Tayyaba Farhat^1,2, Sheeraz Akram^3,*, Hatoon S. AlSagri³, Zulfiqar Ali⁴, Awais Ahmad³, Arfan Jaffar^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 105-126, 2024, DOI:10.32604/cmc.2023.045022

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to evaluate classifier performance and automated… More >

Aysha Naseer¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Abdulwahab Alazeb⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1381-1398, 2024, DOI:10.32604/cmc.2023.042963

Abstract Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images to help identify the objects… More >

Jiansheng Peng^1,2,*, Qing Yang¹, Dunhua Chen¹, Chengjun Yang², Yong Xu², Yong Qin²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 167-196, 2024, DOI:10.32604/cmc.2023.041900

Abstract Dynamic Simultaneous Localization and Mapping (SLAM) in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving. However, in the face of complex real-world environments, current dynamic SLAM systems struggle to achieve precise localization and map construction. With the advancement of deep learning, there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years, and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM. Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic… More >

Duong Q. Nguyen¹, Thinh D. Le³, Phuong D. Nguyen³, Nga T. K. Le², H. Nguyen-Xuan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2197-2214, 2024, DOI:10.32604/cmes.2023.043992

Abstract Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting… More > Graphic Abstract

Juhong Tie, Hui Peng^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 741-757, 2024, DOI:10.32604/cmes.2023.043353

Abstract Accurate automatic segmentation of gliomas in various sub-regions, including peritumoral edema, necrotic core, and enhancing and non-enhancing tumor core from 3D multimodal MRI images, is challenging because of its highly heterogeneous appearance and shape. Deep convolution neural networks (CNNs) have recently improved glioma segmentation performance. However, extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution, resulting in the loss of accurate spatial and object parts information, especially information on the small sub-region tumors, affecting segmentation performance. Hence, this paper proposes a novel multi-level parallel network comprising three different level parallel sub-networks to fully… More >

Shupeng Qiu, Chujin Lin, Wei Zhao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1119-1134, 2024, DOI:10.32604/cmes.2023.030915

Abstract In this paper, we consider the Chan–Vese (C-V) model for image segmentation and obtain its numerical solution accurately and efficiently. For this purpose, we present a local radial basis function method based on a Gaussian kernel (GA-LRBF) for spatial discretization. Compared to the standard radial basis function method, this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain. Additionally, since the Gaussian function has the property of dimensional separation, the GA-LRBF method is suitable for dealing with isotropic images. Finally, a numerical scheme that couples GA-LRBF… More > Graphic Abstract

D. Dorathy Prema Kavitha¹, L. Francis Raj¹, Sandeep Kautish^2,#, Abdulaziz S. Almazyad³, Karam M. Sallam⁴, Ali Wagdy Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 801-816, 2024, DOI:10.32604/cmes.2023.030902

Abstract The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional… More >

Jun Shu^1,2, Shuai Wang^1,2, Shiqi Yu^1,2, Jie Zhang^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818

Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.… More >

Wen Dong¹, Ziyan Liu^1,2,*, Mo Yang¹, Ying Wu¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3551-3572, 2023, DOI:10.32604/cmc.2023.044967

Abstract Autonomous driving technology has made a lot of outstanding achievements with deep learning, and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems. The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information, which is more accurate and reliable than object detection. However, the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed. Therefore, this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT, which fuses the ICIoU (Improved Complete Intersection over Union) and Res2Net for the YOLACT algorithm. Specifically, the… More >

Ming Wan¹, Minglei Hao¹, Jiawei Li¹, Jiangyuan Yao^2,*, Yan Song³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3573-3592, 2023, DOI:10.32604/cmc.2023.044857

Abstract The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged into different functional patterns due… More >