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  • Open Access

    ARTICLE

    Contrastive Clustering for Unsupervised Recognition of Interference Signals

    Xiangwei Chen1, Zhijin Zhao1,2,*, Xueyi Ye1, Shilian Zheng2, Caiyi Lou2, Xiaoniu Yang2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1385-1400, 2023, DOI:10.32604/csse.2023.034543

    Abstract Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning. In the second phase, the original dataset’s k-nearest neighbor set (KNNset) is designed… More >

  • Open Access

    ARTICLE

    Solving Geometry Problems via Feature Learning and Contrastive Learning of Multimodal Data

    Pengpeng Jian1, Fucheng Guo1,*, Yanli Wang2, Yang Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1707-1728, 2023, DOI:10.32604/cmes.2023.023243

    Abstract This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data. A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems. Existing methods either focus on single-modal or multimodal problems, and they cannot fit each other. A general geometry problem solver should obviously be able to process various modal problems at the same time. In this paper, a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image, which… 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

    Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

    Mousa Alhajlah*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5157-5172, 2023, DOI:10.32604/cmc.2023.033339

    Abstract Underwater images degraded due to low contrast and visibility issues. Therefore, it is important to enhance the images and videos taken in the underwater environment before processing. Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images. The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available, low resolution, and blurriness in underwater images caused by the normal camera. Various researchers have proposed different solutions to overcome these problems. Dark channel prior (DCP) is one of the… More >

  • Open Access

    ARTICLE

    Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

    Yong Fang, Zhiying Zhao, Yijia Xu*, Zhonglin Liu

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4099-4118, 2023, DOI:10.32604/cmc.2023.033124

    Abstract System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model… More >

  • Open Access

    ARTICLE

    A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

    Nagwa Elaraby*, Sherif Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288

    Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly… More >

  • Open Access

    ARTICLE

    Ensemble Based Learning with Accurate Motion Contrast Detection

    M. Indirani*, S. Shankar

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1657-1674, 2023, DOI:10.32604/iasc.2023.026148

    Abstract Recent developments in computer vision applications have enabled detection of significant visual objects in video streams. Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization (SPSOM) and Incremental Deep Convolution Neural Networks (IDCNN) for detecting multiple objects. However, the study considered optical flows resulting in assessing motion contrasts. Existing methods have issue with accuracy and error rates in motion contrast detection. Hence, the overall object detection performance is reduced significantly. Thus, consideration of object motions in videos efficiently is a critical issue to be solved. To overcome the above mentioned problems, this research work… More >

  • Open Access

    ARTICLE

    Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

    Prasanalakshmi Balaji1,*, B. Sri Revathi2, Praveetha Gobinathan3, Shermin Shamsudheen3, Thavavel Vaiyapuri4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2275-2291, 2022, DOI:10.32604/cmc.2022.028560

    Abstract Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features; it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases. The Internet of Things (IoT) in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems. In recent years, the Internet of Things (IoT) has been identified as one of the most interesting research subjects in… More >

  • Open Access

    ARTICLE

    Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

    S. Priyadarsini1,*, Carlos Andrés Tavera Romero2, Abolfazl Mehbodniya3, P. Vidya Sagar4, Sudhakar Sengan5

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1057-1068, 2022, DOI:10.32604/csse.2022.024788

    Abstract In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique… More >

  • Open Access

    ARTICLE

    A Post-Processing Algorithm for Boosting Contrast of MRI Images

    B. Priestly Shan1, O. Jeba Shiney1, Sharzeel Saleem2, V. Rajinikanth3, Atef Zaguia4, Dilbag Singh5,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057

    Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More >

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