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  • Open Access


    Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model

    Jiawen Li1,2, Yuesheng Huang1, Yayi Lu1, Leijun Wang1,*, Yongqi Ren1, Rongjun Chen1

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1581-1599, 2024, DOI:10.32604/cmc.2024.052666

    Abstract In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model… More >

  • Open Access


    Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

    Batyrkhan Omarov1,2,*, Meirzhan Baikuvekov1, Daniyar Sultan1, Nurzhan Mukazhanov3, Madina Suleimenova2, Maigul Zhekambayeva3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 341-359, 2024, DOI:10.32604/cmc.2024.052437

    Abstract This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model’s focus on critical segments… More >

  • Open Access


    GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

    Md. Atiqur Rahman1, Mustavi Ibne Masum1, Khan Md Hasib2, M. F. Mridha3,*, Sultan Alfarhood4, Mejdl Safran4,*, Dunren Che5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760

    Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

  • Open Access


    Optimised CNN Architectures for Handwritten Arabic Character Recognition

    Salah Alghyaline*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4905-4924, 2024, DOI:10.32604/cmc.2024.052016

    Abstract Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles. Arabic is morphologically rich, and its characters have a high similarity. The Arabic language includes 28 characters. Each character has up to four shapes according to its location in the word (at the beginning, middle, end, and isolated). This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters. The proposed architectures were derived from the popular CNN architectures, such as VGG, ResNet, and Inception, to make them applicable to recognizing character-size images. The experimental results on three More >

  • Open Access


    CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586

    Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

  • Open Access


    A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

    Jun Wang, Changfu Si, Zhen Wang, Qiang Fu*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4297-4318, 2024, DOI:10.32604/cmc.2024.050223

    Abstract Nowadays, with the rapid development of industrial Internet technology, on the one hand, advanced industrial control systems (ICS) have improved industrial production efficiency. However, there are more and more cyber-attacks targeting industrial control systems. To ensure the security of industrial networks, intrusion detection systems have been widely used in industrial control systems, and deep neural networks have always been an effective method for identifying cyber attacks. Current intrusion detection methods still suffer from low accuracy and a high false alarm rate. Therefore, it is important to build a more efficient intrusion detection model. This paper… More >

  • Open Access


    Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

    Chunming Wu1, Shupeng Zheng2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665

    Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

  • Open Access


    MCIF-Transformer Mask RCNN: Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

    Huiling Lu1,*, Tao Zhou2,3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4371-4393, 2024, DOI:10.32604/cmc.2024.047827

    Abstract The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship… More >

  • Open Access


    Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

    Lin Ma1, Liyong Wang1, Shuang Zeng1, Yutong Zhao1, Chang Liu1, Heng Zhang1, Qiong Wu2,*, Hongbo Ren2

    Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332

    Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

  • Open Access


    An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

    Md. Abdur Rahim1, Fahmid Al Farid2, Abu Saleh Musa Miah3, Arpa Kar Puza1, Md. Nur Alam4, Md. Najmul Hossain5, Sarina Mansor2, Hezerul Abdul Karim2,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714

    Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

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