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 >