Samar Elbedwehy^1,3,*, 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 >

Tusongjiang. Kari¹, Lin Du¹, Aisikaer. Rouzi², Xiaojing Ma^1,*, 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 >

Qiqi Li¹, Longfei Ma¹, Zheng Jiang¹, Mingyong Li^1,*, 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 >

Arfat Ahmad Khan¹, Rashid Jahangir^2,*, 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 >

Sarah Alissa^1,*, 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 >

K. Naresh Kumar^1,*, 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 >

Mingyong Li, Qiqi Li, Zheng Jiang, Yan Ma^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1401-1414, 2023, DOI:10.32604/csse.2023.034757

Abstract In recent years, the development of deep learning has further improved hash retrieval technology. Most of the existing hashing methods currently use Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process image and text information, respectively. This makes images or texts subject to local constraints, and inherent label matching cannot capture fine-grained information, often leading to suboptimal results. Driven by the development of the transformer model, we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs. Specifically, we use a BERT network to extract text… More >

Ahmed Fathalla¹, Zakaria Alameer², Mohamed Abbas³, Ahmed Ali^4,5,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 929-950, 2023, DOI:10.32604/csse.2023.035255

Abstract The oil industries are an important part of a country’s economy. The crude oil’s price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for… More >

Feilu Hang¹, Wei Guo¹, Hexiong Chen¹, Linjiang Xie¹, Chenghao Zhou^2,*, Yao Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 517-529, 2023, DOI:10.32604/cmes.2023.025774

Abstract Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points. These logs are valuable for analyzing performance issues and understanding the status of the system. Anomaly detection plays an important role in service management and system maintenance, and guarantees the reliability and security of online systems. Logs are universal semi-structured data, which causes difficulties for traditional manual detection and pattern-matching algorithms. While some deep learning algorithms utilize neural networks to detect anomalies, these approaches have an over-reliance on manually designed features, resulting in the effectiveness of anomaly detection depending… More >

Haowei Yao^1,3,*, Youxin Li^1,3, Kefeng Lv^1,3, Dong Wang^2,3, Jinguang Zhang⁴, Zhenyu Zhan^2,3, Zhenyu Wang^2,3, Huaitao Song^1,3, Xiaoge Wei^1,3, Hengjie Qin^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 733-747, 2023, DOI:10.32604/cmes.2023.022155

Abstract To thoroughly study the extinguishing effect of a high-pressure water mist fire extinguishing system when a transformer fire occurs, a 3D experimental model of a transformer is established in this work by employing Fire Dynamics Simulator (FDS) software. More specifically, by setting different parameters, the process of the high-pressure water mist fire extinguishing system with the presence of both diverse ambient temperatures and water mist sprinkler laying conditions is simulated. In addition, the fire extinguishing effect of the employed high-pressure water mist system with the implementation of different strategies is systematically analyzed. The extracted results show that a fire source… More > Graphic Abstract