Thai-Viet Dang^1,*, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605 - 17 February 2025

Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >

Ziqiang Tang¹, Chao Han¹, Hongwu Li¹, Zhou Fan¹, Ke Sun¹, Yuntian Huang¹, Yuhang Chen², Chenxing Wang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2813-2829, 2025, DOI:10.32604/cmc.2024.059094 - 17 February 2025

Abstract Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask… More >

Fengju Zhang¹, Kai Zhu^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2353-2372, 2025, DOI:10.32604/cmc.2024.058944 - 17 February 2025

Abstract The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2,… More >

Mahmood Alsaadi, Mohammed E. Seno^*, Mohammed I. Khalaf

Intelligent Automation & Soft Computing, Vol.40, pp. 29-52, 2025, DOI:10.32604/iasc.2024.060390 - 10 January 2025

Abstract The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, More >

Yiqing Lu¹, Liutao Zhao^2,*, Qiankun Zhao³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1401-1416, 2025, DOI:10.32604/cmc.2024.058757 - 03 January 2025

Abstract Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors. These vehicles are crucial in various fields, including environmental science research, ecological and environmental monitoring projects, disaster response, and emergency management. A key method employed in these vehicles for achieving high-precision positioning is LiDAR (lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping (SLAM). However, maintaining high-precision localization in complex scenarios, such as degraded environments or when dynamic objects are present, remains a significant challenge. To address this issue, we integrate both semantic and… More >

Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Manh Vu Minh^*, Cho Do Xuan

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4371-4396, 2024, DOI:10.32604/cmc.2024.058168 - 19 December 2024

Abstract Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity, in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices. In this paper, we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models. The proposed method combines two main techniques: (1) constructing a malware behavior profile and (2) extracting features from the malware behavior profile using graph neural networks. Specifically, to effectively construct an Android malware behavior profile,… More >

Weiyu Hao¹, Jingyi Wang², Huimin Lu^3,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4419-4433, 2024, DOI:10.32604/cmc.2024.055478 - 19 December 2024

Abstract While traditional Convolutional Neural Network (CNN)-based semantic segmentation methods have proven effective, they often encounter significant computational challenges due to the requirement for dense pixel-level predictions, which complicates real-time implementation. To address this, we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving, utilizing the capabilities of Visual Transformers. By leveraging the self-attention mechanism inherent in Visual Transformers, our method enhances global contextual awareness, refining the representation of each pixel in relation to the overall scene. This enhancement is critical for quickly and accurately interpreting the complex elements within driving scenarios—a fundamental… More >

Chen Chen, Kai Qiao, Jie Yang, Jian Chen, Bin Yan^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2629-2642, 2024, DOI:10.32604/cmc.2024.056038 - 18 November 2024

Abstract Computed Tomography (CT) is a commonly used technology in Printed Circuit Boards (PCB) non-destructive testing, and element segmentation of CT images is a key subsequent step. With the development of deep learning, researchers began to exploit the “pre-training and fine-tuning” training process for multi-element segmentation, reducing the time spent on manual annotation. However, the existing element segmentation model only focuses on the overall accuracy at the pixel level, ignoring whether the element connectivity relationship can be correctly identified. To this end, this paper proposes a PCB CT image element segmentation model optimizing the semantic perception… More >

Irum Ilays¹, Yaser Hafeez^1,*, Nabil Almashfi², Sadia Ali¹, Mamoona Humayun^3,*, Muhammad Aqib¹, Ghadah Alwakid⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3761-3784, 2024, DOI:10.32604/cmc.2024.053830 - 12 September 2024

Abstract Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to… More >