With the increasing popularity of artificial intelligence (AI) applications in medical practices, the integration of AI technologies into medical education has garnered significant attention. However, there exists a noticeable research gap when it comes to providing comprehensive guidelines and recommendations for its successful integration into this domain. Addressing this research gap is crucial as the responsible and effective incorporation of AI in medical education not only ensures that current and future healthcare professionals are well-prepared for the demands of modern medicine but also upholds ethical standards, maximizes the potential benefits of AI, and minimizes potential risks. The objective of this chapter is to fill this gap by offering practical tips and actionable insights for incorporating AI into medical education, encompassing practical, ethical, pedagogical, and professional implications. Consequently, it equips medical educators and learners alike with the knowledge and tools necessary to navigate the evolving landscape of medical education in the age of AI.

Energy consumption significantly increased in recent decades, notably at the household level, due to economic development, rising population, and technological advancements. To address this sustainability concern, peer-to-peer energy sharing service (P2PESS) is introduced as a solution to household level energy needs. However, P2PESS has yet to be fully explored in terms of development and adoption. As such, this study attempts to provide an understanding of the adoption intention on artificial intelligence (AI) in P2PESS a developing country. This study is realized by developing an extended adoption intention model analyzed through partial-least squares - structural equation modeling (PLS-SEM). Results show that attitude is the most significant predictor of AI in P2PESS adoption intention. This study also reveals that the trust dimension has the strongest effect on attitude, while attitude toward use has the strongest effect on behavioral intention. Also, this study confirms ease of use and usefulness as critical factors in adoption intention. Meanwhile, AI-anxiety is the least significant predictor in the model. Finally, this study is the first evidence of AI in P2PESS adoption intention from the perspective of household level users.

Sustainability is a present concern in many developing countries, where the role of the household is pivotal in realizing its benefits. This study aims to explore artificial intelligence in green energy technologies (AIGET) adoption intention among household-level respondents selected in the National Capital Region (NCR), Philippines. The study has 446 respondents and analyzed using partial least squares and structural equation modeling approaches (PLS-SEM). Among the factors tested, results revealed that perceived usefulness is the strongest predictor of AIGET adoption intention. Factors such as usefulness, ease of use, subjective norms, and perceived risk have a positive effect on attitude. This confirms that attitude has a positive impact on behavioral intention on AIGET. Finally, this study shows that household-level participants have a positive interest in adopting AIGET, considering its usefulness and ease of use. This study presents useful theoretical and practical contributions to further its uptake in the Philippines and other developing countries.

Modern advancements have the potential to help farmers maximize food production that will result to conservation of resources and profitability maximization. It is beneficial for farmers and to our local industry and economy because it approaches the issues regarding agricultural farming with the help of an up-and- coming field of studies. It would be a step towards food sustainability and conservation of resources and the environment. With this, the researchers came up with an idea of incorporating Artificial Intelligence (AI) through Deep-Learning Neural Network Technology to our food production. With the help of Raspberry Pi Microcomputers, we will develop an AI that will learn the parameters that are needed to control for optimal food production, and then implement the monitoring system through Swarm- based Drone Technology which will perform monitoring and crop maintenance autonomously. All operations shall be processed and deployed through Python Language.

Chloride-induced corrosion (CIC) is a primary reason of deterioration in reinforced concrete (RC), particularly in marine structures which causes cracking, degradation, and decreased service life. Advances in the 4th Industrial Revolution have enabled utilization of machine learning techniques in different fields of civil engineering. This study develops an Artificial Neural Network (ANN) enhanced by Particle Swarm Optimization (PSO) to predict rebar corrosion in polypropylene fiber reinforced concrete (PFRC). Accelerated corrosion tests were performed using the impressed current method on samples with varying polypropylene fiber content, concrete cover (CC), and bar diameter (BD). Experimental results showed that the 3-7-1 network structure (NS) (3 input neurons (IN), 7 hidden neurons (HN), 1 output neuron (ON)) achieved the highest accuracy with correlation coefficient (R) of 0.98969, mean squared error (MSE) of 0.18846, and mean absolute percentage error (MAPE) of 7.832 %. Employing the generated connection weights (CW) from the governing model (GM), through Olden's connection weights approach, observed that the concrete cover had the most significant influence on corrosion (-43.231%), followed by bar diameter (33.717%) and fiber content (-23.052%). It highlights that increasing concrete cover and fiber content significantly reduces corrosion in PFRC, which may be used by civil engineering professionals as it offers insights for enhancing the durability of reinforced concrete structures. This approach supports SDG 9 (Sustainable Development Goal 9: Industry, Innovation, and Infrastructure) by promoting resilient, innovative construction methods and contributes to SDG 11 (Sustainable Development Goal 11: Sustainable Cities and Communities) by enhancing the longevity and sustainability of urban infrastructure.

