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  • Open Access

    ARTICLE

    Detection of Student Engagement in E-Learning Environments Using EfficientnetV2-L Together with RNN-Based Models

    Farhad Mortezapour Shiri1,*, Ehsan Ahmadi2, Mohammadreza Rezaee1, Thinagaran Perumal1

    Journal on Artificial Intelligence, Vol.6, pp. 85-103, 2024, DOI:10.32604/jai.2024.048911

    Abstract Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding More >

  • Open Access

    ARTICLE

    The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

    Xuanchen Zhou1,2,3, Wenzhong Yang2,3,*, Liejun Wang2,3, Fuyuan Wei2,3, KeZiErBieKe HaiLaTi2,3, Yuanyuan Liao2,3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4073-4087, 2023, DOI:10.32604/cmc.2023.040253

    Abstract With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly,… More >

  • Open Access

    ARTICLE

    CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

    Mehwish Zafar1, Javeria Amin2, Muhammad Sharif1, Muhammad Almas Anjum3, Seifedine Kadry4,5,6, Jungeun Kim7,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860

    Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

    Shah Faisal1, Kashif Javed1, Sara Ali1, Areej Alasiry2, Mehrez Marzougui2, Muhammad Attique Khan3,*, Jae-Hyuk Cha4,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

    Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset More >

  • Open Access

    ARTICLE

    FST-EfficientNetV2: Exceptional Image Classification for Remote Sensing

    Huaxiang Song*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3959-3978, 2023, DOI:10.32604/csse.2023.038429

    Abstract Recently, the semantic classification (SC) algorithm for remote sensing images (RSI) has been greatly improved by deep learning (DL) techniques, e.g., deep convolutional neural networks (CNNs). However, too many methods employ complex procedures (e.g., multi-stages), excessive hardware budgets (e.g., multi-models), and an extreme reliance on domain knowledge (e.g., handcrafted features) for the pure purpose of improving accuracy. It obviously goes against the superiority of DL, i.e., simplicity and automation. Meanwhile, these algorithms come with unnecessarily expensive overhead on parameters and hardware costs. As a solution, the author proposed a fast and simple training algorithm based… More >

  • Open Access

    ARTICLE

    An Efficient and Robust Hand Gesture Recognition System of Sign Language Employing Finetuned Inception-V3 and Efficientnet-B0 Network

    Adnan Hussain1, Sareer Ul Amin2, Muhammad Fayaz3, Sanghyun Seo4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3509-3525, 2023, DOI:10.32604/csse.2023.037258

    Abstract Hand Gesture Recognition (HGR) is a promising research area with an extensive range of applications, such as surgery, video game techniques, and sign language translation, where sign language is a complicated structured form of hand gestures. The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers, the orientation of the hand, and the hand’s position concerning the body. The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population. Therefore, real-time HGR is one of the most effective… More >

  • Open Access

    ARTICLE

    A More Efficient Approach for Remote Sensing Image Classification

    Huaxiang Song*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5741-5756, 2023, DOI:10.32604/cmc.2023.034921

    Abstract Over the past decade, the significant growth of the convolutional neural network (CNN) based on deep learning (DL) approaches has greatly improved the machine learning (ML) algorithm’s performance on the semantic scene classification (SSC) of remote sensing images (RSI). However, the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms, e.g., automation and simplicity, partially lost. Traditional ML strategies (e.g., the handcrafted features or indicators) and accuracy-aimed strategies with a high trade-off (e.g., the multi-stage CNNs and ensemble of multi-CNNs) are widely used without any training efficiency optimization involved, which… More >

  • Open Access

    ARTICLE

    CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

    Abishek Mahesh1, Deeptimaan Banerjee1, Ahona Saha1, Manas Ranjan Prusty2,*, A. Balasundaram2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5967-5982, 2023, DOI:10.32604/cmc.2023.033920

    Abstract A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their… More >

  • Open Access

    ARTICLE

    X-ray Based COVID-19 Classification Using Lightweight EfficientNet

    Tahani Maazi Almutairi*, Mohamed Maher Ben Ismail, Ouiem Bchir

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 167-187, 2022, DOI:10.32604/jai.2022.032974

    Abstract The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks… More >

  • Open Access

    ARTICLE

    EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

    R. S. Sandhya Devi1,*, V. R. Vijay Kumar2, P. Sivakumar3

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2249-2263, 2023, DOI:10.32604/csse.2023.032231

    Abstract Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates… More >

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