“…Auflage diejenigen Techniken ein stärkeres Gewicht bekommen, die in der Welt der Big Data genutzt werden…”

Tabla de Contenidos: “…10.7.8 The Genome Sequencer 454 FLX System -- 10.7.9 Illumina/Solexa Genome Analyzer -- 10.7.10 Transition Sequencing Techniques -- 10.7.11 Ion-Torrent's Semiconductor Sequencing -- 10.7.12 Helico's Genetic Analysis Platform -- 10.7.13 Third-Generation Sequencing Techniques -- 10.8 Conclusion -- Abbreviations -- Acknowledgement -- References -- 11 Bioinformatics in Cancer Detection -- 11.1 Introduction -- 11.2 The Era of Bioinformatics in Cancer -- 11.3 Aid in Cancer Research via NCI -- 11.4 Application of Big Data in Developing Precision Medicine -- 11.5 Historical Perspective and Development -- 11.6 Bioinformatics-Based Approaches in the Study of Cancer -- 11.6.1 SLAMS -- 11.6.2 Module Maps -- 11.6.3 COPA -- 11.7 Conclusion and Future Challenges -- References -- 12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression -- 12.1 Introduction -- 12.2 FSHR Gene -- 12.3 IL-10 Gene -- 12.4 IRS-1 Gene -- 12.5 PCR Primers Used -- 12.6 Statistical Analysis -- 12.7 Conclusion -- References -- 13 An Insight of Protein Structure Predictions Using Homology Modeling -- 13.1 Introduction -- 13.2 Homology Modeling Approach -- 13.2.1 Strategies for Homology Modeling -- 13.2.2 Procedure -- 13.3 Steps Involved in Homology Modeling -- 13.3.1 Template Identification -- 13.3.2 Sequence Alignment -- 13.3.3 Backbone Generation -- 13.3.4 Loop Modeling -- 13.3.5 Side Chain Modeling -- 13.3.6 Model Optimization -- 13.3.6.1 Model Validation -- 13.4 Tools Used for Homology Modeling -- 13.4.1 Robetta -- 13.4.2 M4T (Multiple Templates) -- 13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement) -- 13.4.4 ModBase -- 13.4.5 Swiss Model -- 13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2) -- 13.4.7 Modeller -- 13.4.8 Conclusion -- Acknowledgement -- References…”

“…These include advancements in big data, machine learning, and advanced visualization. …”

Tabla de Contenidos: “…6.7 Conclusions and applicability of nanosilver to general nanotoxicology -- References -- 7 A Nanomaterial Registry -- 7.1 Introduction -- 7.1.1 Mission -- 7.1.2 Overview of the current Registry tool -- 7.2 Registry concepts -- 7.2.1 Data content: MIANs -- 7.2.2 Data content: IOC -- 7.2.3 Data quality: compliance to the MIAN -- 7.3 Data curation -- 7.3.1 Example of data curation: assigning IOCs -- 7.3.2 Example of data curation: PCC data -- 7.4 Leveraging initiatives in nanotechnology -- 7.5 Conclusions -- Acknowledgments -- References -- 8 Nanoinformatics: Data-Driven Materials Design for Health and Environmental Needs -- 8.1 Overview -- 8.2 Introduction-the information challenge -- 8.3 Quantifying information complexity in nanoscience -- 8.4 Harnessing nanoinformatics: case studies -- 8.4.1 Data-driven design of nanoparticles: attribute selection methods -- 8.4.2 A data science framework for exploring beyond physics: mapping the property landscape with limited information -- 8.5 Big data for nanotechnology policy -- References -- 3 PERSPECTIVES -- 9 A Case Study of a Nanoscale-Research Facility: Safety Through Design and Operation -- 9.1 The BNC facility -- 9.2 Safety considerations -- 9.3 Designing in safety -- 9.4 Identification of hazard potentials in the BNC -- 9.5 Designing in safety-key examples -- 9.6 Gas hazard mitigation design -- 9.7 Summary -- 10 Commercialization of Cellulose Nanocrystal (NCCTM) Production: A Business Case Focusing on the Importance of Proactive EH ... -- 10.1 Introduction -- 10.2 Regulatory framework in Canada -- 10.3 Physical-chemical characterization of NCCTM -- 10.3.1 Characterization of NCCTM in aqueous solutions -- 10.3.2 Characterization of spray-dried NCCTM -- 10.4 Ecotoxicological and toxicological test results for NCCTM -- 10.5 Occupational and environmental testing at the NCCTM demonstration plant…”

