The convergence of Artificial Intelligence (AI) can overcome the complexity of network defects and support a sustainable and green system. AI has been used in the Cognitive Internet of Things (CIoT), improving a large volume of data, minimizing energy consumption, managing traffic, and storing data. However, improving smart packet transmission scheduling (TS) in CIoT is dependent on choosing an optimum channel with a minimum estimated Packet Error Rate (PER), packet delays caused by channel errors, and the subsequent retransmissions. Therefore, we propose a Generative Adversarial Network and Deep Distribution Q Network (GAN-DDQN) to enhance smart packet TS by reducing the distance between the estimated and target action-value particles. Furthermore, GAN-DDQN training based on reward clipping is used to evaluate the value of each action for certain states to avoid large variations in the target action value. The simulation results show that the proposed GAN-DDQN increases throughput and transmission packet while reducing power consumption and Transmission Delay (TD) when compared to fuzzy Radial Basis Function (fuzzy-RBF) and Distributional Q-Network (DQN). Furthermore, GAN-DDQN provides a high rate of 38 Mbps, compared to actor-critic fuzzy-RBF’s rate of 30 Mbps and the DQN algorithm’s rate of 19 Mbps.

Ultra-reliable and Low-latency Communications (URLLC) is expected to be one of the most critical characteristics in Beyond fifth-Generation (B5G) cellular networks with stringent low latency and high-reliability requirements. The Deep Reinforcement Learning (deep-RL) framework has been applied to predict the optimization of a Resource Block (RB) and minimize Power Allocation (PA) to guarantee a high End-to-End (E2E) reliability and low E2E latency under rate constraints. This paper proposes a novel Policy Gradient-based Actor-Critic Learning (PGACL) algorithm to optimize the policy gradient for optimal rate allocation to solve the RB, minimize power, and guarantee a solution for URLLC scheduling. The purpose of a PGACL algorithm is to provide a good policy with a closer convergence rate and a low computational cost depending on the reduced action space for every user. URLLC systems need to operate in highly reliable systems and account for extreme network conditions. Therefore, we proposed the refiner Generative Adversarial Networks (GANs) that apply enough extreme events for the deep-RL agent to generate synthetic data with high reliability similar to real data based on the regulated number of extreme events in the dataset. This refiner GAN method enables a deep-RL approach to generate large amounts of data practically used in real-time operation. Simulation results showed that the proposed deep-RL for refiner-GAN can omit the transient training time and develop deep-learning based on a controlled set of unlabeled real traffic at a relatively short time. Furthermore, the refiner GAN demonstrated 99.9999% reliability and E2E latency of less than 1.4ms.

University course timetabling problem (UCTP) includes the challenging task of generating an automated timetable for courses under resource limitations. Manual generated timetables might hold some errors and consume a long time to create feasible solutions. Thus, there is a need to find optimal and fast solutions for this problem. The difficulty of the timetabling problem is further increased when tackling faculty-related constraints, according to their requirements, preferences, and availability. Thus, student-related constraints are usually the focus of UCTP generated solutions, in which faculty constraints are limited to their teaching load or preferences. This paper proposes a multi-objective mixed-integer programming model for a preregistration UCTP, combined with faculty-related constraints. The goal is to maximize events assignments and faculty-members preferences satisfaction while balancing the university requirements. At the same time, student learning days and unassigned events are minimized. The model is tested with eight real-world instances. Computational experiments are carried out to show the efficiency of the model. The proposed method can generate optimal timetables for all problem instances that satisfy faculty constraints.

The number of industries using drones is increasing. Although early research was conducted on drones, the prior literature has not emphasized consumer adoption of drones for item delivery. Consequently, this study investigates whether customers are open to receiving packages from drones. This study also examines the elements that influence customers’ willingness to adopt drone package delivery. This study fills a gap in the logistics and service research by examining people’s concerns regarding using drones. The unified theory of acceptance and use of technology (UTAUT2) serves as the basis for the conceptual framework. A self-administered online survey is deployed. The results demonstrate that perceived privacy risks negatively influence performance and effort expectancy as well as facilitating conditions and social influence. This study further validates the UTAUT2 by confirming the influence of performance expectancy and facilitating conditions on attitudes toward adopting drones. Moreover, this study confirms the positive influence of attitude on behavior. This study has managerial implications, one of which is the suggestion that the deployment and use of drones should minimize interference with people’s privacy.

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute’s SEER Program’s November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients’ survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using -nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression ( ) and the greatest acquired from the random forest ( ). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.