Reducing traffic congestion in makkah during Hajj through the use of AI technology.

This research addresses the pervasive issue of traffic congestion during the Hajj, where approximately 250,000 vehicles substantially exacerbate travel times and road accidents, while also escalating pollution levels, thereby adversely affecting public health. Aimed at bolstering the Kingdom's Vision 2030, the study focuses on the incorporation of artificial intelligence (AI) and advanced communication technologies to optimize traffic management in Mecca. Through the innovative deployment of smart cameras and real-time data analytics, the proposed system seeks to predict, manage, and alleviate traffic congestion by providing alternative routes and facilitating smoother vehicular movement. An exploration into the myriad benefits of this AI-integrated system reveals potentials such as enhanced road safety, improved emergency response efficiencies, and elevated air quality, thereby contributing to the overall wellbeing of the community and environment. In addition, the research anticipates that reducing traffic bottlenecks will indirectly invigorate local businesses and augment tourism revenues, aligning with the objectives of enhancing economic prosperity. By advocating a multi-faceted approach to crowd monitoring and management, this study underscores the indispensable role of AI in revolutionizing traffic management strategies, despite the challenges posed by the complexity of real-time data simulation and the unique intricacies of the Hajj.

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning.

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.

Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile.

Antimicrobial peptides (AMPs) are gaining a lot of attention as cutting-edge treatments for many infectious disorders. The effectiveness of AMPs against bacteria, fungi, and viruses has persisted for a long period, making them the greatest option for addressing the growing problem of antibiotic resistance. Due to their wide-ranging actions, AMPs have become more prominent, particularly in therapeutic applications. The prediction of AMPs has become a difficult task for academics due to the explosive increase of AMPs documented in databases. Wet-lab investigations to find anti-microbial peptides are exceedingly costly, time-consuming, and even impossible for some species. Therefore, in order to choose the optimal AMPs candidate before to the in-vitro trials, an efficient computational method must be developed. In this study, an effort was made to develop a machine learning-based classification system that is effective, accurate, and can distinguish between anti-microbial peptides. The position-specific-scoring-matrix (PSSM), Pseudo Amino acid composition, di-peptide composition, and combination of these three were utilized in the suggested scheme to extract salient aspects from AMPs sequences. The classification techniques K-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were employed. On the independent dataset and training dataset, the accuracy levels achieved by the suggested predictor (Target-AMP) are 97.07% and 95.71%, respectively. The results show that, when compared to other techniques currently used in the literature, our Target-AMP had the best success rate.

NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite.

Plasmodium falciparum causes malaria, which is an infectious and fatal disease. In early days, malaria-infected cells were diagnosed using a microscope. owing to a huge number of instances for analysis and intricacy of time, it may lead to false detection. Automated parasite detection technologies are in high demand due to increased time consumption and erroneous detection. To create effective cures and treatments, it is critical to use an accurate approach for predicting malaria parasite. Here, numerous protein sequences formulation techniques namely: discrete methods, Biochemical, physiochemical and Natural language processing techniques are applied for transformation of protein sequences in to numerical descriptors. Four classification algorithms are utilized and the anticipated results of these classifiers were then fused to establish ensemble classification model via simple majority and genetic algorithm. In addition, BCH error correction code is incorporated with support vector machine using all the feature spaces. The simulated results demonstrate the remarkable achievement of proposed compared to previous models. Thus, our proposed model may be an effective tool for discriminating the secretory and non-secretory proteins of malaria parasite.

A Smart Device for a Preliminary Dental Examination Based on the Internet of Things.

The COVID-19 pandemic has threatened the lives of many people, especially the elderly and those with chronic illnesses, as well as threatening the global economy. In response to the pandemic, many medical centers, including dental facilities, have significantly reduced the treatment of patients by limiting clinical practice to exclusively urgent, nondeferred care. Dentists are more vulnerable to contracting COVID-19, due to the necessity of the dentist being close to the patient. One of the precautions that dentists take to avoid transmitting infections is to wear a mask and gloves. However, the basic condition for nontransmission of infection is to leave a safe distance between the patient and the dentist. This system can be implemented by using an Arduino microcontroller, which is designed as a preliminary device by a dentist to examine a patient's teeth so that a safe distance of three meters between the dentist and the patient can be maintained. The project is based on hardware and has been programmed through Arduino. The proposed system uses a small wired camera with a length of five meters that is connected to the dentist's mobile or laptop and is installed on a robotic arm. The dentist can control the movement of the arm in all directions using a joystick at a distance of three meters. The results showed the effectiveness of this system for leaving a safe distance between the patient and the dentist. In our future work, we will control the movement of the arm via Bluetooth, and we will use a wi-fi-based camera.