Application of Meta Learning in Face Recognition

Introduction: The paper explores the trends in the field of artificial intelligence, especially analyzing the improvements in deep learning and machine learning that are revolutionizing the computer science discipline. The study gives details of the current trends and new approaches, possible uses, and other possible advancements. The author discusses a literature review incorporating trends identified in the prior year to discuss what these trends mean for academia and industry as well as the future of AI in computer science. Artificial intelligence has developed quite fast to be one of the key factors in the progress of computer science. Out of all the subfields of AI, DL and ML are receiving substantial attention from academicians as well as industrialists owing to the game-changing effects that these two fields have brought in data processing, decision-making, and automation zones. These technologies have not only helped transform conventional solution-seeking procedures in computer science but have also facilitated various new courses for research. Methodology: This work employs the literature review and analysis from 2015 to 2024 approach for the analysis of current advancements and emerging trends in deep learning and machine learning in computer science. The analysis of the research papers published in recent years, the industry reports, and case studies in the paper presents the views seeking to understand the state of AI in computer science. The examination is built based on categories, which are algorithmic improvement, real-world uses, and artificial intelligence implementation across industries. This approach means a systematic assessment of how deep learning and machine learning affect the field for better. Conclusion: An analysis of the trends in inventiveness of AI shows that the future of AI, especially in areas such as deep learning as well as machine learning, indicates increased inventiveness at a very impressive pace. It is one thing to use these technologies to progress computer sciences to the limits of what is possible today. It is clearly seen that the future of AI heavily relies on the future advancement of deep learning and machine learning, where it is anticipated to increase in its importance within artificial intelligence. The findings of this paper affirm the necessity to advance the studies of the specific areas of artificial intelligence proposed and call for interdisciplinarity to harness the potential of artificial intelligence for computer science.

This book is a platform for anyone who wishes to explore Artificial Intelligence in the field of agriculture from scratch or broaden their understanding and its uses. This book offers a practical, hands-on exploration of Artificial Intelligence, machine learning, deep Learning, computer vision and Expert system with proper examples to understand. This book also covers the basics of python with example so that any anyone can easily understand and utilize artificial intelligence in agriculture field. This book is divided into two parts wherein first part talks about the artificial intelligence and its impact in the agriculture with all its branches and their basics. The second part of the book is purely implementation of algorithms and use of different libraries of machine learning, deep learning and computer vision to build useful and sightful projects in real time which can be very useful for you to have better understanding of artificial intelligence. After reading this book, the reader will an understanding of what Artificial Intelligence is, where it is applicable, and what are its different branches, which can be useful in different scenarios. The reader will be familiar with the standard workflow for approaching and solving machine-learning problems, and how to address commonly encountered issues. The reader will be able to use Artificial Intelligence to tackle real-world problems ranging from crop health prediction to field surveillance analytics, classification to recognition of species of plants etc.

Deep Learning, a buzz in the artificial intelligence field, is the subset of machine learning. It teaches computers to learn from examples in order to perform a task that is intuitive to humans. It is also known as a deep neural network or deep neural learning. In deep learning, neural networks have a significant role. These are a set of algorithms that we implement to identify relevant relationships in datasets, and they follow the process that imitates the human brain. Neural networks depict the behavior of the human brain and enable computer algorithms to identify trends. It also solves complex problems in the domain of machine learning, AI, and data science.Deep learning deploys artificial neural networks to recognize the hidden patterns of data in the dataset provided. These algorithms are trained over an adequate amount of time and applied to a data set.

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.

One of the popularly explored topics for the past few decades in the field of artificial intelligence is face recognition. RGB-D picture-based face recognition has attracted numerous researcher’s interest as it has more benefits compared with two-dimensional face recognitions. This review paper gives a brief review of RGB-D face recognition researches. First, this paper reviewed some of the RGB-D sensors available in the market and its specifications. After that, description and scope of face databases which are utilized to test the accuracy of these face recognition techniques are explored. Finally, this paper presents a summary of traditional techniques and deep learning techniques on RGB-D face recognitions.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

With the rapid development of the information age, the application of artificial intelligence has gradually expanded to various fields, including image and video identification, speech recognition, fingerprint recognition, face recognition, UAV, robot, weather forecast, etc. Deep learning is a very active direction in the field of artificial intelligence. Recently, the state has introduced policies on promoting the classification of domestic waste, and vigorously advocates the classification of waste, while the traditional disposal method wastes manpower, material resources and time. Therefore, this paper mainly introduces the development of deep learning and the application of Python in deep learning, as well as the study of intelligent garbage classification algorithm based on Python deep learning.

