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  • Open Access

    ARTICLE

    Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

    Tanzila Saba1, Muhammad Mujahid1, Shaha Al-Otaibi2, Noor Ayesha3, Amjad Rehman Khan1,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3337-3353, 2025, DOI:10.32604/cmc.2025.060163 - 17 February 2025

    Abstract Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone; however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with… More >

  • Open Access

    ARTICLE

    In-Plane Static Analysis of Curved Nanobeams Using Exact-Solution-Based Finite Element Formulation

    Ömer Ekim Genel*, Hilal Koç, Ekrem Tüfekci

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2043-2059, 2025, DOI:10.32604/cmc.2025.060111 - 17 February 2025

    Abstract Due to their superior properties, the interest in nanostructures is increasing today in engineering. This study presents a new two-noded curved finite element for analyzing the in-plane static behaviors of curved nanobeams. Opposite to traditional curved finite elements developed by using approximate interpolation functions, the proposed curved finite element is developed by using exact analytical solutions. Although this approach was first introduced for analyzing the mechanical behaviors of macro-scale curved beams by adopting the local theory of elasticity, the exact analytical expressions used in this study were obtained from the solutions of governing equations that… More >

  • Open Access

    REVIEW

    Machine Learning-Based Methods for Materials Inverse Design: A Review

    Yingli Liu1,2, Yuting Cui1,2, Haihe Zhou1,2, Sheng Lei3, Haibin Yuan3, Tao Shen1,2,*, Jiancheng Yin4,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1463-1492, 2025, DOI:10.32604/cmc.2025.060109 - 17 February 2025

    Abstract Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. With the development of physics, statistics, computer science, and other fields, machine learning offers opportunities for systematically discovering new materials. Especially through machine learning-based inverse design, machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties. This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials More > Graphic Abstract

    Machine Learning-Based Methods for Materials Inverse Design: A Review

  • Open Access

    ARTICLE

    LSBSP: A Lightweight Sharding Method of Blockchain Based on State Pruning for Efficient Data Sharing in IoMT

    Guoqiong Liao1,3, Yinxiang Lei1,2,*, Yufang Xie1, Neal N. Xiong4

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3309-3335, 2025, DOI:10.32604/cmc.2024.060077 - 17 February 2025

    Abstract As the Internet of Medical Things (IoMT) continues to expand, smart health-monitoring devices generate vast amounts of valuable data while simultaneously raising critical security and privacy challenges. Blockchain technology presents a promising avenue to address these concerns due to its inherent decentralization and security features. However, scalability remains a persistent hurdle, particularly for IoMT applications that involve large-scale networks and resource-constrained devices. This paper introduces a novel lightweight sharding method tailored to the unique demands of IoMT data sharing. Our approach enhances state bootstrapping efficiency and reduces operational overhead by utilizing a dual-chain structure comprising… More >

  • Open Access

    ARTICLE

    A Boundary-Type Meshless Method for Traction Identification in Two-Dimensional Anisotropic Elasticity and Investigating the Effective Parameters

    Mohammad-Rahim Hematiyan*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3069-3090, 2025, DOI:10.32604/cmc.2025.060067 - 17 February 2025

    Abstract The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the… More >

  • Open Access

    ARTICLE

    A Hybrid Transfer Learning Framework for Enhanced Oil Production Time Series Forecasting

    Dalal AL-Alimi1, Mohammed A. A. Al-qaness2,3,*, Robertas Damaševičius4,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3539-3561, 2025, DOI:10.32604/cmc.2025.059869 - 17 February 2025

    Abstract Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions… More >

  • Open Access

    REVIEW

    An Overview of LoRa Localization Technologies

    Huajiang Ruan1,2, Panjun Sun1,2, Yuanyuan Dong1,2, Hamid Tahaei1, Zhaoxi Fang1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1645-1680, 2025, DOI:10.32604/cmc.2024.059746 - 17 February 2025

    Abstract Traditional Global Positioning System (GPS) technology, with its high power consumption and limited performance in obstructed environments, is unsuitable for many Internet of Things (IoT) applications. This paper explores LoRa as an alternative localization technology, leveraging its low power consumption, robust indoor penetration, and extensive coverage area, which render it highly suitable for diverse IoT settings. We comprehensively review several LoRa-based localization techniques, including time of arrival (ToA), time difference of arrival (TDoA), round trip time (RTT), received signal strength indicator (RSSI), and fingerprinting methods. Through this review, we evaluate the strengths and limitations of More >

  • Open Access

    ARTICLE

    A Trusted Distributed Oracle Scheme Based on Share Recovery Threshold Signature

    Shihao Wang1, Xuehui Du1,*, Xiangyu Wu1, Qiantao Yang1,2, Wenjuan Wang1, Yu Cao1, Aodi Liu1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3355-3379, 2025, DOI:10.32604/cmc.2024.059722 - 17 February 2025

    Abstract With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put… More >

  • Open Access

    REVIEW

    An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

    Uddagiri Sirisha1,*, Chanumolu Kiran Kumar2, Sujatha Canavoy Narahari3, Parvathaneni Naga Srinivasu4,5,6

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1757-1810, 2025, DOI:10.32604/cmc.2024.059715 - 17 February 2025

    Abstract The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work,… More >

  • Open Access

    ARTICLE

    MATD3-Based End-Edge Collaborative Resource Optimization for NOMA-Assisted Industrial Wireless Networks

    Ru Hao1,2,3, Chi Xu2,3,*, Jing Liu1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3203-3222, 2025, DOI:10.32604/cmc.2024.059689 - 17 February 2025

    Abstract Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely More >

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