Amal Al-Rasheed¹, Jaber S. Alzahrani², Majdy M. Eltahir³, Abdullah Mohamed⁴, Anwer Mustafa Hilal^5,*, Abdelwahed Motwakel⁵, Abu Sarwar Zamani⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3275-3290, 2022, DOI:10.32604/cmc.2022.029823 - 16 June 2022

Abstract The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources. The developments of artificial intelligence (AI) and machine learning (ML) models assist in the effectual design of medical data classification models. Therefore, this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-to-Sequence Autoencoder (OSAE-LSTM) model for biomedical data classification. The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases. Primarily, the OSAE-LSTM model involves min-max normalization based pre-processing to scale More >

R. Punithavathi¹, R. Thanga Selvi², R. Latha³, G. Kadiravan^4,*, V. Srikanth⁵, Neeraj Kumar Shukla⁶

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 143-156, 2022, DOI:10.32604/iasc.2022.023344 - 05 January 2022

Abstract Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) More >

Hegazy Rezk^1,2,*, Mohammed Mazen Alhato³, Mohemmed Alhaider¹, Soufiene Bouallègue^3,4

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 185-199, 2021, DOI:10.32604/cmc.2021.016175 - 22 March 2021

Abstract In this research paper, an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator (DFIG) based wind energy system has been proposed. The proposed strategy used the robust Fractional-Order (FO) Proportional-Integral (PI) control technique. The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits. It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness. The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization (MRFO) More >