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Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization (CSPSO) algorithm. With this perception measure, characterization and accuracy were improved, while the execution time of the proposed model was decreased. The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face. Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy. The proposed technique demonstrates the model accuracy, which reached 0.97 with the applied dataset. [ABSTRACT FROM AUTHOR]

The price of a product plays a vital role in its market share. Customers usually buy a product when it fits their needs and budget. Therefore, it is an essential area in the business to make decisions about prices for each product. The major portion of the business profit is directly connected with the percentage of the sale, which relies on certain factors of customers including customers' behavior and market competitors. It has been observed in the past that machine learning algorithms have made the decision-making process more effective and profitable in businesses. The fusion of machine learning with business intelligence systems has produced potentially better results for business workers. This study proposes a fusion model of machine learning and business intelligence to facilitate the effective pricing mechanism of products. Three machine learning algorithms namely (1) Multiclass Random Forest, (2) Multiclass Logistic Regression and (3) Multiclass one-vs-all have been applied for product price prediction. This intelligent business model helps business workers to set product pricing and discounts based on customer's buying behavior (i.e., customer's average amount spent in buying items). The model also predicts the demand for the product in the market, which may help in making effective decisions about the product to gain a better productivity. The performance of the model has been evaluated using hold-out cross validation. Multiclass one-vs-all out performed the other two algorithms in terms of average log-loss and it produced better accuracy in prediction for both hold-out as well as cross validation. The outcomes of the proposed model may help businesses in exploring business data and to gain higher share in the market. [ABSTRACT FROM AUTHOR]

In today's competitive business environment, the cost of a product is one of the most important considerations for its sale. Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product. Logistics is one such factor. Numerous products arrive from diverse locations to consumers in today's digital era of online businesses. Clearly, the logistics sector faces several dilemmas from order attributes to environmental changes in this regard. This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold. Consequently, the methodology to optimise delivery cost and its impact on environmental focus by reducing CO2 emissions has gained relevance. The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery. Shipment Consolidation has been categorized in three order scheduling approaches: Time based consolidation, Quantity based consolidation, and a Hybrid (Time-Quantity) based consolidation. In this paper, a new Hybrid Consolidation approach is presented. Using the Hybrid approach, it has been shown that order delivery can be facilitated by taking into account not only the order pick up time, but also the total order quantity. These results have shown that if a time window is available in respect of the order delivery time, then the order can be delayed from pickup to consolidate it with other orders for cost optimization. This hybrid approach is based on four consolidation principles, two of which work on fixed departure and two, on demand departure. Three of these rules have been implemented and tested here with an application case study. Statistical analysis of the results is illustrated with different planning evaluation indicators. The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule. Testing with bigger data sets is required. [ABSTRACT FROM AUTHOR]

In Wuhan, China, a novel Corona Virus (COVID-19) was detected in December 2019; it has changed the entire world and to date, the number of diagnosed cases is 38,756,289¹ and 1,095,216¹ people have died. This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients. One of the precautionary measures for COVID-19 patients is isolation. To support this, there is an urgent need for a platform that makes treatment possible from a distance. Telemedicine systems have been drastically increasing in number and size over recent years. This increasing number intensifies the extensive need for telemedicine for the national healthcare system. In this paper, we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance. Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture (SOA) to avoid the problem of interoperability, vendor lock-in, and data interchange. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided. [ABSTRACT FROM AUTHOR]

With the emerging concept of model transformation, information can be extracted from one or more source models to produce the target models. The conversion of these models can be done automatically with specific transformation languages. This conversion requires mapping between both models with the help of dynamic hash tables. Hash tables store reference links between the elements of the source and target model. Whenever there is a need to access the target element, we query the hash table. In contrast, this paper presents an approach by directly creating aspects in the source meta-model with traces. These traces hold references to target elements during the execution. Illustrating the idea of model driven engineering (MDE), This paper proposes a method that transforms UML class models to EMF ECORE model. [ABSTRACT FROM AUTHOR]