Aljuaid Turkea Ayedh M^1,2,*, Ainuddin Wahid Abdul Wahab^1,*, Mohd Yamani Idna Idris^1,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4663-4686, 2024, DOI:10.32604/cmc.2024.055287 - 12 September 2024

Abstract Organizations are adopting the Bring Your Own Device (BYOD) concept to enhance productivity and reduce expenses. However, this trend introduces security challenges, such as unauthorized access. Traditional access control systems, such as Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC), are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources. This paper proposes a method for enforcing access decisions that is adaptable and dynamic, based on multilayer hybrid deep learning techniques, particularly the Tabular Deep Neural Network TabularDNN method. This technique transforms… More >

M. Iniyan Arasu^1,*, S. Subha Rani¹, G. Raswin Geoffery²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 729-742, 2022, DOI:10.32604/iasc.2022.021973 - 08 February 2022

Abstract LoRa is well-known for its extensive communication range, inexpensive efficiency, and reduced or less power consumption in end devices. End-device energy consumption in LoRa networks is ludicrous because some end-devices use massive dissemination variables to reach the remote doorway. Furthermore, the batteries in these end devices deplete very quickly, reducing network life significantly. To address this issue, an optimal mixed-integer linear programming long-range technique (OMILP-LoRa) was used in this study. The primary goal of this research is to enable adaptive resource allocation using the unique OMILP-LoRa protocol. The ACCURATE heuristic and the OMILP model for More >

Shaleeza Sohail¹, Atif Alvi^2,*, Aasia Khanum³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3211-3225, 2022, DOI:10.32604/cmc.2022.023560 - 07 December 2021

Abstract Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain. Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions. Recently, some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified. However, adaptability is not specifically considered in this domain. This paper presents a new framework based on hybrid statistical More >