Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. Result: The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. Discussion: First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.

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.

The operation of pharmacy systems undergoes transformation through artificial intelligence (AI), which advances from manual procedures to intelligent adaptive tools. These technologies enhance daily operations through prescription verification, drug interaction alerts, and inventory management while decreasing human mistakes. Through AI, patients gain access to customized medication recommendations, automatic appointment alerts, and virtual support services. The advancement of technology creates multiple new difficulties for healthcare systems. The increasing integration of AI in healthcare creates growing concerns about data privacy alongside algorithmic bias and the requirement for decision-making explanations. This paper evaluates AI systems against conventional pharmacy methods through an assessment of their precision and speed and their impact on patient safety and ethical preparedness. The adoption of AI systems requires strong ethical protections together with defined regulatory frameworks to maintain human clinical decision-making authority in patient care.

Agent-based systems (ABS) are the newest and most effective approach in software engineering to solve complex, chaotic, and interconnected problems. Since ABS models the systems as the agents that communicate with each other, it is an effective approach to creating flexible software. This paper aims to discuss the possibility of applying agent-based systems to software engineering with a focus on the opportunities and challenges and further research directions. This paper presents a conceptual model that demonstrates the connections between the agents, their surroundings, and the software engineering process. The study also creates an AI-Driven Agent-Based System Development Framework (AI-ABSD Framework) that uses machine learning (ML) and artificial intelligence (AI) in the Agent-Based System Development Life Cycle (ABSDLC). This framework was created because of the growing need for very smart, self-guiding, and emotionally intelligent computers. Based on a case study, empirical validation, and comprehensive evaluation, the paper ends with a perspective of the future of ABS in software engineering, highlighting how it can change the approach to developing software systems.

Three-dimensional (3D) bioprinting of hydrogel inks is an enabling technology for tissue engineering and the fabrication of personalized drug-delivery systems. However, in most laboratories the extrusion parameters are still optimized by slow, resource-intensive trial-and-error, because printability depends on the combined and often nonlinear influence of ink composition, extrusion pressure, and nozzle geometry rather than on any single variable. Alginate–methylcellulose (Alg–MC) is a widely used pharmaceutical bioink whose shear-thinning behavior makes this optimization particularly demanding. Here we developed a physics-informed machine-learning framework that predicts extrusion print quality before printing. A hybrid dataset of 3280 labeled samples was assembled by combining laboratory extrusion experiments, computational fluid dynamics (CFD) simulations of nozzle flow, and controlled augmentation with a deep belief network (DBN). Five physics-informed features encoding pressure-to-diameter and shear-rate interactions were engineered to embed rheological domain knowledge. Five base classifiers alongside two ensemble strategies, seven models in all, were benchmarked under five-fold stratified cross-validation. The voting ensemble performed best (accuracy, 93.3%; F1 = 0.929), narrowly ahead of the stacking ensemble (93.3% accuracy; F1 = 0.928). Alg:MC ratio, extrusion pressure, and the pressure–diameter interaction term stood out as the dominant predictors, consistent with established alginate rheology. On external validation—withholding one composition ratio and one needle gauge entirely from training and testing on those genuinely unseen conditions—accuracy was 75.9%, a stricter test than a random held-out split. Overall, the framework offers a practical, interpretable way to cut down experimental burden in pharmaceutical bioink development.