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  • Open Access

    ARTICLE

    Leveraging Pre-Trained Word Embedding Models for Fake Review Identification

    Glody Muka1,*, Patrick Mukala1,2,*

    Journal on Artificial Intelligence, Vol.6, pp. 211-223, 2024, DOI:10.32604/jai.2024.049685 - 07 August 2024

    Abstract Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models… More >

  • Open Access

    ARTICLE

    Identification of TMEM159 as a biomarker of glioblastoma progression based on immune characteristics

    JI SHI1,2, YE ZHANG2, YI CHEN2, TANGJUN GUO3,*, HAOZHE PIAO2,*

    BIOCELL, Vol.48, No.8, pp. 1241-1263, 2024, DOI:10.32604/biocell.2024.051049 - 02 August 2024

    Abstract Background: Glioblastoma multiforme (GBM) is the most general malignancy of the primary central nervous system that is characterized by high aggressiveness and lethality. Transmembrane protein 159 (TMEM159) is an endoplasmic reticulum protein that can form oligomers with seipin. The TMEM159-seipin complex decides the site of lipid droplet (LD) formation, and the formation of LDs is a marker of GBM. However, the role of TMEM159 in the progression of GBM has not been investigated to date. Methods: In this study, we examined the genes that may be associated with patient prognosis in GBM by bioinformatics analyses,… More >

  • Open Access

    ARTICLE

    Genome-Wide Discovery and Expression Profiling of the SWEET Sugar Transporter Gene Family in Woodland Strawberry (Fragaria vesca) under Developmental and Stress Conditions: Structural and Evolutionary Analysis

    Shoukai Lin1,3,4,*, Yifan Xiong2, Shichang Xu1,2, Manegdebwaoaga Arthur Fabrice Kabore2, Fan Lin5, Fuxiang Qiu1,2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1485-1502, 2024, DOI:10.32604/phyton.2024.050990 - 30 July 2024

    Abstract The SWEET (sugar will eventually be exported transporter) family proteins are a recently identified class of sugar transporters that are essential for various physiological processes. Although the functions of the SWEET proteins have been identified in a number of species, to date, there have been no reports of the functions of the SWEET genes in woodland strawberries (Fragaria vesca). In this study, we identified 15 genes that were highly homologous to the A. thaliana AtSWEET genes and designated them as FvSWEET1FvSWEET15. We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes. The phylogenetic analysis enabled us… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

    Roseline Oluwaseun Ogundokun1,2, Joseph Bamidele Awotunde3, Hakeem Babalola Akande4, Cheng-Chi Lee5,6,*, Agbotiname Lucky Imoize7,8

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 139-161, 2024, DOI:10.32604/cmc.2024.052153 - 18 July 2024

    Abstract Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size,… More >

  • Open Access

    ARTICLE

    Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2

    Zhilin Li1,2, Yuxin Li1, Chunyu Yan1, Peng Yan1, Xiutong Li1, Mei Yu1, Tingchi Wen4,5, Benliang Xie1,2,3,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526 - 18 July 2024

    Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease More >

  • Open Access

    ARTICLE

    A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

    Jian Luo1,*, Bo Xu1, Tardi Tjahjadi2, Jian Yi1

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018 - 18 July 2024

    Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >

  • Open Access

    ARTICLE

    A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

    Xu Zhang1, Yi He1, Haiwen Yi1, Yulu Zhang2, Yuan Li2, Shuai Ma2, Gui Li3, Zhiyuan Zhao4, Yue Liu1, Junyang Liu1, Guangjun Wen1, Jian Li1,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 897-912, 2024, DOI:10.32604/cmc.2024.050000 - 18 July 2024

    Abstract When the radio frequency identification (RFID) system inventories multiple tags, the recognition rate will be seriously affected due to collisions. Based on the existing dynamic frame slotted Aloha (DFSA) algorithm, a sub-frame observation and cyclic redundancy check (CRC) grouping combined dynamic framed slotted Aloha (SUBF-CGDFSA) algorithm is proposed. The algorithm combines the precise estimation method of the quantity of large-scale tags, the large-scale tags grouping mechanism based on CRC pseudo-random characteristics, and the Aloha anti-collision optimization mechanism based on sub-frame observation. By grouping tags and sequentially identifying them within subframes, it accurately estimates the number More >

  • Open Access

    ARTICLE

    Enhancing Exam Preparation through Topic Modelling and Key Topic Identification

    Rudraneel Dutta*, Shreya Mohanty

    Journal on Artificial Intelligence, Vol.6, pp. 177-192, 2024, DOI:10.32604/jai.2024.050706 - 19 July 2024

    Abstract Traditionally, exam preparation involves manually analyzing past question papers to identify and prioritize key topics. This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation, Optical Character Recognition (OCR), and Latent Dirichlet Allocation (LDA) for topic modelling. This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers, aiding students in efficient exam preparation. The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify… More >

  • Open Access

    ARTICLE

    FDSC-YOLOv8: Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

    Rui Wang1, Zhihui Liu2,*, Hongdi Liu3, Baozhong Su4, Chuanyi Ma5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806 - 08 July 2024

    Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

  • Open Access

    ARTICLE

    A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

    Weijian Song1,, Xi Li1,, Peng Chen1,*, Juan Chen1, Jianhua Ren2, Yunni Xia3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563 - 08 July 2024

    Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

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