American Journal of Business and Operations Research

Osteoporosis affects millions of women around the world, but female athletes are at particular risk. These female athletes are at a higher risk due to the stress of their intensive workouts than the overall female population. In female athletes, the absence or suppression of menstruation results in a low peak bone mass, which weakens their bones. Combined with their physical activity, this domino effect dramatically increases their risk of stress fractures. Increased understanding of the dangers of osteoporosis may help female athletes avoid or decrease the symptoms of the disease. The purpose of this study is to educate female athletes on the need for ssreening the techniques for identifying and treating this disease.

Traditional models for predicting future sales of a product or service are based on previous, not updated data, resulting in unsatisfactory and inaccurate forecasting results, meaning that the data used as inputs to the forecasting process is stable and not dynamic during the forecasting process.The research aims to leverage social media data by extracting features from Facebook platform (features are reactions to posts) and using them as input to the automated forecasting system to try to predict corporate revenues.Machine learning algorithms have been trained to predict returns according to pre-stored data and can be updated on demand, which means that the proposed forecasting system will work in a dynamic environment.The following algorithms were used to predict the profitability of new services and the one with the highest accuracy was selected: (Random Forest, DT, Gradient Boosting, K nearest neighbors, NB).The results showed that Random Forest algorithm is the one with the best accuracy, with an accuracy of 67%, and a slight correlation was observed between the interactions on the target post and the profitability of the service within the post.

In addition to being a game-changer for the cryptocurrency business, Blockchain was also a catalyst for the fast rise of certain Distributed Ledger Technologies (DLTs). An important aspect of a DLT system's design is a consensus mechanism, which ensures that almost all interviewees agreed on the integrity of the data. As a result, a broad variety of consensus protocols have been developed, each with a distinct notion and property (e.g., reduced energy usage, greater scalability). When moving from one blockchain network to another, the main criteria for consensus mechanisms typically vary dramatically, so there is no universal protocol. As a result, choosing the best consensus mechanism for a certain DLT system is critical, but also difficult, since experts must balance competing demands. MCDM approaches are used in this research to provide an approach for choosing the best consensus procedures based on criteria, objectives, and other needs. A genuine bike-rental application is used to show the technology's potential, as well as the preferred consensus mechanisms for 3 of the most popular kinds of current blockchain systems. To top it all off, the information and technologies gathered are openly accessible for anybody to use, allowing for maximum replication and future improvement.

Telemarketing becomes a major tool in enhancing the services of different business sectors. On banking industry, telemarketing is applied to sell products or services. Banking advertisements as well as marketing are majorly based on the detailed information of neutral data related to marketing market and original needs of user for the banks. Decision making becomes an essential part in the telemarketing field that computes a particular class of automated fact in assisting the companies for making decision. Artificial intelligence (AI) is applied for decision making in the telemarketing sector. In this aspect, this paper introduces a social spider optimization (SSOA) with gradient boosting tree (GBT) model for decision making in the telemarketing sector. The main aim of the SSOA-GBT method is to make proper decisions in the telemarketing sectors. To accomplish this, the SSOA-GBT model initially exploits the GBT model for data classification purposes. Next, for improving the performance of the GBT classifier, the SSOA is applied. The performance validation of the SSOA-GBT model is performed using benchmark dataset and the outcomes are investigated in several aspects. The simulation outcomes indicated the better outcomes of the SSOA-GBT approach over the recent approaches.