An earlier research project that dealt with converting ASCII codes into 2D Cartesian coordinates and then applying translation and rotation transformations to construct an encryption system, is improved by this study. Here, we present a variation of the Cantor Pairing Function to convert ASCII values into distinctive 2D Coordinates. Then, we apply some novel methods to jumble the ciphertext generated as a result of the transformations. We suggest numerous improvements to the earlier research via simple tweaks in the existing code and by introducing a novel key generation protocol that generates an infinite integral key space with no decryption failures. The only way to break this protocol with no prior information would be brute force attack. With the help of elementary combinatorics and probability topics, we prove that this encryption protocol is seemingly infeasible to overcome by an unwelcome adversary.

This research paper presents a Gaussian process regression (GPR) model for predicting path loss signal in an urban environment. The Gaussian process regression model was developed using a dataset of path loss signal measurements acquired in two urban environments in Nigeria. Three different kernel functions were selected and compared for their performance in the Gaussian process regression model, including the squared exponential kernel, the Matern kernel, and the rotational quadratic kernel. The GPR model was validated and evaluated using various performance metrics and compared with different regression models. The results show that the Gaussian process regression model with the Matern kernel outperforms the linear regression and the support vector regression, but the decision tree and the random forest regression did better than the GPR in both cities. In the city of Port Harcourt, the GPR has a RMSE value of 3.0776 dB, the DTR has 2.0005 dB, the SVR has 3.6047 dB, the RFR has 1.0459 dB, and the LR 3.5947dB. The proposed GPR model provides more accurate and efficient approach to predict path loss compared to traditional methods. The extensive data collection and analysis conducted has resulted in a well-developed and accurate model.

Day 5 (D5) blastocyst transfers present higher clinical pregnancy and live birth rates than day 6 in both fresh and frozen transfers [1]. To investigate the D5 blastocyst formation rate, in this study, we first collected clinical data from a hospital in Jiaozuo and partitioned the data into training set and validation set. We conducted univariate logistic regression analyses, which were possible predictors of the D5 blastocyst formation rate, on 12 patient covariates. According to the univariate analysis, we determined 10 covariates were suitable for multivariate analysis. Finally, we identified five covariates to construct a logistic regression model to predict the D5 blastocyst formation rate. We also used the receiver operating characteristic curve, the Hosmer–Lemeshow test, and the calibration curve to verify the accuracy of this model. The results showed that logistic regression model of D5 blastocyst formation rate directly reflected the relationship between transplantation results and covariates. According to the model, doctors can provide guidance to patients before treatment and improve the rate of blastocyst formation by changing patients' physical fitness. The model has certain clinical application value.

Forecasting is estimating the magnitude of uncertain future events and provides different results with different supposition. In order to identify the core data pattern of jute bale requirements for yarn production, we examined 10 years' worth of data from Jute Yarn/Twin that were shipped by their member mills Limited. Exponential smoothing and Holt’s methods are commonly used to forecast this output because it provides an adequate result. Selecting the right smoothing constant value is essential for reducing predicting errors. In this work, we created a method for choosing the smoothing constant's ideal value to reduce study errors measured by the mean square error (MSE), mean absolute deviation (MAD), and mean square percent error (MAPE). At the contrary, we discuss research finding result and future possibility so that Jute Mills Limited and similar companies may execute forecasting smoothly and develop the expertise level of the procurement system to stay competitive in the worldwide market.

In this paper, we initiated the idea of interval-valued fuzzy soft set (IVFSS) and a few results, Operations, definitions, and properties also a few properties and characteristics of IVFSSs and Prove some of theorem and we discuss a few examples uses of soft set in finding a selection taking problem. Also initiated the comparable measure of two IVFSSs and discuss with the Presentation of medical application problem.