The rapid development in the field of artificial intelligence is bringing advances in many research fields including the renewable electricity‐driven CO 2 and CO reduction, which features as a promising approach to mitigate carbon emission and electrically synthesize value‐added chemicals. This review focuses on the recent progresses on artificial intelligence and machine learning in the field of CO (2) reduction based on the three aspects: the catalyst screening and design, reaction mechanism investigation, and the knowledge graph construction. For the catalyst development, this work discusses the acceleration of catalyst optimization and the generative artificial intelligence frameworks based on machine learning model predictions, which represent an emerging paradigm for designing efficient catalysts. For the investigation of reaction mechanisms, the combination of machine learning and in situ spectroscopies can present detailed information on the structure evolution and the electric field effects, enabling to bridge experiments and theories. For constructing the knowledge graph in the CO (2) electroreduction, the natural language processing technologies are demonstrating strong capabilities. Finally, the current challenges and potential perspectives on the interdisciplinary fields of CO (2) RR and artificial intelligence are proposed to provide inspiration for the continuous developments of this field.

The cross-fusion research of artificial intelligence technology and spinal surgery represented by machine learning and neural network model is a new research direction and hot issue in the field of artificial intelligence in recent years. The anatomy and disease symptoms of the spine are complex, and the diagnosis and treatment of spinal surgery require rich clinical experience. However, the distribution of medical resources in China is seriously uneven. How to improve the ability of primary medical services so that the most extensive patient groups can benefitis still an urgent problem to be solved. Artificial intelligence is a technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending and expanding human intelligence. With the advent of the era of big data medical technology, artificial intelligence technology may solve this problem by transforming experts sinking into tech sinking . At present, technologies such as confrontation learning, weakly supervised learning, intensive learning and graph neural networks have become research hotspots in the field of artificial intelligence, and have also played an important role in many fields of clinical medicine. Based on the advantages of deep learning and neural network in disease learning, many spine surgeons combine it with the diagnosis and treatment of cervical spondylosis, low back pain, lumbar degenerative diseases, spinal deformity, spinal tumors, and other spine-related diseases. The rapid location and accurate diagnosis of the disease not only makes it an effective tool for the comprehensive diagnosis of spinal diseases but also provides the basis for the most reasonable treatment options for spinal diseases. In the domestic application of artificial intelligence in the diagnosis and treatment of spinal surgery, it can also solve the problems of difficult diagnosis and complicated treatment of spinal diseases faced by primary doctors, reduce the rate of misdiagnosis and missed diagnosis, and effectively reduce the economic and social burden of spinal diseases. This paper reviews the research progress of artificial intelligence represented by deep learning in the field of diagnosis and treatment of spinal surgery at home and abroad, and the advantages and application prospects of artificial intelligence in the diagnosis and treatment of spinal surgery.

The problems of plant leaf disease are rather serious in the world agricultural industry, leading to a significant decrease in crop quantity and quality, consequently, resulting in a huge loss in the economy and food insecurity. Detection and successful classification of plant diseases at the initial stage is essential to further agricultural output and the quality production of food. The recent improvements in the field of artificial intelligence (AI), specifically, machine learning (ML) and deep learning (DL), have shown significant prospects in automating and enhancing methods of diagnosing plant leaf diseases by using a wide variety of ML and DL algorithms. This review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases. They are such conventional classifiers as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with some advanced DL architectures, such as Convolutional Neural Networks (CNN), VGG16, ResNet50, InceptionResNetV2, EfficientNet, and various hybrids. The review analysis takes into consideration the methodologies applied, performance metrics, and insights in practice, as well as the strengths and weaknesses of both. The most crucial findings of the review show that deep learning models, and CNNs in particular, tend to be more accurate, robust, and feature extractors than traditional models of MLs. The performance of classification is also enhanced by numerous hybrid models that will use ML together with DL, and transfer learning has been an effective method to enhance the generalization using small datasets. Nevertheless, with all this progress, the issues of diversity of datasets, computational resource requirements and model interpretability are still to be explored in the future.

