rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Systems simulation and modeling for cloud computing and big data applications
  name:
    name part: Peter, J. Dinesh.
  name:
    name part: Fernandes, Steven L.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    place:
      place term: Place of publication not identified]
    publisher: Academic Press
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Inspired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced several benchmarks, including BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix, which are all covered in this book. Despite the substantial progress, the research community still needs a holistic, comprehensive big data SSM to use in almost every scientific and engineering discipline involving multidisciplinary research. SSM develops frameworks that are applicable across disciplines to develop benchmarking tools that are useful in solutions development.
  table of contents: Front Cover -- Systems Simulation and Modeling for Cloud Computing and Big Data Applications -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Differential color harmony: A robust approach for extracting harmonic color features and perceiving aesthetics ... -- 1.1. Introduction -- 1.2. Related works -- 1.3. Methodology -- 1.3.1. Extracting differential harmonic color features -- 1.3.2. Regression model design -- 1.3.3. Dataset and user study -- 1.4. Results and analysis -- 1.5. Discussion and future work -- Acknowledgments -- References -- Chapter 2: Physiological parameter measurement using wearable sensors and cloud computing -- 2.1. Patient fall monitoring system -- 2.1.1. Methodology -- 2.1.2. ARM LPC2148 microcontroller -- 2.1.3. ADXL 335 3-axis accelerometer -- 2.1.4. Global positioning system -- 2.1.5. Global system for mobile communications -- 2.1.6. Interfacing ADXL335 accelerometer sensor with LPC2148 microcontroller -- 2.1.7. Calibration -- 2.1.8. Interfacing GPS and GSM with LPC2148 -- 2.2. Monitoring of patient body temperature -- 2.2.1. Methodology -- 2.2.2. ESP8266 Wi-Fi module -- 2.2.3. ATmega328 microcontroller -- 2.2.4. LM393 thermistor module -- 2.2.5. Software module ThingSpeak -- 2.2.6. Experimental setup -- 2.2.7. Results and discussion -- 2.3. Vital parameter measurement using PPG signal -- 2.3.1. Significance of remote patient monitoring -- 2.3.2. Importance of PPG signal -- 2.3.3. Experimental setup -- 2.4. Oxygen saturation measurement using PPG signal -- 2.4.1. SpO2 calculation -- 2.5. Heart rate measurement using ECG signal -- 2.5.1. Monitoring of ECG signal over wireless transmission -- 2.5.2. Results and discussion -- 2.6. Conclusion -- References -- Further reading -- Chapter 3: Social media data analytics using feature engineering -- 3.1. Introduction -- 3.2. Social media data analytics.
  table of contents: 3.2.1. Social media data format -- 3.3. Feature representation -- 3.3.1. Textual feature extraction -- 3.3.1.1. Tokenization -- 3.3.1.2. Token preprocessing -- 3.3.1.3. Stemming and lemmatization -- 3.3.1.4. Vectorization -- Term frequency-inverse document frequency -- 3.3.2. Image feature extraction -- 3.3.2.1. Color histogram features -- 3.3.2.2. Histogram of oriented gradients features -- 3.3.2.3. SURF features -- 3.3.2.4. BRISK features -- 3.3.2.5. Deep neural net architectures -- 3.3.2.6. Autoencoders -- 3.3.3. Network relation representation -- 3.3.3.1. Adjacency matrix -- 3.3.3.2. Edge list -- 3.3.3.3. Adjacency list -- 3.4. Clustering methods -- 3.4.1. The k-means clustering algorithm -- 3.4.2. Fuzzy c-means clustering algorithm -- 3.4.3. Mean-shift clustering algorithm -- 3.4.4. Expectation-maximization algorithm -- 3.4.5. Hierarchical clustering algorithm -- 3.4.6. DBSCAN algorithm -- 3.4.7. OPTICS algorithm -- 3.4.8. Multiple-machine clustering techniques -- 3.5. Conclusion -- References -- Chapter 4: A novel framework for quality care in assisting chronically impaired patients with ubiquitous computing and am ... -- 4.1. Introduction -- 4.2. The theoretical perspective of ubiquitous computing -- 4.2.1. Wireless technologies -- 4.2.1.1. Radio frequency identification (RFID) -- 4.2.1.2. Bluetooth -- 4.2.1.3. ZigBee -- 4.2.1.4. Near field communication (NFC) -- 4.2.1.5. Sensors -- 4.2.2. Wearable technologies -- 4.2.3. Human activity recognition -- 4.2.4. Internet of Things -- 4.2.5. Cloud computing -- 4.2.6. Cognitive computing and big data analytics -- 4.2.7. Ambient intelligence -- 4.3. Research challenges -- 4.3.1. Context awareness -- 4.3.2. Security -- 4.3.3. Heterogeneity -- 4.3.4. Data management -- 4.3.5. Scalability -- 4.3.6. Reliability -- 4.4. Existing technologies -- 4.4.1. Telemedicine -- 4.4.2. Smart healthcare.
