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Data-Handling in Biomedical Science
by Peter WhitePacked with worked examples and problems, this book will help the reader improve their confidence and skill in data-handling. The mathematical methods needed for problem-solving are described in the first part of the book, with chapters covering topics such as indices, graphs and logarithms. The following eight chapters explore data-handling in different areas of microbiology and biochemistry including microbial growth, enzymes and radioactivity. Each chapter is fully illustrated with worked examples that provide a step-by-step guide to the solution of the most common problems. Over 30 exercises, ranging in difficulty and length, allow you to practise your skills and are accompanied by a full set of hints and solutions.
Data for the People: How to Make Our Post-Privacy Economy Work for You
by Andreas WeigendA long-time chief data scientist at Amazon shows how open data can make everyone, not just corporations, richerEvery time we Google something, Facebook someone, Uber somewhere, or even just turn on a light, we create data that businesses collect and use to make decisions about us. In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us.In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it.Big Data is here to stay. Now is the time to find out how we can be empowered by it.
Data Driven Strategies: Theory and Applications
by Wang Jianhong Ricardo A. Ramirez-Mendoza Ruben Morales-MenendezA key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.
Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning (Green Energy and Technology)
by Saleh Seyedzadeh Farzad Pour RahimianThis book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
Data-Driven Modeling for Sustainable Engineering: Proceedings of the First International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), Accra, 2017 (Lecture Notes in Networks and Systems #72)
by Kondo H. Adjallah Babiga Birregah Henry Fonbeyin AbandaThis book gathers the proceedings of the 1st International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), a four-day event (18th–21st April 2017) held in Accra, Ghana. It focuses on research work promoting a better understanding of engineering problems through applied sciences and modeling, and on solutions generated in an African setting but with relevance to the world as a whole. The book provides a holistic overview of challenges facing Africa, and addresses various areas from research and development perspectives. Presenting contributions by scientists, engineers and experts hailing from a host of international institutions, the book offers original approaches and technological solutions to help solve real-world problems through research and knowledge sharing. Further, it explores promising opportunities for collaborative research on issues of scientific, economic and social development, making it of interest to researchers, scientists and practitioners looking to conduct research in disciplines such as water supply, control, civil engineering, statistical modeling, renewable energy and sustainable urban development.
Data-driven Modeling for Diabetes: Diagnosis and Treatment (Lecture Notes in Bioengineering)
by Vasilis Marmarelis Georgios MitsisThis contributed volume presents computational models of diabetes that quantify the dynamic interrelationships among key physiological variables implicated in the underlying physiology under a variety of metabolic and behavioral conditions. These variables comprise for example blood glucose concentration and various hormones such as insulin, glucagon, epinephrine, norepinephrine as well as cortisol. The presented models provide a powerful diagnostic tool but may also enable treatment via long-term glucose regulation in diabetics through closed-look model-reference control using frequent insulin infusions, which are administered by implanted programmable micro-pumps. This research volume aims at presenting state-of-the-art research on this subject and demonstrating the potential applications of modeling to the diagnosis and treatment of diabetes. The target audience primarily comprises research and experts in the field but the book may also be beneficial for graduate students.
Data Driven Guide to the Analysis of X-ray Photoelectron Spectra using RxpsG
by Giorgio SperanzaThis book provides a theoretical background to X-ray photoelectron spectroscopy (XPS) and a practical guide to the analysis of the XPS spectra using the RxpsG software, a powerful tool for XPS analysis. Although there are several publications and books illustrating the theory behind XPS and the origin of the spectral feature, this book provides an additional practical introduction to the use of RxpsG. It illustrates how to use the RxpsG software to perform specific key operations, with figures and examples which readers can reproduce themselves. The book contains a list of theoretical sections explaining the appearance of the various spectral features (core‑lines, Auger components, valence bands, loss features, etc.). They are accompanied by practical steps, so readers can learn how to analyze specific spectral features using the various functions of the RxpsG software. This book is a useful guide for researchers in physics, chemistry, and material science who are looking to begin using XPS, in addition to experienced researchers who want to learn how to use RxpsG. In the digital format, the spectral data and step-by-step indications are provided to reproduce the examples given in the textbook. RxpsG is a free software for the spectral analysis. Readers can find the installation information and download the package from https://github.com/GSperanza/ website. RxpsG was developed mainly by Giorgio Speranza with the help of his colleague dr. Roberto Canteri working at Fondazione Bruno Kessler. Key Features: Simplifies the use of RxpsG, how it works, and its applications. Demonstrates RxpsG using a reproduction of the graphical interface of RxpsG, showing the steps needed to perform a specific task and the effect on the XPS spectra. Accessible to readers without any prior experience using the RxpsG software. Giorgio Speranza is Senior Researcher at Fondazione Bruno Kessler – Trento Italy, Associate Member of the Italian National Council of Research, and Associate Member of the Department of Industrial Engineering at the University of Trento, Italy.
