Vehicular ad-hoc networks (VANETs) are the specific sort of ad-hoc networks that are utilized in intelligent transportation systems (ITS). VANETs have become one of the most reassuring, promising, and quickest developing subsets of the mobile ad-hoc networks (MANETs). They include smart vehicles, roadside units (RSUs), and on-board units (OBUs) which correspond through inconsistent wireless network. The current research in the vehicles industry and media transmission innovations alongside the remarkable multimodal portability administrations expedited center-wise ITS, of which VANETs increase considerably more attention. The particular characteristics of the software defined networks (SDNs) use the vehicular systems by its condition of the centralized art having a complete understanding of the network. Security is an important issue in the SDN-based VANETs, as a result of the effect the threats and vulnerabilities can have on driver’s conduct and personal satisfaction. This paper opens a discourse on the security attacks that future SDN-based VANETs should confront and examines how SDNs could be advantageous in building new countermeasures. SDN-based VANETs encourage us to dispose of the confinement and difficulties that are available in the traditional VANETs. It helps us to diminish the general burden on the system by dealing with the general system through a single wireless controller. While SDN-based VANETs provide us some benefits in terms of applications and services, they also have some important challenges which need to be solved. In this study we discuss and elaborate the challenges, along with the applications, and the future directions of SDN-based VANETs. At the end we provide the conclusion of the whole study.

Sleepiness, fatigue, and stress in drivers are the leading causes of car crashes. In the late two decades, there is an endeavor to monitor vital signs, stress levels, and fatigue using adapted sensors supported by technological advances. To the best of our knowledge, this systematic review is the first to investigate the role of HRV measurement for sleepiness, fatigue, and stress level monitoring in car drivers. A search was performed in PubMed, Embase, and Cochrane databases using prespecified keywords. Studies were considered for inclusion if they reported original data regarding the association between different HRV measurements and drivers’ sleepiness, fatigue, or stress levels. Of the retrieved 749 citations, 19 studies were finally included. The sensibility and specificity of HRV significantly varied across studies, respectively 47.1%–95% and 74.6%–98%. Accuracy was also different, ranging from 56.6% to 95%. Nevertheless, in real-world conditions, confounding factors could affect sympathovagal tone and HRV. Multiple HRV parameters measurement rather than one parameter approach seems to be the optimal strategy for evaluating the vigilance state in drivers that it would be possible to achieve a good performance. As all studies were observational, data should be confirmed in randomized controlled trials. In conclusion, HRV represents a potentially valuable marker for sleepiness, fatigue, and stress monitoring in car drivers. HRV measurements could be implemented in future clinical models and sensors to detect early sleepiness and fatigue and prevent car crashes. More studies with larger populations are needed to support this evidence.

The expansion of the concept of the Internet of Things (IoT), together with wireless sensor networks, has given rise to a wide range of IoT applications. This paper presents and describes the concept, theory of operation, and practical results of a Telecare-ECG (Electrocardiogram) Monitoring device, designed for the remote monitoring of out-of-hospital cardiac patients. ECG monitoring using the Telecare-ECG Monitor system ensures a better quality of life for patients and greater possibilities for the real-time monitoring and signaling of sporadic cardiac events, by recording instantaneous cardiac arrhythmias captured during certain activities or in the daily environment of the patient; furthermore, hospital resources are less impacted by this device than other devices. In accordance with the novelty and contribution of this paper to the field of ECG investigations, the results obtained in the analysis, testing, and validation of the Telecare-ECG Monitor system refer to the optimization of the functionality of the mobile ECG device under conditions that were as similar to reality as possible.

According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness’s are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime.

Operating in a dynamic and competitive global market, railway companies have realized many years ago that better management of their logistical operations will enhance their strategic positions on the market. The financial component of daily operations is of utmost importance these days and many companies concluded that maximizing the profit relies on the integration of logistical activities with better income management. This paper presents a system consisting of three components: Ferodata BOX, Ferodata MOBILE, and Ferodata SYS, used to transmit to a web-server the status and operating information of an electric or diesel train. Train information includes data from locomotives, wagons, train driver, route, direction, fuel or electric consumption, speed, etc. All this information is processed in real-time and can be viewed in the web-server application. Additionally, the web-server application could manage and report details that are coming from the wagons, such as valuable information regarding the bogie wear, the identification of the wagons attached to a gasket, and identification the situations in which a wagon or group of wagons comes off the gasket configuration. All information about the status of trains is available on-line and at any moment the person responsible for management can use these data in their work.

The cloud computing networks used in the IoT, and other themes of network architectures, can be investigated and improved by cheminformatics, which is a combination of chemistry, computer science, and mathematics. Cheminformatics involves graph theory and its tools. Any number that can be uniquely calculated by a graph is known as a graph invariant. In graph theory, networks are converted into graphs with workstations or routers or nodes as vertex and paths, or connections as edges. Many topological indices have been developed for the determination of the physical properties of networks involved in cloud computing. The study computed newly prepared topological invariants, K-Banhatti Sombor invariants (KBSO), Dharwad invariants, Quadratic-Contraharmonic invariants (QCI), and their reduced forms with other forms of cloud computing networks. These are used to explore and enhance their characteristics, such as scalability, efficiency, higher throughput, reduced latency, and best-fit topology. These attributes depend on the topology of the cloud, where different nodes, paths, and clouds are to be attached to achieve the best of the attributes mentioned before. The study only deals with a single parameter, which is a topology of the cloud network. The improvement of the topology improves the other characteristics as well, which is the main objective of this study. Its prime objective is to develop formulas so that it can check the topology and performance of certain cloud networks without doing or performing experiments, and also before developing them. The calculated results are valuable and helpful in understanding the deep physical behavior of the cloud’s networks. These results will also be useful for researchers to understand how these networks can be constructed and improved with different physical characteristics for enhanced versions.