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Computational Intelligence and Machine Learning

This Special Issue presents a curated collection of ten research articles that reflect current trends and challenges in computational intelligence and machine learning. The selected works cover a broad spectrum of methods and applications, including personalized recommendation systems, social-aware...

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Formaat: Online
Taal:Engels
Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2025
Onderwerpen:
Arabic
natural language understanding
deep learning
chatbot
recommendation system
collaborative filtering
graph neural networks
self-supervised learning
multi-task learning
machine learning
forecasting models
meteorological variables
Python
graph convolution network
social recommendation
human motion prediction
graph convolutional network
gated recurrent unit
transformers
anomaly detection
log
Sentence-BERT
semantic feature
soft sets
picture fuzzy sets
picture fuzzy soft matrices
distance measures
k-nearest neighbor (kNN)
synthetic data
artificial data
bias
fairness
AI
data generation
automatic speech recognition
artificial intelligence
representation learning
Whisper model
personality model
retrieval augmentation
large language models (LLM)
travel recommendation
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online toegang:ONIX_20250812T110751_9783725838950_234
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Samenvatting:This Special Issue presents a curated collection of ten research articles that reflect current trends and challenges in computational intelligence and machine learning. The selected works cover a broad spectrum of methods and applications, including personalized recommendation systems, social-aware filtering, human motion prediction, automatic speech recognition for under-resourced languages, chatbot frameworks for Arabic natural language understanding, anomaly detection in system logs, and meteorological forecasting. Across these contributions, several themes emerge. Many studies explore the integration of advanced learning architectures, such as transformers, graph neural networks, and retrieval-augmented generation, with domain knowledge to improve performance and generalization. Others focus on addressing practical challenges like data sparsity, bias in AI systems, and the need for lightweight models suitable for low-resource settings. The application domains span travel, healthcare, e-commerce, software engineering, and environmental monitoring, demonstrating the wide-ranging impact of modern AI techniques. Collectively, the papers emphasize not only technical effectiveness but also the importance of interpretability, fairness, and adaptability in real-world deployments. They exemplify the shift toward responsible and context-aware machine learning and offer methodological insights that are relevant for both academic research and industrial innovation.

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