Data Driven Energy Centered Maintenance (Energy Management)
by Marvin T. Howell Fadi AlshakhshirOver recent years, many new technologies have been introduced to drive the digital transformation in the building maintenance industry. The current trend in digital evolution involves data-driven decision making which opens new opportunities for an energy centered maintenance model. Artificial Intelligence and Machine Learning are helping the maintenance team to get to the next level of maintenance intelligence to provide real-time early warning of abnormal equipment performance. This edition follows the same methodology as the First. It provides detailed descriptions of the latest technologies associated with Artificial Intelligence and Machine Learning which enable data-driven decision-making processes about the equipment’s operation and maintenance. Technical topics discussed in the book include: Different Maintenance Types and The Need for Energy Centered Maintenance The Centered Maintenance Model Energy Centered Maintenance Process Measures of Equipment and Maintenance Efficiency and Effectiveness Data-Driven Energy Centered Maintenance Model: Digitally Enabled Energy Centered Maintenance Tasks Artificial Intelligence and Machine Learning in Energy Centered Maintenance Model Capabilities and Analytics Rules Building Management System Schematics The book contains a detailed description of the digital transformation process of most of the maintenance inspection tasks as they move away from being manually triggered. The book is aimed at building operators as well as those building automation companies who are working continuously to digitalize building operation and maintenance procedures. The benefits are reductions in the equipment failure rate, improvements in equipment reliability, increases in equipment efficiency and extended equipment lifespan.
Data-Driven Decision Making in Entrepreneurship: Tools for Maximizing Human Capital
by Nikki Blacksmith Maureen E. McCuskerSince the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital.
Data Driven Approaches on Medical Imaging
by Bin Zheng Stefan Andrei Md Kamruzzaman Sarker Kishor Datta GuptaThis book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical domain. Moreover, the key challenges which affect the design, development, and performance of medical imaging systems are addressed. In addition, the state-of-the-art medical imaging methodologies for efficient, interpretable, explainable, and practical implementation of computer imaging techniques are discussed. At present, there are no textbook resources that address the medical imaging technologies. There are ongoing and novel research outcomes which would be useful for the development of novel medical imaging technologies/processes/equipment which can improve the current state of the art.The book particularly focuses on the use of data driven new technologies on medical imaging vision such as Active learning, Online learning, few shot learning, AutoML, segmentation etc.
Data-Driven Analytics for the Geological Storage of CO2
by Shahab MohagheghData-driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of artificial intelligence and machine learning in data-driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.
Data-driven Analytics for Sustainable Buildings and Cities: From Theory to Application (Sustainable Development Goals Series)
by Xingxing ZhangThis book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.
Data-centric Living: Algorithms, Digitization and Regulation
by Sridhar V.This book explores how data about our everyday online behaviour are collected and how they are processed in various ways by algorithms powered by Artificial Intelligence (AI) and Machine Learning (ML). The book investigates the socioeconomic effects of these technologies, and the evolving regulatory landscape that is aiming to nurture the positive effects of these technology evolutions while at the same time curbing possible negative practices. The volume scrutinizes growing concerns on how algorithmic decisions can sometimes be biased and discriminative; how autonomous systems can possibly disrupt and impact the labour markets, resulting in job losses in several traditional sectors while creating unprecedented opportunities in others; the rapid evolution of social media that can be addictive at times resulting in associated mental health issues; and the way digital Identities are evolving around the world and their impact on provisioning of government services. The book also provides an in-depth understanding of regulations around the world to protect privacy of data subjects in the online world; a glimpse of how data is used as a digital public good in combating Covid pandemic; and how ethical standards in autonomous systems are evolving in the digital world. A timely intervention in this fast-evolving field, this book will be useful for scholars and researchers of digital humanities, business and management, internet studies, data sciences, political studies, urban sociology, law, media and cultural studies, sociology, cultural anthropology, and science and technology studies. It will also be of immense interest to the general readers seeking insights on daily digital lives.
