Yanfen Guo^1,2, Zhe Cui¹, Xiaojie Li^2,*, Jing Peng^1,2, Jinrong Hu², Zhipeng Yang³, Tao Wu², Imran Mumtaz⁴

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1771-1782, 2022, DOI:10.32604/iasc.2022.019785

Abstract Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck, and its incidence is the highest all around the world. Intensive radiotherapy using computer-aided diagnosis is the best technique for the treatment of NPC. The key step of radiotherapy is the delineation of the target areas and organs at risk, that is, tumor images segmentation. We proposed the segmentation method of NPC image based on multi-scale cascaded fully convolutional network. It used cascaded network and multi-scale feature for a coarse-to-fine segmentation to improve the segmentation effect. In coarse segmentation, image blocks and data augmentation… More >

Rabia Saleem¹, Jamal Hussain Shah^1,*, Muhammad Sharif¹, Ghulam Jillani Ansari²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3581-3601, 2021, DOI:10.32604/cmc.2021.017700

Abstract Due to the high demand for mango and being the king of all fruits, it is the need of the hour to curb its diseases to fetch high returns. Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms. Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases, i.e., Anthracnose, apical-necrosis, etc., of a mango plant leaf. To solve this issue, we proposed a CNN based Fully-convolutional-network (FrCNnet) model for the segmentation of the diseased part of the mango leaf. The proposed FrCNnet directly learns the features… More >

Chaowei Lin^1,#, Feifei Lee^1,#,*, Jiawei Cai¹, Hanqing Chen¹, Qiu Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 985-1006, 2021, DOI:10.32604/cmes.2021.014522

Abstract Scene recognition is a fundamental task in computer vision, which generally includes three vital stages, namely feature extraction, feature transformation and classification. Early research mainly focuses on feature extraction, but with the rise of Convolutional Neural Networks (CNNs), more and more feature transformation methods are proposed based on CNN features. In this work, a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation (GEDRR) is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions. In addition, we propose a method using the multi-head attention module to enhance and fuse convolutional… More >

Dengyong Zhang^1,2, Jiawei Hu^1,2, Feng Li^1,2,*, Xiangling Ding³, Arun Kumar Sangaiah⁴, Victor S. Sheng⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1503-1517, 2021, DOI:10.32604/cmc.2021.017089

Abstract In the past several years, remarkable achievements have been made in the field of object detection. Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection. In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets). To address this problem, we propose an improved region-based fully convolutional network (R-FCN). The presented technique improves detection accuracy and eliminates localization misalignment by replacing position-sensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates… More >

Hao Wu^1,*, Arun Kumar Sangaiah²

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 121-132, 2021, DOI:10.32604/iasc.2021.016457

Abstract In oral English teaching in China, teachers usually improve students’ pronunciation by their subjective judgment. Even to the same student, the teacher gives different suggestions at different times. Students’ oral pronunciation features can be obtained from the reconstructed acoustic and natural language features of speech audio, but the task is complicated due to the embedding of multimodal sentences. To solve this problem, this paper proposes an English speech recognition based on enhanced temporal convolution network. Firstly, a suitable UNet network model is designed to extract the noise of speech signal and achieve the purpose of speech enhancement. Secondly, a network… More >

Syed Mustajar Ahmad Shah¹, Hongwei Ge^1,*, Sami Ahmed Haider², Muhammad Irshad³, Sohail M. Noman⁴, Jehangir Arshad⁵, Asfandeyar Ahmad⁶, Talha Younas⁷

Computer Systems Science and Engineering, Vol.36, No.2, pp. 369-382, 2021, DOI:10.32604/csse.2021.014234

Abstract The data generated from non-Euclidean domains and its graphical representation (with complex-relationship object interdependence) applications has observed an exponential growth. The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms. In this study, we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data. Additionally, the quantum information theory has been applied through Graph Neural Networks (GNNs) to generate Riemannian metrics in closed-form of several graph layers. In further, to pre-process the adjacency matrix of graphs, a new… More >

Yong Wu¹, Binjun Wang^{1, *}, Wei Li²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2277-2294, 2020, DOI:10.32604/cmc.2020.011609

Abstract Graph convolutional networks (GCNs) have been developed as a general and powerful tool to handle various tasks related to graph data. However, current methods mainly consider homogeneous networks and ignore the rich semantics and multiple types of objects that are common in heterogeneous information networks (HINs). In this paper, we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph convolutional network architecture that operates on HINs. Specifically, we extract the rich semantics by different metastructures and adopt hyperedge to model the interactions among metastructure-based neighbors. Due to the powerful information extraction capabilities of metastructure and hyperedge, HHCN has the… More >

Shuai Wang¹, Xianyi Chen^{2, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2065-2077, 2020, DOI:10.32604/cmc.2020.09857

Abstract With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed adaptive binary coding, our method… More >

Ao Feng¹, Zhengjie Gao^{1, *}, Xinyu Song¹, Ke Ke², Tianhao Xu¹, Xuelei Zhang¹

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 909-923, 2020, DOI:10.32604/cmc.2020.09913

Abstract Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional… More >

N. Shobha Rani^{1, *}, M. Chandrajith², B. R. Pushpa¹, B. J. Bipin Nair¹

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 679-694, 2020, DOI:10.32604/cmc.2020.08552

Abstract Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during… More >