Shoujian Yu¹, Zhenchi Jie¹, Guowen Wu¹, Hong Zhang¹, Shigen Shen^2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1729-1751, 2023, DOI:10.32604/iasc.2023.039911

Abstract In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While… More >

Qi Liu^1,2, Jing Li^1,2,*, Xianmin Wang^1,*, Wenpeng Zhao¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600

Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further facilitating the classifier to distinguish… More >

Zhizhong Liu^*, Bin Zhou, Lingqiang Meng, Guangyu Huang

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1963-1981, 2023, DOI:10.32604/iasc.2023.038835

Abstract Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to learn the unimodal features of… More >

P. Sabitha^*, G. Meeragandhi

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402

Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained… More >

Jie Deng¹, Ran Guo², Zilong Jin^1,3,*

Journal on Internet of Things, Vol.4, No.3, pp. 141-153, 2022, DOI:10.32604/jiot.2022.038914

Abstract Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage, data privacy leaks, high communication costs, unsatisfactory detection rates, and false positive rate. To address existing issues in intrusion detection, this paper presents a novel approach called CS-FL, which combines Federated Learning and a Self-Attention Fusion Convolutional Neural Network. Federated Learning is a new distributed computing model that enables individual training of client data without uploading local data to a central server. at the same time, local training results are uploaded and integrated across all participating clients to produce a global model. The sharing model reduces… More >

Zunliang Chen^1,2, Chengxu Huang^1,2, Lucheng Duan^1,2, Baohua Tan^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451

Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >

Hong Zhang^1,2,*, Gang Yang¹, Hailiang Yu¹, Zan Zheng¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274

Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs Softmax regression. The experimental results… More >

Wajiha Rahim Khan¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Majed Alhaisoni⁴, Usman Tariq⁵, Jae-Hyuk Cha^6,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188

Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low-… More >

Feng Yan¹, Wushouer Silamu², Yanbing Li^1,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 65-80, 2023, DOI:10.32604/cmc.2023.038177

Abstract Visual question answering (VQA) requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately. However, existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images, which limits the understanding depth of the image content. The images contain more than just visual objects, some images contain textual information about the scene, and slightly more complex images contain relationships between individual visual objects. Firstly, this paper proposes a model using image description for feature enhancement. This model encodes images and their descriptions separately… More >

Siyu Lu¹, Yueming Ding¹, Zhengtong Yin², Mingzhe Liu^3,*, Xuan Liu⁴, Wenfeng Zheng^1,*, Lirong Yin⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598

Abstract Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method. In the case… More >