Data-Centric Biology: A Philosophical Study
by Sabina LeonelliIn recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life--including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities--as well as her own original empirical material--to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices.
Data Cartels: The Companies That Control and Monopolize Our Information
by Sarah LamdanIn our digital world, data is power. Information hoarding businesses reign supreme, using intimidation, aggression, and force to maintain influence and control. Sarah Lamdan brings us into the unregulated underworld of these "data cartels", demonstrating how the entities mining, commodifying, and selling our data and informational resources perpetuate social inequalities and threaten the democratic sharing of knowledge. Just a few companies dominate most of our critical informational resources. Often self-identifying as "data analytics" or "business solutions" operations, they supply the digital lifeblood that flows through the circulatory system of the internet. With their control over data, they can prevent the free flow of information, masterfully exploiting outdated information and privacy laws and curating online information in a way that amplifies digital racism and targets marginalized communities. They can also distribute private information to predatory entities. Alarmingly, everything they're doing is perfectly legal. In this book, Lamdan contends that privatization and tech exceptionalism have prevented us from creating effective legal regulation. This in turn has allowed oversized information oligopolies to coalesce. In addition to specific legal and market-based solutions, Lamdan calls for treating information like a public good and creating digital infrastructure that supports our democratic ideals.
The Data Book of Astronomy
by Patrick MooreFilled with data about the Earth, Moon, the planets, the stars, our Galaxy, and the myriad galaxies in deep space, this invaluable resource reveals the latest scientific discoveries about black holes, quasars, and the origins of the Universe. It includes maps supported by detailed tables of the names, positions, magnitudes, and spectra of the main stars in each constellation along with key data on galaxies, nebulae, and clusters. MNASSA wrote, "This book fills a niche � with detailed astronomical data and concise explanations, all at an accessible level � it is an excellent resource, and probably will be the first book I shall reach for.
Data Baby: My Life in a Psychological Experiment
by Susannah BreslinA Belletrist Book Pick for December 2023Lab Girl meets Brain on Fire in this provocative and poignant memoir delving into a woman's formative experiences as a veritable "lab rat" in a lifelong psychological study, and her pursuit to reclaim autonomy and her identity as a adult. What if your parents turn you into a human lab rat when you&’re a child? Will that change the story of your life? Will that change who you are? When Susannah Breslin is a toddler, her parents enroll her in an exclusive laboratory preschool at the University of California, Berkeley, where she becomes one of over a hundred children who are research subjects in an unprecedented thirty-year study of personality development that predicts who she and her cohort will grow up to be. Decades later, trapped in what she feels is an abusive marriage and battling breast cancer, she starts to wonder how growing up under a microscope shaped her identity and life choices. Already a successful journalist, she makes her own curious history the subject of her next investigation. From experiment rooms with one-way mirrors, to children&’s puzzles with no solutions, to condemned basement laboratories, her life-changing journey uncovers the long-buried secrets hidden behind the renowned study. The question at the gnarled heart of her quest: Did the study know her better than she knew herself? At once bravely honest and sharply witty, Data Baby is a compelling and provocative account of a woman&’s quest to find her true self, and an unblinking exploration of why we turn out as we do. Few people in all of history have been studied from such a young age and for as long as this author, but the message of her book is universal. In an era when so many of us are looking to technology to tell us who to be, it&’s up to us to discover who we actually are.
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV)
by Seon Ki Park Liang XuThis book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including adaptive observations, sensitivity analysis, parameter estimation and AI applications. The book is useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Data and Applications Security and Privacy XXXII: 32nd Annual IFIP WG 11.3 Conference, DBSec 2018, Bergamo, Italy, July 16–18, 2018, Proceedings (Lecture Notes in Computer Science #10980)
by Florian Kerschbaum Stefano ParaboschiThis book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on administration, access control policies, privacy-preserving access and computation, integrity and user interaction, security analysis and private evaluation, fixing vulnerabilities, and networked systems.
Data Analytics for Supply Chain Networks (Greening of Industry Networks Studies #11)
by Niamat Ullah Ibne HossainThe objective of the book is to adopt the application of data analytics to enhance the sustainability and resilience of the green supply chain networks. To demonstrate the applicability and usefulness of the method, the book adopts different data analytic models and approaches against the backdrop of case studies. In summary, this book attempts to address the question of methods, tools, and techniques that can be used to create resilient, anti-fragile, reliable, and invulnerable green supply chain networks.