Tabla de Contenidos: “…Mobile Testing -- 6.5. Big Data Testing -- 6.6. Automotive Testing -- 7. Discussion and Conclusions -- References -- Chapter Four: Optimizing the Symbolic Execution of Evolving Rhapsody Statecharts -- 1. …”

“…TOPICAL -- the topic of neuroscience is huge right now, particularly when combined with information on AI/Big Data/technology overload. It would follow on well from Offline publishing in December 18, AI in Practice publishing in Feb/March. …”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- Chapter 1: Paralysis support system using IoT -- 1.1 Introduction -- 1.2 Literature review -- 1.3 Proposed healthcare system -- 1.4 System architecture -- 1.4.1 ESP32-NODE MCU -- 1.4.2 Gyro and accelerometer sensor -- 1.4.3 RF module -- 1.4.4 Encoder and decoder -- 1.4.5 LCD display -- 1.4.6 Arduino IDE -- 1.5 Results and discussion -- 1.6 Conclusion and future scope -- References -- Chapter 2: Blockchain and its applications: A review -- 2.1 Introduction -- 2.2 Blockchain architecture -- 2.3 Working of blockchain -- 2.4 Application -- 2.4.1 Healthcare -- 2.4.2 Education -- 2.4.3 Insurance -- 2.4.4 E-Commerce -- 2.4.5 Transportation -- 2.4.6 Industry of strength -- 2.5 Conclusion -- References -- Chapter 3: Data analytics tools for smart cities and smart towns -- 3.1 Introduction -- 3.2 The concept of big data analytics -- 3.3 The concept of smart cities -- 3.3.1 Data storage in smart cities -- 3.4 Need for data analytics in smart cities -- 3.4.1 Reliability -- 3.4.2 Transport -- 3.4.3 Planning -- 3.4.4 Future proofing -- 3.4.5 Effective spending -- 3.4.6 Sustainability -- 3.5 Data analytics tools -- 3.6 Data analytics tools for smart cities and towns -- 3.6.1 Hadoop -- 3.6.2 Map Reduce -- 3.6.3 Apache storm -- 3.6.4 Apache spark -- 3.6.5 Apache Flink -- 3.6.6 Flume -- 3.7 Conclusion -- References -- Chapter 4: Industrial Internet of Things and its applications in Industry 4.0 through sensor integration for a process parameter monitor and control -- 4.1 Introduction -- 4.2 Problem statement -- 4.3 Objective -- 4.4 Related works -- 4.4.1 Inferences through the survey -- 4.5 Existing methodology -- 4.5.1 Level process station -- 4.5.2 Monitoring and control of level process -- 4.5.3 SCADA…”

Tabla de Contenidos: “…Regulatory challenges brought by technologies and business models for smart logistics -- Introduction -- Smart logistics overview -- Concepts and key technologies of smart logistics -- Artificial intelligence (AI) -- Robotics -- Augmented reality -- Big data analysis -- Internet of Things -- Virtual reality -- Drones -- Blockchain -- Market outlook for smart logistics -- Market outlook for AGVs -- Global market size for commercial drones -- Emerging topics in smart logistics…”

Tabla de Contenidos: “…Modeling the whole of Wikipedia -- Choosing the number of topics -- Summary -- Chapter 11: Classification III - Music Genre Classification -- Sketching our roadmap -- Fetching the music data -- Converting into WAV format -- Looking at music -- Decomposing music into sine-wave components -- Using FFT to build our first classifier -- Increasing experimentation agility -- Training the classifier -- Using a confusion matrix to measure accuracy in multiclass problems -- An alternative way to measure classifier performance using receiver-operator characteristics -- Improving classification performance with mel frequency cepstral coefficients -- Music classification using Tensorflow -- Summary -- Chapter 12: Computer Vision -- Introducing image processing -- Loading and displaying images -- Thresholding -- Gaussian blurring -- Putting the center in focus -- Basic image classification -- Computing features from images -- Writing your own features -- Using features to find similar images -- Classifying a harder dataset -- Local feature representations -- Image generation with adversarial networks -- Summary -- Chapter 13: Reinforcement Learning -- Types of reinforcement learning -- Policy and value network -- Q-network -- Excelling at games -- A small example -- Using Tensorflow for the text game -- Playing breakout -- Summary -- Chapter 14: Bigger Data -- Learning about big data -- Using jug to break up your pipeline into tasks -- An introduction to tasks in jug -- Looking under the hood -- Using jug for data analysis -- Reusing partial results -- Using Amazon Web Services -- Creating your first virtual machines -- Installing Python packages on Amazon Linux -- Running jug on our cloud machine -- Automating the generation of clusters with cfncluster -- Summary -- Appendi A: Where to Learn More About Machine Learning -- Online courses -- Books -- Blogs…”

