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  • Open Access

    ARTICLE

    An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

    Irfan Haider1, Muhammad Attique Khan1,*, Muhammad Nazir1, Taerang Kim2, Jae-Hyuk Cha2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

    Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

  • Open Access

    ARTICLE

    Defect Detection Model Using Time Series Data Augmentation and Transformation

    Gyu-Il Kim1, Hyun Yoo2, Han-Jin Cho3, Kyungyong Chung4,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1713-1730, 2024, DOI:10.32604/cmc.2023.046324

    Abstract Time-series data provide important information in many fields, and their processing and analysis have been the focus of much research. However, detecting anomalies is very difficult due to data imbalance, temporal dependence, and noise. Therefore, methodologies for data augmentation and conversion of time series data into images for analysis have been studied. This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance, temporal dependence, and robustness to noise. The method of data augmentation is set as the addition of noise. It involves adding Gaussian noise, with the noise… More >

  • Open Access

    ARTICLE

    Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

    Derwin Suhartono1,*, Alif Tri Handoyo1, Franz Adeta Junior2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3637-3657, 2023, DOI:10.32604/cmc.2023.045301

    Abstract Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication. This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network (GAN) based augmentation on diverse datasets, including iSarcasm, SemEval-18, and Ghosh. This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network (RGAN). The proposed RGAN method works by inverting labels between original and synthetic data during the training process. This inversion of labels provides feedback to the generator for generating… More >

  • Open Access

    ARTICLE

    Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

    Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078

    Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in… More >

  • Open Access

    ARTICLE

    Text Augmentation-Based Model for Emotion Recognition Using Transformers

    Fida Mohammad1,*, Mukhtaj Khan1, Safdar Nawaz Khan Marwat2, Naveed Jan3, Neelam Gohar4, Muhammad Bilal3, Amal Al-Rasheed5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202

    Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep neural network (DNN) model, especially… More >

  • Open Access

    ARTICLE

    Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

    S M Hasan Mahmud1,2, Md Mamun Ali3, Mohammad Fahim Shahriar1, Fahad Ahmed Al-Zahrani4, Kawsar Ahmed5,6,*, Dip Nandi1, Francis M. Bui5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020

    Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build… More >

  • Open Access

    ARTICLE

    EfficientShip: A Hybrid Deep Learning Framework for Ship Detection in the River

    Huafeng Chen1, Junxing Xue2, Hanyun Wen2, Yurong Hu1, Yudong Zhang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 301-320, 2024, DOI:10.32604/cmes.2023.028738

    Abstract Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters. Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection. To solve this problem, we present a hybrid ship detection framework which is named EfficientShip in this paper. The core parts of the EfficientShip are DLA-backboned object location (DBOL) and CascadeRCNN-guided object classification (CROC). The DBOL is responsible for finding potential ship objects, and the CROC is used to categorize the potential ship objects. We… More >

  • Open Access

    ARTICLE

    Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

    Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

    Journal on Artificial Intelligence, Vol.5, pp. 15-30, 2023, DOI:10.32604/jai.2023.041341

    Abstract The object detection technique depends on various methods for duplicating the dataset without adding more images. Data augmentation is a popular method that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization. This method is recommended in the case where the amount of high-quality data is limited, and gaining new examples is costly and time-consuming. In this paper, we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes (Car, Bus, Motorcycle, and Person). We used five different data augmentations… More >

  • Open Access

    ARTICLE

    Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

    Qi Liu1,2, Jing Li1,2,*, Xianmin Wang1,*, Wenpeng Zhao1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600

    Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further facilitating the classifier to distinguish… More >

  • Open Access

    ARTICLE

    Ship Detection and Recognition Based on Improved YOLOv7

    Wei Wu1, Xiulai Li2, Zhuhua Hu1, Xiaozhang Liu3,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 489-498, 2023, DOI:10.32604/cmc.2023.039929

    Abstract In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data… More >

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