Mineral prospectivity mapping using a VNet convolutional neural network

Predictive modeling of mineral prospectivity by deep self-organizing map: Implications for copper exploration targeting in saveh district, central Iran

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Porphyry-type mineral prospectivity mapping with imbalanced data via prior geological transfer learning

Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.

Cancer remains a major global health challenge, characterized by high incidence and mortality rates. Accurate early diagnosis and treatment are essential for improving patient survival. Medical imaging technologies, such as X‐ray, computed tomography (CT), positron emission tomography (PET), ultrasound (US), magnetic resonance imaging (MRI), and mammography, play a key role in identifying the location, size, shape, density, and structure of tumors, as well as assessing their spread. The diverse types and large volume of medical images place a heavy workload on radiologists, potentially leading to increased misdiagnosis rates and prolonged diagnostic periods. This situation underscores the growing need for computer‐aided diagnostic systems in clinical practice. Deep learning, a subfield of artificial intelligence (AI), demonstrates significant potential in analyzing and interpreting complex imaging data, enabling precise tumor characterization and improved diagnostic outcomes. This review examines the integration of AI techniques—such as machine learning, deep learning and convolutional neural networks in medical imaging, which have notably enhanced diagnostic accuracy, early detection rates, and workflow efficiency in cancer diagnostics. By applying automated feature extraction and analysis across various imaging modalities, AI systems assist in clinical decision‐making and reduce human oversight. However, challenges remain in areas like data quality, privacy concerns, and model generalizability, which constrain the broader application of AI. Future research should address these challenges to support more robust, reliable, and accessible AI‐driven imaging solutions for cancer diagnosis.

Chapter 4 - Deep Learning in Texture Analysis and Its Application to Tissue Image Classification

Nowadays, people pay more attention in artificial intelligence(AI) research, and they try to make Al smarter. The machine learning became a popular subject, especially in object recognition area. Aiming at providing a faster and more accurate plant species recognition program, the author introduced the deep learning and convolution neural network(CNN), and decided to build a CNN project with pycharm, anaconda, kera to find the best way to improve recognition program accuracy and recognition speed. The author tried to change the learning epoch time and learning data set capacity to found the best solution. After tests were finished, the result of output plots analyze is that both adding learning epochs time and extend training image set are all helpful to improve recognition accuracy and speed. As for the effect of increase learning time, it is more obvious in improving accuracy while extend training set size, which is a better method to reduce recognition time. The end of the thesis contained the experiment result, the deficiency of this essay and the future prospect forecast of the machine learning applied in plant area.

Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping

Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible–near-infrared–shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements.

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.

A review on hyperspectral imagery application for lithological mapping and mineral prospecting: Machine learning techniques and future prospects

Human skin type classification using image processing and deep learning approaches

Classification is one of the most popular topics in image process. In this paper, it provides the specific process of convolutional neural network in deep learning. Here builds typical convolution neural network which parameters and the connection mode can be adjusted. In addition, we present our preliminary classification results. Through the experiments of a convolutional neural network on the Mixed National Institute of Standards and Technology database (MNIST), we compared with the classification results and analyzed in the experimental results with the parameters. The experimental results show that image classification effect is very good used by convolutional neural network.

Use of Artificial Intelligence and Machine Learning in Critical Care Ultrasound.

The majority of machine learning algorithms that have been applied in data-driven predictive mapping of mineral prospectivity require a sufficient number of training samples (known mineral deposits) to obtain results with high performance and reliability. Semi-supervised learning can take advantage of the huge amount of unlabeled data to benefit the supervised learning tasks and hence provide a suitable scheme for mapping mineral prospectivity in cases where only few known mineral deposits are available. Semi-supervised random forest was used in this study to map mineral prospectivity in the southwestern Fujian metallogenic belt of China, where there is still excellent potential for mineral exploration due to the large proportion of areas covered by forest. The findings obtained from the current study include: (1) semi-supervised learning can make use of both the labeled and unlabeled samples to help improve the performance of mapping mineral prospectivity; (2) multi-dimensional scaling can be used to explore the clustering structure within the samples, which provides a tool to validate the usability of semi-supervised learning algorithms. In addition, the prospectivity map obtained in this study can be used to guide further mineral exploration in the southwestern Fujian of China.