Intelligent Data Engineering and Analytics : Proceedings of the 11th International Conference on Frontiers of Intelligent Computing / / Vikrant Bhateja [and four others], editors

• Pubblicazione: Singapore : , : Springer, , [2023]; ©2023
• Edizione: First edition.
• Descrizione fisica: 1 online resource (633 pages)
• Disciplina: 006.3
• Soggetto topico: Computational intelligence
• Persona (resp. second.): BhatejaVikrant
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- About the Editors -- 1 Artificial Bee Colony for Automated Black-Box Testing of RESTful API -- 1.1 Introduction -- 1.2 Aims and Contributions -- 1.3 Literature Survey -- 1.4 ABC Algorithm -- 1.5 Proposed Approach -- 1.6 Experiments and Results -- 1.7 Conclusion -- References -- 2 Classifying Human Activities Using Machine Learning and Deep Learning Techniques -- 2.1 Introduction -- 2.2 Problem Description -- 2.2.1 Dataset Description -- 2.2.2 Exploratory Data Analysis -- 2.3 Related Literature -- 2.4 Proposed Approach -- 2.4.1 Machine Learning Models -- 2.4.2 Deep Learning Models -- 2.5 Results -- 2.6 Conclusion and Future Scope -- References -- 3 Explainable Artificial Intelligence and Mobile Health for Treating Eating Disorders in Young Adults with Autism Spectrum Disorder Based on the Theory of Change: A Mixed Method Protocol -- 3.1 Background -- 3.2 Aims and Objectives -- 3.3 Method -- 3.3.1 Study Design -- 3.3.2 Participants -- 3.3.3 Recruitment -- 3.3.4 Sample Size -- 3.4 Data Analysis -- 3.5 Outcome -- 3.6 Dissemination -- 3.7 Limitation -- 3.8 Impact -- References -- 4 Novel Deep Learning Models for Optimizing Human Activity Recognition Using Wearable Sensors: An Analysis of Photoplethysmography and Accelerometer Signals -- 4.1 Introduction -- 4.1.1 Contributions of the Research Work -- 4.2 Description of the Dataset -- 4.3 Proposed Models -- 4.3.1 ResTime: Residual Network for Time Series -- 4.3.2 Minception: Minimized Inception Architecture -- 4.4 Experiment Results and Discussions -- 4.4.1 ResTime Model Result -- 4.4.2 Minception Model Results -- 4.4.3 Discussions and Performance Comparison -- 4.5 Conclusions and Future Directions -- References -- 5 Link Prediction in Complex Networks: An Empirical Review -- 5.1 Introduction -- 5.1.1 Problem Statement -- 5.2 Link Prediction Measures. ; 5.2.1 Neighborhood-Based Measures -- 5.3 Experimentation and Results -- 5.3.1 Data Set Description -- 5.3.2 Evaluation Metrics -- 5.3.3 Results -- 5.4 Conclusion -- References -- 6 High Utility Itemset Mining and Inventory Management: Theory and Use Cases -- 6.1 Introduction to High Utility Itemset Mining -- 6.2 Inventory Management and Uses -- 6.3 Motivations and Contributions -- 6.3.1 Motivations -- 6.3.2 Contributions -- 6.4 High Utility Itemset Mining Use Cases to Inventory Management -- 6.4.1 Examples -- 6.4.2 Use Cases -- 6.5 Future Directions and Conclusions -- References -- 7 Using Clustering Approach to Enhance Prioritization of Regression Test Cases -- 7.1 Introduction -- 7.2 Background Studies and Related Work -- 7.3 Experimental Result and Analysis -- 7.4 Conclusion -- References -- 8 Nucleus Segmentation Using Adaptive Thresholding for Analysis of Blood and Bone Marrow Smear Images -- 8.1 Introduction -- 8.2 Proposed Approach of Contrast Stretching and Colour-Based Segmentation -- 8.2.1 Contrast Stretching Using Dark Contrast