Application of machine learning methods to detect and classify Core images using GAN and texture recognition

Abstract

During exploration campaigns, oil companies rely heavily on drill core samples as they provide valuable geological information that helps them find important oil deposits. Traditional core logging techniques are laborious and subjective. Core imaging, a new technique in the oil industry, is used to supplement analysis by rapidly characterising large quantities of drill cores in a nondestructive and noninvasive manner. In this paper, we will present the problem of core detection and classification. The first problem is detecting the cores and segmenting the holes in images by using Faster RCNN and Mask RCNN models respectively. The second problem is filling the hole in the core image by applying the Generative adversarial network(GAN) technique and using Contextual Residual Aggregation(CRA) which creates high frequency residual for missing contents in images. And finally applying Texture recognition models for the classification of core images.

Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing

“…Different clustering methods like k-means, RM K-means, and Self-Organized Maps were used for segmentation. In this paper, we proposed a business model for e-commerce organizations based on segmentation according to various categories [10] and RFM positioning to retain and acquire customers in e-commerce. As we know, observing new customers is important, but retaining old customers is even more important.…”

“…Healthcare is another industry where deep learning has found several applications, including diagnosis, treatment planning, drug discovery [5], and medical imaging analysis [6,7,8]. In robotics, deep learning is used for autonomous navigation, object recognition [9,10], and robotic control. handwritten recognition for various languages [11,12,13,14,15].…”

Customer profiling, segmentation, and sales prediction using AI in direct marketing

“…Health care is another industry where deep learning has found several applications, including diagnosis, treatment planning, drug discovery [9], and medical imaging analysis [10][11][12]. In robotics, deep learning is used for autonomous navigation, object recognition [13][14][15], and robotic control, handwritten recognition for various languages [16][17][18][19][20], questions-answering [21][22][23][24][25], intrusion detection in IoT [26][27][28], and energy consumption prediction [29,30].…”