Buch, Englisch, 424 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 680 g
13th International Conference, IACC 2023, Kolhapur, India, December 15-16, 2023, Revised Selected Papers, Part II
Buch, Englisch, 424 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 680 g
Reihe: Communications in Computer and Information Science
ISBN: 978-3-031-56702-5
Verlag: Springer Nature Switzerland
The 66 full papers and 6 short papers presented in these proceedings were carefully reviewed and selected from 425 submissions. The papers are organized in the following topical sections:
Volume I:
The AI renaissance: a new era of human-machine collaboration; application of recurrent neural network in natural language processing, AI content detection and time series data analysis; unveiling the next frontier of AI advancement.
Volume II:
Agricultural resilience and disaster management for sustainable harvest; disease and abnormalities detection using ML and IOT; application of deep learning in healthcare; cancer detection using AI.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
Weitere Infos & Material
Agricultural Resilience and Disaster Management for Sustainable Harvest.- Plant Disease Recognition using Machine Learning and Deep Learning Classifiers.- Securing Lives and Assets: IoT-Based Earthquake and Fire Detection for Real-Time Monitoring and Safety.- An Early Detection of Fall Using Knowledge Distillation Ensemble Prediction Using Classification.- Deep Learning Methods for Precise Sugarcane Disease Detection and Sustainable Crop Management.- An Interactive Interface for Plant Disease Prediction and Remedy Recommendation.- Tilapia Fish Freshness Detection using CNN Models.- Chilli Leaf Disease Detection using Deep Learning.- Damage Evaluation Following Natural Disasters Using Deep Learning.- Total Electron Content Forecasting in Low Latitude Regions of India: Machine & Deep Learning Synergy.- Disease and Abnormalities Detection using ML and IOT.- Early Phase Detection of Diabetes Mellitus Using Machine Learning.- Diabetes Risk Prediction through Fine-Tuned Gradient Boosting.- Early Detection of Diabetes using ML-based Classification Algorithms.- Prediction Of Abnormality Using IoT and Machine Learning.- Detection of Cardiovascular Diseases using Machine Learning Approach.- Mild Cognitive Impairment Diagnosis Using Neuropsychological Tests and Agile Machine Learning.- Heart Disease Diagnosis using Machine Learning Classifiers.- Comparative Evaluation of Feature Extraction Techniques in Chest X Ray Image with Different Classification Model.- Application of Deep Learning in Healthcare.- Transfer Learning Approach for Differentiating Parkinson’s Syndromes using Voice Recordings.- Detection of Brain Tumor Type Based on FANET Segmentation and Hybrid Squeeze Excitation Network with KNN.- Mental Health Analysis using Rasa and Bert: Mindful.- Kidney Failure Identification using Augment Intelligence and IOT Based on Integrated Healthcare System.- Efficient Characterization of Cough Sounds Using Statistical Analysis.- An Efficient Method for Heart Failure Diagnosis.- Novel Machine Learning Algorithms for Predicting COVID-19 Clinical Outcomes with Gender Analysis.- A Genetic Algorithm-Enhanced Deep Neural Network for Efficient and Optimized Brain Tumor Detection.- Diabetes Prediction using Ensemble Learning.- Cancer Detection Using AI.- A Predictive Deep Learning Ensemble Based Approach for Advanced Cancer Classification.- Predictive Deep Learning: An Analysis of Inception V3, VGG16, and VGG19 Models for Breast Cancer Detection.- Innovation in the Field of Oncology: Early Lung Cancer Detection and Classification using AI.- Colon Cancer Nuclei Classification with Convolutional Neural Networks.- Genetic Algorithm-based Optimization of UNet for Breast Cancer Classification: A Lightweight and Efficient approach for IoT Devices.- Classification of Colorectal Cancer Tissue Utilizing Machine Learning Algorithms.- Prediction of Breast Cancer using Machine Learning Technique.
