Artificial Intelligence (AI) is increasingly transforming various engineering disciplines, playing a pivotal role in design, manufacturing, maintenance, and optimization. This paper provides a comprehensive analysis of AI applications in advanced engineering, examining key trends, challenges, and future directions. The study systematically categorizes AI methodologies across different fields, including mechanical, civil, electrical, aerospace, and environmental engineering, as well as emerging areas such as biomedical engineering and material science. Through an extensive literature review and case study analysis, this work highlights the impact of AI-driven optimization in mechanical engineering, predictive maintenance in industrial applications, automation in manufacturing, and AI-enhanced smart infrastructure development.Methodologically, this research synthesizes findings from major scientific databases, including IEEE Xplore, PubMed, Scopus, and Web of Science, ensuring a robust and interdisciplinary perspective. The analysis identifies critical challenges in AI adoption, such as data privacy, scalability, and system integration, and explores strategies to address them. Furthermore, this paper discusses the ethical and societal implications of AI in engineering, emphasizing the need for transparent, explainable, and unbiased AI models.The findings suggest that AI has significantly improved engineering efficiency and innovation but also underline the necessity for interdisciplinary collaboration and standardized frameworks to maximize AI’s transformative potential. The study concludes by outlining future prospects, including the integration of AI with the Internet of Things (IoT) and blockchain, the evolution of AI-driven materials discovery, and the role of AI in personalized medicine and next-generation engineering solutions. Addressing these challenges and leveraging AI’s capabilities will be instrumental in shaping the future of engineering.

The rapid advancement of artificial intelligence (AI) is revolutionizing various sectors, from healthcare to finance. AI-powered technologies, such as machine learning and deep learning, are enabling unprecedented breakthroughs in areas like disease diagnosis, drug discovery, and personalized medicine. This paper explores the influence of Artificial Intelligence (AI) features—such as personalized narratives, adaptive difficulty levels, and virtual companions—on user engagement within interactive and immersive entertainment experiences. Using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, the study analyzes interaction data from 473 users, focusing on behavior patterns and sentiment toward these AI functionalities. Statistical analyses reveal that personalized narratives significantly enhance user sentiment, with an increase in positive sentiment from 45% to 60% after system improvements (t = 8.75, p = 0.0001). Adaptive difficulty levels contribute to sustained engagement, reflected in a notable growth in interaction frequency from 5.0 to 6.2 interactions per user (t = 4.23, p = 0.002). Virtual companions show mixed effectiveness, with their impact heavily influenced by implementation quality and user context. Correlation analysis highlights the importance of session length (r = +0.68, p < 0.001) and abandonment rates (r = -0.56, p < 0.001) as critical factors in shaping user sentiment. The paper includes visual representations of findings and provides actionable recommendations for developers and designers to optimize AI-driven interactive entertainment experiences.

Effective communication is crucial for ensuring inclusivity, yet the hard-of-hearing community faces significant barriers due to a shortage of qualified interpreters. British Sign Language (BSL), officially recognised in the UK, relies on hand gestures, facial expressions, and body movements. However, limited interpreter availability necessitates technological solutions to bridge the communication gap between signers and non-signers. This study proposes a real-time, vision-based BSL recognition system using computer vision and deep learning to interpret fingerspelling and six commonly used BSL words. The system employs OpenCV for video capture, MediaPipe for hand feature extraction, and Long Short-Term Memory (LSTM) networks for sign classification. A dataset incorporating left- and right-handed signers achieved a 94.23% accuracy rate for 26 fingerspelling gestures and 99.07% for six words. To enhance usability, a graphical user interface was developed, enabling seamless real-time interaction. These findings demonstrate the potential of AI-driven sign language recognition to improve accessibility and foster more inclusive communication for the hard-of-hearing community.

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful examination of these issues in an educational context. Using AI and predictive modelling tools, this paper explores the aspects influencing student performance and course success. The Open University Learning Analytics Dataset (OULAD) is analysed using several AI techniques (logistic regression and random forest) in this study to reveal insights about fairness, ethics, and potential biases. This dataset has been used by hundreds of studies to explore how educational data mining can provide information on students. However, potential bias or unfairness in that dataset could undermine the results and any conclusions made from them. To gain insights into the dataset's properties, this was analysed using a typical data science methodology, which included data collecting, cleaning, and exploratory data analysis using Python. By applying AI-based predictive models, this study aims to detect potential biases and their impact on student outcomes. Fairness and ethical considerations are central to the analysis as the representation of various demographic groups and any disparities are evaluated in course results. The goal is to provide useful insights on the proper use of AI in education, while also maintaining equitable and transparent decision-making procedures. The findings shed light on the complicated interplay between artificial intelligence, fairness, and ethics in the context of student performance and course success. As artificial intelligence continues to influence the educational landscape, this study will provide useful ideas for encouraging fairness and minimising biases, resulting in a more inclusive and equal learning environment.

