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Urban air mobility, also known as UAM, is currently being researched in a variety of metropolitan regions throughout the world as a potential new mode of transport for travelling shorter distances inside a territory. In this article, we investigate whether or not the market can back the necessary financial commitments to deploy UAM. A challenge in defining and addressing a critical phase of such guidance is called a demand forecast problem. To achieve this goal, a deep learning model for forecasting temporal data is proposed. This model is used to find and study the scientific issues involved. A benchmark dataset of 150,000 records was used for this purpose. Our experiments used different state-of-the-art DL models: LSTM, GRU, and Transformer for UAM demand prediction. The transformer showed a high performance with an RMSE of 0.64, allowing decision-makers to analyze the feasibility and viability of their investments. [ABSTRACT FROM AUTHOR]

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]

A vehicular ad hoc network (VANET) is a technique that uses vehicles with the ability to sense data from the environment and use it for their safety measures. Flooding is a commonly used term used for sending network packets. VANET may cause redundancy, delay, collision, and the incorrect receipt of the messages to their destination. Weather information is one of the most important types of information used for network control and provides an enhanced version of the network simulation environments. The network traffic delay and packet losses are the main problems identified inside the network. In this research, we propose a routing protocol which can transmit the weather forecasting information on demand based on source vehicle to destination vehicles, with the minimum number of hop counts, and provide significant control over network performance parameters. We propose a BBSF-based routing approach. The proposed technique effectively enhances the routing information and provides the secure and reliable service delivery of the network performance. The results taken from the network are based on hop count, network latency, network overhead, and packet delivery ratio. The results effectively show that the proposed technique is reliable in reducing the network latency, and that the hop count is minimized when transferring the weather information. [ABSTRACT FROM AUTHOR]

Throughout the use of the small battery-operated sensor nodes encourage us to develop an energy-efficient routing protocol for wireless sensor networks (WSNs). The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN. Many routing protocols are available, but the issue is still alive. Clustering is one of the most important techniques in the existing routing protocols. In the clustering-based model, the important thing is the selection of the cluster heads. In this paper, we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster. Initially, the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance. The proposed scheme performs hierarchal routing and direct routing with some energy thresholds. The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance. Moreover, the simulations will be performed in two scenarios, gateway-based and without gateway to achieve more energy-efficient results. [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]

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]