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 >

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 >

Xiaorui Zhang^1,2,3,4,*, Ziquan Zhao¹, Wei Sun^4,5, Qi Cui⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 537-550, 2023, DOI:10.32604/csse.2023.034230

Abstract LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox). However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning. To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate information. To improve the detection… More >

Le Quang Thao^1,2,*, Duong Duc Cuong², Tran Thi Tuong Anh³, Tran Duc Luong⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1309-1321, 2023, DOI:10.32604/csse.2023.031010

Abstract Noise pollution tends to receive less awareness compared to other types of pollution, however, it greatly impacts the quality of life for humans such as causing sleep disruption, stress or hearing impairment. Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control, planning of the soundscape environment, or selection of safe living space. In this paper, we proposed a self-attention long short-term memory (LSTM) method that can improve sound classification compared to previous baselines. An attention mechanism will be designed solely to… More >

Peizhu Gong¹, Jin Liu¹, Zhongdai Wu², Bing Han², Y. Ken Wang³, Huihua He^4,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4203-4220, 2023, DOI:10.32604/cmc.2023.028291

Abstract Speech emotion recognition, as an important component of human-computer interaction technology, has received increasing attention. Recent studies have treated emotion recognition of speech signals as a multimodal task, due to its inclusion of the semantic features of two different modalities, i.e., audio and text. However, existing methods often fail in effectively represent features and capture correlations. This paper presents a multi-level circulant cross-modal Transformer (MLCCT) for multimodal speech emotion recognition. The proposed model can be divided into three steps, feature extraction, interaction and fusion. Self-supervised embedding models are introduced for feature extraction, which give a more powerful representation of the… More >

Hong’an Li¹, Min Zhang^1,*, Dufeng Chen², Jing Zhang¹, Meng Yang³, Zhanli Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering… More > Graphic Abstract

D. Suvitha^*, M. Vijayalakshmi

Computer Systems Science and Engineering, Vol.44, No.1, pp. 873-894, 2023, DOI:10.32604/csse.2023.025189

Abstract Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India. The video obtained from such surveillance are of low quality. Still counting vehicles from such videos are necessity to avoid traffic congestion and allows drivers to plan their routes more precisely. On the other hand, detecting vehicles from such low quality videos are highly challenging with vision based methodologies. In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India, which is mostly an un-attempted entity by most available sources. In this work profound Detection Transformer (DETR)… More >

Muhammad Babar Kamal¹, Arfat Ahmad Khan², Faizan Ahmed Khan³, Malik Muhammad Ali Shahid⁴, Chitapong Wechtaisong^2,*, Muhammad Daud Kamal⁵, Muhammad Junaid Ali⁶, Peerapong Uthansakul²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5547-5562, 2022, DOI:10.32604/cmc.2022.024590

Abstract The deep learning advancements have greatly improved the performance of speech recognition systems, and most recent systems are based on the Recurrent Neural Network (RNN). Overall, the RNN works fine with the small sequence data, but suffers from the gradient vanishing problem in case of large sequence. The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data. Generally, in speech recognition, the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities. The image is classified by the machine learning mechanism to generate a classification transcript. However, the audio… More >

Yue Li, Jin Liu^*, Shengjie Shang

Journal on Big Data, Vol.3, No.3, pp. 111-118, 2021, DOI:10.32604/jbd.2021.017169

Abstract Visual Question Answering (VQA) has attracted extensive research focus and has become a hot topic in deep learning recently. The development of computer vision and natural language processing technology has contributed to the advancement of this research area. Key solutions to improve the performance of VQA system exist in feature extraction, multimodal fusion, and answer prediction modules. There exists an unsolved issue in the popular VQA image feature extraction module that extracts the fine-grained features from objects of different scale difficultly. In this paper, a novel feature extraction network that combines multi-scale convolution and self-attention branches to solve the above… More >

Pu Han^1,2, Mingtao Zhang¹, Jin Shi³, Jinming Yang⁴, Xiaoyan Li^5,*

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 55-72, 2021, DOI:10.32604/iasc.2021.017021

Abstract With the rapid development of Internet, the medical Q&A community has become an important channel for people to obtain and share medical and health knowledge. Online medical entity recognition (OMER), as the foundation of medical and health information extraction, has attracted extensive attention of researchers in recent years. In order to further improve the research progress of Chinese OMER, LSTM-Att-Med model is proposed in this paper to capture more external semantic features and important information. First, Word2vec is used to generate the character-level vectors with semantic features on the basis of the unlabeled corpus in the medical domain and open… More >