| Description |
1 online resource (240 p.) |
| Note |
Description based upon print version of record. |
| Contents |
Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Foreword -- Preface -- [Key Features] -- Key Features -- List of Contributors -- Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision -- P. Sasikumar1 and T. Saravanan1,* -- INTRODUCTION -- Techniques for Improvising Images -- Spatial-Domain Method -- Frequency-Domain Method -- TRANSFORMS: IMAGE IMPROVEMENT -- Wavelet-Transform Oriented Image Improvement -- Scaling and Translation -- IMAGE IMPROVEMENT WITH FILTERS -- DENOISING OF IMAGES -- Frontward Transform |
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IMAGE IMPROVEMENT WITH PRINCIPAL COMPONENT PCA FOR 2D -- Implementing 2D-PCA -- SELECTION AND EXTRACTION OF FEATURES -- Criteria for Selecting Features -- Linear Criteria for Extracting Features -- Discontinuity Handling -- Integration Part: Limitations -- Alteration of Smoothness Terminology -- CONCLUSION -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENT -- REFERENCES -- Algorithm For Intelligent Systems -- Pratik Dhoke1,*, Pranay Saraf1, Pawan Bhalandhare1, Yogadhar Pandey1, H. R. Deshmukh1 and Rahul Agrawal1 -- INTRODUCTION -- Reinforcement Learning -- Q-Learning |
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Game Theory -- Machine Learning -- Decision Tree -- Logistic Regression -- K-Means Clustering -- Artificial Neural Network (ANN) -- Swarm Intelligence -- Swarm Robots -- Swarm Intelligence in Decision Making Algorithm -- Natural Language Processing -- CONCLUSION -- FUTURE SCOPE -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Clinical Decision Support System for Early Prediction of Congenital Heart Disease using Machine learning Techniques -- Ritu Aggarwal1,* and Suneet Kumar2 -- INTRODUCTION -- RELATED WORK -- PROPOSED METHODOLOGY AND DATASET |
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STEPS FOR TRAINING AND TESTING THE DATASET -- MACHINE LEARNING ALGORITHMS FOR PREDICTION -- SUPPORT VECTOR MACHINE -- RANDOM FOREST -- MULTILAYER PERCEPTRON -- INPUT LAYER -- HIDDEN LAYER -- OUTPUT LAYER -- K- NEAREST NEIGHBOR (K-NN) -- EXPERIMENTS AND RESULTS -- Comparison Results -- CONCLUSION -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Review on Covid-19 Pandemic and Role of Multilingual Information Retrieval and Machine Translation for Managing its Effect -- Mangala Madankar1,* and Manoj Chandak2 -- INTRODUCTION -- RELATED WORK |
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OUTBREAK STAGE OF COVID 19 -- Travel history from infected countries -- Local Transmission -- Geographical Cluster of Cases -- Community Transmission -- CURRENT SITUATION IN INDIA -- TREATMENT -- ILLNESS SEVERITY -- ANTIBODY AND PLASMA THERAPY -- VACCINE -- PREVENTIVE MEASURE -- Myths -- EMERGING TECHNOLOGY FOR MITIGATING THE EFFECT OF THE COVID-19 PANDEMIC -- Infodemic and Natural Language Processing -- Arogya Setu App -- Issues of Languages all Over the World and Machine Translation -- Difficulties in Accessing Data in the Native Language -- INFORMATION RETRIEVAL SYSTEM FOR COVID-19 |
| Note |
New Information Retrieval System for COVID-19: TREC COVID |
| Summary |
This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredeve. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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|
Machine learning |
| Added Author |
Verma, Basant.
|
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Thillaiarasu, N.
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| Other Form: |
Print version: Lokulwar, Prasad Machine Learning Methods for Engineering Application Development Piraí : Bentham Science Publishers,c2022 9789815079197 |
| ISBN |
9789815079180 |
|
9815079182 |
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