The majority of undergraduate students finddifficulty to understand the concept of  postulates of quantum mechanics, precisely the postulates of measurement, reduction and evolution. Here, we include In this context we propose a pedagogical innovation technique based on the integration of two simulation projects of Quvis and Quilts. Then, we assess the student's understanding capacity. Here, Our results demonstrate , that the explanation of the postulates of quantum mechanics by manipulating the two simulations by the experimental group , For evaluated our pedagogical innovation , a pre-test and a post-test were administered for two experimental and control groups; the pre-test is composed of 19 multiple choice questions, and the post-test is composed of 12 questions for two groups, one using the simulations during quantum mechanics lectures and the other group learned the course of quantum mechanics without using simulations. A statistical analysis of the results showed that the distribution of pre-test results of the experimental group wasn't normal. In contrast, the distribution of the results of the control group was normal, so the Mann-Whitney U Test analysis showed that there is no difference between the results of the two groups in the pre-test, which shows the homogeneity of the two groups. The distribution of the post-test results of the two groups is normal, so the T-test analysis showed that there is a difference between the results of the two groups, with the average of results of the control group is 7.4, and of the experimental group is 11.4, the comparison of the pre-test and post-test results of the experimental group shows that there is a significant increase in the students' results after the integration of two simulations. The analysis of the results of the quiz showed that there was a significant improvement in understanding of the concepts of measurement and evolution in the group that benefited from the use of the simulations. 

The popularity of mobile games has expanded among individuals of all ages, and the mobile gaming businesses are quickly expanding day by day. The Google Play Store, one of the most well-known platforms for the distribution of Android applications and games, sees a daily influx of thousands of new mobile games. One of the biggest problems in the gaming industry is predicting a mobile game's performance. Every day, thousands of new games are released. But just a couple of them are successful, while most of them fail. The study was done with the intention of analyzing any relationship between a mobile game's success and its distinctive features. Many of the mobile game developers work independently or work in the mobile game industries to make their games successful on the digital market. Before they are released, game makers can increase the quality of their games if they are confident in their products' commercial viability. For that reason, more than 17,000 games were taken into consideration. We show that the success of a mobile game is clearly influenced by its category, number of supported languages, developer profile, and release month. Furthermore, we show that specific aesthetic features of game symbols are more frequently linked to higher rating counts. We analyzed Google Play Store mobile games data and used a variety of machine learning algorithms for predicting the performance of mobile games based on the total number of downloads and the total user rating.

The Indonesian Election is one of the most anticipated political contestations among the Indonesian people. Mainly because the results of the Indonesian Election are leaders in Indonesia ranging from governors and legislative members to the president and vice president of Indonesia, who will lead the next five years, considering the importance of the five-year agenda, the dissemination of good information about work programs, the activities of prospective leaders who will elect in the 2024 election and various news stories are starting to spread on Twitter. Based on this, this research aims to analyze public sentiment on Twitter wa The research method used is SMOTE-Tomek Links to overcome imbalanced data. In contrast, sentiment analysis uses Binary Logistic Regression. Evaluation related to this model measures accuracy and ROC Curves. The evaluation results show that the SMOTE-Tomek Links method is less than optimal for the data used in the research, namely the 2024 election data, with an accuracy value of 0.581 for training data and 0.406 for testing data. Undersampling methods such as Tomek Links and Random (undersampling) show higher values when combined with Binary Logistic Regression in analyzing the sentiment produced in this study, namely 0.983 and 0.938 for the Tomek Links method and 0.964 and 0.902 for the Random (undersampling) method, respectively -each for training and testing data.

Information and communication technology (ICT) is the bedrock of information dissemination and a driving force for better economic planning to achieve its goals and get success stored securely and confidentially. Monkeypox (MPX) epidemic outbreaks affect human beings as a whole and can be a cause of serious illness and death. This epidemic continues to challenge medical systems worldwide in many aspects, including sharp increases in demands for hospital beds and critical shortages in medical equipment, while many healthcare workers have themselves been infected. Thus, the capacity for immediate clinical decisions and effective usage of healthcare resources is crucial. Therefore, this research has developed an effective screening system that will enable quick and efficient diagnosis of Monkeypox (MPX) and can mitigate the burden on healthcare systems. This system would be handy in sharing much-needed expert knowledge in the diagnosis of Monkeypox (MPX) symptoms since it would be used by medical officers, clinical officers, and nurses in the absence of specialists. It could be used to collect medical data, which in this case is the symptoms presented by the patients; it can also be useful in training general practitioners, physicians, inexperienced nurses, and paramedics to guarantee suitable and accurate decision-making in the diagnosis and management of Monkeypox (MPX). The methodology adopted is the machine learning algorithms foranalysis and training of our dataset, to ascertain the level at which this epidemic has caused harm to lives, a linear relationship between an independent and dependent variable is provided by the linear regression technique, and Python programming was used to visualize and predict clinical outcomes.

The use of predictive analytics or models in healthcare has the potential to revolutionize patient care by identifying high-risk patients and intervening with targeted preventative measures to improve health outcomes. This makes the application of analytics in healthcare a concept of utmost interest, which has been explored in various fashions by several scholars. From predicting patients’ ailments to prescribing appropriate drugs, predictive models have seen massive interest. This work studied published works on predictive models in healthcare and observed that the implementation of predictive models in healthcare is experiencing a notable upswing, with a particular focus on research in the United States, where a majority of the top publications originated. Surprisingly, all of the leading nations in this sector have affiliations spanning many continents, with the exception of Africa and South America, together producing a substantially larger volume of research than other countries. The United States also shone out, accounting for 60% of the top five researchers. Notably, although it was published in 2017 (relatively later), Jiang et al. had the most citations (1,346). These studies' core themes were clinical standards, machine learning terminology, and model accuracy. The Journal of Biomedical Informatics topped among journals, with 54 articles, while Luo Gang emerged as the top-performing author, with 12 publications.