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Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs. Exploring individuals’ sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public. An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning (VADER). In this study applied twitter intelligence tool (TWINT), Natural Language Toolkit (NLTK), and VADER constitute the three main tools. VADER represents a gold-standard sentiment lexicon, which is basically tailored to attitudes that are communicated by using social media. The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier called VADER to analyze the sentiment of the general population, particularly among thalassemia carriers on the social media platform Twitter. In this study, the results showed that the proposed approach achieved 0.829, 0.816, and 0.818 regarding precision, recall, together with F-score, respectively. The tweets were crawled using the search keywords, “thalassemia screening,” thalassemia test, “and thalassemia diagnosis”. Finally, results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets, respectively. [ABSTRACT FROM AUTHOR]

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]

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]