Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.

Unmanned aerial vehicles have found applications in fields such as environmental monitoring and the military. Although the collected data in some of these application domains are sensitive, public channels are deployed during the communication process. Therefore, many protocols have been presented to preserve the confidentiality and integrity of the exchanged messages. However, numerous security and performance challenges have been noted in the majority of these protocols. In this paper, an elliptic curve cryptography (ECC) and symmetric key-based protocol is presented. The choice of ECC was informed by its relatively shorter key sizes compared to other asymmetric encryption algorithms such as the Rivest–Shamir–Adleman (RSA) algorithm. Security analysis showed that this protocol provides mutual authentication, session key agreement, untraceability, anonymity, forward key secrecy, backward key secrecy, and biometric privacy. In addition, it is robust against smart card loss, password guessing, known secret session temporary information (KSSTI), privileged insider, side-channeling, impersonation, denial-of-service (DoS), and man-in-the-middle (MitM) attacks. The comparative performance evaluation showed that it has relatively low computation, storage, and communication complexities.

Highly sensitive information about people’s social life and daily activities flows in smart home networks. As such, if attackers can manage to capture or even eavesdrop on this information, the privacy of the users can be compromised. The consequences can be far-reaching, such as knowing the status of home occupancy that can then facilitate burglary. To address these challenges, approaches such as data aggregation and signcryption have been utilized. Elliptic curve cryptography, bilinear pairing, asymmetric key cryptosystem, blockchain, and exponential operations are among the most popular techniques deployed to design these security solutions. However, the computational, storage and communication complexities exhibited by the majority of these techniques are too high. This renders these techniques unsuitable for smart home components such as smart switches and sensors. Some of these schemes have centralized architectures, which present some single points of failure. In this paper, symmetric key authentication procedures are presented for smart home networks. The proposed protocol leverages on cryptographic primitives such as one-way hashing and bitwise exclusive-Or operations. The results indicate that this scheme incurs the lowest communication, storage, and computation costs compared to other related state-of-the-art techniques. Empirically, our protocol reduces the communication and computation complexities by 16.7% and 57.7%, respectively. In addition, it provides backward key secrecy, robust mutual authentication, anonymity, forward key secrecy, and unlinkability. Moreover, it can effectively prevent attacks such as impersonation, session hijacking, denial of service, packet replays, man-in-the-middle, and message eavesdropping.

Path planning is a fundamental aspect of mobile robots and autonomous systems. Methods of path planning are used in robotics to create a path for a robot or autonomous system to follow from a starting position to a goal one while avoiding obstacles and satisfying any additional conditions. There are many different methods to plan the path, including probabilistic methods, heuristics-based approaches, and optimization-based methods. In this paper, we propose a new path planning method called Dynamic Adaptive RRT-connect with a Triangular Segmented Interpolation. Our method improves the traditional RRT algorithms by using an Adaptive-RRT approach, where a random node is chosen as a new node to increase tree exploration. Then, we use a Bi-directional scheme to further enhance the convergence time and cost. Additionally, our method employs Triangular Segmented Interpolation (TSI) method to improve path length and smoothness. Finally, we operate this method within a dynamic environment depending on the Dynamic Window Approach (DWA). Experiments on a variety of environments have shown that our proposed method achieves better than the RRT and RRT-connect algorithms individually in terms of computation time (reduced by 90-80%), cost (reduced by 82-63%), and path length (shorten by 17-12%) besides the ability to avoid dynamic obstacles efficiently.