Advances and Innovations in Deep Learning: Unveiling Multidisciplinary Applications and Challenges

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 819

Special Issue Editors


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Guest Editor
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China
Interests: big data analysis; artificial intelligence and deep learning; intelligent protection of ancient books; multimodal learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information Engineering, Minzu University of China, Beijing 100081, China
Interests: multilingual artificial intelligence; multimodal learning

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) and deep learning has redefined the boundaries of technological innovation. This Special Issue welcomes submissions that traverse a wide spectrum of interdisciplinary research, exploring novel algorithms and architectures that could enhance the performance and efficiency of AI systems. This might involve advancements in neural network design, optimization techniques, or the development of hybrid models. Additionally, the application of AI in various sectors holds great promise. For instance, in healthcare, it can assist in disease diagnosis and treatment planning; in transportation, it enables autonomous driving and traffic optimization; and in finance, it aids in risk assessment and fraud detection. We also encourage investigations into the ethical and social implications of AI, as well as the challenges related to data privacy and security. All high-quality contributions that push the boundaries of this field are welcome, including research on the development of explainable AI models, the utilization of AI in emerging technologies such as the Internet of Things and blockchain, and the exploration of AI’s potential in creative fields such as art and music generation.

This Special Issue focuses on the recent and significant progress made in artificial intelligence and deep learning. It aims to bring together research that showcases the practical applications and theoretical advancements of these technologies. The scope includes, but is not limited to, the following:

  • Artificial intelligence;
  • Deep learning;
  • Large models;
  • Language processing;
  • Image processing;
  • Remote sensing;
  • AI security;
  • Intelligent systems;
  • Data analysis;
  • AI applications and inventions.

Prof. Dr. Yu Weng
Dr. Zheng Liu
Guest Editors

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Keywords

  • artificial intelligence
  • deep learning
  • large models
  • language processing
  • image processing
  • remote sensing
  • ai security
  • intelligent systems
  • data analysis
  • AI applications and inventions

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Published Papers (1 paper)

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Research

22 pages, 5083 KiB  
Article
Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback
by Fengqin Liu, Korawit Orkphol, Natthapon Pannurat, Thanat Sooknuan, Thanin Muangpool, Sanya Kuankid and Montri Phothisonothai
Inventions 2025, 10(4), 46; https://doi.org/10.3390/inventions10040046 - 24 Jun 2025
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Abstract
This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, [...] Read more.
This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article
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