This study investigates the use of neural network and their ability to predict disease progression based on clinical data and biomarkers. Using deep neural networks, a model was developed that efficiently analyzes the complex relationship between various factors and predict the probability of disease. The model was validated using retrospective analysis which indicated a good predictive ability that could be further utilized in better diagnostics and personalized treatment methods. More importantly, reserch detected specific pattern in the data, which enabled a more accurate prediction of disease at different stages. The study tried to improve a model by fine-tuned neural networks and tested other frameworks to gain the highets precision. This research also provides a basic for future work in directing the development of personalized therapeutic approaches based on individual patient characteristics.

This scientific paper investigates the application of the Voltaire-Gurset-Riemann method in solving partial differential equations, using a flickering wire as an example. The method proves to be a powerful tool in the analysis of dynamic systems, providing a deeper understanding of flicker behavior in a wire. The developed numerical solutions enable precise modeling and prediction of the behavior of the flickering structure. This study highlights the key steps in applying the method to a concrete example, providing a useful basis for further research in the field of partial differential equations

This paper presents theoretical and pedagogical considerations as well as the basic results of research on the evaluation and assessment of student achievements in mathematics. The research was carried out in class and subject classes. The paper is based on the hypothesis that descriptive assessment is more successful and increases students' motivation for mathematics as a science. The aim of the paper is to investigate, analyze and interpret the attitudes of students and teachers about descriptive and numerical assessment. The problem of this paper is which type of evaluation has a greater influence on students' motivation towards mathematics. Analytical, theoretical and deductive methods were used in the work. Research techniques for proving the views of this work are a survey. As for the instruments, we made distinctions about the attitudes of students and teachers. The paper ends with a concluding discussion of the problem.

This paper investigates the integration of fuzzy logic and neural networks for disease detection using the Matlab environment. Disease detection is key in medical diagnostics, and the combination of fuzzy logic and neural networks offers an advanced methodology for the analysis and interpretation of medical data. Fuzzy logic is used for modeling and resolving uncertainty in diagnostic processes, while neural networks are applied for indepth processing and analysis of images relevant to disease diagnosis. This paper demonstrates the development and implementation of a simulation system in Matlab, using real medical data and images of organs for the purpose of detecting specific diseases, with a special focus on the application in the diagnosis of kidney diseases. Combining fuzzy logic and neural networks, simulation offers precision and robustness in the diagnosis process, opening the door to advanced medical information systems

Wireless sensor networks play a key role in various applications such as environmental monitoring, smart cities and medical monitoring. Latency and reliability are two basic aspects that affect the efficiency and quality of service of these networks. In this research, delay and reliability analyzes of WSN were conducted through the application of mathematical models. Different parameters were analyzed such as distance, number of devices, and bandwidth to gain a deeper understanding of their impact on network performance. Mathematical models have been developed that take into account random variables and changing factors in order to conduct a more precise analysis. Through simulations and numerical experiments, Matlab codes will be used to simulate and analyze the actual process, all with the aim of achieving minimum delay and maximum reliability. This research provides a deeper insight into the characteristics of WSN and provides guidelines for the design and optimization of these networks.

Partial differential equations are a branch of engineering mathematics that in the last decades in the scientific field has played a very important role in solving certain engineering problems. In this paper, we have applied a kind of partial differential equations when examining the influence of microwaves. We have observed the phenomena occurring in one part of the electromagnetic spectrum in the frequency range of 1 GHz-100 GHz as an aggravating factor in signal transmission by telecommunication water, which was the starting point for research on this topic. The reason for isolating this area is contained in the specific technique that characterizes it. One of the types of partial hyperbolic equations is the telegraphic equation, whose solution eliminates problems related to the transmission line. The graphical interpolation of the telegraph equation is given in the Matlab Engineering Matrix Laboratory. Only the parts of mathematically important functions are derived in the paper and the final results are presented in order to represent our starting differential equation as telegraphic.