As a matter of fact, artificial intelligence (AI) technology has developed by leaps and bounds with the rapid development computation ability and improvements of machine learning scenarios (e.g., deep learning neural network). On this basis, its applications have appeared in various fields in recent years. In order to support complex artificial intelligence computing, AI chips have gradually appeared in people's vision, as well as have played an essential role in the future development of life. With this in mind, based on the development of AI chips in recent years, this paper discusses the definition and classification, principles and design, preparation, and application of AI chips. According to the analysis, the current limitations in terms of the state-of-art facilities will be demonstrated. At the same time, this study predicts AI chips’ future development prospects, so that readers can have a more comprehensive understanding of AI chips and the field of artificial intelligence.

IntroductionArtificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied in a real-world setting and multiple studies have been conducted in the area. The aim of this analysis is to identify the most influential publications on the topic of artificial intelligence in breast imaging.MethodsA retrospective bibliometric analysis was conducted on artificial intelligence in breast radiology using the Web of Science database. The search strategy involved searching for the keywords ‘breast radiology’ or ‘breast imaging’ and the various keywords associated with AI such as ‘deep learning’, ‘machine learning,’ and ‘neural networks’.ResultsFrom the top 100 list, the number of citations per article ranged from 30 to 346 (average 85). The highest cited article titled ‘Artificial Neural Networks In Mammography—Application To Decision-Making In The Diagnosis Of Breast-Cancer’ was published in Radiology in 1993. Eighty-three of the articles were published in the last 10 years. The journal with the greatest number of articles was Radiology (n = 22). The most common country of origin was the United States (n = 51). Commonly occurring topics published were the use of deep learning models for breast cancer detection in mammography or ultrasound, radiomics in breast cancer, and the use of AI for breast cancer risk prediction.ConclusionThis study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.Clinical relevance statementThis article provides a concise summary of the top 100 most-cited articles in the field of artificial intelligence in breast radiology. It discusses the most impactful articles and explores the recent trends and topics of research in the field.Key PointsMultiple studies have been conducted on AI in breast radiology.The most-cited article was published in the journal Radiology in 1993.This study highlights influential articles and topics on AI in breast radiology.Graphical

