Tabla de Contenidos: “…13.5 Wait State Generation -- Questions -- Part II: Assembly Language Programs -- Chapter 14: Simple Assembly Language Programs -- 14.1 Exchange 10 Bytes -- 14.2 Add two Multi-Byte Numbers -- 14.3 Add two Multi-Byte BCD Numbers -- 14.4 Block Movement without Overlap -- 14.5 Block Movement with Overlap -- 14.6 Add N Numbers of Size 8 Bits -- 14.7 Check the Fourth Bit of a Byte -- 14.8 Subtract two Multi-Byte Numbers -- 14.9 Multiply two numbers of Size 8 Bits -- 14.10 Divide a 16-Bit Number by an 8-Bit Number -- Questions -- Chapter 15: Use of PC in Writing and Executing 8085 Programs -- 15.1 Steps Needed to Run an Assembly Language Program -- 15.2 Creation of .ASM File using a Text Editor -- 15.3 Generation of .OBJ File using a Cross-Assembler -- 15.4 Generation of .HEX File using a Linker -- 15.5 Downloading the Machine Code to the Kit -- 15.6 Running the Downloaded Program on the Kit -- 15.7 Running the Program using the PC as a Terminal -- Questions -- Chapter 16: Additional Assembly Language Programs -- 16.1 Search for a Number using Linear Search -- 16.2 Find the Smallest Number -- 16.3 Compute the HCF of Two 8-Bit Numbers -- 16.4 Check for '2 out of 5' Code -- 16.5 Convert ASCII to Binary -- 16.6 Convert Binary to ASCII -- 16.7 Convert BCD to Binary -- 16.8 Convert Binary to BCD -- 16.9 Check for Palindrome -- 16.10 Compute the LCM of Two 8-Bit Numbers -- 16.11 Sort Numbers using Bubble Sort -- 16.12 Sort Numbers using Selection Sort -- 16.13 Simulate Decimal up Counter -- 16.14 Simulate Decimal down Counter -- 16.15 Display Alternately 00 and FF in the Data Field -- 16.16 Simulate a Real-Time Clock -- Questions -- Chapter 17: More Complex Assembly Language Programs -- 17.1 Subtract Multi-Byte BCD Numbers -- 17.2 Convert 16-Bit Binary to BCD -- 17.3 Do an operation on Two Numbers Based on the Value of X.…”

Tabla de Contenidos: “…6.9 Markov Sequence -- 6.10 Markov Process -- 6.11 Random Sequences -- 6.11.1 Bernoulli Sequence -- 6.11.2 Bernoulli Scheme -- 6.11.3 Independent Sequences -- 6.11.4 Bernoulli Random Walk -- 6.11.5 Binomial Counting Sequence -- 6.12 Random Processes -- 6.12.1 Poisson Counting Process -- 6.12.2 Random Telegraph Signal -- 6.12.3 Wiener Process -- 6.12.4 Gaussian Process -- 6.12.5 Pulse Amplitude Modulation -- 6.12.6 Random Sine Signals -- Problems -- Further Reading -- 7 Stochastic Convergence, Calculus, and Decompositions -- 7.1 Introduction -- 7.2 Stochastic Convergence -- 7.3 Laws of Large Numbers -- 7.4 Central Limit Theorem -- 7.5 Stochastic Continuity -- 7.6 Derivatives and Integrals -- 7.7 Differential Equations -- 7.8 Difference Equations -- 7.9 Innovations and Mean-Square Predictability -- 7.10 Doob-Meyer Decomposition -- 7.11 Karhunen-Lo`eve Expansion -- Problems -- Further Reading -- 8 Systems, Noise, and Spectrum Estimation -- 8.1 Introduction -- 8.2 Correlation Revisited -- 8.3 Ergodicity -- 8.4 Eigenfunctions of RXX(τ) -- 8.5 Power Spectral Density -- 8.6 Power Spectral Distribution -- 8.7 Cross-Power Spectral Density -- 8.8 Systems with Random Inputs -- 8.8.1 Nonlinear Systems -- 8.8.2 Linear Systems -- 8.9 Passband Signals -- 8.10 White Noise -- 8.11 Bandwidth -- 8.12 Spectrum Estimation -- 8.12.1 Periodogram -- 8.12.2 Smoothed Periodogram -- 8.12.3 Modified Periodogram -- 8.13 Parametric Models -- 8.13.1 Autoregressive Model -- 8.13.2 Moving-Average Model -- 8.13.3 Autoregressive Moving-Average Model -- 8.14 System Identification -- Problems -- Further Reading -- 9 Sufficient Statistics and Parameter Estimation -- 9.1 Introduction -- 9.2 Statistics -- 9.3 Sufficient Statistics -- 9.4 Minimal Sufficient Statistic -- 9.5 Exponential Families -- 9.6 Location-Scale Families -- 9.7 Complete Statistic -- 9.8 Rao-Blackwell Theorem…”

