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Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approache

An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits Introduction Previous Work Using BPNN and ANFIS Training and Testing Data Power Estimation Using a Neural Network Construction of a Neural Network BPNN Training Phase BPNN Testing Phase Network Parameters Proposed Power Estimation Using ANFIS Technique Overview of the Proposed Work Training and Checking Used in ANFIS Designing the ANFIS Results and Discussions BPNN-Based Metod Calculating Prediction Error ANFIS-Based Method Performance Evaluation Conclusion References Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach Introduction Literature Survey Usage Analysis and Negotiable (UAN) Approach Age Categorization Usage Analysis Rules Defined for 18-25 Age Categorization Rules Defined for 26–30 Age Categorization Rules Defined for 31–35 Age Categorization User View Analysis Likes and Dislikes Views Comments Posting Category Content Sharing User Interested Test Rate (UITR) Analysis Algorithm Rating Statement Analysis Like and Dislikes Rating Algorithm Comments Posting Content Sharing Feedback Analysis Result and Discussions Conclusion References Object Detection and Tracking in Video Using Deep Learning Techniques: A Review Introduction Challenges in Video Tracking Fundamentals of Object Tracking Object Representation Shape Representation Appearance Representation Object Detection Frame Differencing Optical Flow Background Subtraction Object Classification Method Feature Selection Edges Optical Flow Color Texture Object Tracking Techniques Point Kernel-Based Tracking Approach Silhouette Approach Introduction to Deep and Machine Learning Techniques Deep Learning vs Machine Learning Techniques Examples Vehicle Detection Training of a Cascade Detector Feature Types Available for Training Supply Positive Samples Supply Negative Images Conclusion References Fuzzy MCDM: Application in Disease Risk and Prediction Fuzzy MCDM: Introduction Literature Review Methodologies Mathematical Procedure for Applying Fuzzy MCDM Fuzzification Fuzzy Sets Crisp Sets Membership Functions General Architecture of a Fuzzy MCDM Inference System Fuzzy If-Then Rules Case Studies Detection and Risk Prediction of Medical Problem (Heart Disease)Risk prediction of Breast Cancer Using Fuzzy Logic Application of Fuzzy Logic in Determination of Hard Exudates in Diabetic Retinopathy Conclusion and Future Scope References Deep Learning Approach to Predict and Grade Glaucoma from Fundus Images through Constitutional Neural Networks Introduction Related Works Materials and Method Database and Division of Images Preprocessing and Down-Sampling Deep Learning Architecture (DLA)Input Layer Convolutional Layer Batch Normalization Layer Rectified Linear Unit Max Pooling Layer Fully Connected Layer Softmax Layer Methodology Proposed CNN Architecture Training and Testing Schemes Experiments Performance Assessment Discussion and Conclusion Conflict of Interest REFERENCES A Novel Method for Securing Cognitive Radio Communication Network Using the Machine Learning Schemes and a Rule Based Approaches Introduction Related Works and Existing Methods Brief Note on Existing Methods Collaborative Approach Toward Secure Spectrum Sensing Sequential 0/1 for Strategic Byzantine Attack Malicious Cognitive User Identification Algorithm for SSDF Attack XOR Distance Analysis for Securing CSS An Enhanced CSS Scheme Based on Evidence Theory System Model SSDF Attack: Reference and Mathematical Expressions Proposed System Model A Novel Schema Proposed Method An “Improved-apriori” Algorithm Implementation of the Proposed Method Performance Evaluation of the Accuracy Discourse and Performance Rating Basic Simulation Configuration Conclusions References Appendix I: Proof of Error Probability, Pе Detection of Retinopathy of Prematurity Using Convolution Neural Network Deepa Dhanaskodi and Poongodi Chenniappan Introduction Retinopathy of Prematurity Causes of ROP Stages of ROP Literature Survey Proposed Algorithm Convolution Neural Network Input Layer Hidden Layer Output Layer Proposed Work Flow for ROP Detection Survey and Load Image Data Specify Training and Validation Sets Image Input Layer Convolution Layer ReLU Layer Max Pooling Layer Fully Connected Layer Softmax Layer Classification Layer Specify Training Options Classify Evaluation Images and Calculate Accuracy Results and Discussion Conclusion References Impact of Technology on Human Resource Information System and Achieving Business Intelligence in Organizations