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The primary objective of this study is to examine modern applications of artificial intelligence (AI) in construction project management and to explore strategies for improving various processes within this industry. Given the increasing challenges and complexities inthe construction sector, the introduction and evaluation of AI tools can contribute to enhancing project efficiency, reducing costs, improving forecasting accuracy, and accelerating construction processes. This research investigates these aspects and provides suggestions for optimizing project management processes through the utilization of these advanced technologies. The findings of this study can assist entities active in the construction industry in improving their project management procedures by leveraging AI-based tools, thereby completing projects with higher quality and reduced costs. The present study adopts a quantitative research design and is conducted as a descriptive and survey-based study. Data collection was carried out through library research and the compilation of information from documented and scientific sources. The statistical population of the study includes 384 project managers engaged in the residential construction sector. Data analysis was performed using SPSS version 22. The results of the study indicate that AI tools significantly contribute to improving resource allocation in construction projects. Moreover, the study highlights existing challenges related to the adoption of AI technologies within the construction industry, emphasizing the need for focused attention and resolution of these issues.

The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.

artificial intelligence (AI) is transforming crisis management by enabling rapid data processing, predictive analytics, and informed decision-making for disasters, cyber threats, and pandemics. Key applications include scenario modeling, big data analysis, relief team support, and public communication.

Application of ICT in health (eHealth) has become an integral part of modern healthcare systems. Electronic health information management has proven useful in improving quality of health care, reducing costs and facilitating health research. However, the increasing complexity of healthcare and the growing demand for high quality healthcare delivery has created a need for eHealth systems with the capability of anticipating the future need for information, delivering the information timely to patients and professionals, supporting communication, facilitating coordination and enhancing the performance of decision support systems. Agent-based systems, which operate by artificial intelligence, have shown great promise to meet these challenging needs and to realize the full potential of eHealth systems. In this paper we provide a brief review of application of agent-based systems in chronic disease care.

Purpose: This study aimed to explore the applications of artificial intelligence (AI) in school management.Methodology: A qualitative meta-analysis was conducted using the PRISMA method. Articles published between 2020 and 2024 were systematically reviewed from reputable databases, resulting in the selection of 16 studies that met the inclusion criteria. Data were analyzed by categorizing open codes into axial and selective themes, emphasizing the practical applications and implications of AI in school management.Findings: The results highlighted several key applications of AI in school management, including improved administrative efficiency, personalized learning, enhanced decision-making processes, and reduced repetitive tasks. AI technologies were found to facilitate data-driven decision-making, enable real-time feedback, and optimize resource allocation. Additionally, the study identified significant improvements in monitoring and evaluation systems and highlighted the role of AI in addressing challenges such as security, data privacy, and equitable access to technology in underserved regions. However, challenges such as limited infrastructure and resistance to adoption in some settings were noted as potential barriers.Conclusion: The study concludes that AI holds significant potential to transform school management by improving efficiency, personalization, and decision-making. Nonetheless, addressing challenges related to data privacy, infrastructure, and training is crucial for its successful implementation. The findings provide actionable insights for policymakers, educators, and school administrators to harness AI’s capabilities effectively while minimizing associated risks.