In recent times, the control of engineering processes has gained attention, making it critical to protect devices from operating beyond their designed capabilities. Therefore, this study investigates the modelling and optimization of gain factors for a MIMO-based PID controller employing a machine learning classifier approach. Traditional approaches like Ziegler-Nichols and Cohen-Coon may not handle dynamic coupling between channels well, struggle with nonlinearity, and always require manual tuning, which is tediously challenging and time-consuming. Machine-learning (ML) algorithms are introduced in this study to redesign and remodel the general model (GM) in MIMO fashion. We train and test the proposed model in Anaconda3 using Python programming on synthetic and iteratively generated datasets. Four different ML classifiers, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB), are employed to generate confusion matrices with accuracies of 30.67%, 72%, 25.33%, and 76%, respectively, using the PM system. Furthermore, the dataset corresponding to 76% of the NB is predicted to give an optimal output, as the output of the GM serves as the initial solution of the controller, and the test numerical results are fed back into the MATLAB scripts to produce an optimized version of the GM. Comparatively speaking, the GM, with an average settling time of 238.33 s, and the PM, with an average of 93.33 s, demonstrate faster steady-state response times, smaller gain, phase margins, and better overlap of the ideal gain factors on their target open-loop gain curves, depicting excellent stability. In addition, it can be inferred that the PM is more efficient than the existing model (GM) as it yields better-desired results. As a future direction, it is suggested that more machine learning classifiers, such as XGBOOST, neural networks, and K-nearest neighbour, be incorporated into the training and testing of larger datasets for better, improved, and optimized outputs.

The efficacy of Cancer treatment often varies across different types of cancers. This study aims to investigate any pattern relationship in histopathological images of different cancer types to find any potential correlation between those patterns. Using deep image analysis techniques and artificial intelligence (AI), we extract, analyze, and compare the morphological parameters of cancer images to identify potential indicators of that treatment effective for one type that might be applicable for its correspondence. This research applied advanced image analysis, artificial intelligence (AI), machine learning, and more sophisticated statistical analysis to find the required pattern relationship for those parameters. The study answers the question regarding the correlation of different measurement parameters across different varieties of cancer cells. The model achieved an impressive ROC-AUC score of 0.967, an F1-score of 0.805, and Cohen's kappa coefficient of 0.767, indicating a high level of agreement and predictive performance. The overall accuracy of the model was 81%, with both macro and weighted averages also at 81%. These results provide strong evidence of meaningful pattern relationships across different cancer types, potentially enhancing treatments' applicability across various cancers.