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The Next-generation Deep Learning Approaches to Emerging Real-world Applications

Submission Deadline: 31 December 2024 View: 1045 Submit to Special Issue

Guest Editors

Dr. Yu Zhou, Shenzhen University, China
Dr. Eneko Osaba, TECNALIA Research & Innovation, Spain
Dr. Xiao Zhang, South-Central Minzu University, China

Summary

Artificial intelligence techniques, such as Deep learning (DL) methods, have demonstrated their great success in the past ten years for various applications, such as computer vision, bioinformatics, healthcare and transportation. As the field of deep learning evolves rapidly, new and innovative approaches continue to emerge, addressing complex challenges in real-world applications. When facing emerging real-world applications, current DL models still suffer from high-dimensionality issue, robustness, data uncertainty and lack of global convergence and interpretability. Recent developments in intelligent computing approaches suggest the potential for next-generation deep learning methodologies that effectively address these challenges, such as bio-inspired computing, brain-inspired computing and other new computing schemes. This special issue aims to bring together cutting-edge research that showcases the next-generation deep learning approaches and their applications in emerging real-world scenarios, which aims to explore:

1) Novel deep learning models and structures.

2) New optimization method for deep learning training

3) Emerging real-world applications

 

The topics include those related to novel deep learning approaches and emerging real-world applications, but not limited to, the following:

Bio-inspired computing methods with deep leaning

Brain-inspired computing methods with deep learning

Attention mechanism in deep learning

Sparse deep learning

Soft computing with deep learning

Ensemble deep learning

Fine-tune methods for deep learning

Graph deep learning

Emerging topics in healthcare and sports with deep learning

Emerging topics in smart city and transportation with deep learning

Emerging topics in industrial informatics and intelligent manufacturing with deep learning

Emerging topics in social sciences with deep learning

Keywords

Deep learning, emerging real-world applications, intelligent computing

Published Papers

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  • Open Access

    ARTICLE

    Multi-Head Encoder Shared Model Integrating Intent and Emotion for Dialogue Summarization

    Xinlai Xing, Junliang Chen, Xiaochuan Zhang, Shuran Zhou, Runqing Zhang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056877
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets More >

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    130

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    24

  • Open Access

    ARTICLE

    Context-Aware Feature Extraction Network for High-Precision UAV-Based Vehicle Detection in Urban Environments

    Yahia Said, Yahya Alassaf, Taoufik Saidani, Refka Ghodhbani, Olfa Ben Rhaiem, Ali Ahmad Alalawi
    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

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    189

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    56

  • Open Access

    ARTICLE

    Learning Dual-Layer User Representation for Enhanced Item Recommendation

    Fuxi Zhu, Jin Xie, Mohammed Alshahrani
    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 949-971, 2024, DOI:10.32604/cmc.2024.051046
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract User representation learning is crucial for capturing different user preferences, but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data, and thus cannot be measured directly. Text-based data models can learn user representations by mining latent semantics, which is beneficial to enhancing the semantic function of user representations. However, these technologies only extract common features in historical records and cannot represent changes in user intentions. However, sequential feature can express the user’s interests and intentions that change time by time. But the sequential recommendation… More >

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    448

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    211

  • Open Access

    ARTICLE

    A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

    Mingyuan Li, Yuanli Gu, Qingqiao Geng, Hongru Yu
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3877-3896, 2024, DOI:10.32604/cmc.2024.047765
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more… More >

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    542

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    249

  • Open Access

    ARTICLE

    Enhancing Deep Learning Semantics: The Diffusion Sampling and Label-Driven Co-Attention Approach

    Chunhua Wang, Wenqian Shang, Tong Yi, Haibin Zhu
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

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    544

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    254

  • Open Access

    ARTICLE

    Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners: A Recommendation System

    Ameni Ellouze, Nesrine Kadri, Alaa Alaerjan, Mohamed Ksantini
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the… More >

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    740

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    437

  • Open Access

    ARTICLE

    An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

    Yu Zhou, Bosong Lin, Siqi Hu, Dandan Yu
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 161-182, 2024, DOI:10.32604/cmc.2024.047836
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view More >

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    677

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    393

  • Open Access

    ARTICLE

    Aspect-Level Sentiment Analysis Based on Deep Learning

    Mengqi Zhang, Jiazhao Chai, Jianxiang Cao, Jialing Ji, Tong Yi
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them More >

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    793

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    307

  • Open Access

    ARTICLE

    ASLP-DL —A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

    Saba Awan, Zahid Mehmood
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through More >

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    752

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    316

  • Open Access

    ARTICLE

    SDH-FCOS: An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

    Bin Zhou, Bo Li, Wenfei Lan, Congwen Tian, Wei Yao
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 633-652, 2024, DOI:10.32604/cmc.2023.046667
    （This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last… More >

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    1016

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    401

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