Advances and Applications of Artificial Intelligence and Machine Learning : Proceedings of ICAAAIML 2022

• Autore: Unhelkar Bhuvan
• Pubblicazione: Singapore : , : Springer, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (782 pages)
• Altri autori: PandeyHari Mohan AgrawalArun Prakash ChoudharyAnkur
• Nota di contenuto: Intro -- Contents -- About the Editors -- Development of Big Data Dimensionality Reduction Methods for Effective Data Transmission and Feature Enhancement Algorithms -- 1 Introduction -- 2 Works -- 3 Objectives -- 4 Proposed Dimensionality Reduction Method -- 5 Analysis of the Obtained Results -- 6 Conclusion -- References -- IndianFood-7: Detecting Indian Food Items Using Deep Learning-Based Computer Vision -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Preparation -- 3.2 Our Experimentation on Object Detection Models -- 4 Results -- 5 Conclusion -- References -- Prediction of Protein-Protein Interaction Using Support Vector Machine Based on Spatial Distribution of Amino Acids -- 1 Introduction -- 2 Experimental Setup -- 3 Methodology -- 3.1 Data Set -- 3.2 Feature Representation -- 3.3 Support Vector Machines (SVM) -- 4 Results and Discussion -- 4.1 Evaluation Metrics -- 4.2 Performance of Proposed Model -- 4.3 Proposed Model Comparison Against Various Predictors -- 5 Conclusion -- References -- A Computational Comparison of VGG16 and XceptionNet for Mango Plant Disease Recognition -- 1 Introduction -- 2 Methodology and Dataset -- 2.1 Architecture of the Proposed System -- 2.2 Dataset Description -- 2.3 Data Pre-processing -- 2.4 Models Used -- 2.5 Training and Compiling the Model -- 3 Result and Analysis -- 4 Conclusion -- References -- Generate Artificial Human Faces with Deep Convolutional Generative Adversarial Network (DCGAN) Machine Learning Model -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Experimental Setup -- 3.2 Dataset Description -- 3.3 Model Description -- 4 Results -- 5 Future Scope and Conclusion -- References -- Robust Approach for Person Identification Using Three-Triangle Concept -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Block Diagram of Recommended System. ; 3.2 Algorithm Used -- 4 Circuit Layout -- 5 Interfacing of Components -- 6 Experimental Results -- 7 Conclusions -- 8 Future Scope -- References -- COVID-19 Disease Detection Using Explainable AI -- 1 Introduction -- 2 Explainable Artificial Intelligence -- 3 Dataset Description -- 4 Approach to the Proposed System -- 4.1 Support Vector Machine -- 4.2 Convolutional Neural Networks -- 4.3 ResNet50 -- 4.4 Implementation of Explainable AI -- 5 Proposed Methodology -- 6 Results -- 7 Conclusion and Future Scope -- References -- Towards Helping Visually Impaired People to Navigate Outdoor -- 1 Introduction -- 1.1 Convolutional Neural Network -- 1.2 Visual Geometry Group -- 2 Literature -- 3 Methodology -- 3.1 Create the Dataset -- 3.2 Applying Existing Approach -- 3.3 Analyzing the Existing Approach -- 3.4 Detect Objects in Image -- 3.5 Train and Test the Model -- 3.6 Analyzing the Results -- 4 Experimentation -- 5 Conclusion and Future Work -- References -- An Analysis of Deployment Challenges for Kubernetes: A NextGen Virtualization -- 1 Introduction -- 2 Origin, History of Kubernetes, and the Community Behind -- 3 Related Works -- 3.1 Literature Review -- 3.2 Objective -- 4 Deployment of Application in Kubernetes Cluster in Public Cloud -- 4.1 Survey to Examine Kubernetes Impact -- 5 Analysis of Deployment Failure Strategies and Measures -- 6 Result Analysis -- 7 Result Analysis -- 8 Conclusion -- References -- A New Task Offloading Scheme for Geospatial Fog Computing Environment Using M/M/C Queueing Approach -- 1 Introduction -- 2 Related Work -- 3 Establishing the Model -- 4 Numerical and Simulation Examples -- 5 Conclusions and Future Work -- References -- Face Recognition Using Deep Neural Network with MobileNetV3-Large -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 MobileNetV3Large Model. ; 3.4 Hyperparameter Tuning -- 4 Result -- 5 Conclusion -- References -- Detection of BotNet Using Extreme Learning Machine Tuned by Enhanced Sine Cosine Algorithm -- 1 Introduction -- 2 Background and Related Work -- 2.1 BotNet and DDOS -- 2.2 Extreme Learning Machine -- 2.3 Population-Based Metaheuristics -- 3 Proposed Method -- 3.1 Suggested Improved SCA -- 4 Experiments and Discussion -- 4.1 Dataset Description, Pre-processing and Evaluation Metrics -- 4.2 Research Findings and Comparative Analysis -- 5 Conclusion -- References -- Cloud Services Management Using LSTM-RNN -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Forecast Utilizing LSTM-RNN -- 3.2 Workload Prediction Using LSTM Pseudocode -- 4 Result -- 5 Conclusion and Future Scope -- References -- Detection of Various Types of Thyroid-Related Disease Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed System -- 3.1 Dataset -- 3.2 