Tabla de Contenidos: “…7.3 System Architecture -- 7.4 System Development -- 7.5 Algorithm-LSTM -- 7.6 Result -- 7.7 Conclusions -- References -- 8 Deep Learning Era for Future 6G Wireless Communications-Theory, Applications, and Challenges -- 8.1 Introduction -- 8.2 Study of Wireless Technology -- 8.3 Deep Learning Enabled 6G Wireless Communication -- 8.4 Applications and Future Research Directions -- Conclusion -- References -- 9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks -- 9.1 Introduction -- 9.2 Spectrum Sensing in Cognitive Radio Networks -- 9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments -- 9.4 Cooperative Sensing Among Cognitive Radios -- 9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems -- 9.6 Spectrum Agile Radios: Utilization and Sensing Architectures -- 9.7 Some Fundamental Limits on Cognitive Radio -- 9.8 Cooperative Strategies and Capacity Theorems for Relay Networks -- 9.9 Research Challenges in Cooperative Communication -- 9.10 Conclusion -- References -- 10 Natural Language Processing -- 10.1 Introduction -- 10.2 Conclusions -- References -- 11 Class Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Class Level Semantic Similarity-Based Retrieval -- 11.4 Results and Discussion -- Conclusion -- References -- 12 Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling With Diurnal Changes -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Proposed Work -- 12.4 Results -- 12.5 Conclusion and Future Work -- References -- 13 Multi-Layer UAV Ad Hoc Network Architecture, Protocol and Simulation -- 13.1 Introduction -- 13.2 Background -- 13.3 Issues and Gap Identified -- 13.4 Main Focus of the Chapter -- 13.5 Mobility…”

Tabla de Contenidos: “…Capital One's cloud transformation journey -- Capital One's talent acquisition strategy -- Re-architecting the Capital One mobile app on the cloud -- Summary -- Further reading -- Part 3: Lead and Transform Your Business with Cloud -- Chapter 8: Architecting a Cloud-Ready Enterprise -- Building a framework for a cloud-ready enterprise -- Cloud foundation layer -- Legacy apps -- Application development and operations -- API orchestration, and integration layer -- Cloud architecture frameworks from major cloud service providers -- AWS Well-Architected Framework -- Google Cloud Architecture Framework -- Microsoft Azure Well-Architected Framework -- Private and hybrid cloud strategies -- Summary -- Further reading -- Chapter 9: Facets of Digital Transformation -- Building a security-first cloud architecture -- Foundational security principles -- Automating cloud asset management -- IAM -- Network security -- Application security -- Data security -- Data sovereignty and data residency -- Rethinking infrastructure -- Compute infrastructure -- Network infrastructure -- Storage infrastructure -- Application modernization strategy -- Traditional application modernization -- Cloud-native DevOps -- Data modernization -- Cloud optimization and cost governance -- The inform phase -- The optimize phase -- The operate phase -- Summary -- Further readings -- Chapter 10: Leading and Innovating with the Cloud -- Innovating with the cloud -- Setting up innovation labs -- Vision and governance -- Talent and partnerships -- Innovate and deliver -- Innovating with big data and serverless -- Banking and financial services -- Retail -- Healthcare -- Embedding AI across the enterprise -- GenAI -- XR -- Edge computing -- Blockchain -- Quantum computing -- Responsible uses of emerging technologies -- Summary -- Further reading -- Chapter 11: ESG and Sustainability…”

