Lu Chen^1,#, Huaqiang Chen^2,#, Zhikai Pan⁷, Sheng Xu², Guangsheng Lai², Shuwen Chen^2,5,6, Shuihua Wang^3,8, Xiaodong Gu^2,6,*, Yudong Zhang^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 361-382, 2024, DOI:10.32604/cmes.2023.031229 - 30 December 2023

Abstract Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately. Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model’s generalization ability. Third, we introduce strategies for augmenting… More >

Thi-Kien Dao¹, Trong-The Nguyen^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2201-2237, 2024, DOI:10.32604/cmes.2023.029880 - 15 December 2023

Abstract Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging and fundamental operations in various monitoring or tracking applications because the network deploys a large area and allocates the acquired location information to unknown devices. The metaheuristic approach is one of the most advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditional methods that often suffer from computational time problems and small network deployment scale. This study proposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on the siege mechanism… More >

Zhenyu Huang^1,2, Gun Li², Xudong Sun¹, Yong Chen¹, Jie Sun¹, Zhangsong Ni^1,*, Yang Yang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3219-3238, 2023, DOI:10.32604/cmc.2023.039489 - 08 October 2023

Abstract Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attracted much interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlation… More >

Bilal Hassan^1,*, Hafiz Husnain Raza Sherazi², Mubashir Ali³, Yusra Siddiqi²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2727-2743, 2023, DOI:10.32604/iasc.2023.039275 - 11 September 2023

Abstract Following the success of soft biometrics over traditional biometrics, anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video. Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics. However, one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video. The level of accuracy is usually affected by several contextual factors such as overlapping body components, an angle from the camera, and… More >

Jinxing Niu^1,*, Shaokui Gu¹, Junmin Du², Yongxing Hao¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2015-2031, 2023, DOI:10.32604/cmc.2023.040489 - 30 August 2023

Abstract With the continuous development of the economy and society, plastic pollution in rivers, lakes, oceans, and other bodies of water is increasingly severe, posing a serious challenge to underwater ecosystems. Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste. However, it often causes significant challenges such as noise interference, low contrast, and blurred textures in underwater optical images. A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed, which combines weighted logarithmic transformations, adaptive gamma correction, improved multi-scale Retinex (MSR) algorithm, and the contrast… More >

Lina Al-joudi, Muhammad Arif^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1763-1780, 2023, DOI:10.32604/cmc.2023.039413 - 30 August 2023

Abstract The subcellular localization of human proteins is vital for understanding the structure of human cells. Proteins play a significant role within human cells, as many different groups of proteins are located in a specific location to perform a particular function. Understanding these functions will help in discovering many diseases and developing their treatments. The importance of imaging analysis techniques, specifically in proteomics research, is becoming more prevalent. Despite recent advances in deep learning techniques for analyzing microscopy images, classification models have faced critical challenges in achieving high performance. Most protein subcellular images have a significant… More >

Qilin Xiong^1,2,*, Wen An^1,2, Chuanzhi Liu^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010044

Abstract Shear localization is an important failure mode, or even the dominant mode in metals at high-strain rates. However, it is a great challenge to accurately predict the occurrence and evolution of shear localization in metals at the high-strain rate deformation. Here, a dislocation-based crystal plasticity constitutive model with a crucial mechanism of shear instability, namely dynamic recrystallization, was developed. The evolution equations of dislocation density and grain size in the process of dynamic recrystallization were proposed and incorporated into the new constitutive model. The threshold of the stored energy in crystals was used as the… More >

Gyu-Il Kim¹, Hyun Yoo², Kyungyong Chung^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2135-2152, 2023, DOI:10.32604/csse.2023.041030 - 28 July 2023

Abstract Artificial intelligence is increasingly being applied in the field of video analysis, particularly in the area of public safety where video surveillance equipment such as closed-circuit television (CCTV) is used and automated analysis of video information is required. However, various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging. Video analysis technology applies object classification, detection, and relationship analysis to continuous 2D frame data, and the various meanings within the video are thus analyzed based on the extracted basic data. Motion recognition is key in this analysis.… More >

Yuanyuan Li^1,#, Jinze Mu^2,#, Qingliang Li¹, Huabing Liu³, Xuefeng Yuan^2,*, Deya Wang^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.7, pp. 2079-2085, 2023, DOI:10.32604/phyton.2023.028237 - 29 May 2023

Abstract Infection of plum bark necrosis stem pitting associated virus (PBNSPaV) has been reported in many Prunus species in several countries, causing significant economic losses. The very small proteins encoded by plant viruses are often overlooked due to their short sequences and uncertain significance. However, numerous studies have indicated that they might play important roles in the pathogenesis of virus infection. The role of small hydrophobic protein P6, encoded by the open reading frame 2 of PBNSPaV, has not been well explored. In this study, we amplified the P6 fragment from a PBNSPaV isolate by RT-PCR using specific… More >

Muhammad Aamir¹, Ziaur Rahman^1,*, Waheed Ahmed Abro², Uzair Aslam Bhatti³, Zaheer Ahmed Dayo¹, Muhammad Ishfaq¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6351-6373, 2023, DOI:10.32604/cmc.2023.038173 - 29 April 2023

Abstract Object detection in images has been identified as a critical area of research in computer vision image processing. Research has developed several novel methods for determining an object’s location and category from an image. However, there is still room for improvement in terms of detection efficiency. This study aims to develop a technique for detecting objects in images. To enhance overall detection performance, we considered object detection a two-fold problem, including localization and classification. The proposed method generates class-independent, high-quality, and precise proposals using an agglomerative clustering technique. We then combine these proposals with the… More >