Open Access
RETRACTION
Fangyu Ye1, Xiaoshu Xu2,*, Yunfeng Zhang3, Yan Ye4, Jingyu Dai5,*
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.045930
Abstract This article has no abstract. More >
Open Access
ARTICLE
Laith Abualigah1,2,3,4,5,6,*, Serdar Ekinci7, Davut Izci7,8, Raed Abu Zitar9
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.040291
Abstract Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and
reliability. This study proposes a novel approach for designing a fractional order proportional-integral-derivative
(FOPID) controller that utilizes a modified elite opposition-based artificial hummingbird algorithm (m-AHA) for
optimal parameter tuning. Our approach outperforms existing optimization techniques on benchmark functions,
and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.
Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient
method for FOPID controller design that can enhance the driving experience while ensuring safety and… More >
Open Access
ARTICLE
Ghawar Said1, Ata Ullah2, Anwar Ghani1,*, Muhammad Azeem1, Khalid Yahya3, Muhammad Bilal4, Sayed Chhattan Shah5,*
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.036079
Abstract The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks
(SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce
communication costs and storage overhead. Existing State of the art schemes
suffer from computational overhead due to deterministic or random treebased tags generation which further increases as the file size grows. This
paper presents an efficient file-level de-duplication scheme (EFDS) where
the cost of creating tags is reduced by employing a hash table with keyvalue pair for each… More >