Introduction Objective of the Study Research Methodology Literature Review Evolution of HRIS and the Changing Concept of HR Integration with IT Before 1945 (Pre World War II)-1960 (Post World War II)–1980 (Legislative Era and Emerging HRM)–1990 (Low-Cost Era and Integration of HR with IT)–2000 (Technology Era and the Emergence of Strategic Human Resource Management)–2010 (Emergence of High Technology and Introduction of Diversified Technological Tools)Onwards (Recent Trends of HRIS)Technology as a Competitive Strategy Implications Scope of the Study Conclusion References Proficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning Algorithm Introduction Problem Solving Problem Statement Solution System Design and Implementation Image Preprocessing k-Means Clustering Maximum iteration Epsilon Type Watershed Segmentation Feature Extraction SVM Classifier PCA Graph for Linear and RBF Kernel Conclusion References Role of Machine Learning in Social Area Networks Introduction to Machine Learning Framework to Develop Machine Learning Models Two Phases of Machine Learning Types of Machine Learning Types of Supervised Learning Classification Linear Regression Logistic Regression Types of Unsupervised Learning Dimensionality Reduction Feature Selection Feature Extraction Methods of Dimensionality Reduction Reinforcement Learning Challenges and Limitations of Machine Learning Applications of ML Social Area Networks Research Areas in Social Networking Sentimental Analysis Recommender System Introduction to Rule-Based Algorithms Experiment 1: Sentimental Analysis Sample Experimentation Results Experiment 2: Movie Recommendation Sample Experimentation Results Conclusion and Future Scope References Breast Cancer and Machine Learning: Interactive Breast Cancer Prediction Using Naive Bayes Algorithm Introduction to Breast Cancer Types of Breast Cancer Breast Cancer Symptoms Treating Breast Cancer Introduction to Machine Learning Algorithm Classification of Machine Learning Grouping Machine Learning Based on the Results Obtained Implementing Machine Learning Risk Factors of Breast Cancer Risk Factors TNM Staging System T Category N Category M Category Cancer Staging, Grouping, and Grading Staging and Grading Staging Evaluating and Implementing Naive Bayes Algorithm Assumptions Bayes Theorem Areas Utilization Naive Bayes Algorithms Building Primary Modules Using Naive Bayes System Workflow Architecture Exploratory Data Analysis Splitting the Dataset Data Wrangling Data Gathering Preprocessing Building the Classification Model Training the Data Testing the Dataset System Workflow Implementation of Naive Bayes Algorithm Using Anaconda Software Evaluating the Accuracy of Software Modules Confusion Matrix Basic Terms Comparing the Algorithm with Prediction in the Form of Best Accuracy Result Prediction Result by Accuracy Accuracy Calculation Results of Testing Using Confusion Matrix Conclusion References Deep Networks and Deep Learning Algorithms Introduction Deep Learning Why Deep Learning The Perceptron (Neuron)Neural Networks Network Topologies Feedforward Neural Networks The Artificial Neuron Gradient Descent The Backpropagation Algorithm TensorFlow and Its Use Neurons in Human Vision The Predictors: Decision Trees Learning Lower-Dimensional Representations Convolutional Layers Reinforcement Learning Recurrent Neural Networks On the Future of Neural Networks Conclusion References Machine Learning for Big Data Analytics, Interactive and Reinforcement Introduction Big Data Characteristics of Big Data The Big Data Revolution Why Is Big Data Analytics Important?Challenges FACED BY Big Data Machine Learning Types of Machine Learning Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning Machine Learning Steps Collection of Data Preparing Data Selecting a Model Training Model Evaluation of the Model Tuning of Parameter Making Predictions (Matthew Mayo, 2018)Applications of Machine Learning Virtual Assistants Prognosis Videos Supervision Social Media Activities Spam E-mail and Filtering of Malware Supporting Customers Online Search Engine Result Refining Online Fraud Detection Big Data Using Machine Learning Enter Machine Learning ML and Big Data—Real-World Applications Implementing Machine Learning in Big Data Empowering Big Data and Machine Learning Conclusion and the Future of Big Data Analytics References Fish Farm Monitoring System Using IoT and Machine Learning Introduction Related Work Proposed System Model Hardware Section Experimental Setup Result Conclusion Future Work References

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