## 2 Summary of the Contributions
This concept involves the utilization of DNA computers for intelligent and personalized diagnostics, particularly in the context of medical treatment. A new approach to designing diagnostic biochips that combines ML methods with the concept of biomolecular queue automata is introduced. This enables the scheduling of computational tasks at the molecular level by manipulating DNA molecules through cutting and ligating sequences. The authors stress the importance of ML methods in the design of biochips based on biomolecular computers, as accurate knowledge of unique DNA sequences is a fundamental aspect of these solutions. The study highlights the potential of biomolecular computers in constructing biochips and emphasizes the significance of deterministic input-driven queue automata as a theoretical model for these systems. The use of specific restriction enzymes, particularly type IIB restriction endonucleases, is emphasized for the design of biomolecular computers with memory capabilities, such as queue automata. The paper introduces the concept of \"Queue-PCR\" as an innovative way to automate the polymerase chain reaction (PCR) method using biomolecular computers, thereby suggesting new avenues for the automation of molecular genetics techniques. Paper [9] focuses on the application of ML algorithms to predict the clinical evolution of patients diagnosed with COVID-19. It aims to optimize the diagnostic process by utilizing predictive modeling to classify the clinical course of COVID-19 cases. The research involves a comparative analysis of various classification algorithms, including k-nearest neighbors, naive Bayes, decision trees (DT), multilayer perceptrons (MLPs), and support vector machines. It analyzes 30,000 cases during the training and testing phases of the prediction models. The authors underscore the significance of accurate predictions for patients' vital prognosis and the efficiency of initial consultations in hospitals. The conclusion of this study highlights the achievement of predicting the clinical evolution of COVID-19 patients using optimized ML models. The MLP algorithm is identified as the most effective for this purpose, based on comparative benchmarks. The research suggests potential future work, including analyzing clinical data using different algorithms, applying ensemble learning, and exploring additional neural network algorithms. However, the study acknowledges limitations such as data quality concerns in medical applications of ML, which can impact diagnoses and introduce biases. Paper [10] explores the use of generative adversarial networks (GANs) for regression tasks, focusing on multi-output regression in non-image data. The study introduces the concept of MOR-GANs, which employ Wasserstein GAN (WGAN) as a regression method. The paper compares the performance of MOR-GANs with Gaussian process regression (GPR) on various datasets and introduces a prediction algorithm for GANs to generate responses based on independent variables. The authors emphasize that WGANs perform well in regression tasks, often surpassing GPR, and showcase their effectiveness across diverse datasets. MOR-GANs show notable performance in handling variable uncertainty, multi-modal distributions, and multi-output regression tasks. Importantly, this performance is achieved without requiring extensive modifications to the GAN architecture. The authors also speculate on potential applications in domains like image reconstruction and high-dimensional modeling, where MOR-GANs could provide invaluable insights and predictions. In [11], a novel compressed convolutional neural network designed to address the challenges of human detection in computer vision, ReSTiNet, is introduced. Human detection is crucial for applications like public safety and security surveillance. ReSTiNet aims to incorporate a compact size, high detection speed, and accuracy in its design, inspired by recent advances in deep learning techniques.