The production of new apartments in Sweden has varied strongly during the period from 1975 to 2021. A new statistical function, which explains these production changes, has been developed. This function is designed, based on a set of hypotheses of how the production level should be affected by different explaining factors, such as the GNP, the size of the population, the growth of the population, and the cost of construction. The following hypotheses could not be rejected: the apartment production is a strictly increasing and strictly convex function of GNP, and a strictly increasing function of the size of the population and the growth of the population, and a strictly decreasing function of the cost of construction. The parameters of the statistical function have been estimated with high precision, via multiple regression analysis. It was not possible to detect heteroscedasticity via residual analysis. Furthermore, no indications that nonlinear transformations would improve the selected model were found. The apartment production model contains a strongly significant negative time trend. The estimated function is used to predict the future apartment production until the year 2050. The predictions are based on assumed growth levels of GNP and the population, and on alternative future time trends of the construction cost index. If the real construction cost index continues to grow with the same average trend as from the year 1993 to 2021, the future apartment construction level will stay almost constant at 40,000 apartments per year until 2050. If the future real construction cost index stays constant at the level in 2022, the production of new apartments will grow to almost 90,000 apartments per year in 2050. If the real construction cost index can be decreased to the level in 1993, the production of new apartments will grow to almost 130,000 apartments per year in the year 2050.

As an ultimate generalisation to several kinds of generalised entropy in the literature, a novel entropy measure, namely, Ismail’s entropy, or (IE), is presented. This article spotlights the significance of fractal dimension, through highlighting several possible applications of fractal dimension to structural engineering. In addition to several difficult open problems and the next step of inquiry, the paper ends with some concluding observations.

The current paper provides a cutting-edge information data length-theoretic approach to the transient M/M/∞ queueing system. This is the first investigation that unifies information data length and queueing theories. Notably, an exposition of a significant real-life application of the M/M/∞ queueing system was addressed, namely the computation of the common average time for unsaturated site visitor flows beneath double-parking situations. This adds another dimension of the significance of the current work. On another note, the significant impact of both time and the number of states for the transient M/M/∞ queue on both the upper and lower bounds of the obtained information data length is observed and noted, which is a completely unprecedented innovative research methodology. The undertaken analytical technique in this paper is based on calculating the information data length for the M/M/∞ transient queue rather than going into higher complexities to go through a non-standard integral to be accomplished. It was a necessity to find both the upper and lower bounds of such a desired-to-be-calculated integration. The data collection process to carry out the numerical validation of the key analytic findings was conducted by choosing values for the parameters of the M/M/∞ transient queue to reveal both obtained upper and lower bounds numerically. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

This paper proposes a multi agent value decomposition network (VDN) based multi UAV collaborative reconnaissance and control method to address the issue of insufficient strategies for multi UAV collaborative reconnaissance and control. By designing corresponding algorithm networks and training processes, the goal of autonomy, collaboration, and intelligence among multiple unmanned aerial vehicle systems has been achieved, assisting unmanned aerial vehicle combat forces in achieving collaborative operations and decision-making. This article uses AirSim as the simulation verification environment to verify the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in this paper can achieve multi UAV collaborative reconnaissance tasks in complex environments, providing an intelligent solution for UAV collaborative control.

Nowadays, cybersecurity stands out as a prominent topic frequently discussed by companies aiming to safeguard their data from hacking attempts. The rise of cyberspace has fuelled the expansion of electronic systems, creating a virtual digital realm that links computers and smartphones within the Internet of Things framework. Thus, the purpose of this study is to present the progress made thus far in applying AI-based intrusion detection systems (IDSs) to address cybercrimes, demonstrating their efficacy in detecting and preventing cyberattacks. The review utilized 4 scholarly databases; ScienceDirect, IEEE-Explore, Web of science, and Springer. 437 studies were extracted from the 4 chosen databases out of which only 54 studies were found to be relevant, thus included. The review found AI-based intrusion detection systems (IDSs) to be more robust and flexible compared to other conventional IDSs. Interestingly, the study results highlight how AI-based intrusion detection systems such as; ANN-based intrusion detection systems, agent-based intrusion detection systems, and Genetic-fuzzy intrusion detection systems, and other machine learning detection systems can be used to assist security experts in analyzing, designing, and developing security frameworks for combating cybercrimes. Lastly, the study proposed some areas for future studies.

This study proposes a marketing strategy framework tailored to address the challenges faced by small and medium-sized enterprises (SMEs) in Uzbekistan. Amidst a fiercely competitive business landscape, SMEs encounter obstacles in establishing brand awareness, attracting customers, and optimizing financial performance. To address these challenges and sustain growth, SMEs can leverage the extended marketing mix, encompassing product, price, place, promotion, people, process, and physical evidence. Employing a combination of descriptive and exploratory research methodologies, this study utilizes quantitative data-gathering techniques, including a survey of entrepreneurs’ perspectives on the implementation of marketing mix strategies by SMEs. Regression analysis, facilitated by STATA software, examines the correlation between marketing mix variables and SME development. Key findings underscore the importance of integrating marketing mix elements to enhance SME marketing efforts, cultivate brand loyalty, and gain a competitive advantage. These findings contribute to a deeper understanding of marketing strategies for SMEs in developing economies like Uzbekistan, offering actionable insights for policymakers and entrepreneurs alike to foster SME growth and economic development.

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator perceptions. It then examines the repercussions of AI-enabled content proliferation, referencing the limitation of three books self-published per day in September 2023 by Amazon due to a suspected influx of AI-generated material. The discourse extends to the potential of AI in accelerating research akin to the contributions of digital humanities and computational linguistics, highlighting its accessibility to the general public. The article further delves into the implications of AI on pedagogical approaches to research and writing, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities. Finally, it proposes revisions to writing curricula, adapting to the transformative influence of AI in academic contexts.