Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Wei Zhang¹, Haijun Geng^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 427-448, 2024, DOI:10.32604/cmc.2024.051871

Abstract Currently, distributed routing protocols are constrained by offering a single path between any pair of nodes, thereby limiting the potential throughput and overall network performance. This approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is disrupted. In contrast, routing protocols that leverage multiple paths within the network offer a more resilient and efficient solution. Multipath routing, as a fundamental concept, surpasses the limitations of traditional shortest path first protocols. It not only redirects traffic to unused resources, effectively mitigating network congestion, but… More >

Jinqiao Dai, Pengsen Cheng, Jiayong Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 547-565, 2024, DOI:10.32604/cmc.2024.051745

Abstract Generating diverse and factual text is challenging and is receiving increasing attention. By sampling from the latent space, variational autoencoder-based models have recently enhanced the diversity of generated text. However, existing research predominantly depends on summarization models to offer paragraph-level semantic information for enhancing factual correctness. The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models. In this paper, a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text. Specifically, our model encodes the input sentences and uses them as facts to… More >

Zhan Shi^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530

Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

Haiyue Li¹, Jing Xie², Jing Ke³, Ye Yuan¹, Xiaoyong Pan¹, Hongyi Xin⁴, Hongbin Shen^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 487-506, 2024, DOI:10.32604/cmc.2024.049640

Abstract Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue. Convolutional neural network (CNN) and generative adversarial network (GAN) are pivotal in medical image registration. However, existing methods often struggle with severe interference and deformation, as seen in histological images of conditions like Cushing’s disease. We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator in GAN. In this study, we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration. To… More >

SNJEŽANA KAŠTELAN^1,2, ANA DIDOVIĆ PAVIČIĆ³, DARIA PAŠALIĆ⁴, TAMARA NIKUŠEVA-MARTIĆ⁵, SAMIR ČANOVIĆ^3,7, PETRA KOVAČEVIĆ^1,6,*, SUZANA KONJEVODA^3,7

Oncology Research, Vol.32, No.8, pp. 1265-1285, 2024, DOI:10.32604/or.2024.048437

Abstract Uveal and conjunctival melanomas are relatively rare tumors; nonetheless, they pose a significant risk of mortality for a large number of affected individuals. The pathogenesis of melanoma at different sites is very similar, however, the prognosis for patients with ocular melanoma remains unfavourable, primarily due to its distinctive genetic profile and tumor microenvironment. Regardless of considerable advances in understanding the genetic characteristics and biological behaviour, the treatment of uveal and conjunctival melanoma remains a formidable challenge. To enhance the prospect of success, collaborative efforts involving medical professionals and researchers in the fields of ocular biology… More > Graphic Abstract

Punam Gulande^*, R. N. Awale

Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123

Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

Jiawei Qin^1,2, Hua Zhang^1,*, Hanbing Yan², Tian Zhu², Song Hu¹, Dingyu Yan²

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 527-544, 2024, DOI:10.32604/iasc.2024.047509

Abstract By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, More >

Xiaojun Li, Fuyong Su^*

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 719-732, 2024, DOI:10.32604/fhmt.2024.051950

Abstract In order to study the effect of oxygen-enriched combustion technology on the temperature field and NO emission in the continuous heating furnace, this paper studies the oxygen-enriched combustion of a pushing steel continuous heating furnace in a domestic company. This study utilizes numerical simulation method, establishes the mathematical models of flow, combustion and NO generation combustion process in the furnace and analyzes the heat transfer process and NO generation in the furnace under different air oxygen content and different wind ratio. The research results show that with the increase of oxygen content in the air, More > Graphic Abstract

Shuai Li¹, Xiaodong Zhao^1,2,*, Jinghu Zhou¹, Xiyue Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2587-2611, 2024, DOI:10.32604/cmes.2024.052203

Abstract Accurate quantification of the uncertainty in the mechanical characteristics of dielectric solids is crucial for advancing their application in high-precision technological domains, necessitating the development of robust computational methods. This paper introduces a Conditional Generation Adversarial Network Isogeometric Analysis (CGAN-IGA) to assess the uncertainty of dielectric solids’ mechanical characteristics. IGA is utilized for the precise computation of electric potentials in dielectric, piezoelectric, and flexoelectric materials, leveraging its advantage of integrating seamlessly with Computer-Aided Design (CAD) models to maintain exact geometrical fidelity. The CGAN method is highly efficient in generating models for piezoelectric and flexoelectric materials, More >