This book constitutes the proceedings of the First International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024, held in Mohali, India, in March 1-2, 2024. The 44 full papers presented in these two volumes were carefully reviewed and selected from 417 submissions. The papers are organized in the following topical sections: Part I: machine learning and deep learning; pattern and speech recognition; internet of things (IoT). Part II: data science and data analytics; communication, network and security.
This book constitutes the proceedings of the First International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024, held in Mohali, India, in March 1-2, 2024.
The 44 full papers presented in these two volumes were carefully reviewed and selected from 417 submissions. The papers are organized in the following topical sections:
Part I: machine learning and deep learning; pattern and speech recognition; internet of things (IoT).
Part II: data science and data analytics; communication, network and security.
Produktdetails
Produktdetails
Communications in Computer and Information Science 2125
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Inhaltsangabe
.- Machine Learning and Deep Learning.
.- A Novel Smart Facial Features for Real-Time Motorists Sleepiness Prediction and Alerting System Using Hybrid Convolutional Neural Network in Computer Vision.
.- Enhancing Power Transformer Reliability with Machine Learning-Based Fault Detection and Data Analysis.
.- Type 2 Diabetes Prediction using Machine Learning: A Game-Changer for Healthcare.
.- Classification and Prognosis of Brain Tumors in MRI Images Using Deep Learning.
.- Improving Breast Cancer Prediction with Validation: Optimized feature.
.- Diabetes Prediction Precision: Evaluating XGBoost and LightGBM Performance with IQR Preprocessing.
.- Enhancing Cardiovascular Health Prediction: A Machine Learning Perspective.
.- Measuring AI's Impact on HR Strategies and Operational Effectiveness in the Era of Industry 4.0.
.- Implementation of deep learning and machine learning for designing and analyzing IDS (Intrusion detection System) through Novel framework.
.- Performance Evaluation of Medicinal Leaf Classification Using DeepLabv3 and ML Classifiers.
.- Android App Permission Detector based on Machine Learning Models.
.- Histopathological Analysis Advancements in Deep Learning for the Diagnosis of Lung and Colon Cancer with Ex-planatory Power via Visual Saliency.
.- Deep Learning Approaches for Chest X-Ray-Based Covid-19 Identification.
.- Pattern and Speech Recognition.
.- Bi-LSTM based Speech Emotion Recognition.
.- Real Time Voice Language Interaction.
.- Exploring In-Context Learning: A Deep Dive into Model Size, Templates, and Few-Shot Learning for Text Classification.
.- A Novel Smart Facial Features for Real-Time Motorists Sleepiness Prediction and Alerting System Using Hybrid Convolutional Neural Network in Computer Vision.
.- Enhancing Power Transformer Reliability with Machine Learning-Based Fault Detection and Data Analysis.
.- Type 2 Diabetes Prediction using Machine Learning: A Game-Changer for Healthcare.
.- Classification and Prognosis of Brain Tumors in MRI Images Using Deep Learning.
.- Improving Breast Cancer Prediction with Validation: Optimized feature.
.- Diabetes Prediction Precision: Evaluating XGBoost and LightGBM Performance with IQR Preprocessing.
.- Enhancing Cardiovascular Health Prediction: A Machine Learning Perspective.
.- Measuring AI's Impact on HR Strategies and Operational Effectiveness in the Era of Industry 4.0.
.- Implementation of deep learning and machine learning for designing and analyzing IDS (Intrusion detection System) through Novel framework.
.- Performance Evaluation of Medicinal Leaf Classification Using DeepLabv3 and ML Classifiers.
.- Android App Permission Detector based on Machine Learning Models.
.- Histopathological Analysis Advancements in Deep Learning for the Diagnosis of Lung and Colon Cancer with Ex-planatory Power via Visual Saliency.
.- Deep Learning Approaches for Chest X-Ray-Based Covid-19 Identification.
.- Pattern and Speech Recognition.
.- Bi-LSTM based Speech Emotion Recognition.
.- Real Time Voice Language Interaction.
.- Exploring In-Context Learning: A Deep Dive into Model Size, Templates, and Few-Shot Learning for Text Classification.