Tabla de Contenidos: “…3.2 Security Issues in e-Business -- 3.2.1 Vulnerabilities -- 3.2.2 Security Attacks -- 3.2.3 Malware as a Threat -- 3.3 Common Vulnerabilities in e-Business -- 3.3.1 Phishing -- 3.3.2 Cross-Site Scripting (XSS) -- 3.4 Threats in e-Business -- 3.4.1 Ransomware -- 3.4.2 Spyware -- 3.4.3 Worms -- 3.4.4 Trojan Horse -- 3.5 Prevention Mechanism -- 3.6 Conclusion -- References -- 4 e-Commerce Security: Threats, Issues, andMethods -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 e-Commerce -- 4.3.1 Characteristics of e-Commerce Technology -- 4.3.2 Architectural Framework of e-Commerce -- 4.3.2.1 Two-Tier Architecture -- 4.3.2.2 Three-Tier Architecture -- 4.3.3 Advantages and Disadvantages of e-Commerce -- 4.4 Security Overview in e-Commerce -- 4.4.1 Purpose of Security in e-Commerce -- 4.4.2 Security Element at Different Levels of e-Commerce System -- 4.4.2.1 Hardware -- 4.4.2.2 Software -- 4.4.2.3 Environment -- 4.5 Security Issues in e-Commerce -- 4.5.1 Client Level -- 4.5.2 Front-End Servers and Software Application Level -- 4.5.3 Network and Server Level -- 4.6 Security Threats in e-Commerce -- 4.7 Security Approaches in e-Commerce -- 4.8 Comparative Analysis of Various Security Threats in e-Commerce -- 4.9 e-Commerce Security Life-Cycle Model -- 4.10 Conclusion -- References -- 5 Cyberwar is Coming -- 5.1 Introduction -- 5.2 Ransomware Attacks -- 5.2.1 Petya -- 5.2.2 WannaCry -- 5.2.3 Locky -- 5.3 Are Nations Ready? …”

Tabla de Contenidos: “…6.4 Boolean methods -- 6.5 Splitting strings -- 6.6 Coding challenge -- 6.6.1 Problems -- 6.6.2 Solutions -- 6.7 A note on regular expressions -- Summary -- 7 MultiIndex DataFrames -- 7.1 The MultiIndex object -- 7.2 MultiIndex DataFrames -- 7.3 Sorting a MultiIndex -- 7.4 Selecting with a MultiIndex -- 7.4.1 Extracting one or more columns -- 7.4.2 Extracting one or more rows with loc -- 7.4.3 Extracting one or more rows with iloc -- 7.5 Cross-sections -- 7.6 Manipulating the Index -- 7.6.1 Resetting the index -- 7.6.2 Setting the index -- 7.7 Coding challenge -- 7.7.1 Problems -- 7.7.2 Solutions -- Summary -- 8 Reshaping and pivoting -- 8.1 Wide vs. narrow data -- 8.2 Creating a pivot table from a DataFrame -- 8.2.1 The pivot_table method -- 8.2.2 Additional options for pivot tables -- 8.3 Stacking and unstacking index levels -- 8.4 Melting a data set -- 8.5 Exploding a list of values -- 8.6 Coding challenge -- 8.6.1 Problems -- 8.6.2 Solutions -- Summary -- 9 The GroupBy object -- 9.1 Creating a GroupBy object from scratch -- 9.2 Creating a GroupBy object from a data set -- 9.3 Attributes and methods of a GroupBy object -- 9.4 Aggregate operations -- 9.5 Applying a custom operation to all groups -- 9.6 Grouping by multiple columns -- 9.7 Coding challenge -- 9.7.1 Problems -- 9.7.2 Solutions -- Summary -- 10 Merging, joining, and concatenating -- 10.1 Introducing the data sets -- 10.2 Concatenating data sets -- 10.3 Missing values in concatenated DataFrames -- 10.4 Left joins -- 10.5 Inner joins -- 10.6 Outer joins -- 10.7 Merging on index labels -- 10.8 Coding challenge -- 10.8.1 Problems -- 10.8.2 Solutions -- Summary -- 11 Working with dates and times -- 11.1 Introducing the Timestamp object -- 11.1.1 How Python works with datetimes -- 11.1.2 How pandas works with datetimes -- 11.2 Storing multiple timestamps in a DatetimeIndex…”