Exploratory Data Analysis -- 3.3 Data Preprocessing -- 3.4 Training Phase -- 3.5 Testing the Model -- 4 Results and Discussion -- 5 Conclusion -- References -- Implementation of WSN in the Smart Hanger to Facilitate MRO Operations on Aircraft Fuselage Using Machine Learning -- 1 Introduction -- 2 Acquisition and Dataset -- 2.1 Complexity of Model and Training Dataset -- 2.2 Learning from Imbalanced Data -- 3 Existing Machine Learning Approaches -- 3.1 DNN (Deep Neural Networks) -- 3.2 Support Vector Machine (SVM) -- 3.3 Algorithmic Approach Using Minimal Data: Few-Shot Learning -- 4 State of the Art Method Evaluation -- 4.1 Experiment -- 4.2 Result -- 5 Proposed Approach -- 6 Conclusion and Prospects -- References -- Wi-Fi Controlled Smart Robot for Objects Tracking and Counting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Wi-Fi Controlled Smart Robot Through Web Server. ; 3.2 Proposed Methodology for Color Detection -- 3.3 Object Tracking for Counting Objects -- 4 Results and Discussion -- 5 Conclusion -- References -- Speech Recognition for Kannada Using LSTM -- 1 Introduction -- 2 Literature Review -- 3 Overview of LSTM and Kaldi -- 3.1 Markov Models -- 3.2 RNN -- 3.3 LSTM -- 3.4 Kaldi -- 4 Methodology -- 4.1 Audio Data Collection -- 4.2 Text Data Pre-processing -- 4.3 Feature Extraction and Preparing Language Models -- 4.4 Experiments with Monophone, Triphone Models -- 4.5 Experiments with DNN and LSTM -- 5 Results -- 6 Conclusion -- References -- Computer Vision-Based Smart Helmet with Voice Assistant for Increasing Driver Safety -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Deep Learning Model Development -- 3.2 Rear-End Collision Warning System -- 3.3 Deployment of Deep Learning Model via Server -- 3.4 Architecture of Helmet Software -- 3.5 Building Helmet (Hardware) Prototype -- 3.6 Comparison and Analysis of Models -- 4 Result -- 5 Conclusion and Future Scope -- References -- Predicting Aging Related Bugs with Automated Feature Selection Techniques in Cloud Oriented Softwares -- 1 Research Motivation and Aim -- 1.1 Imbalanced Data -- 1.2 High Dimensional Data -- 2 Related Work -- 3 Research Contributions -- 4 Research Framework -- 4.1 Automated Bug Report Extraction -- 4.2 Feature Selection Techniques -- 4.3 Datasets -- 4.4 Software Metrics -- 4.5 Imbalance Mitigation Procedure-SMOTE -- 4.6 Machine Learning Classifiers -- 4.7 Performance Measures -- 5 Experimental Setup -- 6 Results and Discussions -- 6.1 Detailed Analysis of Feature Ranking -- 6.2 Relative Comparison of Techniques -- 7 Conclusion -- 8 Threats to Validity and Future Work -- References -- Time Series Analysis of Crypto Currency Using ARIMAX -- 1 Introduction -- 2 Literature Review -- 3 Methodology. ; 4 Components Taken into Consideration -- 4.1 Factors that Affect Crypto Currency -- 4.2 Dataset Used -- 4.3 ARIMAX Algorithm -- 5 Experimental Setup -- 5.1 Cointegrated Pair -- 5.2 Selection of Features -- 5.3 Building the Model -- 6 Result Analysis -- 7 Conclusion -- References -- A Machine Learning Approach Towards Prediction of User's Responsiveness to Notifications with Best Device Identification for Notification Delivery -- 1 Introduction -- 2 Related Work -- 3 Proposed System Architecture -- 3.1 Notification Module -- 3.2 User Identification Module -- 3.3 Active Device and Proximity Detection Module -- 3.4 Privacy and Access Control Module -- 3.5 Intelligent Delivery System Module -- 3.6 Notification Storage Bucket -- 4 Predicting User's Responsiveness to Notifications -- 4.1 Dataset -- 4.2 Predicting User's Responsiveness Using Machine Learning -- 5 Results and Discussions -- 6 Conclusion and Future Work -- References -- Real-Time Full Body Tracking for Life-Size Telepresence -- 1 Introduction -- 2 Related Work -- 3 Material and Method -- 3.1 Full-Body Tracking -- 3.2 Background Removal -- 3.3 Remote User Setting -- 4 Results and Discussion -- 5 Conclusion -- References -- Solar Power Generation Forecasting Using Deep Learning -- 1 Introduction -- 2 Use of Artificial Intelligence in Predicting Data -- 3 Methodology -- 3.1 Data Collection -- 3.2 Pre-processing -- 3.3 Split Data into Train and Testing Sets -- 3.4 Data Standardization -- 3.5 Building Model -- 3.6 Training Model -- 4 Model Building and Implementation -- 5 Model Evaluation -- 6 Results -- 7 Conclusion -- References -- Applications of Big Five Personality Test in Job Performance -- 1 Introduction -- 1.1 Dimensions of Job Performance -- 1.2 Personality Model with Five Traits -- 2 Literature Review -- 3 Objectives of the Study -- 4 Research Design -- 4.1 Measuring Instruments. ; 5 Data Analysis Technique Employed.
• Titolo autorizzato: Advances and Applications of Artificial Intelligence and Machine Learning
• ISBN: 981-9959-74-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910763599703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Electrical Engineering Series