Modeling the Dynamics of the Random Demand Inventory Management System

At any given time, a product stock manager is expected to carry out activities to check his or her holdings in general and to monitor the condition of the stock in particular. He should monitor the level or quantity available of a given product, of any item. On the basis of the observation made in relation to the movements of previous periods, he may decide to order or not a certain quantity of products. This paper discusses the applicability of discrete-time Markov chains in making relevant decisions for the management of a stock of COTRA-Honey products. A Markov chain model based on the transition matrix and equilibrium probabilities was developed to help managers predict the likely state of the stock in order to anticipate procurement decisions in the short, medium or long term. The objective of any manager is to ensure efficient management by limiting overstocking, minimising the risk of stock-outs as much as possible and maximising profits. The determined Markov chain model allows the manager to predict whether or not to order for the period following the current period, and if so, how much.

Development of a Quantitative Prediction Support System Using the Linear Regression Method

The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company’s areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.

Study on the Development and Implementation of Different Big Data Clustering Methods

Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different classes. In this day and age, the very rapid increase in the amount of data being produced brings new challenges in the analysis and storage of this data. Recently, there is a growing interest in key areas such as real-time data mining, which reveal an urgent need to process very large data under strict performance constraints. The objective of this paper is to survey four algorithms including K-Means algorithm, FCM algorithm, EM algorithm and BIRCH, used for data clustering and then show their strengths and weaknesses. Another task is to compare the results obtained by applying each of these algorithms to the same data and to give a conclusion based on these results.

Risk Factor Hypertension Prediction Model

According to the 2020 Ministry of Health reports, the public health sector is facing an acute shortage of logistical resources and qualified competent human resources, as evidenced by the doctor-to-hospital ratio in relation to population [1]. Aside from these structural and cyclical issues, the above ratios are even lower in rural areas with low incomes. Underdevelopment is a major impediment to establishing a normal public health situation, though the Burundian government is working hard to ensure that it is at an acceptable level. Furthermore, some Burundian traditions, customs, and practices are undermining efforts to build an international-standard public health facility. Indeed, the mental state of a people (tradition, culture, and practices) has a significant impact on the fluctuation of risk factors in public health. It is determined by the socioeconomic development and sociocultural behavior of the population. This demonstrates that hypertension is a public health concern in Burundi. Unfortunately, the vast majority of people are completely unaware of the risks that high blood pressure poses to public health. High blood pressure, on the other hand, has always been a key physiological measure in medical examinations, serving as one of the most important biological markers in clinical evaluation. As a result, cardiovascular diseases caused by high blood pressure have a significant impact on mortality worldwide, particularly in Burundi. Predicting high blood pressure based on risk factors can help to reduce complications associated with this disease, which is known as a silent killer. The digital era provides a variety of tools for studying, analyzing, managing, and monitoring the risk factors that contribute to and degenerate high blood pressure. The primary goal of this work is to create a decision-making tool based on the outcomes of high blood pressure epidemic and/or pandemic predictions from sanitarian districts. The current paper work employs a prediction support tool created using linear regression methods from machine learning, one of the fields of artificial intelligence. It is especially useful for optimizing the cost function. The latter allows the predicted values to be determined and defined using the gradient descent algorithm.