R. Nandakumar¹, Vijayakumar Ponnusamy^2,*, Aman Kumar Mishra²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2805-2819, 2023, DOI:10.32604/iasc.2023.028645

Abstract In the Internet of Things (IoT) scenario, many devices will communicate in the presence of the cellular network; the chances of availability of spectrum will be very scary given the presence of large numbers of mobile users and large amounts of applications. Spectrum prediction is very encouraging for high traffic next-generation wireless networks, where devices/machines which are part of the Cognitive Radio Network (CRN) can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sensing radio spectrum. Long short-term memory (LSTM) is employed to simultaneously predict the Radio Spectrum State (RSS) for two-time slots,… More >

S. Prabu, K. Joseph Abraham Sundar^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2071-2086, 2023, DOI:10.32604/iasc.2023.029105

Abstract Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain… More >

S. Girirajan, A. Pandian^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1987-2001, 2023, DOI:10.32604/iasc.2023.028090

Abstract Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural… More >

Jian Liu^1,*, Xiaodong Xia¹, Chunyang Han², Jiao Hui³, Jim Feng⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 265-278, 2022, DOI:10.32604/cmc.2022.029227

Abstract As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the… More >

Jia Chen¹, Zhiqiang He¹, Dayong Zhu¹, Bei Hui^1,*, Rita Yi Man Li², Xiao-Guang Yue^3,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565

Abstract Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly consists of three parts: contracting… More >

Muhammad Usman Younus^1,*, Rabia Shafi², Ammar Rafiq³, Muhammad Rizwan Anjum⁴, Sharjeel Afridi⁵, Abdul Aleem Jamali⁶, Zulfiqar Ali Arain⁷

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2617-2631, 2022, DOI:10.32604/cmc.2022.022236

Abstract The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion… More >

Mingyang Duan, Jin Liu^*, Shiqi Lv

Journal on Big Data, Vol.3, No.2, pp. 77-83, 2021, DOI:10.32604/jbd.2021.016674

Abstract Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in the traditional Encoder-Decoder architectures, only the global features of the images are extracted, while the local information of the images is not well utilized. This paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption text. We use VGG16 and Faster R-CNN to extract global and local features in the encoder first. Then, we train the bidirectional LSTM network with the fused… More >

Aziguli Wulamu^1,2,*, Jingyue Sang³, Dezheng Zhang^1,2, Zuxian Shi^1,2

Journal of New Media, Vol.2, No.4, pp. 149-155, 2020, DOI:10.32604/jnm.2020.014115

Abstract Cultivated land extraction is essential for sustainable development and agriculture. In this paper, the network we propose is based on the encoderdecoder structure, which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation solutions. The encoder consists of two part: the first is the modified Xception, it can used as the feature extraction network, and the second is the atrous convolution, it can used to expand the receptive field and the context information to extract richer feature information. The decoder part uses the conventional upsampling operation to restore the original resolution.… More >

Yan Wang^*, Zhangjie Fu, Xingming Sun

Journal of Cyber Security, Vol.2, No.3, pp. 115-121, 2020, DOI:10.32604/jcs.2020.012275

Abstract Nowadays, with the popularization of network technology, more and more people are concerned about the problem of cyber security. Steganography, a technique dedicated to protecting peoples’ private data, has become a hot topic in the research field. However, there are still some problems in the current research. For example, the visual quality of dense images generated by some steganographic algorithms is not good enough; the security of the steganographic algorithm is not high enough, which makes it easy to be attacked by others. In this paper, we propose a novel high visual quality image steganographic neural network based on encoder-decoder… More >

Chao Luo¹, Canghong Shi¹, Xiaojie Li^{1, *}, Xin Wang⁴, Yucheng Chen³, Dongrui Gao¹, Youbing Yin⁴, Qi Song⁴, Xi Wu¹, Jiliu Zhou¹

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 995-1012, 2020, DOI:10.32604/cmc.2020.07968

Abstract Myocardial segmentation and classification play a major role in the diagnosis of cardiovascular disease. Dilated Cardiomyopathy (DCM) is a kind of common chronic and life-threatening cardiopathy. Early diagnostics significantly increases the chances of correct treatment and survival. However, accurate and rapid diagnosis of DCM is still challenge due to high variability of cardiac structure, low contrast cardiac magnetic resonance (CMR) images, and intrinsic noise in synthetic CMR images caused by motion artifact and cardiac dynamics. Moreover, visual assessment and empirical evaluation are widely used in routine clinical diagnosis, but they are subject to high inter-observer variability and are both subjective… More >