Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Najm Alotaibi³, Mohamed K. Nour⁴, Mahmoud Othman⁵, Gouse Pasha Mohammed¹, Mohammed Rizwanullah¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.48, No.1, pp. 97-114, 2024, DOI:10.32604/csse.2023.034520

Abstract Computational linguistics is the scientific and engineering discipline related to comprehending written and spoken language from a computational perspective and building artefacts that effectively process and produce language, either in bulk or in a dialogue setting. This paper develops a Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification and dictarization (CBSOEDL-APCD) technique. The presented CBSOEDL-APCD technique involves the classification and dictarization of Arabic text into Arabic poetries and prose. Primarily, the CBSOEDL-APCD technique carries out data pre-processing to convert it into a useful format. Besides, the ensemble deep learning (EDL) model comprising deep belief network (DBN),… More >

A. C. Filankembo Kava^*, B. C. Ndjengue Bengono, P. L. Nzamba Bissielou, C. Nziengui Tirogo, A. Kabena, T. Mpami, E. Belembaogo

Psycho-Oncologie, Vol.17, No.4, pp. 267-273, 2023, DOI:10.32604/po.2023.044512

Abstract Objectif. Le syndrome d’épuisement professionnel est fréquent chez les travailleurs de la santé en oncologie. Non diagnostiqué et incorrectement pris en charge, le burnout peut avoir un impact négatif sur le rendement professionnel. L’Institut de Cancérologie d’Akanda (ICA) est un centre hospitalier ultra-moderne qui se veut une référence en matière de prise en charge du cancer en Afrique centrale. L’objectif de l’étude est de mesurer la fréquence du burnout au sein du personnel soignant de l’Institut de Cancérologie d’Akanda et d’évaluer les principaux facteurs de risque. Patients et méthodes. Nous avons mené une étude transversale à l’Institut de cancérologie d’Akanda… More >

Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255

Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… 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 >

Liuyi Ling^1,2,3, Yiwen Wang^1,*, Fan Ding⁴, Li Jin¹, Bin Feng³, Weixiao Li³, Chengjun Wang¹, Xianhua Li¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2771-2790, 2023, DOI:10.32604/cmc.2023.043383

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 >

Manas Ranjan Prusty¹, Rishi Dinesh², Hariket Sukesh Kumar Sheth², Alapati Lakshmi Viswanath², Sandeep Kumar Satapathy^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718

Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time augmentation, to improve segmentation accuracy.… More >

Siyu Yang¹, Ge Song^1,*, Yuqiao Deng², Changyu Liu¹, Zhuoyu Ou¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4055-4072, 2023, DOI:10.32604/cmc.2023.042277

Abstract Deep neural networks have achieved tremendous success in various fields, and the structure of these networks is a key factor in their success. In this paper, we focus on the research of ensemble learning based on deep network structure and propose a new deep network ensemble framework (DNEF). Unlike other ensemble learning models, DNEF is an ensemble learning architecture of network structures, with serial iteration between the hidden layers, while base classifiers are trained in parallel within these hidden layers. Specifically, DNEF uses randomly sampled data as input and implements serial iteration based on the weighting strategy between hidden layers.… More >

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

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3209-3236, 2023, DOI:10.32604/cmc.2023.041898

Abstract Visual simultaneous localization and mapping (SLAM) is crucial in robotics and autonomous driving. However, traditional visual SLAM faces challenges in dynamic environments. To address this issue, researchers have proposed semantic SLAM, which combines object detection, semantic segmentation, instance segmentation, and visual SLAM. Despite the growing body of literature on semantic SLAM, there is currently a lack of comprehensive research on the integration of object detection and visual SLAM. Therefore, this study aims to gather information from multiple databases and review relevant literature using specific keywords. It focuses on visual SLAM based on object detection, covering different aspects. Firstly, it discusses… More >

Md. Shohan Parvez^1,2, Md. Mustafizur Rahman^1,3,*, Mahendran Samykano¹, Mohammad Yeakub Ali⁴

Energy Engineering, Vol.121, No.1, pp. 1-12, 2024, DOI:10.32604/ee.2023.027472

Abstract With the exponential development in wearable electronics, a significant paradigm shift is observed from rigid electronics to flexible wearable devices. Polyaniline (PANI) is considered as a dominant material in this sector, as it is endowed with the optical properties of both metal and semiconductors. However, its widespread application got delineated because of its irregular rigid form, level of conductivity, and precise choice of solvents. Incorporating PANI in textile materials can generate promising functionality for wearable applications. This research work employed a straightforward in-situ chemical oxidative polymerization to synthesize PANI on Cotton fabric surfaces with varying dopant (HCl) concentrations. Pre-treatment using… More >