The q-symmetric analogues of Hölder, Minkowski, and power mean inequalities are presented in this paper. The obtained inequalities along with a Montgomery identity involving q-symmetric integrals are used to extend some Ostrowski-type inequalities. The q-symmetric derivatives of the functions involved in these Ostrowski-type inequalities are convex or s-convex. Moreover, some Hermite–Hadamard inequalities for convex functions as well as for s-convex functions are also acquired with the help of q-symmetric calculus in the present work. Some examples are included to support the effectiveness of the proved results.

The Hajj is a religious event that attracts a significant number of Muslims from various countries who perform rituals in Mecca, Saudi Arabia. Despite the high volume of pilgrims that typically participate in the event, the number has been reduced in recent years due to the COVID-19 pandemic. The satisfaction of Hajj pilgrims with the quality of hospitality services provided during the event is a crucial factor that must be studied and understood. To achieve this goal, various psychological theories have been employed to explain the phenomenon. The advancement of big data and artificial intelligence has enabled the development of new analytical methodologies for evaluating psychological theories in the hospitality industry. In this study, we present a novel deep learning model that leverages the expectation-confirmation theory to examine the satisfaction of Hajj pilgrims with hospitality services. The model was trained and tested on data obtained from hotel review posts related to the Hajj. Based on our results, the proposed model achieved a high accuracy of 97 % in predicting the satisfaction of Hajj pilgrims. In addition, the results can be used to improve the quality of services provided to pilgrims and enhance their overall experience during the Hajj.

The performance of an operating system (OS) is affected by the algorithm policy that is used by a CPU to schedule the running processes. Thus, a better CPU scheduling algorithm results in faster OS performance using minimal resources over small amounts of time [11]. For that reason, many algorithms were proposed and implemented to enhance the performance of CPU scheduling. Round Robin is considered an efficient and fair algorithm because all processes are given the same amount of time quantum. However, its efficiency depends on the selected time quantum. In this paper, we present a comparative study of four different Round Robin algorithms namely: Adaptive Round Robin Algorithm, Best Time Quantum Round Robin CPU Scheduling, Optimal Round Robin Scheduling Using Manhattan Distance Algorithm, and Improved Round Robin Scheduling Algorithm. We compare these algorithms in terms of four performance factors including: Average Waiting Time (AWT), Average Turnaround Time (ATT), Average Response Time (ART) and Number of Contexts Switching (NCS). The simulation results show that both Adaptive Round Robin and Optimal Round Robin Scheduling Using Manhattan Distance algorithms are more efficient to be adopted as they recorded the minimum values of performance factors.

Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The predictive model is embedded in a new regularization based on decaying the weights according to the weight matrices’ standard deviation and comparing the results against its parents (RSD‐ANN). The performance of RSD‐ANN is far better than that of the existing methods. Based on our experiments, the average validation accuracy computed was 96.30% using either the tenfold cross‐validation or holdout method.