An uncontrolled growth of brain cells is known as a brain tumor. When brain tumors are accurately and promptly diagnosed using magnetic resonance imaging scans, it is easier to start the right treatment, track the tumor’s development over time, and select the best surgical techniques. This paper applies advanced and popular methods for preprocessing, segmentation, grading of tumors, and lifetime prediction. On exploring various encoder-decoder architectures, UNet++ architecture was chosen for detecting brain tumors and obtained an accuracy of 98% during the testing phase. The segmented image is used to extract the radiomics features. Despite of several difficulties, data imbalance among the dataset is the common obstacle to survival prediction. To solve and increase the sample size and preserve the class distribution, the synthetic minority oversampling technique and adaptive synthetic approaches are used. After balancing the dataset, the important characteristics are selected using principal component analysis and tree-based feature selection techniques. The collected characteristics are used as input for machine learning techniques including stochastic gradient descent, decision tree, random forest, and support vector machine. The distinction between low-grade glioma and high-grade glioma is investigated as a binary classification. Accuracy, precision, recall, and F1score are used in the performance evaluation. The highest accuracy of 96% is achieved using stochastic gradient descent. Lifetime prediction of high-grade glioma patients is made using regression techniques: Linear, ridge, stochastic gradient descent, and extreme gradient boosting. We have obtained the least mean square error of 93726.45 using the extreme gradient boosting method. The proposed approach is contrasted with the most recent segmentation, grading, and lifetime prediction methods described in the literature.

Parkinson’s disease (PD) is a rapidly growing neurodegenerative disorder that primarily affects the elderly population. Until now, there has been no antidote for PD. However, diagnosing Parkinson’s disease in its early stages is difficult. Early treatment will help people with Parkinson’s disease improve their quality of life. The primary goal of this work is to increase the early diagnostic accuracy of Parkinson’s disease using deep learning models and to make the models more transparent and trustworthy. It proved challenging to comprehend the methods by which the classifiers made predictions about Parkinson’s disease. It would be valuable if the outcomes generated by these classifiers could be clarified in a reliable and trustworthy manner. Explainable Artificial Intelligence (EXAI) focuses on enhancing clinical health practises and bringing transparency to predictive analysis, both of which are critical in the healthcare arena. We proposed a new hybrid deep transfer learning model to distinguish PD patients from healthy individuals. The proposed architecture combines the advantages of both VGG19 Net and Google Net. This study also shows the experimental outcomes of various pre-trained models such as Alex Net, DenseNet-201, VGG-19 Net, Squeeze Net1.1, and ResNet-50. The VGG19-INC model predicts PD with an accuracy of 98.45%, which is greater than other state-of-the-art approaches, demonstrating the proposed work’s superiority and robustness. To demystify the VGG19-INC model, explainable AI approaches such as LIME are used to identify the specific parts of the spiral and wave drawings that contribute most to the model’s prediction. These methods provide local interpretation, making it easier to understand how the model arrives at its conclusions.

Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.