Pengwei Guo¹, Xiao Tan^2,3,*, Yiming Liu⁴

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071317 - 08 January 2026

Abstract Crack detection accuracy in computer vision is often constrained by limited annotated datasets. Although Generative Adversarial Networks (GANs) have been applied for data augmentation, they frequently introduce blurs and artifacts. To address this challenge, this study leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic crack images, enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation. The proposed framework involves a two-stage pipeline: first, DDPMs are used to synthesize high-fidelity crack images that capture fine structural details. Second, these generated samples are combined with real data to train… More >

Hemn Barzan Abdalla^1,*, Yulia Kumar², Jose Marchena², Stephany Guzman², Ardalan Awlla³, Mehdi Gheisari⁴, Maryam Cheraghy¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1073-1099, 2025, DOI:10.32604/cmc.2025.063551 - 09 June 2025

Abstract Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence (AI), and it will be interesting to see how we overcome this obstacle in the future, but for now, “THE SHOW MUST GO ON!!!” As AI spreads and transforms more industries, the lack of data is a significant obstacle: the best methods for teaching machines how real-world processes work. This paper explores the considerable implications of data scarcity for the AI industry, which threatens to restrict its growth and potential, and proposes plausible solutions and perspectives. In addition, this article focuses highly on… More >

Vatche Bahudian, John Valdovinos^*

Congenital Heart Disease, Vol.20, No.1, pp. 115-127, 2025, DOI:10.32604/chd.2025.063991 - 18 March 2025

Abstract Background: The population of Fontan patients, patients born with a single functioning ventricle, is growing. There is a growing need to develop algorithms for this population that can predict health outcomes. Artificial intelligence models predicting short-term and long-term health outcomes for patients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solution for generating realistic and useful synthetic data that can be used to train such models. Methods: Despite their promise, GANs have not been widely adopted in the congenital heart disease research community due, in some part, to a lack of knowledge… More >

Hussein Ibrahim Hussein¹, Said Amirul Anwar^2,*, Muhammad Imran Ahmad²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025 - 06 February 2023

Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly,… More >

Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143 - 27 September 2021

Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a… More >

Kang-Cheng Chen¹, Chia-Mu Yu^2,*, Tooska Dargahi³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 761-787, 2021, DOI:10.32604/cmc.2021.014984 - 22 March 2021

Abstract The advancement of information technology has improved the delivery of financial services by the introduction of Financial Technology (FinTech). To enhance their customer satisfaction, Fintech companies leverage artificial intelligence (AI) to collect fine-grained data about individuals, which enables them to provide more intelligent and customized services. However, although visions thereof promise to make customers’ lives easier, they also raise major security and privacy concerns for their users. Differential privacy (DP) is a common privacy-preserving data publishing technique that is proved to ensure a high level of privacy preservation. However, an important concern arises from the… More >

Yajun Zhang^1,*, Jianjun Yi¹, Jiahao Zhang¹, Yuanhao Chen¹, Liang He²

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 425-439, 2021, DOI:10.32604/iasc.2021.013795 - 18 January 2021

Abstract Image recognition algorithms based on deep learning have been widely developed in recent years owing to their capability of automatically capturing recognition features from image datasets and constantly improving the accuracy and efficiency of the image recognition process. However, the task of training deep learning networks is time-consuming and expensive because large training datasets are generally required, and extensive manpower is needed to annotate each of the images in the training dataset to support the supervised learning process. This task is particularly arduous when the image scenes involve randomly stacked objects. The present work addresses… More >

Hee-Jin Yoon¹, Kang-Jik Kim¹, Jun-Chul Chun^1,*

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 459-472, 2020, DOI:10.32604/cmc.2020.08799

Abstract Recently, virtual reality technology that can interact with various data is used for urban design and analysis. Reality, one of the most important elements in virtual reality technology, means visual expression so that a person can experience threedimensional space like reality. To obtain this realism, real-world data are used in the various fields. For example, in order to increase the realism of 3D modeled building textures real aerial images are utilized in 3D modelling. However, the aerial image captured during the day can be shadowed by the sun and it can cause the distortion or… More >

Menghua Luo^1,2, Ke Wang¹, Zhiping Cai^1,*, Anfeng Liu³, Yangyang Li⁴, Chak Fong Cheang⁵

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 15-26, 2019, DOI:10.32604/cmc.2019.03708

Abstract The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative More >