## 3 ML Techniques for Adaptive Plasma System
### Deep Learning  
Deep learning is proficient at autonomously extracting hierarchical features and patterns from raw data, rendering them highly effective in tasks like processing and decision making. It is composed of an input layer, multiple hidden layers, and an output layer, each containing interconnected nodes. These networks learn to execute tasks by fine-tuning the weights assigned to connections between nodes. The capacity to automatically uncover intricate patterns and representations in data without explicit programming is attained through a training process. In this process, the neural network learns from labeled examples and adjusts its parameters to minimize the disparity between predicted and actual outcomes. Deep learning uses artificial neural networks (ANNs) with multiple layers (deep neural networks) to make predictions from the labeled data (for instance, real-time spectra from optical emission spectroscopy). The self-adaptive plasma system empowered by deep learning holds great promise for advancing precision medicine and improving cancer treatment outcomes [122]. The self-adaptive plasma system is a plasma control system that can dynamically and autonomously diagnose plasma chemistry and adjust its operating  
Figure 4: Model Predictive Learning Control (MPLC) framework: merging real-time data with an adaptive plasma control system to modify the treatment parameters online, making treatment more effective and personalized to each cancer cell’s response [125]. parameters (e.g., gas and power inputs) to control and optimize plasma chemical composition in real time according to the required species/dosage to kill cancer cells. This system typically employs artificial neural networks and gradual mutation algorithms (GMA) to continuously monitor, analyze, control, and optimize the datasets of plasma chemistry and thus make real-time decisions on how to modify the plasma conditions. The real-time spectra of plasma chemical compositions and temperatures are obtained using spontaneous emission spectroscopy and processed via a diagnostic ANN. The diagnostic ANN provides valuable outputs on plasma chemistry, which are then used by a control ANN as inputs to adjust plasma parameters like gas injections and energy levels. The control ANN is trained with the GMA to adjust the production of specific RONS associated with the apoptosis of cancer cells. It receives feedback from the diagnostic ANN and dynamically adjusts the gas and power inputs to optimize the concentration of these species used in the plasma. The GMA is used to train both the diagnostic and control ANNs effectively, allowing the self-adaptive plasma system to diagnose, control, and optimize the plasma chemistry in real time based on the OES data and specific objectives. Such an empowered, self-adaptive plasma system allows personalized treatments tailored to the specific characteristics of the patient's cancer. The integration of diagnostic ANN, chemical process network (CPN), and control ANN creates an adaptive and intelligent system for effective and personalized cancer treatment [122; 126] (Figure 5). AI-driven gas plasma can continuously learn from treatment outcomes, adapt to changing conditions, and optimize treatment plans. This personalized and dynamic approach has the potential to improve the efficacy of cancer therapies, reduce side effects, and advance precision medicine. The absence of performance metrics and considerations is a critical limitation in the above-discussed AI-based approaches. Performance evaluation is a fundamental aspect of any AI system. However, these research efforts primarily focus on the theoretical and conceptual aspects of AI-empowered plasma systems and their potential applications in cancer treatment. Given their early stage and limited access to real-world data, providing precise performance metrics is challenging. Addressing this gap necessitates future studies with interdisciplinary collaborations involving experts in computational science, machine learning, and medical engineering.