Artificial intelligence in cancer urology

Introduction. Artificial intelligence (AI) refers to computing technologies that simulate human intellectual processes. The use of AI in the near future will contribute to the widespread introduction of telemedicine technologies into practice. Materials and methods. The authors analyzed publications in PubMed and in the Electronic Scientific Library for the keywords oncology , urology , cancer urology , artificial intelligence . In PubMed, out of 127 articles that met the queries, 32 publications were selected, in the Electronic Scientific Library 3 articles were selected. Results. In kidney cancer, CT texture analysis with support vector method (SVM) can be considered promising; in order to predict the recurrence of bladder cancer, machine learning algorithms (support vector method) are used to identify the recurrence of bladder cancer by detecting urine micro-RNA. In order to reduce unnecessary biopsies based on clinical characteristics, an artificial neural network has been developed to predict the presence of prostate cancer. Conclusion. Artificial intelligence methods are constantly evolving, the range of their application in the field of oncourology is expanding. In the near future, we are not talking about replacing traditional methods, but in addition to them, artificial intelligence can provide more information about the patient. For the widespread introduction of these methods, mechanisms for overseeing the safety and efficiency of artificial intelligence algorithms should be developed. More research is needed to clinically and statistically compare the results obtained with AI with those obtained using traditional methods.

Behavior Recognition Algorithm of Mobile Phone Users Based on Sensor Networks

To improve the efficiency of data analysis in the process of mobile phone users’ interaction, an algorithm on behavior recognition of mobile phone users based on sensor networks is proposed in this paper. The algorithm and model uses the multidimensional receptive-field function and the support vector method to improve the accuracy and efficiency. Simulation result shows that the algorithm and the new model can improve the performance of analysis efficiency and correctness substantially.