Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Duong Tien Dung^1,2,3, Ha Hai Nam⁴, Nguyen Long Giang³, Luong Thi Hong Lan^5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5625-5642, 2025, DOI:10.32604/cmc.2025.069636 - 23 October 2025

Abstract Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance. This method effectively integrates prior knowledge using pre-labeled data. While semi-supervised fuzzy clustering (SSFC) methods leverage limited labeled data to enhance accuracy, they remain highly susceptible to inappropriate or mislabeled prior knowledge, especially in noisy or overlapping datasets where cluster boundaries are ambiguous. To enhance the effectiveness of clustering algorithms, it is essential to leverage labeled data while ensuring the safety of the previous knowledge. Existing solutions, such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method (TS3FCM),… More >

Osnamir E. Bru-Cordero¹, Cecilia Castro², Víctor Leiva^3,*, Mario C. Jaramillo-Elorza⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1401-1431, 2025, DOI:10.32604/cmes.2025.064487 - 30 May 2025

Abstract Most reliability studies assume large samples or independence among components, but these assumptions often fail in practice, leading to imprecise inference. We address this issue by constructing confidence intervals (CIs) for the reliability of two-component systems with Weibull distributed failure times under a copula-frailty framework. Our construction integrates gamma-distributed frailties to capture unobserved heterogeneity and a copula-based dependence structure for correlated failures. The main contribution of this work is to derive adjusted CIs that explicitly incorporate the copula parameter in the variance-covariance matrix, achieving near-nominal coverage probabilities even in small samples or highly dependent settings. More >

Lijun He¹, Weibo Yang^2,*, Jialing Miao², Jingru Chen³

International Journal of Mental Health Promotion, Vol.26, No.11, pp. 943-955, 2024, DOI:10.32604/ijmhp.2024.055730 - 28 November 2024

Abstract Background: In the pursuit of fostering employees’ well-being, leaders are recognized as playing a vital role. However, so far, most of the existing research has focused on leadership behavior and the superficial interaction between leaders and members but has unexpectedly ignored the specific supporting role of supervisors in the career development of employees, that is, supervisory career support. Additionally, the internal mechanism of how career support from supervisors is related to and promotes employees’ well-being is still unclear. Based on social cognitive career theory (SCCT), this study aimed to explore whether, how, and when supervisory… More >

Yan Shi¹, Wenjie Li¹, Gongbo Fan^2,*, Luxi Zhang¹, Fengjiu Yang¹

Energy Engineering, Vol.121, No.2, pp. 461-482, 2024, DOI:10.32604/ee.2023.043835 - 25 January 2024

Abstract Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation, this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side. A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation, with the overarching goal of optimizing the system for low-carbon operation. To begin with, an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation… More >

Karim Gasmi^1,*, Lassaad Ben Ammar^2,, Hmoud Elshammari⁴, Fadwa Yahya²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.033270 - 31 March 2023

Abstract Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and… More >

Sunisa Junnumtuam, Sa-Aat Niwitpong^*, Suparat Niwitpong

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1229-1254, 2023, DOI:10.32604/cmes.2022.022098 - 27 October 2022

Abstract A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) distribution is proposed for the first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of the new distribution, such as the moment generating function, mean, and variance are presented. Furthermore, confidence intervals are constructed by using the Wald, Bayesian, and highest posterior density (HPD) methods to estimate the true confidence intervals for the parameters of the ZICG distribution. Their efficacies were investigated by using both simulation and real-world data comprising the number of daily More > Graphic Abstract

Muhammad Shaheen^1,*, Umair Abdullah²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3305-3322, 2021, DOI:10.32604/cmc.2021.016766 - 06 May 2021

Abstract This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative… More >

Rashad Bantan¹, Amal S. Hassan², Ehab Almetwally³, M. Elgarhy⁴, Farrukh Jamal⁵, Christophe Chesneau⁶, Mahmoud Elsehetry^7,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2859-2875, 2021, DOI:10.32604/cmc.2021.015422 - 06 May 2021

Abstract Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress, such as pressure, temperature, vibration, voltage, or load to induce early failures. In this paper, a step stress partially accelerated life test (SS-PALT) is regarded under the progressive type-II censored data with random removals. The removals from the test are considered to have the binomial distribution. The life times of the testing items are assumed to follow length-biased weighted Lomax… More >

Zhibin Zhang^1,2,*, Ping Li^1,3, Shuailing Zhao^1,2, Zhimin Lv^1,2, Fang Du^1,2, Yajian An^1,2

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 1043-1056, 2021, DOI:10.32604/cmc.2020.012517 - 30 October 2020

Abstract As the agricultural internet of things (IoT) technology has evolved, smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments. In this paper, we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots, which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters. First, the speeded-up robust feature (SURF) extracting and matching algorithm is used to obtain featuring point pairs from the green crop row… More >