Tabla de Contenidos: “…Departamento datificado: investigar, medir y evaluar -- Medición y evaluación: el viejo cri du coeur de la gestión de comunicación -- La evaluación: el alfa y el omega de la estrategia -- Etapas de medición y evaluación aplicadas en los departamentos de comunicación -- Utilización de los resultados de la investigación evaluativa -- Big data y automatización: la gota que ha colmado el vaso en la evaluación de comunicación -- ¿Cri du coeur o lágrimas de cocodrilo? …”

“…Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology…”

Tabla de Contenidos: “…9.2.2.2 Second leader stage -- 9.2.2.3 First leader stage -- 9.2.2.4 First leader learning -- 9.2.2.5 Second leader learning -- 9.2.2.6 Second leader decision -- 9.2.2.7 First leader decision -- 9.2.3 Control parameters in spider monkey optimization -- 9.3 Related work -- 9.3.1 Optimization problems -- 9.3.2 Deep learning -- 9.3.3 Data clustering -- 9.3.4 Big data problems -- 9.3.5 Networking problems -- 9.3.6 Cloud computing -- 9.3.7 Scheduling issues -- 9.3.8 Privacy problems -- 9.3.9 Image processing -- 9.3.10 Software engineering field -- 9.3.11 Other applications -- 9.4 Discussion -- 9.5 Conclusion and future works -- References -- 10 Marine predator's algorithm: a survey of recent applications -- 10.1 Introduction -- 10.2 Marine Predator's Algorithm -- 10.3 Related Works -- 10.3.1 Engineering Problems -- 10.3.2 Image Processing -- 10.3.3 Benchmark Function -- 10.3.4 Feature Selection -- 10.4 Discussion -- 10.5 Conclusion and Future Work -- References -- 11 Quantum approximate optimization algorithm: a review study and problems -- 11.1 Introduction -- 11.2 Methods -- 11.2.1 Fixed p algorithm -- 11.2.2 Concentration -- 11.2.3 The ring of disagrees -- 11.2.4 Maxcut on 3-regular graphs -- 11.2.5 Relation to the quantum adiabatic algorithm -- 11.2.6 A variant of the algorithm -- 11.3 Related works -- 11.4 Result -- 11.5 Discussion -- 11.6 Conclusion -- References -- 12 Crow search algorithm: a survey of novel optimizer and its recent applications -- 12.1 Introduction -- 12.2 Crow search algorithm -- 12.2.1 Inspiration -- 12.2.2 Continuous crow search algorithm -- 12.3 Related work -- 12.4 Conclusion and future work -- References -- 13 A review of Henry gas solubility optimization algorithm: a robust optimizer and applications -- 13.1 Introduction -- 13.2 Henry gas solubility optimization -- 13.2.1 Henry's law -- 13.2.2 Inspiration source…”

“…The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing the Google Cloud Platform -- ML and the cloud -- The nature of the cloud -- Public cloud -- Managed cloud versus unmanaged cloud -- IaaS versus PaaS versus SaaS -- Costs and pricing -- ML -- Introducing the GCP -- Mapping the GCP -- Getting started with GCP -- Project-based organization -- Creating your first project -- Roles and permissions -- Further reading -- Summary -- Chapter 2: Google Compute Engine -- Google Compute Engine -- VMs, disks, images, and snapshots -- Creating a VM -- Google Shell -- Google Cloud Platform SDK -- Gcloud -- Gcloud config -- Accessing your instance with gcloud -- Transferring files with gcloud -- Managing the VM -- IPs -- Setting up a data science stack on the VM -- BOX the ipython console -- Troubleshooting -- Adding GPUs to instances -- Startup scripts and stop scripts -- Resources and further reading -- Summary -- Chapter 3: Google Cloud Storage -- Google Cloud Storage -- Box-storage versus drive -- Accessing control lists -- Access and management through the web console -- gsutil -- gsutil cheatsheet -- Advanced gsutil -- Signed URLs -- Creating a bucket in Google Cloud Storage -- Google Storage namespace -- Naming a bucket -- Naming an object -- Creating a bucket -- Google Cloud Storage console -- Google Cloud Storage gsutil -- Life cycle management -- Google Cloud SQL -- Databases supported -- Google Cloud SQL performance and scalability -- Google Cloud SQL security and architecture -- Creating Google Cloud SQL instances -- Summary -- Chapter 4: Querying Your Data with BigQuery -- Approaching big data -- Data structuring -- Querying the database -- SQL basics -- Google BigQuery -- BigQuery basics -- Using a graphical web UI -- Visualizing data with Google Data Studio…”

