Initially, in vitro fertilization (IVF) was solely performed in couples with bilateral tubal occlusion. In 1992 intracytoplasmic sperm injection (ICSI) was discovered and initiated in couples with severe male subfertilty. Later on IVF/ICSI was also applied in couples who did not suffer from sterility, such as couples with cervical hostility, failed ovulation induction, endometriosis, unilateral tubal pathology or even unexplained subfertility. The major difference between the original indication and the indications for which IVF is conducted nowadays is that the couples with bilateral tubal pathology or severe male subfertility are sterile and have a (near) zero chance of natural conception and completely depend on IVF/ICSI for a pregnancy, while the other couples are subfertile: they do have chances of natural conception, which may or may not be better than with IVF.Despite the lack of evidence that IVF is effective in subfertile couples, IVF is often considered as a last resort for all couples regardless of the etiology of their subfertility.Contrary to the perception of many, IVF does not guarantee success; almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. Subfertile couples should therefore be well informed about the chances of success with IVF before starting their first or before continuing with a new IVF cycle.Based on a couple’s specific probability, one should decide whether the chances of success with IVF outweigh the burden, risks and costs of the treatment.To do so, Prediction models have been developed. Most existing models are of limited use for several reasons.They were developed before current clinical and laboratory protocols were established. They do not include the transfer of frozen-thawed embryos. They calculate pregnancy chances only for the first IVF cycle or after one failed IVF.We developed a model that would calculate pregnancy chances during the complete IVF procedure, after failed cycles, and that included pregnancies after fresh and frozen-thawed embryo transfer which performed well after internal and external, temporal validation.If couples start or continue with IVF, the aim should be to achieve optimal pregnancy chances with a low risk multiple pregnancies. The optimal embryo transfer strategy would be a ‘‘individualized embryo transfer strategy’’ that takes the woman’s prognostic profile and embryo characteristics into account; a Prediction model that is able to select which and how many embryo (s) should be transferred to obtain optimal ongoing pregnancy rates with low multiple pregnancy rates. To develop such a model, we first constructed a model that was able to rank embryos on day 3 after oocyte retrieval based on their ongoing implantation potential. We then developed an embryo transfer model that consisted of two variables: one variable being the sum of all coefficients of the IVF model and the second variable being the sum of all coefficients of the embryo implantation model. These models will be presented.With help of these Prediction models a more uniform treatment strategy is possible, and also a more ‘patient tailored’ treatment.

Background: Technologies can predict various aspects of COVID-19, such as early Prediction of cases and those ‎at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases ‎and deaths. Herein, we aimed to review the published models and techniques that predict various ‎COVID-19 outcomes and identify their role in the management of the COVID-19. ‎ Methods: This study was a review identifying the Prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until ‎September 4th, 2021. In addition, Google Scholar was also searched. ‎ Results: We have reviewed 59 studies. The authors reviewed Prediction techniques in COVID-19 disease ‎management. Studies in these articles have shown that in the section medical setting, most of the subjects were ‎inpatients. In the purpose of the Prediction section, mortality was also the most item. In the type of data/predict ‎section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of ‎technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section. ‎ Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and ‎prevention. They accelerate the process of diagnosis and move toward great progress in emergency ‎circumstances like the COVID-19 pandemic. ‎

The overall purpose of this study is to use probabilistic methods for the estimation of the advanced rate of full-face tunnel boring machines. To collect appropriate input parameters, Monte Carlo Simulation was utilized. Then, the calculation phase was conducted applying established models on input data and probability density functions of output data were obtained. The results show that the average advance rates calculated by QTBM and CSM models were closer to the average value of the actual advance rates. In addition, using probabilistic methods in combination with TBM Prediction models helps to estimate the range of advance rates more confidently.

Background: According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms. Methods: The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14. 2) software on the prepared data,and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software. Results: This study was able to predict MI % 75. 28 and 77. 77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively. Conclusions: Predicting studies aim to support rather than replace clinical judgment. Our Prediction models are not sufficiently accurate to supplant decision‑, making by physicians but have considerable tips about MI risk factors.

Background: Knee osteoarthritis (OA) is a prevalent joint disease. Clinical Prediction models consider a wide range of risk factors for knee OA. This review aimed to evaluate published Prediction models for knee OA and identify opportunities for future model development. Methods: We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, Prediction model, deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded information on methodological characteristics and findings. We only included articles that were published after 2000 and reported a knee OA incidence or progression Prediction model. Results: We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning (ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0. 6 and 1. 0. Regarding external validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set. Conclusion: Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of current knee OA Prediction models.

There is an urgent need for precise and trustworthy models to forecast device behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Naïve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device behavior forecasting. Our results show that Naïve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device behavior Prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.