Application of deep learning in image recognition

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.

With the rapid development of high-tech in China, the field of artificial intelligence has also been promoted. Image recognition is an important topic in the field of artificial intelligence, which mainly includes two modules: classification recognition and feature extraction; At the same time, as an important research direction of AI, deep learning has made rapid progress in recent years. It has been widely used in image recognition, speech recognition and other fields and has achieved great success. This paper analyzes the application of deep learning in image recognition, mainly from the aspects of face recognition, remote sensing image classification, etc. Its purpose is to provide help for relevant practitioners, so as to promote the development of image recognition in the tide of AI development.

This research aims to study the deep learning applications in image recognition and classification tasks. The advantages and limitations of deep learning are explored by analysing existing deep learning algorithms and their applications on tasks such as using images to do classification, image segmentation, and target detection. In the experimental part, this paper evaluates the performance of deep learning in image recognition by using classical neural network models, including training and testing models on a large size of image datasets. The results show that neural network has good classification and detection capabilities in image recognition tasks and also achieves good results for image segmentation tasks. However, the model training process of shallow neural network models is time-consuming and performs poorly for small-scale datasets. Based on these situations, this paper proposes some optimization strategies to achieve high performance and efficiency of neural networks in image recognition and analyze the performance differences between different strategies .

Crater extraction and recognition is an important research content of deep space planetary science. Traditional crater detection algorithms (CDAs) are mainly based on crater feature construction which relies on high-quality data. With the application of deep learning in image recognition and semantic segmentation, new ideas have been brought to meteorite crater extraction. Many crater extraction algorithms based on artificial intelligence have been proposed which greatly simplifies the crater extraction process and improves detection accuracy. However, with the improvement of the accuracy, the convolution kernels become more and more, and the huge parameters and the consumption of storage and computing resources limit the application of these algorithms in mobile device. In order to solve this problem, we propose a compact crater extraction network based on model pruning. In the combination of U-Net and residual block, the network structure is optimized under the premise of retaining the longitude of large model extraction, and the balance of hardware resources and algorithm accuracy is achieved. The experimental results show that we compress the model to 4.9% of raw size and only lose almost 0.5% accuracy. It provides a reference for the application of CDAs on resource constrained platforms.

Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in image recognition. We review the different deep learning approaches which have been used in the field of image classification and localization.

This paper researches the localization method based on the finer-grained channel parameters using a deep learning model with a single access point. The basic principle is to transform the localization problem into a classification problem by using the advantages of deep learning in image recognition. First, we use the 2D recursively applied and projected-multiple signal classification algorithm to estimate the path parameters, and then the signal spectrum in 2D space is obtained which is plotted in a image. Then we present a structure based on convolutional neural network to obtain the local signal features and learn the correlation between its two-dimensional parameters and the coordinates of the target. Finally, we use the commercial-grade ray tracing software to evaluate our proposed methods, and the simulation results show that the proposed method can locate the target accurately using only a single access point.

Race against the Machine: can deep learning recognize microstructures as well as the trained human eye?

Big data technology has not only become an important driving force of industry digitalization and intelligence, but also a fundamental source of innovation and development. Through the study of pricing model, the application of big data technology in service trading can be more standardized and orderly. As a special data form in the recommendation field, traditional recommendation methods are often difficult to model session sequence data. Although some scholars have improved the traditional recommendation method to solve the problem of session sequence modeling, the effectiveness of the recommendation system is limited by many problems in the model structure. In recent years, with deep learning in image recognition, natural language processing, speech recognition and other breakthroughs in the field of artificial intelligence, will study deep into the recommendation algorithm, can help to effectively solve the traditional recommendation algorithm of multi-source heterogeneous information, data sparse, cold start, data characteristics of the design of artificial rely on such issues, in this case, The results of traditional recommendation methods are often inaccurate, and the results are often lagging and repeatable.

