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Advanced Computing: 13th International Conference, IACC 2023, Kolhapur, India, December 1516, 2023, Revised Selected Papers, Part II 2024 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 424 pages, kõrgus x laius: 235x155 mm, 183 Illustrations, color; 27 Illustrations, black and white; XXVIII, 424 p. 210 illus., 183 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 2054
  • Ilmumisaeg: 26-Mar-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031567021
  • ISBN-13: 9783031567025
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  • Formaat: Paperback / softback, 424 pages, kõrgus x laius: 235x155 mm, 183 Illustrations, color; 27 Illustrations, black and white; XXVIII, 424 p. 210 illus., 183 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 2054
  • Ilmumisaeg: 26-Mar-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031567021
  • ISBN-13: 9783031567025
The two-volume set CCIS 2053 and 2054 constitutes the refereed post-conference proceedings of the 13th International Advanced Computing Conference, IACC 2023, held in Kolhapur, India, during December 15–16, 2023.

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.
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 Parkinsons 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.