Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (57)
  • Open Access


    Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

    Tusongjiang Kari1, Zhiyang He1, Aisikaer Rouzi2, Ziwei Zhang3, Xiaojing Ma1,*, Lin Du1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617

    Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

  • Open Access


    Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

    Asma Qaiser1, Saman Hina1, Abdul Karim Kazi1,*, Saad Ahmed2, Raheela Asif3

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 73-90, 2023, DOI:10.32604/iasc.2023.036784

    Abstract In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using… More >

  • Open Access


    MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN

    Kaiyue Wang1, Jian Gao1,2,*, Xinyan Lei1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 619-638, 2023, DOI:10.32604/iasc.2023.036701

    Abstract Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for… More >

  • Open Access


    PCATNet: Position-Class Awareness Transformer for Image Captioning

    Ziwei Tang1, Yaohua Yi2,*, Changhui Yu2, Aiguo Yin3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6007-6022, 2023, DOI:10.32604/cmc.2023.037861

    Abstract Existing image captioning models usually build the relation between visual information and words to generate captions, which lack spatial information and object classes. To address the issue, we propose a novel Position-Class Awareness Transformer (PCAT) network which can serve as a bridge between the visual features and captions by embedding spatial information and awareness of object classes. In our proposal, we construct our PCAT network by proposing a novel Grid Mapping Position Encoding (GMPE) method and refining the encoder-decoder framework. First, GMPE includes mapping the regions of objects to grids, calculating the relative distance among objects and quantization. Meanwhile, we… More >

  • Open Access


    Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

    Samar Elbedwehy1,3,*, T. Medhat2, Taher Hamza3, Mohammed F. Alrahmawy3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3637-3652, 2023, DOI:10.32604/csse.2023.038376

    Abstract One of the issues in Computer Vision is the automatic development of descriptions for images, sometimes known as image captioning. Deep Learning techniques have made significant progress in this area. The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. This paper aims to find optimized models for these two subsystems. For the image feature extraction subsystem, the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image. For the caption generation lingual subsystem, this… More >

  • Open Access


    Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

    Tusongjiang. Kari1, Lin Du1, Aisikaer. Rouzi2, Xiaojing Ma1,*, Zhichao Liu1, Bo Li1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4573-4592, 2023, DOI:10.32604/cmc.2023.037954

    Abstract The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.… More >

  • Open Access


    TECMH: Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

    Qiqi Li1, Longfei Ma1, Zheng Jiang1, Mingyong Li1,*, Bo Jin2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3713-3728, 2023, DOI:10.32604/cmc.2023.037463

    Abstract In recent years, cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage. Cross-modal retrieval technology can be applied to search engines, cross-modal medical processing, etc. The existing main method is to use a multi-label matching paradigm to finish the retrieval tasks. However, such methods do not use fine-grained information in the multi-modal data, which may lead to sub-optimal results. To avoid cross-modal matching turning into label matching, this paper proposes an end-to-end fine-grained cross-modal hash retrieval method, which can focus more on the fine-grained semantic information of multi-modal data. First,… More >

  • Open Access


    An Efficient Text-Independent Speaker Identification Using Feature Fusion and Transformer Model

    Arfat Ahmad Khan1, Rashid Jahangir2,*, Roobaea Alroobaea3, Saleh Yahya Alyahyan4, Ahmed H. Almulhi3, Majed Alsafyani3, Chitapong Wechtaisong5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4085-4100, 2023, DOI:10.32604/cmc.2023.036797

    Abstract Automatic Speaker Identification (ASI) involves the process of distinguishing an audio stream associated with numerous speakers’ utterances. Some common aspects, such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording, make the ASI task much more complicated and complex. This research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation resources. In this research, the performance of the transformer model is investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs, delta-delta MFCCs and spectral… More >

  • Open Access


    Text Simplification Using Transformer and BERT

    Sarah Alissa1,*, Mike Wald2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3479-3495, 2023, DOI:10.32604/cmc.2023.033647

    Abstract Reading and writing are the main interaction methods with web content. Text simplification tools are helpful for people with cognitive impairments, new language learners, and children as they might find difficulties in understanding the complex web content. Text simplification is the process of changing complex text into more readable and understandable text. The recent approaches to text simplification adopted the machine translation concept to learn simplification rules from a parallel corpus of complex and simple sentences. In this paper, we propose two models based on the transformer which is an encoder-decoder structure that achieves state-of-the-art (SOTA) results in machine translation.… More >

  • Open Access


    Analysis of Power Quality for Distribution Networks Using Active Compensator

    K. Naresh Kumar1,*, S. Srinath2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2623-2638, 2023, DOI:10.32604/iasc.2023.031713

    Abstract This paper concentrates on compensating the power quality issues which have been increased in day-to-day life due to the enormous usage of loads with power electronic control. One such solution is compensating devices like Pension Protection Fund (PPF), Active power filter (APF), hybrid power filter (HPF), etc., which are used to overcome Power Quality (PQ) issues. The proposed method used here is an active compensator called unified power quality conditioner (UPQC) which is a combination of shunt and series type active filter connected via a common DC link. The primary objective is to investigate the behavior of the compensators in… More >

Displaying 1-10 on page 1 of 57. Per Page  

Share Link