| Description |
1 online resource (539 p.) |
| Series |
Lecture Notes on Data Engineering and Communications Technologies Series ; v.203 |
|
Lecture Notes on Data Engineering and Communications Technologies Series
|
| Contents |
Intro -- Welcome Message from AINA-2024 Organizers -- AINA-2024 Organizing Committee -- AINA-2024 Keynote Talks -- Agile Edge: Harnessing the Power of the Intelligent Edge by Agile Optimization -- Challenges in Entity Matching in AI Era -- Contents -- Application of Soft Computing Techniques for Clustering in Vehicular Ad Hoc Networks: A Survey -- 1 Introduction -- 2 Fuzzy Logic-Based Clustering Techniques for VANETs -- 3 Metaheuristic-Based Clustering Techniques for VANETs -- 4 Machine Learning-Based Clustering Techniques for VANETs -- 5 Discussion -- 6 Conclusions -- References |
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EarlyStage Diabetes Risk Detection Using Comparison of Xgboost, Lightgbm, and Catboost Algorithms -- 1 Introduction -- 2 Methodology -- 2.1 XGBoost -- 2.2 LightGBM -- 2.3 CatBoost -- 3 Experimental Analysis -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Result Analysis -- 3.4 XGBoost -- 3.5 LightGBM -- 3.6 CatBoost -- 3.7 Model Evaluation -- 4 Conclusion -- References -- Blockchain Based Artificial Intelligence of Things (AIoT) for Wildlife Monitoring -- 1 Introduction -- 2 Literature Review -- 3 Proposed Framework -- 3.1 AIoT for Collection of Data -- 3.2 IPFS for Storage of Data |
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3.3 Blockchain for Storage of Hash of Data -- 3.4 Consortium Blockchain for Decision-Making -- 3.5 Mobile Application for Monitoring Wild Animal Movement -- 4 Simulation Results -- 4.1 The Classification of Images Using CNN -- 4.2 Uploading Data to IPFS and Blockchain -- 5 Conclusion and Future Work -- References -- Research on Screw Detection Using Artificial Intelligence Technology -- 1 Introduction -- 2 Current 2D Inspection Technology -- 3 AI Inspection Technology -- 4 The Referred CNN Model for Screw Dataset in Kaggle -- 5 The CNN Model for Anomaly Images Detection and Experiments Results |
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6 Conclusions -- References -- An Efficient Method for Lung Cancer Image Segmentation and Nodule Type Classification Using Deep Learning Algorithms -- 1 Introduction -- 2 Lung Cancer Segmentation and Discrimination Proposed System -- 2.1 Data Exploration -- 2.2 Image Preprocessing -- 2.3 Mask Creation -- 2.4 Segmentation -- 2.5 Classification -- 3 Results and Discussion -- 3.1 Pre-Processing and Mask Creation Results -- 3.2 Segmentation and Classification Results -- 4 Conclusion -- References -- Targeted and Automatic Deep Neural Networks Optimization for Edge Computing -- 1 Introduction |
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2 DNN Compression Techniques -- 2.1 Network Pruning -- 2.2 Weight Sharing -- 2.3 Quantization -- 3 Related Work -- 3.1 Pruning Rate Search -- 3.2 Number of Clusters Search -- 4 Proposed Method -- 4.1 Weight Pruning -- 4.2 Number of Clusters -- 4.3 Quantization -- 4.4 Overall Procedure -- 5 Results -- 6 Conclusion -- References -- Edge to Cloud Network Function Offloading in the ADAPTO Framework -- 1 Introduction -- 2 Technological Context -- 2.1 5G Architecture -- 2.2 Network Function Virtualization Scaling and Placement -- 2.3 SD-WAN for 5G Backhauls -- 3 The ADAPTO Project |
| Notes |
Description based upon print version of record |
|
3.1 Seamless Offloading Based on Reinforcement Learning |
| Genre/Form |
Electronic books
|
| Form |
Electronic book
|
| ISBN |
9783031579318 |
|
3031579313 |
|
