Yushan Yin¹, Witold Pedrycz^1,2, Zhiwu Li^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 623-650, 2024, DOI:10.32604/cmc.2024.048787

Abstract Utilizing granular computing to enhance artificial neural network architecture, a new type of network emerges—the granular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability to process both numerical and granular data, leading to improved interpretability. This paper proposes a novel design method for constructing GNNs, drawing inspiration from existing interval-valued neural networks built upon NNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzy numbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizes a uniform distribution of information granularity to granulate connections with… More >

Aizaz Ali¹, Maqbool Khan^1,2, Khalil Khan³, Rehan Ullah Khan⁴, Abdulrahman Aloraini^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712

Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features, presents an additional hurdle due… More >

Meng Huang, Honglei Wei^*, Xianyi Zhai

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510

Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of… More >

Yasmine M. Ibrahim^1,2, Reem Essameldin³, Saad M. Darwish^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840

Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore and determine… More >

Chengzhang Zhu^1,2, Renmao Zhang¹, Yalong Xiao^1,2,*, Beiji Zou¹, Xian Chai¹, Zhangzheng Yang¹, Rong Hu³, Xuanchu Duan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453

Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer. To address… More >

Fangfang Shan^1,2,*, Fuyang Li¹, Zhenyu Wang¹, Peiyu Ji¹, Mengyi Wang¹, Huifang Sun¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1029-1044, 2024, DOI:10.32604/cmes.2024.047665

Abstract A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks. Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs. Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network. Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are used to update user nodes… More >

Xianwei Jiang¹, Yanqiong Zhang^1,*, Juan Lei¹, Yudong Zhang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649

Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived… More >

Lujuan Deng, Ruochong Fu^*, Zuhe Li, Boyi Liu, Mengze Xue, Yuhao Cui

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4071-4089, 2024, DOI:10.32604/cmc.2024.048200

Abstract Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the… More >

Jiadong Wu, Yinan Wang^*, Zhiwei Li^*, Lun Lu, Qingjiang Li

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2909-2939, 2024, DOI:10.32604/cmc.2024.047240

Abstract Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance and practicability compared with the… More >

Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >