<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>

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.

Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means.

Abstract

Most of the information (more than 80%) is stored as text, and text mining is a very important process as it is an initial step in the process of text classification, and this is especially the case in the Arabic language. The Aim of The Study is to classify Arabic texts according to specific categories using advanced performance indicators We used Data Templates as a platform for managing and organizing Apache Spark to solve big data challenges. Apache Spark offers several integrated language APIs. nlp lib was used for text processing. The data is pre-processed through several steps, namely separating the words into one text on the basis of the space between words, cleaning the text of unwanted words, restoring the words to their roots, as well as the feature selection process is a critical step. in text classification. It is a preprocessing technology. In this paper, one way to determine which TF attributes are used how often each feature appears in the document is that they consider the first level of the feature selection process. Then we use TF-IDF to determine the significance of the feature in the document, and this is the last step in the preprocessing Outcomes Text classification . Results were evaluated using advanced performance indicators such as accuracy, Precision and recall. A high accuracy of 96.94% was achieved.The main objective of this paper is to classify basic texts quickly and accurately, according to the results as long as the feature size is suitable, the most advanced technology is superior to other pass rate methods due to the reasonable reliability and perfect pruning level.