A way to make job matching work better in Nigeria, where the jobless rate is consistently high. Businesses and users alike might gain from the app's user-friendly layout, which makes it simple to publish jobs. Post jobs and submit resumes. The foundation of the program is the SVM algorithm, which searches job ads and user profiles for appropriate matches depending on parameters like education, experience, and the kind of role. This system learns from user interactions and comments to produce even better matches than job boards, which have significantly lower prediction accuracy. We develop secure and scalable applications using front-end and back-end methodologies with React Native and Node.js. This article outlines the system architecture, algorithmic implementation, and first testing results, illustrating how machine learning might transform the employment sector in poor countries such as Nigeria.

This study explores the integration of two methods, namely K-Means and k-NN. K-means is used to identify categories of learning outcome data, while k-NN is used to predict students' learning outcomes into relevant categories. Through the calculation of the Elbow method, it was established that the optimal number of clusters for grouping is three. The learning outcome data, which include Arithmetic and Statistics scores, are processed to produce a mapping that differentiates students into three categories: Adequate, Moderate, and Good. In the 12th iteration, the clustering results using K-Means achieved convergence, with 64 students in the Adequate category (C1), 60 students in the Moderate category (C2), and 59 students in the Good category (C3). This indicates that the students in each group are evenly distributed based on their mathematical and statistical abilities. The prediction results using k-NN for a student with an Arithmetic score of 85 a Statistics score of 75, and a k-value of 61, found that 7 data fell into Category 1 (Adequate), 3 data into Category 2 (Moderate), and dominant 51 data in Category 3 (Good). Thus, the prediction results are placed in Category 3, indicating a 'Good' rating in their academic performance. By using data mining techniques to enhance understanding of student learning outcomes, this study provides a significant contribution to the field of education. It demonstrates substantial progress toward a data-driven learning approach that can be tailored to specific needs and improve student learning outcomes.

With the technological advancements, global communication has largely shifted to text-based communication. As a result, the process of extracting meaningful insights from human behavior by machine learning techniques applied to textual data has now been significantly simplified. This research utilizes text mining methods to analyze customers feedback from food reviews, employing them as effective tools for opinion analysis and rating prediction from feedback. This research utilizes two neural network techniques (Normal Neural Network and LSTM) to analyze textual data and generate predicted scores ranging from 1 to 5 for each review from Amazon food review dataset. After implementing two neural network models, the system automatically generates a predicted score on a scale from 1 to 5. This study employs widely-used neural network techniques and provides a foundation for advancing text-based emotion detection in future research. The primary focus of this study is on unaltered customer feedback and it aims to solve the problem of accurately analyzing customer sentiments or opinion and extracting meaningful insights from their feedback. By comparing the performance of LSTM and standard neural networks, we achieve a 62.12% accuracy, showcasing superior results in emotion prediction from unstructured textual reviews. These insights pave the way for more scalable and efficient solutions in text mining for emotion detection.

Forecasts of births and deaths play an important role in determining the dynamics of both population size and gender-age structure. Since population forecasts are the basis of long-term planning of socio-economic development, the statistical accuracy of forecasts is particularly important, and the applied methods play a special role here. The purpose of this study is to evaluate Autoregressive Integrated Moving Average (ARIMA) model ability to forecast the yearly number of births and deaths in Azerbaijan. In the analysis, the Box-Jenkins methodology was followed when building the suggested model. Besides, Akaike’s information criterion (AIC) and Bayesian Information Criteria (BIC) are used to select the best ARIMA model, compared to another estimated models.  The prediction results of the models are evaluated using the mean absolute percentage error (MAPE) and the root mean square error (RMSE) . Comparing the predicted data from the ARIMA models shows that the correct selection of model parameters, it possible to fairly accurately predict the yearly number of births and deaths. Thus, using the advantages of the ARIMA model, it is possible to obtain forecasts of birth and death rates for the near future and it possible to observe changes that will occur in the age structure of the population. And these interpretations can guide policymakers to focus on socio-economic development and comprehensive healthcare system strengthening as crucial strategies for raising the fertility level and further reducing mortality rate.

The purpose of the scientific article is to identify a possible solution to the problem of improving students’ training effectiveness through the combined usage of active forms and methods of teaching students among the dependent learning parameters set. The authors came to this hypothetical opinion based on the comparative analysis results of previous studies, which confirm that the usage of active forms and methods of teaching students is the right vector for solving the lifelong problem of learning improving the effectiveness. 
It has been established that the available psychological and pedagogical literature does not provide specific solutions for modern students - cyber-socialised youth, which would help to substantiate the best ways to intensify students' learning and cognitive activity. 
Previous scientific studies have confirmed that the group of people who are most addicted to computer games, as practice shows, is difficult to motivate to study using traditional approaches when there is a distracting and, to some extent, gambling factor. 
Based on these circumstances, the proposed research is obviously logical from the need to improve the theory and methodology of vocational education through the usage of active forms and methods of teaching students. New circumstances have determined the subject of the study, which is a professional business game. It has been experimentally determined that during a professional business game students will use simulation models to solve professional problems. To organise and conduct a professional business game, teachers define game and functional goals. In the above variant of the professional business game structural scheme, the goal of accelerating the discipline development through students' learning activation is achieved. Organising and conducting a professional business game as an active teaching method involves preparation of both students and teachers for it, and also requires the methodological materials and technical means availability, which helps to increase the classes effectiveness conducted in a game form and to form professional competencies in students. The article provides practical steps for preparing participants of a professional business game.