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  • Open Access

    ARTICLE

    Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

    Jae-Seok Jang1, Hyuk-Yoon Kwon2,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3471-3489, 2023, DOI:10.32604/csse.2023.035570

    Abstract Question-answering (QA) models find answers to a given question. The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets. In this paper, we deal with the QA pair matching approach in QA models, which finds the most relevant question and its recommended answer for a given question. Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies. In contrast, we aim to automatically find the category to which the question belongs by employing the text classification model and… More >

  • Open Access

    ARTICLE

    Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

    Arwa Saif Fadel1,2,*, Osama Ahmed Abulnaja1, Mostafa Elsayed Saleh1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4419-4444, 2023, DOI:10.32604/cmc.2023.037112

    Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the… More >

  • Open Access

    ARTICLE

    Augmenting Android Malware Using Conditional Variational Autoencoder for the Malware Family Classification

    Younghoon Ban, Jeong Hyun Yi, Haehyun Cho*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555

    Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their behaviors by conducting the familial… More >

  • Open Access

    ARTICLE

    Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

    K. Kavin Kumar1, P. M. Dinesh2, P. Rayavel3, L. Vijayaraja4, R. Dhanasekar4, Rupa Kesavan5, Kannadasan Raju6, Arfat Ahmad Khan7, Chitapong Wechtaisong8,*, Mohd Anul Haq9, Zamil S. Alzamil9, Ahmed Alhussen10

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1845-1861, 2023, DOI:10.32604/csse.2023.033927

    Abstract A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal. In India, around 15 million cases are diagnosed yearly. To mitigate the seriousness of the tumor it is essential to diagnose at the beginning. Notwithstanding, the manual evaluation process utilizing Magnetic Resonance Imaging (MRI) causes a few worries, remarkably inefficient and inaccurate brain tumor diagnoses. Similarly, the examination process of brain tumors is intricate as they display high unbalance in nature like shape, size, appearance, and location. Therefore, a precise and expeditious prognosis of brain tumors is essential for implementing… More >

  • Open Access

    ARTICLE

    A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

    Qixin Lan, Binqiang Chen*, Bin Yao

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2017-2037, 2023, DOI:10.32604/cmes.2023.025307

    Abstract Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance training data through distribution fusion.… More >

  • Open Access

    ARTICLE

    Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

    Samra Rehman1, Muhammad Attique Khan1, Majed Alhaisoni2, Ammar Armghan3, Fayadh Alenezi3, Abdullah Alqahtani4, Khean Vesal5, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324

    Abstract Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation. The authors present computer vision techniques for detecting and classifying fruit leaf diseases. Examples of computer vision techniques are preprocessing original images for visualization of infected regions, feature extraction from raw or segmented images, feature fusion, feature selection, and classification. The following are the major challenges identified by researchers in the literature: (i) low-contrast infected regions extract irrelevant and redundant information, which misleads classification accuracy; (ii) irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy. This paper proposed… More >

  • Open Access

    ARTICLE

    Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

    S. J. K. Jagadeesh Kumar1, P. Parthasarathi2, Mofreh A. Hogo3, Mehedi Masud4, Jehad F. Al-Amri5, Mohamed Abouhawwash6,7,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2363-2378, 2023, DOI:10.32604/iasc.2023.033800

    Abstract Women from middle age to old age are mostly screened positive for Breast cancer which leads to death. Times over the past decades, the overall survival rate in breast cancer has improved due to advancements in early-stage diagnosis and tailored therapy. Today all hospital brings high awareness and early detection technologies for breast cancer. This increases the survival rate of women. Though traditional breast cancer treatment takes so long, early cancer techniques require an automation system. This research provides a new methodology for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best characteristics.… More >

  • Open Access

    ARTICLE

    Data Augmentation and Random Multi-Model Deep Learning for Data Classification

    Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420

    Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this… More >

  • Open Access

    ARTICLE

    Improving Brain Tumor Classification with Deep Learning Using Synthetic Data

    Muhammed Mutlu Yapici1, Rukiye Karakis2,*, Kali Gurkahraman3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5049-5067, 2023, DOI:10.32604/cmc.2023.035584

    Abstract Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers. By… 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 across variable sized datasets. The… More >

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