Tabla de Contenidos: “…-- Defining the Corporate Mission -- Establishing Strategic Business Units -- Assigning Resources to Each SBU -- Assessing Growth Opportunities -- Organization and Organizational Culture -- Marketing Innovation -- Marketing Insight: Creating Innovative Marketing -- Business Unit Strategic Planning -- The Business Mission -- SWOT Analysis -- Marketing Memo: Checklist for Evaluating Strengths/Weaknesses Analysis -- Goal Formulation -- Strategic Formulation -- Program Formulation and Implementation -- Marketing Insight: Businesses Charting a New Direction -- Feedback and Control -- The Nature and Contents of a Marketing Plan -- Marketing Memo: Marketing Plan Criteria -- The Role of Research -- The Role of Relationships -- From Marketing Plan to Marketing Action -- Summary -- Applications -- Marketing Excellence: Electrolux -- Marketing Excellence: Emirates -- Sample Marketing Plan: Pegasus Sports International -- Part 2: Capturing Marketing Insights -- Chapter 3: Collecting Information and Forecasting Demand -- Components of a Modern Marketing Information System -- Internal Records -- The Order-to-payment Cycle -- Sales Information Systems -- Databases, Data Warehousing, and Data Mining -- Marketing Insight: Digging Into Big Data -- Marketing Intelligence -- The Marketing Intelligence Syst Em -- Collecting Marketing Intelligence on the Internet -- Communicating and acting on Marketing Intelligence -- Analyzing the Macroenvironment -- Needs and Trends -- Identifying the Major Forces -- The Demographic Environment -- Marketing Memo: Finding Gold at the Bottom of the Pyramid -- The Economic Environment -- The Sociocultural Environment…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 IoT Technologies and Applications -- 1.1 Introduction -- 1.2 Traditional IoT Technologies -- 1.2.1 Traditional IoT System Architecture -- 1.2.1.1 Sensing Layer -- 1.2.1.2 Network Layer -- 1.2.1.3 Application Layer -- 1.2.2 IoT Connectivity Technologies and Protocols -- 1.2.2.1 Low‐power and Short‐range Connectivity Technologies -- 1.2.2.2 Low Data Rate and Wide‐area Connectivity Technologies -- 1.2.2.3 Emerging IoT Connectivity Technologies and Protocols -- 1.3 Intelligent IoT Technologies -- 1.3.1 Data Collection Technologies -- 1.3.1.1 mmWave -- 1.3.1.2 Massive MIMO -- 1.3.1.3 Software Defined Networks -- 1.3.1.4 Network Slicing -- 1.3.1.5 Time Sensitive Network -- 1.3.1.6 Multi‐user Access Control -- 1.3.1.7 Muti‐hop Routing Protocol -- 1.3.2 Computing Power Network -- 1.3.2.1 Intelligent IoT Computing Architecture -- 1.3.2.2 Edge and Fog Computing -- 1.3.3 Intelligent Algorithms -- 1.3.3.1 Big Data -- 1.3.3.2 Artificial Intelligence -- 1.4 Typical Applications -- 1.4.1 Environmental Monitoring -- 1.4.2 Public Safety Surveillance -- 1.4.3 Military Communication -- 1.4.4 Intelligent Manufacturing and Interactive Design -- 1.4.5 Autonomous Driving and Vehicular Networks -- 1.5 Requirements and Challenges for Intelligent IoT Services -- 1.5.1 A Generic and Flexible Multi‐tier Intelligence IoT Architecture -- 1.5.2 Lightweight Data Privacy Management in IoT Networks -- 1.5.3 Cross‐domain Resource Management for Intelligent IoT Services -- 1.5.4 Optimization of Service Function Placement, QoS, and Multi‐operator Network Sharing for Intelligent IoT Services -- 1.5.5 Data Time stamping and Clock Synchronization Services for Wide‐area IoT Systems -- 1.6 Conclusion -- References -- Chapter 2 Computing and Service Architecture for Intelligent IoT…”

“…Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. …”

Tabla de Contenidos: “…(What Is Not in This Book) -- 1.5.1 What About ``Big Data''? -- 1.5.2 What About Related Work? -- 1.5.3 Why All the Defect Prediction and Effort Estimation? …”