Tabla de Contenidos: “…3.2.5 Calculating CVA and DVA Using Monte Carlo Simulation -- 3.3 Overnight Index Swap Discounting -- Chapter 4 An Introduction to Derivative Instruments -- 4.1 FX Forwards -- 4.1.1 Product Description -- 4.1.2 Forward Points -- 4.2 Interest Rate Swaps -- 4.2.1 Product Description -- 4.2.2 IFRS 9 Accounting Implications -- 4.3 Cross-Currency Swaps -- 4.3.1 Product Description -- 4.3.2 IFRS 9 Accounting Implications -- 4.4 Standard (Vanilla) Options -- 4.4.1 Product Description -- 4.4.2 Standard Equity Options -- 4.4.3 Standard Foreign Exchange Options -- 4.4.4 Interest Rate Options - Caps, Floors and Collars -- 4.5 Exotic Options -- 4.6 Barrier Options -- 4.6.1 Knock-out Barrier Options - Product Description -- 4.6.2 Knock-in Barrier Options - Product Description -- 4.7 Range Accruals -- Chapter 5 Hedging Foreign Exchange Risk -- 5.1 Types of Foreign Exchange Exposure -- 5.2 Introductory Definitions -- 5.2.1 Functional Currency and Presentation Currency -- 5.2.2 Relevant Dates in an FX Transaction -- 5.3 Summary of IAS 21 Translation Rates -- 5.3.1 Monetary versus Non-monetary Items -- 5.3.2 Translation Rates -- 5.4 Foreign Currency Transactions -- 5.4.1 Summary of Most Commonly Used FX Derivatives -- 5.5 Case Study: Hedging A Forecast Sale and Subsequent Receivable with an FX Forward (Forward Element Included in Hedging Relationship) -- 5.5.1 Background -- 5.5.2 Setting the Hedging Relationship Term -- 5.5.3 Hedging Relationship Documentation -- 5.5.4 Hedge Effectiveness Assessment - Hypothetical Derivative -- 5.5.5 Hedge Effectiveness Assessment Performed at Hedge Inception -- 5.5.6 Fair Valuation of Hedged Item and Hypothetical Derivative at the Relevant Dates -- 5.5.7 Accounting Entries - Hedge Objective Unchanged: No Discontinuation -- 5.5.8 Accounting Entries - Hedge Risk Management Objective Changed: Discontinuation…”

Tabla de Contenidos: “…Automating forecasting at scale -- 19 Automating time series forecasting with Prophet -- 19.1 Overview of the automated forecasting libraries -- 19.2 Exploring Prophet -- 19.3 Basic forecasting with Prophet -- 19.4 Exploring Prophet's advanced functionality -- 19.4.1 Visualization capabilities -- 19.4.2 Cross-validation and performance metrics -- 19.4.3 Hyperparameter tuning -- 19.5 Implementing a robust forecasting process with Prophet…”

Tabla de Contenidos: “…8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem…”

