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Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications

Submission Deadline: 06 March 2024 Submit to Special Issue

Guest Editors

Dr. Khursheed Aurangzeb, King Saud University, Saudi Arabia
Prof. Yudong Zhang, University of Leicester, UK
Dr. Muhammad Shahid Anwar, Gachon University, South Korea
Dr. Shuihua Wang, University of Leicester, UK.

Summary

The recent progress in the fields of Artificial Intelligence (AI), Machine Learning (ML), data analytics, High-Performance Computing (HPC), and image processing enabled intelligent sensor nodes with decision-making power for different real-life applications such as surveillance, healthcare, smart transportation, smart cities, precision agriculture, etc. The cameras and other sensors are used for surveillance and interaction with the environment in these real-life applications, which generate big data that require the development of sophisticated AI/ML models for interpretation and decision-making.


A healthier lifestyle is of utmost importance for the sustainable progression of modern society. The development of advanced and robust AI/ML models for surveillance and healthcare applications is highly desired due to its direct impact on our lives and lifestyle. There are stringent limitations on the performance of the ML and AI models, which in turn leads to numerous challenges, and opportunities for applying the latest developments in these fields for the betterment of our lifestyles and society.


This special issue welcomes the latest advances and trends related to the development and exploration of advanced AI and machine/deep learning models for signal and image processing applications. It will attract the attention of the research community from around the Globe, addressing genuine issues, emerging applications, and useful solutions for open problems related to the development of AI and machine/deep learning algorithms and techniques for surveillance and other smart cities applications. Therefore, we welcome the submission of original contributions, surveys and review articles including, but not limited to, the following topics:

• Exploration and development of advanced AL, ML, and deep learning models for smart cities, with a focus on surveillance and healthcare application   

• Signal and image processing

• Sophisticated machine vision, signal processing, image processing, and evolutionary techniques and algorithms targeting different smart cities applications

• Expanding the domain knowledge for enhancing the training of the machine and deep learning models

• AI Model development for semantic segmentation of biomedical images

• Use cases of vision transformer in Medical, Agriculture and surveillance applications

• Disease detection and diagnosis using advanced machine vision and image processing methods


Keywords

Smart cities, artificial intelligence, data analytics, machine learning, deep learning, electronics systems, surveillance, healthcare, semantic segmentation

Published Papers


  • Open Access

    ARTICLE

    An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

    Junpeng Wu, Zhenpeng Liu, Xingfan Jiang, Xinguang Tao, Ye Zhang
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 677-694, 2024, DOI:10.32604/cmc.2023.045759
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy… More >

  • Open Access

    ARTICLE

    An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

    Liuyi Ling, Yiwen Wang, Fan Ding, Li Jin, Bin Feng, Weixiao Li, Chengjun Wang, Xianhua Li
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2771-2790, 2023, DOI:10.32604/cmc.2023.043383
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion states. Firstly, continuous wavelet transform… More >

  • Open Access

    ARTICLE

    Improved Speech Emotion Recognition Focusing on High-Level Data Representations and Swift Feature Extraction Calculation

    Akmalbek Abdusalomov, Alpamis Kutlimuratov, Rashid Nasimov, Taeg Keun Whangbo
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2915-2933, 2023, DOI:10.32604/cmc.2023.044466
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract The performance of a speech emotion recognition (SER) system is heavily influenced by the efficacy of its feature extraction techniques. The study was designed to advance the field of SER by optimizing feature extraction techniques, specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients (MFCC). This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches. Ultimately, the primary objective was to elevate both the intricacy and effectiveness of our SER model, with a focus on augmenting its proficiency in the accurate identification of emotions… More >

  • Open Access

    ARTICLE

    PP-GAN: Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

    Jongwook Si, Sungyoung Kim
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3119-3138, 2023, DOI:10.32604/cmc.2023.043797
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract The objective of style transfer is to maintain the content of an image while transferring the style of another image. However, conventional methods face challenges in preserving facial features, especially in Korean portraits where elements like the “Gat” (a traditional Korean hat) are prevalent. This paper proposes a deep learning network designed to perform style transfer that includes the “Gat” while preserving the identity of the face. Unlike traditional style transfer techniques, the proposed method aims to preserve the texture, attire, and the “Gat” in the style image by employing image sharpening and face landmark, with the GAN. The color,… More >

  • Open Access

    ARTICLE

    Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

    Tariq Qayyum, Zouheir Trabelsi, Asadullah Tariq, Muhammad Ali, Kadhim Hayawi, Irfan Ud Din
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality,… More >

  • Open Access

    ARTICLE

    A Lightweight Road Scene Semantic Segmentation Algorithm

    Jiansheng Peng, Qing Yang, Yaru Hou
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1929-1948, 2023, DOI:10.32604/cmc.2023.043524
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract In recent years, with the continuous deepening of smart city construction, there have been significant changes and improvements in the field of intelligent transportation. The semantic segmentation of road scenes has important practical significance in the fields of automatic driving, transportation planning, and intelligent transportation systems. However, the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation edges. Therefore, this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these issues. The model uses the lightweight backbone network… More >

  • Open Access

    ARTICLE

    DM Code Key Point Detection Algorithm Based on CenterNet

    Wei Wang, Xinyao Tang, Kai Zhou, Chunhui Zhao, Changfa Liu
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1911-1928, 2023, DOI:10.32604/cmc.2023.043233
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Data Matrix (DM) codes have been widely used in industrial production. The reading of DM code usually includes positioning and decoding. Accurate positioning is a prerequisite for successful decoding. Traditional image processing methods have poor adaptability to pollution and complex backgrounds. Although deep learning-based methods can automatically extract features, the bounding boxes cannot entirely fit the contour of the code. Further image processing methods are required for precise positioning, which will reduce efficiency. Because of the above problems, a CenterNet-based DM code key point detection network is proposed, which can directly obtain the four key points of the DM code.… More >

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