The information exchanged over the smart grid networks is sensitive and private. As such, proper mechanisms must be put in place to protect these messages from security and privacy violations. Although many schemes have been presented in literature to address these challenges, a number of them rarely consider concurrent authentication of smart meters, while some are inefficient or still lack some of the smart grid network security and privacy requirements. In this article, a novel concurrent smart meters authentication algorithm is presented, based on some trusted authority. Formal security analysis of this algorithm is executed using Burrows‐Abadi‐Needham logic, which shows that this algorithm provides strong authentication among the smart meter, utility service provider and trusted authority. In addition, session keys are independently computed and verified between the smart meter and utility service provider with the help of the trusted authority. Informal security analysis shows that this algorithm provides device anonymity, perfect forward key secrecy, strong mutual authentication and is resilient against replay, de‐synchronization, privileged insider, impersonation, eavesdropping, side‐channel, and traceability attacks. In terms of performance, the proposed algorithm exhibits the least communication and computation overheads when compared with other related schemes.

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Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

Mankind is vulnerable to artificial seismic sources and accompanying explosions' consequences. Recently, seismicity catalog contamination is among the main problems faced by seismologists. Since identifying artificial seismic sources is the first and always challenging stage, it is imperative to develop an automated control system that will discriminate tectonic from non-tectonic events. Detection and removal of the artificial seismic sources have become urgent. Early treatments and cleaning of contaminated seismicity catalogs are crucial to assist in accurate seismic hazard identification and enhance the planning of future urban developments. With the advancement of machine learning (ML) techniques, artificial seismic source detection accuracy has been improved. Today, there are different kinds of methods, ML techniques, and diverse processes like knowledge discovery are developed for discriminating artificial seismic sources and earthquakes. ML techniques offer various probabilistic and statistical methods that allow intelligent systems to learn from reoccurring experiences to detect and identify patterns from a dataset. This study aims to build an automated system that is able to detect the existence of artificial seismic sources in seismicity catalogs. More concretely, we classify seismic activity reports into two classes using classical and ensemble ML algorithms. Classical seismicity parameters or features are supplied to linear and nonlinear ML classifiers. The proposed scheme based on the four features (Latitude, Longitude, depth, and Magnitude) can enhance the performance. To assure the enhanced performance, we have examined the proposed scheme by both the accuracy of each model, ROC curves, Precision-Recall, and Calibration. The obtained results prove that the ensemble learning algorithms exhibit better results compared to other classical ML algorithms by having 98.14% testing accuracy.

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