All Outputs (7)

A new class of zero-truncated counting models and its application (2024)
Journal Article
Tang, X. P., Tian, Y. Z., Wu, C. H., Wang, Y., & Mian, Z. B. (2024). A new class of zero-truncated counting models and its application. Communications in Statistics - Simulation and Computation, https://doi.org/10.1080/03610918.2024.2384561

Count data is a type of data derived from the number of times an event occurs per unit of time, and zero-truncated count data refers to count data without zero, which often appears in various fields. In this paper, a new zero-truncated Bell (ZTBell)... Read More about A new class of zero-truncated counting models and its application.

An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm (2024)
Journal Article
Niu, X. Y., Tian, Y. Z., Tang, M. L., & Mian, Z. B. (2024). An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm. Air Quality, Atmosphere and Health, https://doi.org/10.1007/s11869-024-01606-2

Agricultural and rural carbon (ARC) emissions are a major source of greenhouse gas emissions in China and have profound implications for implementing the rural revitalization strategy. This study takes Shandong Province, a leading agricultural provin... Read More about An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm.

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach (2024)
Journal Article
Rajak, U., Chaurasiya, P. K., Verma, T. N., Dasore, A., Ağbulut, Ü., Meshram, K., …Mian, Z. (2024). Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach. Environmental science and pollution research, https://doi.org/10.1007/s11356-024-33210-3

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such... Read More about Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach.

Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization (2024)
Journal Article
Tian, Y.-Z., Wu, C.-H., Tai, L.-N., Mian, Z., & Tian, M.-Z. (2024). Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization. Statistical Analysis and Data Mining, 17(2), Article e11683. https://doi.org/10.1002/sam.11683

Ordinal data frequently occur in various fields such as knowledge level assessment, credit rating, clinical disease diagnosis, and psychological evaluation. The classic models including cumulative logistic regression or probit regression are often us... Read More about Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems (2024)
Journal Article
Li, X., Ma, Z., Mian, Z., Liu, Z., Huang, R., & He, N. (2024). Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems. Computers, Materials & Continua, 78(3), 4129-4152. https://doi.org/10.32604/cmc.2024.047168

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications. Although there is an extensive literature on qualitative properties such as safety and liveness, there is sti... Read More about Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems.

Assessing the Impact of Usability from Evaluating Mobile Health Applications (2024)
Journal Article
Busari, A., Jaber, T., Gordon, N., & Mian, Z. (2024). Assessing the Impact of Usability from Evaluating Mobile Health Applications. International Journal on Engineering Technologies and Informatics, 5(2), https://doi.org/10.51626/ijeti.2024.05.00074

Software applications that are used to monitor, track, and improve health are called Mobile Health Applications or mHAs. They are developed with or without the help of medical professionals to potentially aid health, achieve health goals and improve... Read More about Assessing the Impact of Usability from Evaluating Mobile Health Applications.

Application of Machine Learning Techniques for the Prediction of Heart Disease (2024)
Journal Article
Owodunni, A. A., Jaber, T., & Mian, Z. (2024). Application of Machine Learning Techniques for the Prediction of Heart Disease. Acta Scientific Computer Sciences, 6(3), 13-23

As important as the heart is to humans, unfortunately, 43% of death is from heart disease [2] declared by Global Burden of Disease research. By 2030, deaths from cardiovascular disease will reach 23.6 million where heart disease takes the lead [3]. A... Read More about Application of Machine Learning Techniques for the Prediction of Heart Disease.