Advanced Computing [[electronic resource] ] : 13th International Conference, IACC 2023, Kolhapur, India, December 15–16, 2023, Revised Selected Papers, Part II / / edited by Deepak Garg, Joel J. P. C. Rodrigues, Suneet Kumar Gupta, Xiaochun Cheng, Pushpender Sarao, Govind Singh Patel

• Record Nr.: UNINA9910845499703321
• Autore: Garg Deepak
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• ISBN: 3-031-56703-X
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (445 pages)
• Collana: Communications in Computer and Information Science, , 1865-0937 ; ; 2054
• Altri autori (Persone): RodriguesJoel J. P. C; GuptaSuneet Kumar; ChengXiaochun; SaraoPushpender; PatelGovind Singh
• Disciplina: 006.3
• Soggetti: 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
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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.
• Sommario/riassunto: 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.