performs the crime forecasting based on data mining classification method. In this research analyze various classification methods which are predicting crime hotspots more accurately. The final result of the research model shows the improvement of spatial and temporal crime data to create consistent crime forecasting. Key Words: spatial-temporal crime data, crime forecasting, classification, crime hotspot and cold spot