Tabla de Contenidos: “…Project Manager -- 1.3.3 Relating the Product Management Role to PDMA's BoK -- 1.4 The Product Life Cycle -- 1.4.1 Introduction to the Product Life Cycle -- 1.4.2 The Impact of the PLC Stages on Cash Flow -- 1.4.3 The Length and Shape of the Product Life Cycle -- 1.4.4 Customer Adoption of New Products -- 1.5 Managing the Product Life Cycle -- 1.5.1 Impact of the PLC on the Product Portfolio -- 1.5.2 The Product Manager's Role in the Product Life Cycle -- 1.6 The Chasm in the Product Life Cycle? -- 1.6.1 Crossing the Chasm -- 1.6.2 The "Go-to-Market" Processes -- 1.7 Roadmaps Used in Product Innovation -- 1.7.1 Product Roadmaps -- 1.7.2 Kanban boards -- 1.7.3 Technology Roadmaps -- 1.7.4 Platform Roadmaps -- 1.7.5 Best Practices for Using Roadmaps -- 1.8 Project Management…”

Tabla de Contenidos: “…., Literature Review -- 4.3 Objective of Research and Research Methodology -- 4.4 Results and Analysis of the Selected Papers -- 4.5 Overview of Data Used in the Earlier Studies Selected for the Current Research -- 4.6 Data Source -- 4.7 Technical Indicators -- 4.7.1 Other (Advanced Technical Indicators) -- 4.8 Stock Market Prediction: Need and Methods -- 4.9 Process of Stock Market Prediction -- 4.10 Reviewing Methods for Stock Market Predictions -- 4.11 Analysis and Prediction Techniques -- 4.12 Classification Techniques (Also Called Clustering Techniques) -- 4.13 Future Direction -- 4.13.1 Cross-Market Evaluation or Analysis -- 4.13.2 Various Data Inputs -- 4.13.3 Unexplored Frameworks -- 4.13.4 Trading Strategies Based on Algorithm -- 4.14 Conclusion -- References -- Chapter 5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions -- 5.1 Introduction -- 5.2 Literature Survey -- 5.3 Analysis of Popular Deep Learning Techniques for Stock Market Prediction -- 5.3.1 Blind Quantum Computing (BQC) in Stock Market Prediction -- 5.3.2 Quantum Neural Networks (QNNs) for Time Series Forecasting -- 5.3.3 Artificial Intelligence-Based Algorithms -- 5.3.3.1 Deep Learning Models -- 5.3.3.2 Support Vector Machines (SVM) -- 5.3.3.3 Reinforcement Learning (RL) -- 5.3.4 Quantum Computing-Based Algorithms…”

Tabla de Contenidos: “…Campaign management solution examples -- 6.1 Campaign management overview -- 6.2 Trigger-based marketing -- 6.2.1 The business issue -- 6.2.2 Mapping the business issue to data mining functions -- 6.2.3 The business application -- 6.2.4 Environment, components, and implementation flow -- 6.2.5 Step-by-step implementation -- 6.2.6 Benefits -- 6.3 Retention campaign -- 6.3.1 The business issue -- 6.3.2 Mapping the business issue to data mining functions -- 6.3.3 The business application -- 6.3.4 Environment, components, and implementation flow -- 6.3.5 Step-by-step implementation -- 6.3.6 Benefits -- 6.4 Cross-selling campaign -- 6.4.1 The business issue -- 6.4.2 Mapping the business issue to data mining functions -- 6.4.3 The business application -- 6.4.4 Environment, components, and implementation flow -- 6.4.5 Step-by-step implementation -- 6.4.6 Other considerations -- Chapter 7. …”

Tabla de Contenidos: “…Using the softmax activation function in the last layer -- 6.5.2. Cross-entropy loss for classification problems -- 6.6. …”