  table of contents: 4.4.3. PDA-based healthcare -- 4.4.4. Ubiquitous healthcare -- 4.5. Proposed framework -- 4.5.1. Tier 1: Ubiquitous devices -- 4.5.2. Tier 2: Edge computing -- 4.5.3. Tier 3: Fog computing -- 4.5.4. Tier 4: Cloud computing with big data and cognitive analytics -- 4.6. Conclusion -- References -- Chapter 5: Dynamic and static system modeling with simulation of an eco-friendly smart lighting system -- 5.1. Introduction -- 5.2. Related work -- 5.3. Modeling a smart street-lighting system -- 5.3.1. Factors influencing smart lighting -- 5.3.2. System modeling -- 5.3.2.1. Luminaire modeling -- 5.3.2.2. Zone modeling -- 5.3.2.3. Duration modeling -- 5.4. Static and dynamic CEFRAS -- 5.4.1. TOPSIS -- 5.4.2. Static-CEFRAS -- 5.4.3. Dynamic-CEFRAS -- 5.5. Simulation analysis and results -- 5.5.1. Active duration analysis -- 5.5.2. Power utilization analysis -- 5.5.3. Lifetime analysis -- 5.6. Conclusion -- References -- Chapter 6: Predictive analysis of diabetic women patients using R -- 6.1. Introduction -- 6.1.1. Type 1 diabetes -- 6.1.2. Type 2 diabetes [1] -- 6.1.3. Gestational diabetes -- 6.2. Literature survey -- 6.3. Proposed work -- 6.3.1. Attributes -- 6.3.2. Predictive models -- 6.3.2.1. Logistic regression model -- Implementing the logistic regression model -- 6.3.2.2. Decision tree -- Implementing the decision tree model -- 6.3.2.3. Random forest model -- Implementing random forest model -- 6.3.2.4. Support vector machine -- Implementing support vector model -- Comparing the four models -- 6.4. Experimental analysis -- 6.5. Conclusion and future enhancements -- Acknowledgment -- References -- Further reading -- Chapter 7: IoT-based smart mirror for health monitoring -- 7.1. Introduction -- 7.2. Literature review -- 7.3. Benchmarks -- 7.4. Proposed system -- 7.5. Components -- 7.5.1. One-way mirror -- 7.5.2. Display -- 7.5.3. Raspberry Pi.
  table of contents: 7.5.4. Microphones -- 7.5.5. Frame and support -- 7.5.6. Sensors -- 7.6. Modules -- 7.6.1. Mirror surface module -- 7.6.2. Sensing module -- 7.6.3. Storage module -- 7.6.4. Communication module -- 7.7. Conclusion -- References -- Chapter 8: Discovering human influenza virus using ensemble learning -- 8.1. Introduction -- 8.2. Literature survey -- 8.3. Proposed work -- 8.3.1. Data collection -- 8.3.2. Feature extraction -- 8.3.2.1. Element periodicity -- 8.3.2.2. Subsequence periodicity -- 8.3.2.3. Latent periodicity -- 8.3.2.4. Amino acid composition -- 8.3.2.5. Composition/transition/distribution -- 8.3.2.6. Lengthpep -- 8.3.2.7. Molecular weight -- 8.3.2.8. pI -- 8.3.2.9. Z-scales -- 8.3.3. Dataset -- 8.3.4. Ensemble learning classifiers -- 8.4. Experiments and results -- 8.4.1. Comparative analysis -- 8.5. Conclusion -- References -- Further reading -- Chapter 9: Mining and monitoring human activity patterns in smart environment-based healthcare systems -- 9.1. Introduction -- 9.2. Literature review -- 9.3. Impulse-based Markov model -- 9.4. Summary and conclusion -- References -- Chapter 10: Early detection of cognitive impairment of elders using wearable sensors -- 10.1. Introduction -- 10.2. Related work -- 10.3. Proposed method -- 10.3.1. Activity recognition (AR) module -- 10.3.2. Abnormal activity detection (AAD) module -- 10.4. Results and discussion -- 10.5. Conclusion -- References -- Index.
  note: edited by J. Dinesh Peter and Steven L. Fernandes.
  note: Includes index.
  note: Includes bibliographical references and index.
  subject:
    topic: Computer simulation
  subject:
    topic: Cloud computing
  subject:
    topic: Big data
  subject:
    topic: Digital computer simulation
  subject:
    topic: Simulation par ordinateur
  subject:
    topic: Infonuagique
  subject:
    topic: Données volumineuses
  subject:
    topic: simulation
  subject:
    topic: Digital computer simulation
  subject:
    topic: Big data
  subject:
    topic: Cloud computing
  subject:
    topic: Computer simulation
  classification: QA76.9.C65 S97 2020
  classification: 003.3
  related item:
    identifier: (OCoLC)1112425534
  related item:
    title info:
      title: Advances in ubiquitous sensing applications for healthcare
  identifier: 9780128197806
  identifier: 0128197803
  identifier: 9780128197790
  identifier: 012819779X
  identifier: 9780128197806 Ingram Content Group
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 200303
    record change date: 20260327164232.7
    record identifier: in00005766212
    language of cataloging:
      language term: eng