04272nam 2200517 450 99641828200331620210218003008.03-030-59612-510.1007/978-3-030-59612-5(CKB)4100000011457733(DE-He213)978-3-030-59612-5(MiAaPQ)EBC6352846(PPN)25022075X(EXLCZ)99410000001145773320210218d2020 uy 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierBig Data - BigData 2020 9th International Conference, held as part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, proceedings /Surya Nepal [and five others]1st ed. 2020.Cham, Switzerland :Springer,[2020]©20201 online resource (XIV, 254 p. 130 illus., 91 illus. in color.) Information Systems and Applications, incl. Internet/Web, and HCI ;12402Includes index.3-030-59611-7 Research Track -- Entropy-based Approach to Efficient Cleaning of Big Data in Hierarchical Databases -- A Performance Prediction Model for Spark Applications -- Predicting the DJIA with news headlines and historic data using hybrid genetic algorithm/support vector regression and BERT -- Big Data Applications on Large-Scale Infrastructures -- Fake News Classification of Social Media through Sentiment Analysis -- Scalable reference genome assembly from compressed pan-genome index with Spark -- A Web Application for Feral Cat Recognition through Deep Learning -- MCF：Towards Window-based Multiple Cuckoo Filters in Stream Computing -- A Data-Driven Method for Dynamic OD Passenger Flow Matrix Estimation in Urban Metro Systems -- Ensemble learning for heterogeneous missing data imputation -- Validating Goal-Oriented Hypotheses of Business Problems Using Machine Learning: An Exploratory Study of Customer Churn -- The collaborative influence of multiple interactions on successive POI recommendation -- Application Track -- Chemical XAI to Discover Probable Compounds' Spaces based on Mixture of Multiple Mutated Exemplars and Bioassay Existence Ratio -- Clinical Trials Data Management in the Big Data Era -- Cross-Cancer Genome Analysis on Cancer Classification Using Both Unsupervised and Supervised Approaches -- Heavy Vehicle Classification through Deep Learning -- Short Paper Track -- Spatial Association Pattern Mining using In-Memory Computational Framework -- Dissecting Biological Functions for BRCA Genes and their Targeting MicroRNAs within Eight Clusters.This book constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions. .Information Systems and Applications, incl. Internet/Web, and HCI ;12402Society & social sciencesBig dataCongressesEducational equipment & technology, computer-aided learning (CAL)Society & social sciences.Big dataEducational equipment & technology, computer-aided learning (CAL)005.7Nepal SuryaMiAaPQMiAaPQMiAaPQBOOK996418282003316Big Data - BigData 20202047301UNISA05804nam 2200817 a 450 991095818190332120200520144314.097866134102769781283410274128341027397801238701310123870135(CKB)2550000000084147(EBL)858694(OCoLC)775872059(SSID)ssj0000599792(PQKBManifestationID)11382289(PQKBTitleCode)TC0000599792(PQKBWorkID)10598880(PQKB)10315781(CaSebORM)9780123869814(Au-PeEL)EBL858694(CaPaEBR)ebr10528203(CaONFJC)MIL341027(PPN)170604071(OCoLC)810072339(OCoLC)ocn810072339 (FR-PaCSA)88812230(MiAaPQ)EBC858694(FRCYB88812230)88812230(EXLCZ)99255000000008414720120124d2012 uy 0engur|n|---|||||txtccrProbability and random processes with applications to signal processing and communications /Scott L. Miller, Donald ChildersEd. 2.Waltham, Mass. Elsevier20121 online resource (625 p.)Description based upon print version of record9780128102459 0128102454 9780123869814 0123869811 Includes bibliographical references and index.Front Cover; Probability and Random Processes: With Applications to Signal Processingand Communications; Copyright; Contents; Preface; Chapter 1: Introduction; 1.1 A Speech Recognition System; 1.2 A Radar System; 1.3 A Communication Network; Chapter 2: Introduction to Probability Theory; 2.1 Experiments, Sample Spaces, and Events; 2.2 Axioms of Probability; 2.3 Assigning Probabilities; 2.4 Joint and Conditional Probabilities; 2.5 Basic Combinatorics; 2.6 Bayes's Theorem; 2.7 Independence; 2.8 Discrete Random Variables; 2.9 Engineering Application-An Optical Communication System; ExercisesSection 2.1: Experiments, Sample Spaces, and EventsSection 2.2: Axioms of Probability; Section 2.3: Assigning Probabilities; Section 2.4: Joint and Conditional Probabilities; Section 2.5: Basic Combinatorics; Section 2.6: Bayes's Theorem; Section 2.7: Independence; Section 2.8: Discrete Random Variables; Miscellaneous Problems; MATLAB Exercises; Chapter 3: Random Variables, Distributions,and Density Functions; 3.1 The Cumulative Distribution Function; 3.2 The Probability Density Function; 3.3 The Gaussian Random Variable; 3.4 Other Important Random Variables; 3.4.1 Uniform Random Variable3.4.2 Exponential Random Variable3.4.3 Laplace Random Variable; 3.4.4 Gamma Random Variable; 3.4.5 Erlang Random Variable; 3.4.6 Chi-Squared Random Variable; 3.4.7 Rayleigh Random Variable; 3.4.8 Rician Random Variable; 3.4.9 Cauchy Random Variable; 3.5 Conditional Distribution and Density Functions; 3.6 Engineering Application: Reliability and Failure Rates; Exercises; Section 3.1: The Cumulative Distribution Function; Section 3.2: The Probability Density Function; Section 3.3: The Gaussian Random Variable; Section 3.4: Other Important Random VariablesSection 3.5: Conditional Distribution and Density FunctionsSection 3.6: Reliability and Failure Rates; Miscellaneous Exercises; MATLAB Exercises; Chapter 4: Operations on a Single Random Variable; 4.1 Expected Value of a Random Variable; 4.2 Expected Values of Functions of Random Variables; 4.3 Moments; 4.4 Central Moments; 4.5 Conditional Expected Values; 4.6 Transformations of Random Variables; 4.6.1 Monotonically Increasing Functions; 4.6.2 Monotonically Decreasing Functions; 4.6.3 Nonmonotonic Functions; 4.7. Characteristic Functions; 4.8. Probability-Generating Functions4.9 Moment-Generating Functions4.10 Evaluating Tail Probabilities; 4.11 Engineering Application-Scalar Quantization; 4.12 Engineering Application-Entropy and Source Coding; Exercises; Section 4.1: Expected Values of a Random Variable; Section 4.2: Expected Values of Functions of a Random Variable; Section 4.3: Moments; Section 4.4: Central Moments; Section 4.5: Conditional Expected Values; Section 4.6: Transformations of Random Variables; Section 4.7: Characteristic Functions; Section 4.8: Probability-Generating Functions; Section 4.9: Moment-Generating FunctionsSection 4.10: Evaluating Tail ProbabilitiesMiller and Childers have focused on creating a clear presentation of foundational concepts with specific applications to signal processing and communications, clearly the two areas of most interest to students and instructors in this course. It is aimed at graduate students as well as practicing engineers, and includes unique chapters on narrowband random processes and simulation techniques. The appendices provide a refresher in such areas as linear algebra, set theory, random variables, and more. Probability and Random Processes also includes applications in digital communicatSignal processingMathematicsProbabilitiesStochastic processesSignal processingMathematics.Probabilities.Stochastic processes.621.382/20151621.38220151Miller Scott L223807Childers Donald G1797933MiAaPQMiAaPQMiAaPQBOOK9910958181903321Probability and random processes4340478UNINA