Tabla de Contenidos: “…8.5.2 Exploiting domain rules -- 8.6 Validating configuration -- 8.6.1 Causes for configuration-related security flaws -- 8.6.2 Automated tests as your safety net -- 8.6.3 Knowing your defaults and verifying them -- Summary -- 9 Handling failures securely -- 9.1 Using exceptions to deal with failure -- 9.1.1 Throwing exceptions -- 9.1.2 Handling exceptions -- 9.1.3 Dealing with exception payload -- 9.2 Handling failures without exceptions -- 9.2.1 Failures aren't exceptional -- 9.2.2 Designing for failures -- 9.3 Designing for availability -- 9.3.1 Resilience -- 9.3.2 Responsiveness -- 9.3.3 Circuit breakers and timeouts -- Always specify a timeout -- 9.3.4 Bulkheads -- 9.4 Handling bad data -- Cross-site scripting and second-order attacks -- 9.4.1 Don't repair data before validation -- 9.4.2 Never echo input verbatim -- Summary -- 10 Benefits of cloud thinking -- 10.1 The twelve-factor app and cloud-native concepts -- 10.2 Storing configuration in the environment -- 10.2.1 Don't put environment configuration in code -- 10.2.2 Never store secrets in resource files -- 10.2.3 Placing configuration in the environment -- 10.3 Separate processes -- 10.3.1 Deploying and running are separate things -- 10.3.2 Processing instances don't hold state -- 10.3.3 Security benefits -- 10.4 Avoid logging to file -- 10.4.1 Confidentiality -- 10.4.2 Integrity -- 10.4.3 Availability -- 10.4.4 Logging as a service -- 10.5 Admin processes -- 10.5.1 The security risk of overlooked admin tasks -- 10.5.2 Admin tasks as first-class citizens -- Admin of log files -- 10.6 Service discovery and load balancing -- 10.6.1 Centralized load balancing -- 10.6.2 Client-side load balancing -- 10.6.3 Embracing change -- 10.7 The three R's of enterprise security -- 10.7.1 Increase change to reduce risk -- 10.7.2 Rotate -- 10.7.3 Repave -- 10.7.4 Repair -- Summary…”

Tabla de Contenidos: “…10.2.4 Availability of Human and Physical Resources -- 10.2.5 Network of Infrastructure -- 10.2.6 Fiscal, Monetary, and Industrial Policies -- 10.2.7 Strength of External Sector -- 10.3 Process of Analysing Economic Environment -- Summary -- Review Questions -- Objective Type Questions -- Study Topic -- References -- Select Further Readings -- Chapter 11: Socio-Culturaland Ethical Environment -- 11.1 Concept of Culture -- 11.1.1 Meaning of Culture -- 11.1.2 Elements of Culture -- 11.2 Cultural Diversity -- 11.2.1 Basis of Cultural Diversity -- 11.2.2 Diverse Culture and Competitive Advantage -- 11.3 Management of Cultural Diversity -- 11.3.1 Two Schools of Thought -- 11.3.2 Management Process -- 11.4 Ethics and Social Responsibility -- 11.4.1 Concept -- 11.4.2 Normative and Relative Ethics -- 11.4.3 Some Ethical Issues in International Business -- Summary -- Review Questions -- Objective Type Questions -- Case Study -- References -- Select Further Readings -- Chapter 12: International Financial Environment: Exchange Rate -- 12.1 International Monetary System -- 12.1.1 Early System -- 12.1.2 Bretton Woods System of Exchange Rates -- 12.1.3 Exchange Rate Regime Since 1973 -- 12.2 Exchange Rate Quotation -- 12.2.1 Direct and Indirect Quotes -- 12.2.2 Buying and Selling Rates -- 12.2.3 Forward Rates -- 12.2.4 Cross Rates -- 12.3 Determination of Exchange Ratein Spot Market -- 12.3.1 Process of Determination -- 12.3.2 Factors Infl Uencing Exchange Rate -- 12.4 Exchange Rate Determination in Forward Market -- 12.4.1 Interest Rate Parity Theory -- 12.4.2 Covered Interest Arbitrage -- 12.5 Theories of Exchange Rate Behaviour -- 12.5.1 Balance of Payments Approach -- 12.5.2 Monetary Approach of Flexible Price Version -- 12.5.3 Monetary Approach of Sticky-Price Version -- 12.5.4 Portfolio Balance Approach -- 12.6 Exchange Rate Forecasting…”

Tabla de Contenidos: “…8.11.5 Spread Trades -- 8.11.6 Arbitrage Transactions -- 8.11.7 Adjusting the Duration of the Portfolio -- 8.11.8 Cross-hedging -- 8.12 Cautions in Using Interest Rate Futures -- Chapter Summary -- Review Questions -- Problems -- Case Study -- Chapter 9: Currency Futures -- Learning Objectives -- 9.1 What Are Currency Futures? …”

