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  • Open Access

    ARTICLE

    DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

    Pengchao Li1,2,3,*, Fang Xu1,2,3,4, Jintao Wang1,2, Haibing Guo4, Mingmin Liu4, Zhenjun Du4

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1755-1771, 2024, DOI:10.32604/cmc.2023.047057

    Abstract We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations. Initially, to enhance the capability of deep neural networks in extracting geometric attributes from depth images, we developed a novel deep geometric convolution operator (DGConv). DGConv is utilized to construct a deep local geometric feature extraction module, facilitating a more comprehensive exploration of the intrinsic geometric information within depth images. Secondly, we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network (FCN8) to establish a high-performance deep neural network algorithm… More >

  • Open Access

    ARTICLE

    Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

    R. Sujatha, K. Nimala*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

    Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

  • Open Access

    ARTICLE

    An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

    Asma Hassan Alshehri*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2767-2786, 2024, DOI:10.32604/cmc.2023.046838

    Abstract Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervised machine learning method to detect fake reviews on any website, among other things. Online reviews… More >

  • Open Access

    ARTICLE

    Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

    Afnan M. Alhassan*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427

    Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf… More >

  • Open Access

    ARTICLE

    Detecting APT-Exploited Processes through Semantic Fusion and Interaction Prediction

    Bin Luo1,2,3, Liangguo Chen1,2,3, Shuhua Ruan1,2,3,*, Yonggang Luo2,3,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739

    Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit logs, fits normal system entity… More >

  • Open Access

    ARTICLE

    Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble

    Muhammad Rizwan1,2, Muhammad Faheem Mushtaq1, Maryam Rafiq2, Arif Mehmood3, Isabel de la Torre Diez4, Monica Gracia Villar5,6,7, Helena Garay5,8,9, Imran Ashraf10,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2047-2066, 2024, DOI:10.32604/cmc.2024.037347

    Abstract Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter… More >

  • Open Access

    ARTICLE

    Morphometry and Mineral Content in the Seeds and Soil of Two Species of Argemone L. (Papaveraceae) in the Central Part of the Chihuahuan Desert

    Perla Patricia Ochoa-García1, Jaime Sánchez-Salas2, Ricardo Trejo-Calzada1, Jesús Josafath Quezada-Rivera2, Fabián García-González1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 371-386, 2024, DOI:10.32604/phyton.2024.048338

    Abstract The genus Argemone L. (Papaveraceae) is found widely distributed in Mexico’s Chihuahuan Desert (CD). Some species of this genus are of phytochemical or ethnobotanical interest. They are inedible plants considered as scrubs. To date they have not been broadly studied; thus, their ecology is, to our knowledge, unknown. The present work was centered around carrying out a morphometric analysis and the determination of minerals in the soil and seeds of the wild populations of Argemone at sites belonging to two ecoregions of the CD in Mexico. In April 2021 and April 2022, seeds of Argemone spp., and soil samples were… More >

  • Open Access

    ARTICLE

    Leveraging Augmented Reality, Semantic-Segmentation, and VANETs for Enhanced Driver’s Safety Assistance

    Sitara Afzal1, Imran Ullah Khan1, Irfan Mehmood2, Jong Weon Lee1,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1443-1460, 2024, DOI:10.32604/cmc.2023.046707

    Abstract Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead. However, limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers, leading to accidents and fatalities. In this paper, we consider atrous convolution, a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation. This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety. See-through vehicles leverage… More >

  • Open Access

    ARTICLE

    A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

    Wei Liu, Meijuan Yin*, Jialong Zhang, Lunchong Cui

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475

    Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed, which is abbreviated as… More >

  • Open Access

    ARTICLE

    A Video Captioning Method by Semantic Topic-Guided Generation

    Ou Ye, Xinli Wei, Zhenhua Yu*, Yan Fu, Ying Yang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418

    Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

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