Simple SummaryMalignant melanomas (MMs) with aypical clinical presentation constitute a diagnostic pitfall, and false negatives carry the risk of a diagnostic delay and improper disease management. Among the most common, challenging presentation forms of MMs are those that clinically resemble seborrheic keratosis (SK). On the other hand, SK may mimic melanoma, producing ‘false positive overdiagnosis’ and leading to needless excisions. The evolving efficiency of deep learning algorithms in image recognition and the availability of large image databases have accelerated the development of advanced computer-aided systems for melanoma detection. In the present study, we used image data from the International Skin Image Collaboration archive to explore the capacity of deep knowledge transfer in the challenging diagnostic task of the atypical skin tumors of MM and SK.Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.

Deep learning is a complex machine learning algorithm that can learn the deep inner rules of a sample. In recent years, deep learning has achieved good results in the field of image processing. The essence of deep learning is to build a multi-layered machine learning architecture that trains on large-scale data. With the development of computer technology, deep learning has achieved good results in image recognition and natural language processing. Deep learning is also becoming more and more widely used in industry. This paper introduces the applications of deep learning in the industrial field, and finally discusses development and challenges of deep learning in industrial applications in the future.

In today’s digital environment, the pattern is everywhere. It is present in many aspects of our daily life. Algorithms can be used to detect or mathematically observe a pattern physically. Vector feature values represent a pattern in the digital world. With the advent of artificial intelligence (AI) techniques in the recent era, there are so many machine learning (ML) and deep learning (DL) models that have been developed. ML is the branch of AI, which can perform tasks like data analysis, analytical model building, and classification without being explicitly programmed. DL is the subset of ML in AI. In DL, mostly artificial neural networks (ANNs) are utilized. It has the capability of work based upon unsupervised learning from the data that is unlabeled and unstructured. Using these DL and ML models, extract the meaningful features from the given image or video is known as pattern recognition (PR). PR is used in many engineering applications such as computer vision, natural language processing, character recognition, robotics, speech recognition, and so on. It is also used in many medical image processing applications and telemedicine. The present work discussed the pattern recognition problem and its various stages in detail. In addition to that, the application of deep learning and machine learning in pattern recognition is also explained briefly.

It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. This talk is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies that we currently adopt to deal with the Big Data problems. The second part is to discuss the deep learning role in Big Data. In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. This talk will also discuss how the big data relates with the deep learning.

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

Machine learning is a rapidly evolving field that relies heavily on mathematical principles and techniques. In this paper, we provide a comprehensive review of the mathematical aspects of machine learning, focusing on key concepts and their applications in various machine learning algorithms. We begin by discussing the basic concepts and terminology of machine learning, followed by an exploration of linear algebra, calculus, probability theory, and information theory in the context of machine learning. We then present case studies and applications of machine learning in image recognition, natural language processing, recommender systems, and autonomous vehicles. Finally, we discuss the current limitations of mathematical models in machine learning, emerging trends in mathematical research, and the ethical and societal implications of machine learning. This paper aims to provide a foundational understanding of the mathematical principles underlying machine learning and their significance in advancing the field.

Information Theory (IT) has been used in Machine Learning (ML) from early days of this field. In the last decade, advances in Deep Neural Networks (DNNs) have led to surprising improvements in many applications of ML. The result has been a paradigm shift in the community toward revisiting previous ideas and applications in this new framework. Ideas from IT are no exception. One of the ideas which is being revisited by many researchers in this new era, is Information Bottleneck (IB); a formulation of information extraction based on IT. The IB is promising in both analyzing and improving DNNs. The goal of this survey is to review the IB concept and demonstrate its applications in deep learning. The information theoretic nature of IB, makes it also a good candidate in showing the more general concept of how IT can be used in ML. Two important concepts are highlighted in this narrative on the subject, i) the concise and universal view that IT provides on seemingly unrelated methods of ML, demonstrated by explaining how IB relates to minimal sufficient statistics, stochastic gradient descent, and variational auto-encoders, and ii) the common technical mistakes and problems caused by applying ideas from IT, which is discussed by a careful study of some recent methods suffering from them.