Tabla de Contenidos: “…Johnston / Cardiorespiratory Fitness and Peak Torque Differences between Vegetarian and Omnivore Endurance Athletes: A Cross-Sectional Study, Reprinted from: Nutrients 2016, 8(11), 726; doi: 10.3390/nu8110726 -- Justin D. …”

Tabla de Contenidos: “…-- 13.2.5 UN Cluster System: A tool for NGO Diplomats -- 13.2.6 ISDR - International Strategy on Disaster Reduction (Geneva) -- 13.2.7 IASC: Interagency Standing Committee and OCHA (New York) -- 13.2.8 IFRC and ICRC The International Red Cross Red Crescent Movement (Geneva) -- 13.2.9 SPHERE Project and LEGS (Boston, Geneva, New York, Washington, Ethiopia) -- 13.3 Help is Available -- 13.4 Working with Non Traditional NGO: Operation Gwamba, 1964 -- Chapter 14: Creation of the League of Nations and the UN -- Extract: -- 14.1 Introduction -- 14.2 The Study Group “Inquiry:” and the “CSOP” -- 14.3 The Peer Review: -- 14.4 The Red Scare - What Does the Other Side Fear? …”

Tabla de Contenidos: “…Analysing changes over time -- 12.2.3. Making cross-country comparisons -- 12.3. Obtaining insight into possible reasons for implausible results -- 12.3.1. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Acknowledgments -- General Introduction -- Chapter 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning -- 1.1 Learning Styles for Machine Learning -- 1.1.1 Supervised Learning -- 1.1.1.1 Overfitting and Underfitting -- 1.1.1.2 K-Folds Cross-Validation -- 1.1.1.3 Train/Test Split -- 1.1.1.4 Confusion Matrix -- 1.1.1.5 Loss Functions -- 1.1.2 Unsupervised Learning -- 1.1.3 Semi-Supervised Learning -- 1.1.4 Reinforcement Learning -- 1.2 Essential Python Tools for Machine Learning -- 1.2.1 Data Manipulation with Python -- 1.2.2 Python Machine Learning Libraries -- 1.2.2.1 Scikit-learn -- 1.2.2.2 TensorFlow -- 1.2.2.3 Keras -- 1.2.2.4 PyTorch -- 1.2.3 Jupyter Notebook and JupyterLab -- 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs -- 1.3.1 Installation -- 1.3.2 HephAIstos Function -- 1.4 Where to Find the Datasets and Code Examples -- Further Reading -- Chapter 2 Feature Engineering Techniques in Machine Learning -- 2.1 Feature Rescaling: Structured Continuous Numeric Data -- 2.1.1 Data Transformation -- 2.1.1.1 StandardScaler -- 2.1.1.2 MinMaxScaler -- 2.1.1.3 MaxAbsScaler -- 2.1.1.4 RobustScaler -- 2.1.1.5 Normalizer: Unit Vector Normalization -- 2.1.1.6 Other Options -- 2.1.1.7 Transformation to Improve Normal Distribution -- 2.1.1.8 Quantile Transformation -- 2.1.2 Example: Rescaling Applied to an SVM Model -- 2.2 Strategies to Work with Categorical (Discrete) Data -- 2.2.1 Ordinal Encoding -- 2.2.2 One-Hot Encoding -- 2.2.3 Label Encoding -- 2.2.4 Helmert Encoding -- 2.2.5 Binary Encoding -- 2.2.6 Frequency Encoding -- 2.2.7 Mean Encoding -- 2.2.8 Sum Encoding -- 2.2.9 Weight of Evidence Encoding -- 2.2.10 Probability Ratio Encoding -- 2.2.11 Hashing Encoding -- 2.2.12 Backward Difference Encoding…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 BBUCAF: A Biometric-Based User Clustering Authentication Framework in Wireless Sensor Network -- 1.1 Introduction to Wireless Sensor Network -- 1.2 Background Study -- 1.3 A Biometric-Based User Clustering Authentication Framework -- 1.3.1 Biometric-Based Model -- 1.3.2 Clustering -- 1.4 Experimental