Muutke küpsiste eelistusi

E-raamat: Intelligence Science V: 6th IFIP TC 12 International Conference, ICIS 2024, Nanjing, China, October 25-28, 2024, Proceedings

Edited by , Edited by , Edited by
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 111,14 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Intelligence Science V: 6th IFIP TC 12 International Conference, ICIS 2024, Nanjing, China, October 25-28, 2024, ProceedingsSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book constitutes the refereed proceedings of the 6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024, held in Nanjing, China, in October 25-28, 2024. 





The 23 full papers and 2 short papers presented here were carefully reviewed and selected from 32 submissions. These papers have been categorized into the following sections: Machine Learning; Causal Reasoning; Large Language Model; Intelligent Robot; Perceptual Intelligence;  AI for Science; Medical Artificial Intelligence.
.-Machine Learning.



.- Difference-Enhanced Learning of the Deep Semantic Segmentation Networks
for First Break Picking.



.- A Framework of Reinforcement Learning for Truncated L´evy Flight
Exploratory.



.- Detection of depression in EEG Signals Based on Convolutional transformer
and adaptive transfer learning.



.- Twin Bounded Least Squares Support Vector Regression.



.-MLEE: Event Extraction as Multi-Label Classification Task at Token Level.



.- Research on Improvement of Sweeping Learning Chain Algorithm Based on
Factor Space Theory.



.- End-to-End Control of a Quadrotor Using Gaussian Ensemble Model-Based
Reinforcement Learning.



.- Causal Reasoning.



.- Research on the Causal Forest Algorithm based on Factor Space Theory.



.- Superpositioner A Non-logical Computation Model.



.- Research on Factor Support Vector Multi-classification Algorithm based on
Factor Space Theory.



.- Large Language Model.



.-Improve LLM Inference Performance with Matrix Decomposition Strategies.



.- Intelligent Robot.



.- Trajectory Prediction of Unmanned Surface Vehicle Based on Improved
Transformer.



.- Deep Neural Network Based Relocalization of Mobile Robot in Visual SLAM.



.- A Vision-Based Method for UAV Autonomous Landing Area Detection.



.- Perceptual Intelligence.



.- Research on Object Detection for Intelligent Sensing of Navigation Mark in
Yangtze River.



.- Cascaded Sliding-Window-based Relativistic GAN Fusion for Perceptual and
Consistent Video Super-Resolution.



.- Integration of Raman Spectroscopy, On-line Microscopic Imaging and Deep
learning-based Image Analysis for Real-time Monitoring of Cell Culture
Process.



.- DRL-SLAM: Enhanced Object Detection Fusion with Improved YOLOv8.



.- Driver Fatigue Recognition Based on EEG Signal and Semi-Supervised
Learning.



.- SC-EcapaTdnn : ECAPA-TDNN with Separable Convolutional for Speaker
Recognition.



.- AI for Science.



.-  Evolving Financial Markets: The Impact and Efficiency of AI-Driven
Trading Strategies.



.-DSFM Method: A New Approach to Enhancing Discrimination Ability on
AI-Generated Datasets.



.- Medical Artificial Intelligence.



.- Enhancing Weakly Supervised Medical Segmentation via Heterogeneous
Co-training with Box-wise Augmentation and Pseudo-label Filtering.



.- FCGA-Former: A Hybrid Factor Space Classification Model for Predicting the
Tumor Mutation Burden of Lung Adenocarcinoma.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97830317125312e.html