Suchergebnisse

Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities

Cover -- Half Title -- Title -- Copyright -- Contents -- Preface -- About the Editors -- List of Contributors -- Chapter 1 ◾ A Detailed Study on Deep Learning versus Machine Learning Approaches for Pest Classification in Field Crops -- 1.1 Introduction -- 1.1.1 Background -- 1.1.2 Dataset -- 1.1.3 Machine Learning -- 1.1.4 Deep Learning -- 1.2 Related Work -- 1.2.1 Pest Classification in Field Crops Based on ML Techniques -- 1.2.2 Pest Classification in Field Crops Based on DL Algorithms -- 1.3 Open-Source Tools for Deep Learning Frameworks -- 1.4 Challenges in the Area of Pest Classification Using ML/DL -- 1.4.1 Lack of Dataset -- 1.4.2 Selection of Hyperparameters -- 1.4.3 Algorithm Selection -- 1.4.4 Performance Metric Selection -- 1.5 Conclusion -- References -- Chapter 2 ◾ Integration of Artificial Intelligence (AI) and Other Cutting-Edge Technologies -- 2.1 Introduction -- 2.2 Functional Blocks of an IoT Ecosystem -- 2.2.1 Sensors -- 2.2.2 Processors -- 2.2.3 Gateways -- 2.2.4 Applications -- 2.3 Technologies Involved in IoT Development -- 2.4 Working of IoT -- 2.5 IoT Platform -- 2.6 IoT Platforms Overview -- 2.6.1 Arduino -- 2.6.2 Raspberry Pi -- 2.7 Wearable IoT Technology -- 2.7.1 IoT Connectivity - 5G, Wi-Fi 6, LORA WAN -- 2.7.2 AIoT - Artificial Intelligence and IoT Technology -- 2.8 Applications -- 2.8.1 IoT for Smart Cities: Real-Time Examples -- 2.8.2 IoT in Smart Home -- 2.8.3 IoT in Healthcare -- 2.8.4 Automation in Agriculture -- 2.8.5 Industrial Automation -- 2.8.6 IoT in Transportation -- 2.9 Conclusion -- References -- Chapter 3 ◾ A Detailed Case Study on Various Challenges in Vehicular Networks for Smart Traffic Control System Using Machine Learning Algorithms -- 3.1 Introduction -- 3.1.1 Benefits of Using STCS -- 3.1.2 Reinforcement Learning (RL) -- 3.1.3 Traffic Light Management System (TLMS) -- 3.1.4 Machine Learning (ML).

Abstract
Purpose
This study addresses the critical health issue of brain tumors, focusing on enhancing the accuracy of tumor segmentation from Magnetic Resonance Imaging (MRI) images. The primary research question investigates the effectiveness of a novel Hybrid Watershed–Clustering framework and its underlying Progressive Segmentation of the MR Images using the Radius and Intensity Measure (PS-RIM) algorithm. The aim is to improve the detection and segmentation of brain tumors within MR images, surpassing the efficacy of current methodologies.

Methods
The methodology involves a three-stage process. In the preprocessing stage, noise reduction and intensity normalization techniques are applied to clarify the images. The next stage is region-based segmentation, which includes morphological processing, edge detection, and thresholding to delineate tumor areas accurately. The final post-processing stage enhances segmentation accuracy and reduces false positives by integrating clustering machine learning techniques, specifically the K-Means cluster algorithm, to refine tumor identification.

Results
The framework's comprehensive evaluation across various MR images shows a significant improvement in accuracy over existing segmentation methods. The PS-RIM algorithm within the framework effectively captures the diverse presentations of tumor appearances in MR images. The research recorded an impressive accuracy rate of 98.11% in tumor detection, demonstrating enhanced identification and segmentation quality.

Conclusions
The study concludes that the proposed Hybrid Watershed–Clustering framework, powered by the PS-RIM algorithm, markedly improves the detection and differentiation of brain tumors in MR images. It exhibits exceptional accuracy, resilience, and computational efficiency. These findings hold substantial potential for advancing computer vision and image analysis in medical diagnostics, which could improve patient outcomes in managing brain tumors.

Graphical abstract