Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. Breakthroughs in deep learning (DL) make it feasible to address such challenges and construct a complete intelligent framework that automatically detects such malicious behaviors. Motivated by this, we propose a convolutional neural network (CNN)-based Xception model, i.e., BlockCrime, to detect crimes and improve public safety. Furthermore, we integrate blockchain technology to securely store the detected crime scene locations and alert the nearest law enforcement authorities. Due to the scarcity of the dataset, transfer learning has been preferred, in which a CNN-based Xception model is used. The redesigned Xception architecture is evaluated against various assessment measures, including accuracy, F1 score, precision, and recall, where it outperforms existing CNN architectures in terms of train accuracy, i.e., 96.57%.

An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance and giving performance ratings to employees is essential for companies nowadays. It is evident that different people have different skill sets and behavior, so data should be gathered from all parts of an employee’s life. This paper aims to provide the performance rating of an employee based on various factors. First, we compare various AI-based algorithms, such as random forest, artificial neural network, decision tree, and XGBoost. Then, we propose an ensemble approach, RanKer, combining all the above approaches. The empirical results illustrate that the efficacy of the proposed model compared to traditional models such as random forest, artificial neural network, decision tree, and XGBoost is high in terms of precision, recall, F1-score, and accuracy.

The emerging demand for electric vehicles in urban cities leads to the need to install a huge number of charging stations. With this requirement, electric vehicle coordination and scheduling at charging stations in real-time becomes highly tedious. Thus, there is a need for an efficient scheduling mechanism for electric vehicle charging at charging stations. This paper proposes a novel blockchain and Internet of Things-based consensus mechanism called COME for secure and trustable electric vehicle scheduling at charging stations. The proposed mechanism is intending to resolve conflicts at charging stations. The integrated InterPlanetary File System protocol facilitates a cost-efficient mechanism with minimized bandwidth for electric vehicle scheduling. The proposed mechanism ensures that there is no loss for either the electric vehicle or the charging station. We formulate different scenarios for electric vehicle charging and apply different scheduling algorithms, including first-come first-served, longest remaining time first, and coalition game theory. The performance of the proposed COME consensus mechanism is estimated by comparing it with the practical Byzantine Fault Tolerance consensus protocol and traditional systems based on the charging demand, wait time, conflict resolution, scalability, and InterPlanetary File System bandwidth parameters. The performance results show that the proposed COME consensus mechanism ensures that electric vehicles can have their vehicle charged without any conflict and that the charging station can be satisfied in terms of profit. Moreover, the proposed COME consensus mechanism outperforms the both practical Byzantine Fault Tolerance consensus protocol and the traditional system in terms of scalability and conflict resolution along with additional parameters such as wait time, charging demand, and bandwidth analysis.

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