Atjaunināt sīkdatņu piekrišanu

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

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 88,63 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Advanced Computing: 13th International Conference, IACC 2023, Kolhapur, India, December 15-16, 2023, Revised Selected Papers, Part IIPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97830315670322e.html