<p>Image mining is the method of searching and discovering valuable information and knowledge from a huge image dataset. Image mining is based on data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining handled with the hidden information extraction, an association of image data and additional pattern which are not clearly visible in the image. Choosing the proper objects or the feature of the image to be suitable for image mining process is the main challenge would face the programmer. The process includes fine out the most efficient routes at a shorter time and saving the users effort. The main objective of this paper is to design and implement the image classification system with a higher performance, where a CIFAR-10 data set is used to train and testing classification models using CNN. A convolutional neural network is trustworthy, and it could lead to high-quality results. The high accuracy of 98% has been obtained using deep convolutional neural network (DCNN).</p>

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).

The growing interest in the database generated many techniques in this area and the Intelligent Systems Laboratory Where graphical models were used in the theoretical developments of computer vision and reasoning, and their application in various fields. Our proposal provides a database of the face under the circumstances of the real, without knowing the people who are taking pictures of them. The present study focuses on the which recording images and places them in the database through active inference and effective reasoning. We are interested in active reasoning because it manages sensor algorithms and guidance unit until the visual translation process is completed.Thus, this sophisticated capture technique processes each frame whenever a face or eye is selected. We are developing a face detection and eye identification process by building a facial recognition algorithm using databases collected from previous experiments. This procedure was applied to a database of a previous set of 40,000 images for 40 people, which illustrates how difficult it is to identify faces. This paper includes a detailed research methodology. In Section 3, face evaluation is discussed, depending on key characteristics within a specific protocol, followed by a definition of the most accurate performance criteria for face verification, and identification through statistical measures. The evaluation protocols in our paper provide researchers the means to recognize faces using modern methods. The results obtained were mentioned in the figure and indicated the strength of the technique used.