Computational Collective Intelligence

16th International Conference, ICCCI 2024, Leipzig, Germany, September 9–11, 2024, Proceedings, Part I

AvNgoc Thanh Nguyen,Bogdan Franczyk

Häftad, Engelska, 2024

Del 14810 i serien Lecture Notes in Computer Science

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Beskrivning

This two-volume set LNAI 14810-14811 constitutes the refereed proceedings of the 16th International Conference on Computational Collective Intelligence, ICCCI 2024, held in Leipzig, Germany, during September 9–11, 2024.The 59 revised full papers presented in these proceedings were carefully reviewed and selected from 234 submissions. They cover the following topics:Part I: Collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning.Part II: Social networks and intelligent system; cybersecurity, blockchain technology, and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for industry 4.0.

Produktinformation

Utgivningsdatum:2024-09-06
Mått:155 x 235 x 24 mm
Vikt:645 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:397
Förlag:Springer International Publishing AG
ISBN:9783031708152

Utforska kategorier

Artificiell intelligens inom Data och IT
Hårdvara inom Data och IT
Programmeringsböcker inom Data och IT

Innehållsförteckning

.- Collective Intelligence and Collective Decision-Making..- Collective Computational Intelligence – challenges and opportunities..- Reward-based Hybrid Genetic Algorithm for Solving the Class Scheduling Problem..- A Novel Multi-Criteria Approach Supporting Strong Sustainability Assessment..- Enhancing Focused Ant Colony Optimization for Large-Scale Traveling Salesman Problems through Adaptive Parameter Tuning..- Parallelized Population-based Multi-heuristic Approach for Solving RCPSP and MRCPSP Instances..- A Collective Intelligence To Predict Stock Market Indices Applying An Optimized Hybrid Ensemble Learning Model..- Deep Learning Techniques..- Melanoma detection using CBR approach within a possibilistic framework..- GANet - Learning tabular data using global attention..- COVID-19 Detection based on Deep Features and SVM..- Hybrid Convolutional Network Fusion: Enhanced Medical Image Classification with Dual-Pathway Learning from Raw and Enhanced Visual Features..- Interpreting results of VGG-16 for COVID-19 diagnosis on CT images..- A hybrid approach using 2D CNN and attention-based LSTM for Parkinson’s Disease Detection from video..- Improved CNN Model Stability and Robustness with Video Frame Segmentation..- Deep Learning for Cardiac Diseases Classification..- Natural Language Processing..- BABot: a Framework for the LLM-based Chatbot Supporting Business Analytics in e-Commerce..- BioBERT for Multiple knowledge-based question expansion and biomedical extractive question answering..- AMAMP: A Two-Phase Adaptive Multi-hop Attention Message Passing Mechanism For Logical Reasoning Machine Reading Comprehension..- Enhancing Low-Resource NER via Knowledge Transfer from LLM..- Efficient Argument Classification with Compact Language Models and ChatGPT-4 Refinements..- Refining Natural Language Inferences using Cross-Document Structure Theory..- Data Mining and Machine Learning..- Intelligent Handling of Noise in Federated Learning with Co-training for Enhanced Diagnostic Precision..- Detection and Classification of olive leaves diseases using machine learning algorithms..- Investigation of Machine Learning and Deep Learning Approaches for Early PM2.5 Forecasting: A Case Study in Vietnam..- Detection of candidate skills from job offers and comparison with ESCO database..- Multi-objective and Randomly Distributed Fuzzy Logic-based Unequal Clustering in Heterogeneous Wireless Sensor Networks..- nMITP-Miner: An efficient method for mining frequent maximal inter-transaction patterns..- A heterogeneous ensemble of classifiers for sports betting: based on the English Premier League..- The New K-Means Initialization Method..- Efficiently discover multi-level maximal high-utility patterns from hierarchical databases.