Principles of Big Graph: In-depth Insight PDF Download
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Author:
Publisher: Elsevier
ISBN: 0323898114
Category : Computers
Languages : en
Pages : 460
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Book Description
Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers
Author:
Publisher: Elsevier
ISBN: 0323898114
Category : Computers
Languages : en
Pages : 460
Get Book Here
Book Description
Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers
Author: Biplab Das
Publisher: Springer Nature
ISBN: 9811924120
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
This book discusses smart computing techniques which offer an effective solution for investigating and modeling the stochastic behavior of renewable energy generation, operation of grid-connected renewable energy systems, and smart decision-making among alternatives. It also discusses applications of soft computing techniques to make an intelligent decision for optimum use of suitable alternatives which gives an upper hand compared to conventional systems. It includes upgradation of the existing system by embedding of machine intelligence. The authors present combination of use of neutral networks, fuzzy systems, and genetic algorithms which are illustrated in several applications including forecasting, security, verification, diagnostics of a specific fault, efficiency optimization, etc. Smart energy systems integrate a holistic approach in diverse sectors including electricity, thermal comfort, power industry, transportation. It allows affordable and sustainable solutions to solve the future energy demands with suitable alternatives. Thus, contributions regarding integration of the machine intelligence with the energy system, for efficient collection and effective utilization of the available energy sources, are useful for further advanced studies.
Author: Biplab Das
Publisher: Springer Nature
ISBN: 9811908362
Category : Technology & Engineering
Languages : en
Pages : 661
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Book Description
This book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization (CoMSO 2021), organized by National Institute of Technology, Silchar, Assam, India, during December 16–18, 2021. The book covers topics of modeling, simulation and optimization, including computational modeling and simulation, system modeling and simulation, device/VLSI modeling and simulation, control theory and applications, modeling and simulation of energy systems and optimization. The book disseminates various models of diverse systems and includes solutions of emerging challenges of diverse scientific fields.
Author: Abdellah Idrissi
Publisher: Springer Nature
ISBN: 3031650387
Category :
Languages : en
Pages : 591
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Book Description
Author: Sheng-Lung Peng
Publisher: Springer Nature
ISBN: 3030335968
Category : Technology & Engineering
Languages : en
Pages : 636
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Book Description
This book discusses the evolution of future-generation technologies through the Internet of things, bringing together all the related technologies on a single platform to offer valuable insights for undergraduate and postgraduate students, researchers, academics and industry practitioners. The book uses data, network engineering and intelligent decision- support system-by-design principles to design a reliable IoT-enabled ecosystem and to implement cyber-physical pervasive infrastructure solutions. It takes readers on a journey that begins with understanding the insight paradigm of IoT-enabled technologies and how it can be applied. It walks readers through engaging with real-time challenges and building a safe infrastructure for IoT-based, future-generation technologies. The book helps researchers and practitioners to understand the design architecture through IoT and the state of the art in IoT countermeasures. It also highlights the differences between heterogeneous platforms in IoT-enabled infrastructure and traditional ad hoc or infrastructural networks, and provides a comprehensive discussion on functional frameworks for IoT, object identification, IoT domain model, RFID technology, wearable sensors, WBAN, IoT semantics, knowledge extraction, and security and privacy issues in IoT-based ecosystems. Written by leading international experts, it explores IoT-enabled insight paradigms, which are utilized for the future benefit of humans. It also includes references to numerous works. Divided into stand-alone chapters, this highly readable book is intended for specialists, researchers, graduate students, designers, experts, and engineers involved in research on healthcare-related issues.
Author: Ripon Patgiri
Publisher: Springer Nature
ISBN: 9811597359
Category : Technology & Engineering
Languages : en
Pages : 384
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Book Description
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.
Author: Barron's Educational Series
Publisher: Simon and Schuster
ISBN: 1506292003
Category : Study Aids
Languages : en
Pages : 445
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Book Description
Be prepared for exam day with Barron’s. Trusted content from AP experts! Barron’s AP Computer Science Principles Premium, 2025 includes in-depth content review and online practice. It’s the only book you’ll need to be prepared for exam day. Written by Experienced Educators Learn from Barron’s--all content is written and reviewed by AP experts Build your understanding with comprehensive review tailored to the most recent exam Get a leg up with tips, strategies, and study advice for exam day--it’s like having a trusted tutor by your side Be Confident on Exam Day Sharpen your test‑taking skills with 6 full‑length practice tests–3 in the book, including a diagnostic test to target your studying, and 3 more online–plus detailed answer explanations for all questions Strengthen your knowledge with in‑depth review covering all Big Ideas on the AP Computer Science Principles Exam Reinforce your learning with practice questions at the end of each chapter that cover all frequently tested topics Prepare for the AP Computer Science Principles Create Performance Task with 6 full sample Create Performance Tasks with complete written reports and requirements for scoring Robust Online Practice Continue your practice with 3 full‑length practice tests on Barron’s Online Learning Hub Simulate the exam experience with a timed test option Deepen your understanding with detailed answer explanations and expert advice Gain confidence with scoring to check your learning progress Going forward, this exam will only be offered in a digital format. Barron's AP online tests offer a digital experience with a timed test option to get you ready for test day. Visit the Barron's Learning Hub for more digital practice.
Author: Seth Reichelson
Publisher: Simon and Schuster
ISBN: 1506286313
Category : Reference
Languages : en
Pages : 505
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Book Description
Be prepared for exam day with Barron’s. Trusted content from AP experts! Barron’s AP Computer Science Principles Premium, 2023 includes in-depth content review and online practice. It’s the only book you’ll need to be prepared for exam day. Written by Experienced Educators Learn from Barron’s--all content is written and reviewed by AP experts Build your understanding with comprehensive review tailored to the most recent exam Get a leg up with tips, strategies, and study advice for exam day--it’s like having a trusted tutor by your side Be Confident on Exam Day Sharpen your test-taking skills with 6 full-length practice tests--3 in the book, including a diagnostic test to target your studying, and 3 more online Strengthen your knowledge with in-depth review covering all Units on the AP Computer Science Principles Exam Reinforce your learning with practice questions at the end of each chapter Interactive Online Practice Continue your practice with 3 full-length practice tests on Barron’s Online Learning Hub Simulate the exam experience with a timed test option Deepen your understanding with detailed answer explanations and expert advice Gain confidence with automated scoring to check your learning progress
Author: N. Gregory Mankiw
Publisher: Cengage Canada
ISBN: 1774740281
Category : Business & Economics
Languages : en
Pages : 46
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Book Description
Principles of Macroeconomics, Ninth Canadian Edition, breaks down concepts and emphasizes important themes for students. It is the most widely used economics textbook on the market, perfectly complementing instructor lessons. Students should expect to gain a solid understanding of economic theory through real-world applications. While it prepares students for advanced economics studies, it also speaks to people in other fields. Mankiw stresses big-picture ideas, ensuring learners are grounded in essential economic concepts and principles.
Author: Richard Brath
Publisher: John Wiley & Sons
ISBN: 1118845870
Category : Computers
Languages : en
Pages : 544
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Book Description
Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences – until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritative resource.
