Finding the optimum subset of genes for microarray classification is laborious because microarray data are often high-dimensional and contain many irrelevant and redundant genes. To overcome this problem, we have prop...
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Finding the optimum subset of genes for microarray classification is laborious because microarray data are often high-dimensional and contain many irrelevant and redundant genes. To overcome this problem, we have proposed a two-step technique. In the first step, to reduce the vast number of genes or features, an ensemble of popular rank-based feature selection algorithms with filter evaluation metrics are used to select a group of top-ranking genes. In the next step, the quantum-inspired owl search algorithm (QIOSAf), a new filter fitness function-based metaheuristic search technique incorporating concepts from quantum computing, is developed to identify the best subset of genes from the predetermined list. The experimental findings reveal that the ensemble approach in the first step can select more dominant groups of genes than each of the individual filters. Furthermore, it has been found that QIOSAf can reduce the cardinality of the selected optimum gene subset with comparable classification accuracy and requires lesser computational time than our earlier proposed QIOSA-based wrapper approach (i.e. QIOSAw). Besides, compared with three popular evolutionary feature subset selection algorithms, QIOSAf efficiently reduces the optimum cardinality of the gene subset while maintaining acceptable classification accuracy.
Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assi...
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Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owlsearchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owlsearchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.
This article proposes an optimal approach of improving the dynamic stability in power system by using hybrid method based power system-stabiliser. The proposed hybrid approach is an integration of owl search algorithm...
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This article proposes an optimal approach of improving the dynamic stability in power system by using hybrid method based power system-stabiliser. The proposed hybrid approach is an integration of owl search algorithm (OSA) and Improved Grey Wolf Optimizer (IGWO);hence, it is known as OSA-IGWO method. The objective of the proposed method is to enhance the power system's dynamic stability. This notion offers a singular approach to design the sturdy Power System Stabiliser (PSS) and enhance the dynamic energy machine balance. The Modified Heffron-Phillip's version can be evolved through generator facet transformer voltage because endless bus voltage and connection are considered to lessen the complexity with computational time. An energy machine stabiliser is referred to as Modified Power System Stabiliser (MPSS) that can be evolved and incorporated with PID controller;the usage of proposed approach in a Single-Machine-Infinity Bus-System uses Modified Heffron-Phillips (MHP) version. The OSA-IGWO approach may be used to select the benefit sets of PID-MPSS. The proposed version complements the dynamic overall performance of machine via the way of means of growing the damping ratio of the energy machine below diverse running conditions. The OSA-IGWO method is done in MATLABplatform, and its performance is compared with existing methods.
This paper represents a new technique for optimal modelling and simulating a proton exchange membrane fuel cell (PEMFC) system to assure dependable modelling. The main idea is to utilise a newly developed meta-heurist...
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This paper represents a new technique for optimal modelling and simulating a proton exchange membrane fuel cell (PEMFC) system to assure dependable modelling. The main idea is to utilise a newly developed meta-heuristic, called Chaos owl search algorithm (COSA) to optimal selecting of the model parameters of the PEMFC stacks by minimising the Sum of Squared Error (SSE) between the estimated and the measured output voltage for two different case studies. By applying 50 independent runs with the algorithm, it is analysed and compared with some literature meta-heuristics including Bat algorithm (BA) Firefly algorithm (FFA), and Multi-verse optimiser (MVO) in terms of convergence speed and minimum SSE. The final results declare that the proposed method achieves the best convergence speed in comparison with others. The results also determine the high efficiency of the presented method.
Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed sol...
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Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the owl search algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process.
In this manuscript, an energy management system (EMS) is proposed to the distribution system (DS) using Internet of Things (IoT) framework with a hybrid system. The proposed hybrid method is the combination of the Sea...
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In this manuscript, an energy management system (EMS) is proposed to the distribution system (DS) using Internet of Things (IoT) framework with a hybrid system. The proposed hybrid method is the combination of the Seagull Optimization algorithm (SOA) and owl search algorithm (OSA), hence it is called SO(2)SA technique. The principle objective of the SO(2)SA technique is to optimize managing distribution system power and resources through continuous monitoring of the data from a communication framework based on IoT. In SO(2)SA technique, every home device is connected to the module of data acquisition, which indicates an IoT object along with a unique IP address as a result of huge mesh wireless network devices. The sending data are processed through SO(2)SA technique. Similarly, the IoT architecture of the distribution system enhances the flexibility of these networks and gives optimal utilization of obtainable resources. In addition, the SO(2)SA technique is responsible for meeting the overall power and supply requirements. The proposed method is implemented in MATLAB/Simulink site and the efficiency is likened to the other different methods. In 50 trail numbers, the RMSE, MAPE, and MBE range of SO(2)SA technique represents 5.63, 0.90, and 1.035. Thus, the proposed technique is highly competent over all the existing approaches.
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