Jingang Kim, Suengbum Lim, Taejin Lee^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 471-490, 2023, DOI:10.32604/csse.2023.038413

Abstract AI-related research is conducted in various ways, but the reliability of AI prediction results is currently insufficient, so expert decisions are indispensable for tasks that require essential decision-making. XAI (eXplainable AI) is studied to improve the reliability of AI. However, each XAI methodology shows different results in the same data set and exact model. This means that XAI results must be given meaning, and a lot of noise value emerges. This paper proposes the HFD (Hybrid Feature Dropout)-based XAI and evaluation methodology. The proposed XAI methodology can mitigate shortcomings, such as incorrect feature weights and impractical feature selection. There are… More >

Azam Zaka¹, Riffat Jabeen^2,*, Kanwal Iqbal Khan³

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4781-4802, 2022, DOI:10.32604/cmc.2022.020316

Abstract The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The findings suggest that an extended… More >

Mahmoud Higazy^1,2,*, Taher A. Nofal¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3401-3418, 2021, DOI:10.32604/cmc.2021.015790

Abstract The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems. Furthermore, combinatorial design theory has several applications like detecting or correcting errors in communication systems. Network (graph) designs (GDs) are introduced as a generalization of the symmetric balanced incomplete block designs (BIBDs) that are utilized directly in the above mentioned application. The networks (graphs) have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it. Here, a general method is proposed and applied to construct new networks designs.… More >

Xiaolei Ma^{1, 2}, Yang Lu^{1, 2}, Yinan Lu^{1, *}, Zhili Pei², Jichao Liu³

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 923-941, 2020, DOI:10.32604/cmc.2020.07711

Abstract Supervised machine learning approaches are effective in text mining, but their success relies heavily on manually annotated corpora. However, there are limited numbers of annotated biomedical event corpora, and the available datasets contain insufficient examples for training classifiers; the common cure is to seek large amounts of training samples from unlabeled data, but such data sets often contain many mislabeled samples, which will degrade the performance of classifiers. Therefore, this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data. First, we construct the mislabeled dataset through error data analysis with the development… More >