Del i serien Communications in Computer and Information Science

Advanced Computing

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

Häftad, Engelska, 2024

AvDeepak Garg,Joel J. P. C. Rodrigues,Suneet Kumar Gupta,Xiaochun Cheng,Pushpender Sarao,Govind Singh Patel

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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.

Produktinformation

Utgivningsdatum2024-03-26
Mått155 x 235 x 25 mm
Vikt680 g
FormatHäftad
SpråkEngelska
SerieCommunications in Computer and Information Science
Antal sidor424
Upplaga2024
FörlagSpringer International Publishing AG
ISBN9783031567025

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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.