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In recent years, underwater wireless sensor networks (UWSNs) have been widely applied to aquatic and military applications. Network survivability is an essential attribute to be considered in UWSN circumstance and various stratifications like node survivability, connectivity and rapid fault node detection and recovery. However, efficient and accurate fault tolerance mechanisms are required to prolong the network survivability in UWSN. In this research work, the energy-efficient fault detection and recovery management (EFRM) approach is proposed for the UWSN with relatively better network survivability. The hidden Poisson Markov model has been incorporated in EFRM to achieve efficient fault detection throughout the whole network. Thereafter, the recovered node can be selected by using the analytical network process model which facilitates to recover the larger number of nodes in the damaged region. The simulation results manifest that when the fault probability is 40%, the detection accuracy of the proposed EFRM is over 99%, and the false positive rate is below 2%. The detection accuracy is improved by up to 12% when compared with the existing state-of-the-art schemes.

Wireless sensor network (WSN) consists of a large amount of limited battery-powered sensor nodes. In general, energy consumption will be a significant concern for WSN owing to irreplaceable battery constraints of sensor nodes. The zone formation approach could be an adequate data aggregation technique which efficiently minimizes the energy consumption by categorizing sensor nodes into zones. However, the main constraints like zone head (ZH) selection, frequent change of ZH, and multi-hop communication from ZH to the sink have a direct impact on the network consistency of WSN. In this paper, a novel efficient intra- and inter-zone routing scheme has been proposed in order to prolong the network consistency of WSN. In the proposed scheme, the hybrid algorithm is established in which harmony search algorithm incorporates with modified moth flame optimization algorithm. This hybrid algorithm provides the appropriate ZH selection for intra-zone routing that reduces the frequent change of ZH in the network. Furthermore, the path balancing in inter-zone routing is acquired through multi-criteria-based optimal path routing algorithm. The performance results confirm that the proposed scheme enhances the network consistency compared with an existing scheme.

In today’s world, brain stroke is considered as a life-threatening disease provoked by undesirable blockage among the arteries feeding the human brain. The timely diagnosis of this brain stroke detection in Magnetic Resonance Imaging (MRI) images increases the patient’s survival rate. However, automated detection plays a significant challenge owing to the complexity of the shape, dimension of size and the location of stroke lesions. In this paper, a novel optimized fuzzy level segmentation algorithm is proposed to detect the ischemic stroke lesions. After segmentation, the multi-textural features are extracted to form a feature set. These features are given as input to the proposed weighted Gaussian Naïve Bayes classifier to discriminate normal and abnormal stroke lesion classes. The experimental result manifests that the proposed methodology achieves a higher accuracy as compared with the existing state-of-the-art techniques. The proposed classifier discriminates normal and abnormal classes efficiently and attains 99.32% of accuracy, 96.87% of sensitivity and 98.82% of F1 measure.

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In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain-based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state-of-the-art techniques.

In today's world, the advancement of telediagnostic equipment plays an essential role to monitor heart disease. The earlier diagnosis of heart disease proliferates the compatibility of treatment of patients and predominantly provides an expeditious diagnostic recommendation from clinical experts. However, the feature extraction is a major challenge for heart disease prediction where the high dimensional data increases the learning time for existing machine learning classifiers. In this article, a novel efficient Internet of Things-based tuned adaptive neuro-fuzzy inference system (TANFIS) classifier has been proposed for accurate prediction of heart disease. Here, the tuning parameters of the proposed TANFIS are optimized through Laplace Gaussian mutation-based moth flame optimization and grasshopper optimization algorithm. The simulation scenario can be carried out using11 different datasets from the UCI repository. The proposed method obtains an accuracy of 99.76% for heart disease prediction and it has been improved upto 5.4% as compared with existing algorithms. K E Y W O R D Sclassification, grasshopper optimization algorithm, heart disease prediction, internet of things, moth flame optimization 610