Algorithm (DCA) -- 8.2.2 Colour-Based Segmentation Using Adaptive Thresholding -- 8.3 Experimental Results and Analysis -- 8.3.1 Dataset -- 8.3.2 Simulation Results and Analysis -- 8.4 Conclusion -- References -- 9 A Systematic Review on Automatic Speech Recognition for Odia Language -- 9.1 Introduction -- 9.2 Data Collection Techniques in ASR -- 9.3 Related Work -- 9.4 Toolkits and Online Resources -- 9.5 Proposed Model for ASR -- 9.6 Discussion and Future Scope -- References -- 10 A Study on Influence Maximization in Complex Networks -- 10.1 Introduction -- 10.2 State of the Art -- 10.3 Conclusion -- References -- 11 A Survey on Smart Hydroponics Farming: An Integration of IoT and AI-Based Efficient Alternative to Land Farming -- 11.1 Introduction -- 11.1.1 Different Types of Agriculture Process. ; 11.1.2 Hydroponics -- 11.1.3 Techniques of Hydroponics -- 11.1.4 Why Hydroponics Over Other Smart Agriculture Procedures -- 11.2 Related Work -- 11.3 Methodology -- 11.3.1 Intelligent Farming -- 11.3.2 IoT-Based Farming -- 11.3.3 Intelligent Agriculture -- 11.4 Discussion -- 11.5 Conclusion -- References -- 12 Angiosperm Genus Classification by RBF-SVM -- 12.1 Introduction -- 12.2 Methodology -- 12.2.1 Checking the PDF File Gray-Level Co-Occurrence Matrices -- 12.2.2 Radial Basis Function Kernel Support Vector Machine -- 12.2.3 Multi-class Classification Using SVM -- 12.2.4 Classification -- 12.3 Experiment -- 12.3.1 Dataset -- 12.3.2 Cross-Validation Results -- 12.4 Conclusion -- References -- 13 Signage Detection Based on Adaptive SIFT -- 13.1 Introduction -- 13.2 Method -- 13.2.1 Overview -- 13.2.2 Build SM -- 13.2.3 Signage Identification Based on SIFT Matching -- 13.3 Experimental Results -- 13.4 Conclusion -- References -- 14 Surface Electromyography Assisted Hand Gesture Recognition Using Bidirectional LSTM and Unidirectional LSTM for the Hearing Impaired -- 14.1 Introduction -- 14.2 Related Works -- 14.3 Proposed Methodology -- 14.3.1 Long Short-Term Memory (LSTM) -- 14.3.2 Bi-LSTM -- 14.3.3 Deep Stacked LSTM -- 14.3.4 Proposed Stacked Bidirectional and Unidirectional LSTM -- 14.4 Experiment -- 14.4.1 Data Acquisition -- 14.4.2 Data Preprocessing -- 14.4.3 Model Architecture -- 14.5 Results -- 14.6 Conclusions -- References -- 15 The Potential of Using Corpora and Concordance Tools for Language Learning: A Case Study of 'Interested in (Doing)' and 'Interested to (Do)' -- 15.1 Introduction -- 15.2 Methodology -- 15.2.1 Corpus Tools -- 15.2.2 Stages of Investigation -- 15.3 Results -- 15.3.1 Stage One: A General Picture -- 15.3.2 Stage Two: Verbs that Occur in the Two Constructions -- 15.3.3 Stage Three: In-Depth Observations and the Rules. ; 15.4 Discussion and Conclusion -- References -- 16 Analysis of Various Video-Based Human Action Recognition Techniques Using Deep Learning Techniques -- 16.1 Introduction -- 16.2 Literature Review -- 16.2.1 Classification of Human Action Recognition Methods -- 16.3 Research Gaps -- 16.4 Analysis and Discussion -- 16.4.1 Analysis Based on Techniques -- 16.4.2 Analysis Using Evaluation Metrics -- 16.5 Conclusion -- References -- 17 Penetration Testing of Web Server Using Metasploit Framework and DVWA -- 17.1 Introduction -- 17.2 Problem Statement -- 17.3 Our Implementation -- 17.4 Conclusion -- References -- 18 Periodic Rampart Line Inspired Circular Microstrip