Yingpeng Dai¹, Junzheng Wang¹, Jing Li^1,*, Lingfeng Meng², Songfeng Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 135-156, 2023, DOI:10.32604/cmes.2022.021525 - 24 August 2022

Abstract It is important for the autonomous system to understand environmental information. For the autonomous system, it is desirable to have a strong generalization ability to deal with different complex environmental information, as well as have high accuracy and quick inference speed. Network ensemble architecture is a good choice to improve network performance. However, it is unsuitable for real-time applications on the autonomous system. To tackle this problem, a new neural network ensemble named partial-shared ensemble network (PSENet) is presented. PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks More >

P. Anantha Prabha^1,*, G. Suchitra², R. Saravanan³

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3065-3079, 2023, DOI:10.32604/iasc.2023.030017 - 17 August 2022

Abstract Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist. Manual observation and identification take time and are always contingent on the involvement of experts. A system is proposed to alleviate this challenge that uses transfer learning techniques to classify the cephalopods automatically. In the proposed method, only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition. First, the efficiency of the chosen models is determined by evaluating their performance and comparing the findings. Second, the models are fine-tuned by adding dense layers… More >

Sara A. Alameen^*, Areej M. Alhothali

Computer Systems Science and Engineering, Vol.44, No.1, pp. 895-912, 2023, DOI:10.32604/csse.2023.024643 - 01 June 2022

Abstract Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features… More >

Tahani Maazi Almutairi^*, Mohamed Maher Ben Ismail, Ouiem Bchir

Journal on Artificial Intelligence, Vol.4, No.3, pp. 167-187, 2022, DOI:10.32604/jai.2022.032974 - 01 December 2022

Abstract The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks… More >

Mu Ye^1,2,3,4, Li Ji¹, Luo Tianye¹, Li Sihan⁵, Zhang Tong¹, Feng Ruilong¹, Hu Tianli^1,2,3,4, Gong He^1,2,3,4, Guo Ying^1,2,3,4, Sun Yu^1,2,3,4, Thobela Louis Tyasi⁶, Li Shijun^7,8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6195-6205, 2022, DOI:10.32604/cmc.2022.026880 - 28 July 2022

Abstract Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of… More >

Jianan Wu¹, Ke Xue², Zhaowei Ding³, Lei Lang³, Kang Gu³, Xiaolin Li⁴, Mingli Zhang⁵, Desheng Li^3,6,*

Journal of Renewable Materials, Vol.10, No.12, pp. 3167-3183, 2022, DOI:10.32604/jrm.2022.020265 - 14 July 2022

Abstract Thermal insulation is an important indicator to evaluate the construction material in cold region engineering. As we know, adding the industrial waste as lightweight aggregate or creating the pore inside the cement-based composite could make the texture loose, and the thermal insulating capacity of the material would be improved with this texture. Using these methods, the industrial by-product and engineering waste could be cycled in an efficient way. Moreover, after service the fragmented cement composites paste could be used as aggregate in the thermal insulating concrete again. While the porous texture is not favorable for… More > Graphic Abstract

Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263 - 27 June 2022

Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental… More >

Jiabao Shi¹, Zhao Qiu^1,*, Tao Chen¹, Jiale Lin¹, Hancheng Huang², Yunlong He³, Yu Yang³

Journal of New Media, Vol.4, No.2, pp. 97-106, 2022, DOI:10.32604/jnm.2022.027176 - 13 June 2022

Abstract Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point More >

Fadia Ali Khan¹, Jameel Ahmed¹, Fehaid Alqahtani², Suliman A. Alsuhibany³, Fawad Ahmed⁴, Jawad Ahmad^5,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 279-294, 2022, DOI:10.32604/cmc.2022.028789 - 18 May 2022

Abstract In this paper, we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm. The method works on the principle of confusion and diffusion. The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes. Initially, an image (red, green, and blue components) is partitioned into blocks with an equal number of pixels. Each block is then processed with Tinkerbell Chaotic Map (TBCM) to get shuffled pixels and shuffled blocks. Composite Fractal Function (CFF) change the value of pixels of each color component More >

Jie Chen¹, Chengsheng Yuan^1,2,*, Chen Cui², Zhihua Xia¹, Xingming Sun^1,3, Thangarajah Akilan⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 719-733, 2022, DOI:10.32604/cmc.2022.027984 - 18 May 2022

Abstract Fingerprint identification systems have been widely deployed in many occasions of our daily life. However, together with many advantages, they are still vulnerable to the presentation attack (PA) by some counterfeit fingerprints. To address challenges from PA, fingerprint liveness detection (FLD) technology has been proposed and gradually attracted people's attention. The vast majority of the FLD methods directly employ convolutional neural network (CNN), and rarely pay attention to the problem of over-parameterization and over-fitting of models, resulting in large calculation force of model deployment and poor model generalization. Aiming at filling this gap, this paper… More >