Abstract In the smart city, the presence of intelligent transportation systems (ITS) is much essential. The emerging vehicular ad‐hoc networks (VANET) technology offers a wide range of applications in transportation, mobility, connectivity, and safety applications. In this decade, vehicular traffic is a prime hassle. The actual‐instant traffic data to drivers along with navigation system would facilitate vehicles to select an excellent path. This article proposes hybrid VANET architecture for effective communication between vehicles. The proposed hybrid architecture uses the multihop routing algorithm to permit vehicles to enlarge their driving performance and road safety. The proposed hybrid vehicular multihop routing algorithm with intelligent transportation system (Hybrid VMR‐ITS) allows the transportation system for instantaneous communications between vehicles and road‐side units (RSUs) and vehicles‐traffic servers effectively. The proposed work enhanced the whole spatial usage of a road network by reducing average vehicle travel costs with a reduced traffic overhead of 4%–9% compared with existing works.

Abstract In this article, an auto‐regressive integrated moving average (ARIMA) modeling has been proposed to predict the mobility speed of the nodes in the mobile ad hoc networks (MANETs). The mobility speed prediction supports effective route discovery to enhance efficient and reliable routing. The random walk mobility model had been used to forecast the mobility of the nodes. Subsequently, various the recurrent neural network (RNN) model has been developed with different number hidden neurons, and the prediction results are compared with proposed ARIMA based results. The Akaike information criterion, auto‐correlation function metrics are evaluated to assess the dataset's quality. Several network scenarios are evaluated with various speeds and the number of nodes. The results of the proposed ARIMA model are compared with the RNN results. It demonstrates that the proposed model has higher prediction rates with reduced error rates of 0.1%–1.4% than RNN values. Such that the proposed methods facilitate the higher prediction rate of 10%–26% as compared with the RNN. Therefore, the proposed mobility speed prediction techniques support the effective routing in the MANETs.

Abstract Chronic kidney disease is a global health issue that affects millions of people worldwide and causes significant social, economic, and medical issues. Several automated detection systems can diagnose chronic kidney disease. This paper proposes the recursive random forest feature selection (RFFS) based ensemble learning algorithm to diagnose chronic kidney diseases (CKD). In the decision point, decision tree‐based classifiers are used. The accuracy and kappa scores are used to determine the classification results. According to the results of the proposed algorithm's performance analyses, the ensemble learning classifiers outperform other classifiers for classifying CKD. The proposed RFFS algorithm achieves 6%–40% improved prediction accuracy using the feature selection algorithm. Further, it attains 15%–39% of reduced mean square error. The performance metrics, precision, sensitivity, specificity, f1 score, and Jaccard scores, have been analysed and show greater results for the RFFS algorithm. Thus, the proposed RFE‐GB algorithm results prove it as a prominent model for CKD estimation and treatment.

Abstract Cardiovascular diseases are one of the most common chronic illnesses that affect people's health. Early detection of cardiovascular diseases's can reduce mortality rates by preventing or reducing the severity of the disease. Machine learning algorithms are a promising method for identifying risk factors. This article proposes a recursive feature elimination‐based gradient boosting algorithm in order to obtain accurate heart disease prediction. The patients' health record with important cardiovascular disease features has been analysed for the evaluation of the results. Several other machine learning methods were also used to build the prediction model, and the results were compared with the proposed model. The results of this proposed model infer that the combined recursive feature elimination and gradient boosting algorithm achieves the highest accuracy (89.7%). Further, with an area under the curve of 0.84, the proposed algorithm was found superior and had obtained a substantial gain over other techniques. Thus, the proposed gradient boosting algorithm will serve as a prominent cardiovascular disease estimation and treatment model.

Summary The percentage of patients hospitalized due to hyponatremia is getting higher. Hyponatremia is the deficiency of sodium electrolyte in the human serum. This deficiency might indulge adverse effects and also be associated with longer hospital stay or mortality if it was not actively treated and managed. This work predicts the futuristic sodium levels of patients based on their history of health problems using a multilayer perceptron (MLP) and multivariate linear regression (MLR) algorithm. This work analyzes the patient's age, information about other diseases such as diabetes, pneumonia, liver disease, malignancy, pulmonary, sepsis, SIADH, and a sodium level of the patient during admission to the hospital. The results of the proposed MLP algorithm is compared with the MLR algorithm‐based results. The MLP prediction results generate 23%–72% of higher prediction results than the MLR algorithm. Thus, the proposed MLP algorithm has produced 57.1% of the reduced mean squared error rate than the MLR results on predicting future sodium ranges of patients. Further, the proposed MLP algorithm produces 27%–50% of the higher prediction precision rate. Therefore, the MLP algorithm can be used for forecasting patient's hyponatremia.