#cnn-lstm News & Analysis

4 articles tagged with #cnn-lstm. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 106/10

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Towards Robust Arabic Speech Emotion Recognition with Deep Learning

Researchers propose a CNN-Transformer hybrid architecture for Arabic Speech Emotion Recognition that achieves 98.1% accuracy, outperforming CNN-LSTM and fine-tuned wav2vec 2.0 models. The study addresses the underexplored challenge of emotion detection in Arabic speech by combining convolutional feature extraction with Transformer-based context modeling, demonstrating effectiveness in low-resource, dialectally diverse settings.

AINeutralarXiv – CS AI · Jun 96/10

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Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management

Researchers developed a hybrid CNN-LSTM deep learning model for coffee supply chain demand forecasting, achieving 90% accuracy and outperforming benchmarks by 12-30%. This forecasting feeds a multi-objective optimization system that simultaneously minimizes costs and emissions while maximizing product freshness in circular supply chains, demonstrating that sustainability policies can reduce emissions by 22.4% with minimal cost overhead.

AINeutralarXiv – CS AI · Jun 85/10

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Phonetic Error Analysis of Raw Waveform Acoustic Models

Researchers achieved state-of-the-art performance on raw waveform acoustic models for phone recognition using CNN-LSTM architectures, with error rates of 13.9%/15.3% on TIMIT benchmarks. Analysis reveals that different phonetic classes benefit differently from model components, and transfer learning from WSJ data improves consonant recognition significantly more than vowels.

AINeutralarXiv – CS AI · Jun 56/10

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An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

Researchers present an improved CNN-LSTM neural network model for detecting intrusions in IoT networks, achieving 97% accuracy by combining convolutional and recurrent layers to analyze network traffic patterns. The advancement addresses growing security vulnerabilities as IoT device proliferation outpaces defensive capabilities.