Lithium-ion (Li-ion) batteries have gained considerable attention in the Electric Vehicle (EV) industry due to their high energy density, better lifespan, and higher nominal voltage.However, accurately estimating the State of Charge (SOC) and State of Health (SOH) for Li-ion batteries remains challenging due to its aging and nonlinear behaviour.This paper explores Battery Management System (BMS) models potential incorporating Artificial Intelligence (AI) estimation techniques, particularly Deep Learning (DL), to improve SOC and SOH model estimations.This research paper summarized and analyzed current BMS approaches by identify the potential gaps in existing research focus and propose another technique for further exploration in the EV Li-ion battery.Currently, there is a research gap in the existing studies, especially in the application of DL for SOC and SOH estimation.and underscores the need for more comprehensive exploration and refinement of DL methods.Future research should address these gaps to advance the integration of DL into BMS to ensure robust and reliable SOC and SOH estimations.Because of its features and capacity to improve SOC and SOH estimating health models accurately, deep learning has a lot of potential for studying SOC & SOH in BMS.As a result, there is opportunity to investigate the DL technique further in order to thoroughly and clearly examine the correctness of SOC & SOH model estimations in BMS.Index Terms-Battery management system (BMS), lithium-ion, artificial intelligence, state of charge (SOC), state of health (SOH) I. INTRODUCTIONDue to rising concerns about environmental pollution and global warming, modern society has made sustainability one of its top priorities.To ease these worries and move forward to a sustainable future, global energy strategies emphasise the switch from fossil fuels to renewable energy (RE) sources.The ability of RE to be efficiently used to manage energy consumption and cut carbon emissions makes it essential in the commercial, industrial, and residential sectors [1].Major contributors to carbon emissions include electricity and heat production, transport, manufacturing and construction, as well as agriculture [2].The transportation sector is among the leading causes of environmental pollution, contributing over one-third carbon dioxide emissions, of which vehicle transportation accounts for over 70% [3].Technological disruptions in the transportation sector facilitate decarbonisation because of rising environmental pollution and concerns about global warming.This transformation includes a move toward adopting electric vehicles (EVs), which have several benefits, such as shorter payback period and longer lifespan, helping to reduce CO2 emissions and change the way transportation will be in the future [4].With the promise of lower emissions and less reliance on oil, EVs have drawn much interest as a leading the way for environmental sustainability and emissions-free mobility [4], [5].Artificial intelligence (AI) is the most fascinating and discussed technology in the current decade for its nature to mimic human intelligence.The field of AI has shown an upward trend of growth in the 21 st century (from 2000 to 2015) [6].Artificial Intelligence (AI) is the study of creating machines that can perceive, analyze, comprehend, and react like humans.Simply said, the ultimate goal of artificial intelligence (AI) is to extend and argue humanity's capacity and efficiency in the work of changing nature and regulating society through intelligent machines.Simultaneously, artificial intelligence technologies that enhance the system's overall capabilities have significant importance.The legal standing of AI technology is examined in this study.In the current societal development stage, AI is rapidly developing its capabilities as a future technology.This research study aims to provide an overview of AI and explain how it relates to the concepts of SOC and SOH.A difficulty at the junction of AI is data integration.AI finds extensive application in the automotive, logistics, healthcare, stock trading, robotics, finance, transportation, and educational domains.AI techniques provide promise for the vehicle, the infrastructure, the driver, or the transport user-and especially for the interactions among them.Transportation is changing as a result of the EV industry's adoption of AI, particularly deep learning (DL) and machine learning (ML), which enhances user experience, efficiency, sustainability, and safety.

With the rapid increase of interest in independent living along with the continual growth of the global aging population, there is a significant need for developing technological solutions that are trustworthy, functional, and scalable. Towards this vision, we developed a novel framework that provides an organic, evolving, and immersive assistive interaction between human, robot, and their environment. Our contribution is in the system design and the computing aspect of the proposed solution. At the system level, we are utilizing the concept of the Internet of Robotic Things (IoRT) to integrate smart personal devices and smart space automation technologies with social companion robot(s) and mixed/augmented reality smart glasses. At the computing level, we propose utilizing advancements in the field of Artificial Intelligence (AI) and Machine Learning (ML) towards providing task assistance in daily activities, smart space control, and improving emotion and mood through Affective Computing. We provide details of our IoRT system integration and quantitative experimental results on Facial Emotion Recognition (FER) which is one of the core components for Affective Computing. We improved FER utilizing Deep Learning on the edge and then deployed it on our Social Companion Robot (SCR) called Miro. We integrated the SCR with a Mixed Reality smart glass (Microsoft Hololens 2) and smart home automaton hub (Google). While the smart automation controller is directly in charge of controlling the environment (e.g., lighting, temperature), the robot can increase the control level with a more context-aware decision providing a socially embodied control.KeywordsHuman-robot-interactionInternet of Robotic Things (IoRT)Social companion robotsSmart home automationSocial embodimentAffective computingMixed/augmented realityIndependent living

Recently, automatic face recognition method has become one of the key issues in the field of pattern recognition and artificial intelligence. Typically, the face recognition process can be divided into three parts: the detection and recognition of human face, facial feature extraction and face recognition, and among which the facial feature extraction is the key to face recognition technology. In this paper, an extraction algorithm of face recognition feature, which is based on face recognition feature, is proposed. The experimental results based on the ORL face database demonstrate that this algorithm works well.