Procedural aspects of change of accusation in court proceeding

After making a decision on the appointment of a judicial review, the nature of the charge laid down by the general conditions for the exercise of judicial review is examined. In this case, the general terms of the trial must be adhered in such a manner that the prosecution can be considered in court. These terms of judicial review are generally applicable to independent court proceedings where any decision made without their observance is invalid .After making a decision on the appointment of a judicial review, the nature of the charge laid down by the general conditions for the exercise of judicial review is examined. In this case, the general terms of the trial must be adhered in such a manner that the prosecution can be considered in court. These terms of judicial review are generally applicable to independent court proceedings where any decision made without their observance is invalid

Criteria for assessing the adequacy of image recognition methods and their verification using examples of artificial series of signals

The article discusses four criteria for assessing the adequacy of the most well-known image recognition methods. Verification of two of these criteria is carried out by empirical analysis using the example of the most well-known signal recognition methods, such as DTW, DDTW, as well as methods based on the Wavelet transform and Fourier transform. Two artificial sets of images are used as recognition objects, formed by uniformly shifting the base image both horizontally and vertically. In general, the goal of this research is to develop a new method for extracting recognition features using the example of the image of the State Emblem of the Republic of Azerbaijan. In the context of this study, a verification of a previously proposed signal recognition algorithm is carried out based on the artificial family of curves, for which the most accessible and acceptable method of displacement is established: horizontally or simultaneously horizontally and vertically

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future

Fuzzy Approach in Implementation of E-Government in the Field of Regional Development Regulation

Fuzzy Approach in Implementation of E-Government in the Field of Regional Development Regulatio

Volatile time series forecasting on the example of the dynamics of the Dow Jones index

The paper discusses a new predictive model of a fuzzy volatile time series, in the framework of which a new approach to the data fuzzification is proposed as the results of observations based on “Sоft Measurements”. As an example, the index of the Dow Jones Industrial Average is chosen, the readings of which are established by usual arithmetic averaging of cоntextual indicators. This allows to consider the daily readings of the Dow Jones index as weakly structured, and to interpret the dynamics of its change as a fuzzy time series. The data fuzzification is realized by applying the fuzzy inference system that provides the values of the membership functions of the appropriate fuzzy sets on the universe covering the set of Dow Jones index for the period from June 15, 2018 to October 10, 2019. The prоpоsed predictive mоdel is based on the identified internal relatiоnships, designed as 1st оrder fuzzy relatiоns between evaluation criteria (or fuzzy sets) that describe weakly structured Dow Jones indexes. At the end of the study, the proposed model is evaluated for adequacy using the statistical criteria MAPE, MPE and MSE