Current traffic signals in Jordan suffer from severe congestion due to many factors, such as the considerable increase in the number of vehicles and the use of fixed timers, which still control existing traffic signals. This condition affects travel demand on the streets of Jordan. This study aims to improve an intelligent road traffic management system (IRTMS) derived from the human community-based genetic algorithm (HCBGA) to mitigate traffic signal congestion in Amman, Jordan’s capital city. The parameters considered for IRTMS are total time and waiting time, and fixed timers are still used for control. By contrast, the enhanced system, called enhanced-IRTMS (E-IRTMS), considers additional important parameters, namely, the speed performance index (SPI), speed reduction index (SRI), road congestion index (Ri), and congestion period, to enhance IRTMS decision. A significant reduction in congestion period was measured using E-IRTMS, improving by 13% compared with that measured using IRTMS. Meanwhile, the IRTMS result surpasses that of the current traffic signal system by approximately 83%. This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI, SRI, and Ri compared with IRTMS.

Chatbots are important in artificial intelligence (AI) and natural language processing (NLP). The development of the chatbot is viewed as a continuous issue in the field. This is suitable for Arabic chatbots that are not widely available. This study aims to fill the gap in Arabic chatbot development by creating an Arabic chatbot system for university admissions. The system uses a deep neural network model and a manually constructed dataset for conversation pairings, utilizing the Jordanian Arabic dialect from Al-Zaytoonah University of Jordan’s (ZUJ) website. The system efficiently answers most user queries, improving the counseling experience and reducing workload in the admissions department. The adoption of this system also minimizes website traffic congestion. The study contributes to the improvement of Arabic chatbot technology by creating a deep learning-based system optimized for university admissions, demonstrating its potential impact in the Arabic-speaking context. Future research can further enhance the system’s capabilities and its applicability in other disciplines.

A conversational agent (chatbot) is a software that communicates with people using natural language. Natural language processing and artificial intelligence rely heavily on conversation modeling. (AI).Indeed, creating a reliable chatbot system has been one of the most difficult challenges in AI since its inception. Although this system can do a variety of things, it must be able to recognize what users are saying in chat and reply to their questions. As a result, this study proposes a deep learning Arabic chatbot system built on a deep neural network model. The proposed method allows for text chats with others about common topics. The suggested system makes use of a Jordanian dialect derived from the website of Jordan’s Al-Zaytoonah University. The model is also created and tested in the deep learning frameworks Tensorflow and Keras. Furthermore, with no handcrafted rules, a dataset of manually produced pairs of conversations is used. This leads to the development of a chat bot system to assist users in counseling via the Al-Zaytoonah website. While the findings are promising and significant, the obtained results show that this system is capable of reproducing efficient and satisfying responses to users for the majority of queries. It is also demonstrated by the obtained results that an extreme burden is placed on the admissions department, including less online utilization of the website.