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In the light of the Industry 4.0 revolution, we find ourselves contending with datasets characterized by a multitude of features, thereby introducing a novel challenge in effectively managing high-featured data in real time. To address this challenge, we propose a new hybrid classification model that leverages the support vector machine (SVM) and firebug swarm optimization (FSO). SVM stands out as a fundamental and widely used classification technique in data science. The optimal determination of kernel parameters and the selection of the most impactful features significantly influence SVM performance. Additionally, the FSO algorithm is noteworthy for its utilization of element-wise Hadamard matrix multiplication operations to update positions. This operation enables parallel execution on multiple data items, thereby reducing overall execution time. Given the ability of FSO algorithm to run on multi-core …