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

by Deepak Garg (Editor), Joel J. P. C. Rodrigues (Editor), Suneet Kumar Gupta (Editor), Xiaochun Cheng (Editor), Pushpender Sarao (Editor)

Overview

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.

Product Details

ISBN-13:	9783031567025
Publisher:	Springer Nature Switzerland
Publication date:	03/26/2024
Series:	Communications in Computer and Information Science , #2054
Edition description:	2024
Pages:	424
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

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.