Analysis -- 1.5 Conclusion -- References -- Chapter 2 DeepNet: Dynamic Detection of Malwares Using Deep Learning Techniques -- 2.1 Introduction -- 2.2 Literature Survey -- 2.2.1 ML or Metaheuristic Methods for Malware Detection -- 2.2.2 Deep Learning Algorithms for Malware Detection -- 2.3 Malware Datasets -- 2.3.1 Android Malware Dataset -- 2.3.2 SOREL-20M Dataset -- 2.4 Deep Learning Architecture -- 2.4.1 Deep Neural Networks (DNN) -- 2.4.2 Convolutional Neural Networks (CNN) -- 2.4.3 Recurrent Neural Networks (RNN) -- 2.4.4 Deep Belief Networks (DBN) -- 2.4.5 Stacked Autoencoders (SAE) -- 2.5 Proposed System -- 2.5.1 Datasets Used -- 2.5.2 System Architecture -- 2.5.3 Data Preprocessing -- 2.5.4 Proposed Methodology -- 2.5.5 DeepNet -- 2.5.6 DBN -- 2.5.7 SAE -- 2.5.8 Categorisation -- 2.6 Result and Analysis -- 2.7 Conclusion &amp -- Future Work -- References -- Chapter 3 State of Art of Security and Risk in Wireless Environment Along with Healthcare Case Study -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Applications of Wireless Networks -- 3.4 Types of Attacks -- 3.4.1 Passive Attacks -- 3.4.2 Release of Message Contents -- 3.4.3 Traffic Analysis -- 3.4.4 Eavesdropping -- 3.5 Active Attacks -- 3.5.1 Malware -- 3.5.2 Password Theft -- 3.5.3 Bandwidth Stealing -- 3.5.4 Phishing Attacks -- 3.5.5 DDoS -- 3.5.6 Cross-Site Attack -- 3.5.7 Ransomware -- 3.5.8 Message Modification -- 3.5.9 Message Replay -- 3.5.10 Masquerade -- 3.6 Layered Attacks in WSN…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Self-Powered Sensory Transducers: A Way Toward Green Internet of Things -- 1.1 Introduction -- 1.2 Need of the Work -- 1.3 Energy Scavenging Schemes in WSAN -- 1.3.1 Photovoltaic or Solar Cell -- 1.3.2 Temperature Gradient -- 1.3.3 Pressure Variations -- 1.3.4 Plant Microbial Fuel -- 1.3.5 Wind/Liquid Flow -- 1.3.6 Vibrations -- 1.3.7 Friction -- 1.4 Self Powered Systems and Green IoT (G-IoT) -- 1.5 Application Area and Scope of Self-Powered System in G-IoT -- 1.5.1 Terrestrial Applications -- 1.5.1.1 Agriculture -- 1.5.1.2 Smart Home and Cities -- 1.5.1.3 Industry -- 1.5.1.4 Medicines -- 1.5.1.5 Environment Monitoring -- 1.5.1.6 Structural Monitoring -- 1.5.1.7 Indoor Applications -- 1.5.1.8 Arial Vehicles -- 1.5.1.9 Military Applications -- 1.5.1.10 Underwater Applications -- 1.5.1.11 Submarine and Event Localization -- 1.5.1.12 Water Contamination -- 1.5.1.13 Intelligent Water Distribution and Smart Meter -- 1.5.1.14 Underground Applications -- 1.5.1.15 Coal and Petroleum Mining Application -- 1.5.1.16 Underground Structural Monitoring -- 1.6 Challenges and Future Scope of the Self-Powered G-IoT -- 1.6.1 Challenges Pertain to Energy Efficient Design and Protocols -- 1.6.2 Size and Cost of the Harvester -- 1.6.3 Energy-Efficient Routing and Scheduling Protocols -- 1.6.4 Design of Application-Specific Passive Wake-Up Receivers -- 1.6.5 Redefined Protocol with Application-Specific Goals -- 1.6.6 Embedded Operating Systems -- 1.6.7 AI and Cloud-Assisted Lifetime Prediction Techniques -- 1.6.8 Design of Energy-Efficient/Harvested Service-Oriented Architecture -- 1.6.9 Smart Web Interfaces for Monitoring -- 1.6.10 Cross Layer Exploitations with Energy Harvesting -- 1.6.11 Security Aspects and Need of Standardization…”