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society: 6th ECML PKDD Workshop, DARE 2018, Dublin, Ireland, September 10, 2018, Revised Selected Papers (Lecture Notes in Computer Science #11325)
by Wei Lee Woon Zeyar Aung Alejandro Catalina Feliú Stuart MadnickThis book constitutes the revised selected papers from the 6th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2018, held in Dublin, Ireland, in September 2018. The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others.
Data Analytics for Protein Crystallization
by Marc L. Pusey Ramazan Savaş AygünThis unique text/reference presents an overview of the computational aspects of protein crystallization, describing how to build robotic high-throughput and crystallization analysis systems. The coverage encompasses the complete data analysis cycle, including the set-up of screens by analyzing prior crystallization trials, the classification of crystallization trial images by effective feature extraction, the analysis of crystal growth in time series images, the segmentation of crystal regions in images, the application of focal stacking methods for crystallization images, and the visualization of trials.Topics and features: describes the fundamentals of protein crystallization, and the scoring and categorization of crystallization image trials; introduces a selection of computational methods for protein crystallization screening, and the hardware and software architecture for a basic high-throughput system; presents an overview of the image features used in protein crystallization classification, and a spatio-temporal analysis of protein crystal growth; examines focal stacking techniques to avoid blurred crystallization images, and different thresholding methods for binarization or segmentation; discusses visualization methods and software for protein crystallization analysis, and reviews alternative methods to X-ray diffraction for obtaining structural information; provides an overview of the current challenges and potential future trends in protein crystallization.This interdisciplinary work serves as an essential reference on the computational and data analytics components of protein crystallization for the structural biology community, in addition to computer scientists wishing to enter the field of protein crystallization.
Data Analytics for Process Engineers: Prediction, Control and Optimization (Synthesis Lectures on Mechanical Engineering)
by Daniela Galatro Stephen DaweThis book provides an industry-oriented data analytics approach for process engineers, including data acquisition methods and sources, exploratory data analysis and sensitivity analysis, data-based modelling for prediction, data-based modelling for monitoring and control, and data-based optimization of processes. While many of the current data analytics books target business-related problems, the rationale for this book is a specific need to understand and select applicable data analytics approaches pragmatically to analyze process engineering–related problems; this tailored solution for engineers gets amalgamated with governing equations, and in several cases, with the physical understanding of the phenomenon being analyzed. We also consider this book strategically conceived to help map Education 4.0 with Industry 4.0 since it can support undergraduate and graduate students to gain valuable and applicable data analytics stills that can be further used in their workplace. Moreover, it can be used as a reference book for professionals, a quick reference to data analytics tools that can facilitate and/or optimize their process engineering tasks.
Data Analytics for Drilling Engineering: Theory, Algorithms, Experiments, Software (Information Fusion and Data Science)
by Qilong XueThis book presents the signal processing and data mining challenges encountered in drilling engineering, and describes the methods used to overcome them. In drilling engineering, many signal processing technologies are required to solve practical problems, such as downhole information transmission, spatial attitude of drillstring, drillstring dynamics, seismic activity while drilling, among others. This title attempts to bridge the gap between the signal processing and data mining and oil and gas drilling engineering communities. There is an urgent need to summarize signal processing and data mining issues in drilling engineering so that practitioners in these fields can understand each other in order to enhance oil and gas drilling functions. In summary, this book shows the importance of signal processing and data mining to researchers and professional drilling engineers and open up a new area of application for signal processing and data mining scientists.
Data Analytics and Learning: Proceedings of Dal 2018 (Lecture Notes in Networks and Systems #43)
by P. Nagabhushan D. S. Guru B. H. Shekar Y. H. Sharath KumarThis paper describes a method to localize and recognize seven-segment displays on digital energy meters. Color edge detection is first performed on a camera-captured image of the device which is then followed by a run-length technique to detect horizontal and vertical lines. The region of interest circumscribing the LCD panel is determined based on the attributes of intersecting horizontal and vertical lines. The extracted display region is preprocessed using the morphological black-hat operation to enhance the text strokes. Adaptive thresholding is then performed and the digits are segmented based on stroke features. Finally, the segmented digits are recognized using a support vector machine classifier trained on a set of syntactic rules defined for the seven-segment font. The proposed method can handle images exhibiting uneven illumination, the presence of shadows, poor contrast, and blur, and yields a recognition accuracy of 97% on a dataset of 175 images of digital energy meters captured using a mobile camera.