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

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Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering
Zhenyu Qian, Yizhang Jiang, Zhou Hong, Lijun Huang, Fengda Li, KhinWee Lai, Kaijian Xia
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More >
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Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method
Xin Xiong, Jing Zhang, Sanli Yi, Chunwu Wang, Ruixiang Liu, Jianfeng He
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

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Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition
Yi-Chun Lai, Shu-Yin Chiang, Yao-Chiang Kan, Hsueh-Chun Lin
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More >
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SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation
Suyi Liu, Jianning Chi, Chengdong Wu, Fang Xu, Xiaosheng Yu
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >

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Real-Time Object Detection and Face Recognition Application for the Visually Impaired
Karshiev Sanjar, Soyoun Bang, Sookhee Ryue, Heechul Jung
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3569-3583, 2024, DOI:10.32604/cmc.2024.048312
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types… More >

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Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm
Mutasem K. Alsmadi
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5175-5200, 2024, DOI:10.32604/cmc.2024.044065
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally,… More >

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A Novel Approach to Energy Optimization: Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN
Muhammad Salman Qamar, Ihsan ul Haq, Amil Daraz, Atif M. Alamri, Salman A. AlQahtani, Muhammad Fahad Munir
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs… More >

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Image Segmentation-P300 Selector: A Brain–Computer Interface System for Target Selection
Hang Sun, Changsheng Li, He Zhang
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2505-2522, 2024, DOI:10.32604/cmc.2024.049898
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions without necessitating extensive training. However, the complexity of operational instructions and the slow pace of character spelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects for classification and tracking. By applying predefined stimulation rules and object-specific masks, the proposed system triggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in the patient’s… More >

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features
Mahmood Ul Haq, Muhammad Athar Javed Sethi, Najib Ben Aoun, Ala Saleh Alluhaidan, Sadique Ahmad, Zahid farid
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2169-2186, 2024, DOI:10.32604/cmc.2024.049645
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are… More >

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies
Muhammad Ahmad Nawaz Ul Ghani, Kun She, Muhammad Arslan Rauf, Shumaila Khan, Javed Ali Khan, Eman Abdullah Aldakheel, Doaa Sami Khafaga
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

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Optimizing Optical Fiber Faults Detection: A Comparative Analysis of Advanced Machine Learning Approaches
Kamlesh Kumar Soothar, Yuanxiang Chen, Arif Hussain Magsi, Cong Hu, Hussain Shah
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

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Model Agnostic Meta-Learning (MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks
Yasir Maqsood, Syed Muhammad Usman, Musaed Alhussein, Khursheed Aurangzeb, Shehzad Khalid, Muhammad Zubair
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing… More >

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KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network
Sardar Hasen Ali, Maiwan Bahjat Abdulrazzaq
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits,… More >

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Image Fusion Using Wavelet Transformation and XGboost Algorithm
Shahid Naseem, Tariq Mahmood, Amjad Rehman Khan, Umer Farooq, Samra Nawazish, Faten S. Alamri, Tanzila Saba
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 801-817, 2024, DOI:10.32604/cmc.2024.047623
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring,… More >

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Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach
Syeda Shamaila Zareen, Guangmin Sun, Mahwish Kundi, Syed Furqan Qadri, Salman Qadri
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model More >

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Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN
Jongwook Si, Sungyoung Kim
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2893-2908, 2024, DOI:10.32604/cmc.2023.046081
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract In the context of high compression rates applied to Joint Photographic Experts Group (JPEG) images through lossy compression techniques, image-blocking artifacts may manifest. This necessitates the restoration of the image to its original quality. The challenge lies in regenerating significantly compressed images into a state in which these become identifiable. Therefore, this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks (GAN). The generator in this network is based on the U-Net architecture. It features a new hourglass structure that preserves the characteristics… More >

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram
Umer Sadiq Khan, Zhen Liu, Fang Xu, Muhib Ullah Khan, Lerui Chen, Touseef Ahmed Khan, Muhammad Kashif Khattak, Yuquan Zhang
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3323-3348, 2024, DOI:10.32604/cmc.2024.046094
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model. Although the Gaussian mixture model enhances the flexibility of image segmentation, it does not reflect spatial information and is sensitive to the segmentation parameter. In this study, we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model (GMM) without parameter estimation. The proposed model highlights the residual region with considerable information and constructs color saliency. Second, we incorporate the content-based color saliency as spatial information in the Gaussian mixture model. The segmentation is performed by clustering… More >

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features
Asifa Mehmood Qureshi, Naif Al Mudawi, Mohammed Alonazi, Samia Allaoua Chelloug, Jeongmin Park
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with… More >

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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 the 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,… More >

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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 the 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 More >

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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 the 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… More >

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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 the 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,… More >

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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 the 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… More >

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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 the 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… More >

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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 the 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… More >

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

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