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RETRACTION

Fangyu Ye¹, Xiaoshu Xu^2,*, Yunfeng Zhang³, Yan Ye⁴, Jingyu Dai^5,*
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.045930
Abstract This article has no abstract. More >

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ARTICLE

Laith Abualigah^{1,2,3,4,5,6,*}, Serdar Ekinci⁷, Davut Izci^7,8, 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 >

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ARTICLE

Ghawar Said¹, Ata Ullah², Anwar Ghani^1,*, Muhammad Azeem¹, Khalid Yahya³, Muhammad Bilal⁴, Sayed Chhattan Shah^5,*
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 >

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