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Advanced Computing 13th International Conference, IACC 2023, Kolhapur, India, December 15–16, 2023, Revised Selected Papers, Part II /

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 rev...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Garg, Deepak (Editor), Rodrigues, Joel J. P. C. (Editor), Gupta, Suneet Kumar (Editor), Cheng, Xiaochun (Editor), Sarao, Pushpender (Editor), Patel, Govind Singh (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Udgivelse:1st ed. 2024.
Serier:Communications in Computer and Information Science, 2054
Fag:
Artificial intelligence.
Computer engineering.
Computer networks .
Application software.
Education > Data processing.
Image processing > Digital techniques.
Computer vision.
Artificial Intelligence.
Computer Engineering and Networks.
Computer and Information Systems Applications.
Computers and Education.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Online adgang:https://doi.org/10.1007/978-3-031-56703-2
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Indholdsfortegnelse:
  • 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.

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