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This two-volume set, CCIS 2389 and CCIS 2390, constitutes selected papers presented at the 6th International Conference on Artificial Intelligence and Speech Technology, AIST 2024, held in Delhi, India, during November 13 14, 2024.
The 40 full papers and 15 short papers presented in these proceedings were carefully reviewed and selected from 398 submissions.These papers focus on Speech Technology using AI and AI innovations for CV and NLP. They have been categorized under the following topical sections:- Part I : Trends and Applications in Speech Processing; Recent Trends in Speech and NLP; Emerging trends in Speech Processing; Advances in Computational Linguistics and NLP. Part II : Recent Trends in Machine Learning and Deep Learning; Analysis using Hybrid technologies with Artificial Intelligence; Exploring New Horizons in Computer Vision Research.
List of contents
.- Recent Trends in Machine Learning and Deep Learning.
.- Exploring State-of-the-Art Approaches for Heart Disease Detection: A Detailed Analysis.
.- A Comprehensive Analysis of Recent Methodologies for Irregular Heart Beat Prediction.
.- Next-Gen Imaging: The Power of Hyperspectral Data and Autoencoders.
.- Redefining Vision Tasks: The Power of Transformers in Classification, Detection and Segmentation.
.- Harnessing Machine Learning for Feature Extraction in Plant Imaging and Analysis: A Review.
.- DeepFake Classification using Fine-Tuned Wave2Vec2.0.
.- Deepfake Detection: A Comprehensive Analysis of Modern Techniques.
.- Transfer Learning for Leaf Disease Image Classification via CNN Model.
.- Real-Time Detection of Household Objects Using Single-Shot with MobileNet.
.- Analysis using Hybrid technologies with Artificial Intelligence.
.- Investigating AI Explanations In Medical Diagnosis.
.- Assessing ChatGPT in Medical Domain.
.- Turbocharging Pull Request Reviews: Exploring Generative AI for Code Review.
.- Leveraging Depth Data and Parameter Sharing in Vision Transformers for Improved Face Anti-Spoofing.
.- Comparative Analysis of Multiple Embedding Models for Text Based Document Similarity.
.- Electricity Prediction using Machine Learning.
.- A Metric-Driven Comparative Study of Text Summarization Model: Insights from State-of-the- art LLMs.
.- Automatic Assessment of Program Code using CodeBERTScore: A Transformer-based Approach.
.- Comparative Analysis of Deep Learning Techniques on Tomato Plant Disease Detection.
.- Exploring New Horizons in Computer Vision Research.
.- Customer Churn Prediction Using Artificial Neural Networks.
.- Recent Advances in Denoising Techniques for Hyperspectral Image Enhancement.
.- Creating and Analyzing a Dictionary-Based Lexical Resource for Somali Homograph Disambiguation.
.- CodeBERT-BiGRU for Software Defect Prediction.
.- AccuRep: A Real-Time Android Application for Exercise Tracking Using MoveNet Pose Detection and Joint Angle Analysis.
.- Comparative Evaluation of 3D U-Net and SegResNet Architectures for Brain Tumor Segmentation Using Adam and Ranger21 Optimizers.
.- Advancements in Alzheimer's Disease Detection: A Comprehensive Review of Deep Learning Approaches in MRI Imaging.
.- Predictive Models in Biomedical Applications: A Machine Learning Approach.
.- Image Captioning Using Deep Learning Models: A Comprehensive Overview.
.- NLP-Driven Sentiment Analysis for Fake News Identification: The Responsive Truth.
Summary
This two-volume set, CCIS 2389 and CCIS 2390, constitutes selected papers presented at the 6th International Conference on Artificial Intelligence and Speech Technology, AIST 2024, held in Delhi, India, during November 13–14, 2024.
The 40 full papers and 15 short papers presented in these proceedings were carefully reviewed and selected from 398 submissions.These papers focus on Speech Technology using AI and AI innovations for CV and NLP. They have been categorized under the following topical sections:-
Part I : Trends and Applications in Speech Processing; Recent Trends in Speech and NLP; Emerging trends in Speech Processing; Advances in Computational Linguistics and NLP.
Part II : Recent Trends in Machine Learning and Deep Learning; Analysis using Hybrid technologies with Artificial Intelligence; Exploring New Horizons in Computer Vision Research.