In recent years, the construction sector in the Philippines has faced significant challenges stemming from various events and occurrences, leading to cost overruns and delays in project timelines. A critical element for every construction undertaking's accomplishment is cost evaluation. Precisely approximating the cost of a project involves thorough consideration of various elements, making it a difficult undertaking to forecast. Several building constructions nowadays produce high cost overrun because of unforeseen change in the project budget that raises the overall project cost such as the complexity of the building system and the organization’s environment. The aim of this paper is to offer a potential prediction for cost estimation, with the goal of minimizing the substantial risk of cost overruns in low- to mid-rise buildings. In this study, the structural elements for low- to mid-rise buildings were utilized from building constructions, such as the number of exterior walls (QEW), type of construction material (TCM), building height (HB), total gross area (TGA), building footprint area (BFA), type of occupancy (TO), number of floors (NF), quantity of shear walls (QSW), and number of columns (NC); an artificial neural network (ANN) model was employed in this research to establish a model for forecasting the total construction cost (TCC). With a correlation value (R) of 0.999890 and a mean absolute percentage error (MAPE) of 0.601%, the modeling results shown that the best model structure was 9-25-1 (input-hidden-output), indicating its effectiveness and efficacy in forecasting the TCC. The impact of each variable employed as an input variable (IV) in the model establishment was seen employing the connection weights (CW) through Garson’s algorithm (GA). The calculation exhibited the order of influence observed as QSW > NC > HB > NF > QEW > TGA > BFA > TO > TCM, wherein the quantity of the shear walls is seen to have the most contribution to the construction cost. Moreover, to check its performance versus other prediction modeling tools, a multiple linear regression (MLR) model was also created and compared to the governing prediction model (GPM). The MAPE of the BP-NN is 7.108 times better than that of the created MLR model.

crucial component of growth in infrastructure is estimating construction costs (CC) for pipelaying projects (PP) related to water distribution networks, which guarantees the effective and long-term provision of safe drinking water to communities. In this paper, an artificial neural network (ANN) and multiple linear regression (MLR) model was developed for predicting construction cost for pipelaying projects. The governing model (GM) has a model structure of 9-20-1 (input-hidden-output) with an R = 0.99992. The findings revealed that the ANN-based network was 13.127 times better than the MLR model, based on its MAPE of 3.214 and 42.194%, for ANN and MLR, respectively. The best network also has the lowest Akaike Information Criterion (AIC) among the simulated network structures indicating that it is the best network. The relative importance (RI) of the independent variables including the length, diameter, material type, hydrotesting works, disinfection works, demolition works, restoration works, duration delay, and liquidated damages were calculated utilizing the Garson’s algorithm (GA). It was seen using GA to compute the relative importance of each parameter that the order of influence is seen as restoration works (RW) > length > demolition works (DeW) > material type (MT) > diameter > disinfection works (DiW) > hydrotesting works (HW) > duration delay (D%) > liquidated damages (LD) wherein the restoration works is the most influential parameter. The findings of the study could be used as a reference for better planning and managing pipelaying project activities.

Wind forces on structures have the potential to cause significant damage. A database involving the distance from the ridge, enclosure classification, surface type, elevation above ground level, wind direction, basic wind speed, presence of wall/surface openings, and effective net wind pressure (ENWP) was created using computation fluid dynamics (CFD). This paper focuses on the development of a model for predicting ENWP using a backpropagation-artificial neural network (BP-ANN). Utilizing the Levenberg-Marquardt algorithm (LMA) and hyperbolic tangent sigmoid function (HTSF) as the model hyperparameters, the study investigated several network structures and the simulations revealed that the 7-20-1 is the best model among the topologies observed in this study. The results showed an R value of 0.99868, MSE and MAPE of 0000749 and 5.036%, respectively. Additionally, the Akaike Information Criterion (AIC) was used as another layer of metric to measure the effectiveness of the model. The least was observed in the 7-20-1 network structure indicating that this is the best among the topologies observed in this study. Moreover, a sensitivity analysis (SA) through Garson's Algorithm (GA) was performed to determine the relative contribution (RC) of the input parameters (IP) including the distance from the ridge, enclosure classification, surface type, elevation above ground level, wind direction, basic wind speed, and presence of wall/surface opening to the effective net wind pressure. The findings presented that the basic wind speed is the most significant parameter to the effective net wind pressure value. The results of this study can be utilized in considering appropriate configuration to minimize the effects of wind pressure in structures.

Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundaries, and the development of new collaborative and economic models. The paper posits that the integration of generative AI in art is not just a technological advancement but a significant cultural shift, which necessitates a reevaluation of our understanding of art and the artist. It concludes with a forward-looking perspective, advocating for a collaborative approach to harness the potential of this technology in enriching human creativity and ensuring the vibrant evolution of the art world in the era of AI-driven generation.

As technology rapidly advances, the complexity of societal challenges grows, necessitating intelligent solutions that can adapt and evolve. However, developing such solutions requires a deep understanding of computational intelligence (CI) and its application in addressing real-world problems. Moreover, ethical considerations surrounding AI, such as bias and accountability, are crucial to ensure responsible development and deployment of intelligent systems. Open AI and Computational Intelligence for Society 5.0 offers a comprehensive exploration of CI, providing insights into intelligent systems' theory, design, and application. This book is a practical guide for scientists, engineers, and researchers seeking to develop thoughtful solutions for complex societal issues. Integrating disruptive technologies and frameworks illuminates the path toward creating intelligent machines collaborating with humans to enhance problem-solving and improve quality of life.