Patch Antenna -- 18.1 Introduction -- 18.2 Antenna Design -- 18.3 Results and Discussion -- 18.4 Conclusions -- References -- 19 A Deep Learning-Based Prediction Model for Wellness of Male Sea Bass Fish -- 19.1 Introduction -- 19.2 Literature Study -- 19.3 Proposed Work -- 19.3.1 Dataset -- 19.3.2 Preprocessing -- 19.3.3 Design Methodology -- 19.4 Results and Observations -- 19.5 Advantages -- 19.6 Conclusion -- 19.7 Future Work -- References -- 20 Depression Detection Using Deep Learning -- 20.1 Introduction -- 20.2 Literature Survey -- 20.3 Methods and Experimental Procedure -- 20.3.1 Feed-Forward Deep Neural Network (FNN) -- 20.3.2 Simple Recurrent Neural Network (RNN) -- 20.3.3 Long Short-Term Memory (LSTM) -- 20.3.4 Gated Recurrent Units (GRU) -- 20.3.5 Convolutional Neural Network (CNN) -- 20.3.6 Sigmoid -- 20.3.7 Binary Cross-Entropy Loss -- 20.4 Results and Discussion -- 20.4.1 Dataset Description -- 20.4.2 Evaluation Metrics -- 20.4.3 Performance of the Deep Learning Models -- 20.5 Conclusion -- References -- 21 Fusion of Variational Autoencoder-Generative Adversarial Networks and Siamese Neural Networks for Face Matching -- 21.1 Introduction -- 21.1.1 About Variational Auto-Encoders. ; 21.1.2 About Generative Adversarial Networks -- 21.1.3 Applications of Face Matching -- 21.2 Related Works -- 21.3 Methodology -- 21.4 Results -- 21.5 Conclusion and Future Scope -- References -- 22 An Invasion Detection System in the Cloud That Use Secure Hashing Techniques -- 22.1 Introduction -- 22.2 Literature Review -- 22.3 Proposed System -- 22.4 Methodology -- 22.5 Experimental Results -- 22.6 Conclusion -- References -- 23 Detection of Suspicious Human Activities from Surveillance Camera Using Neural Networks -- 23.1 Introduction -- 23.2 Related Work -- 23.3 Existing System -- 23.4 Proposed System -- 23.4.1 Uploading Video Input File -- 23.4.2 Frames Processing by CNN Model -- 23.4.3 Detecting Suspicious Images -- 23.5 Architecture -- 23.6 Process Execution -- 23.7 Results -- 23.8 Future Scope -- 23.9 Conclusion -- References -- 24 High Resolution Remote Sensing Image Classification Using Convolutional Neural Networks -- 24.1 Introduction -- 24.2 Related Work -- 24.2.1 Grey Level Co-occurrence Matrix and Statistical Parameters (GLCM) -- 24.2.2 Feature Selection -- 24.2.3 Image Classification -- 24.3 Proposed Methodology -- 24.3.1 Pre-processing of Data -- 24.3.2 Conventional Neural Networks -- 24.4 Experimental Results -- 24.5 Conclusion -- References -- 25 FSVM: Time Series Forecasting Using Fuzzy Support Vector Machine -- 25.1 Introduction -- 25.2 Background -- 25.2.1 Concept of Time Series -- 25.2.2 Concept of Time Series Forecasting (TSF) -- 25.2.3 Conventional Sets and Fuzzy Sets -- 25.2.4 Support Vector Machine (SVM) -- 25.3 Proposed Fuzzy SVM Model -- 25.4 Experimental Analysis -- 25.4.1 Accuracy Measures Using RMSE and SMAPE -- 25.4.2 Performance Analysis by Considering All Datasets Together -- 25.5 Conclusions -- References -- 26 Deep Learning Framework for the Detection of Invasive Ductal Carcinoma -- 26.1 Introduction. ; 26.2 Literature Survey.
• Titolo autorizzato: Intelligent Data Engineering and Analytics
• ISBN: 981-9967-06-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910766886203321
• Lo trovi qui: Univ. Federico II
• Serie: Smart innovation, systems, and technologies ; ; Volume 371.