Scopus ID: 85190896154
Transformative Approaches to Patient Literacy and Healthcare Innovation, (2024), pp. 20-45
Book Chapter | Published: March 22, 2024
Abstract
Serious games offer an innovative blend of entertainment and learning in medical education. This chapter examines the technological underpinnings, challenges, and potential future developments in this domain. Drawing from publications between 2018 and 2023, this systematic review highlights the role of technologies such as web and mobile applications, game engines, augmented reality, virtual reality, mixed reality, and artificial intelligence in personalizing and enhancing the learning experience. However, the use of serious games in medical education also faces several challenges, including the need for adequate technological infrastructure, complex effectiveness assessments, and integration into existing curricula. Moreover, this chapter outlines projections for further research. The authors reveal how serious games have the potential to transform medical education to be more engaging, interactive, and effective, and inspire future research in the development of innovative technologies and learning methods.
Scopus ID: 85190896059
Transformative Approaches to Patient Literacy and Healthcare Innovation, (2024), pp. 242-260
Book Chapter | Published: March 22, 2024
Abstract
Assessing the preparedness of Ghana's health sector is a crucial task that requires a comprehensive and multi-faceted approach. Ghana's health sector faces many challenges, including limited resources, inadequate infrastructure, and workforce shortages, which can impede the delivery of quality healthcare services to the population. Thus, building a strong health resilience system is essential to cope with catastrophic events, and the capacity to prepare and effectively respond to pandemics. The COVID-19 pandemic has highlighted the critical role of digital technologies in managing public health emergencies. In the context of long-term pandemic management, digital transformation can provide numerous benefits, such as improving the speed and efficiency of response, enhancing communication and collaboration, and enabling remote access to essential services. Empirically, our study found that individual and systemic resilience are significant predictors of long-term pandemic management. Conversely, community resilience in times of crisis is not a significant predictor.
Scopus ID: 85188943410
DOI: 10.3390/info15030172
Information, (2024), Vol. 15, No. 3, pp. 1-20
Mamoon Riaz, Hammad Dilpazir, ... Tanveer Ahmad
Journal Article | Published: March 1, 2024
Abstract
In the past few decades, the transmission of data over an unsecure channel has resulted in an increased rate of hacking. The requirement to make multimedia data more secure is increasing day by day. Numerous algorithms have been developed to improve efficiency and robustness in the encryption process. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of taking less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using a substitution–permutation and one-time pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as keyspace analysis, complexity analysis, sensitivity analysis, strict avalanche criteria (SAC), histogram analysis, entropy analysis, mean of absolute deviation (MAD) analysis, correlation analysis, contrast analysis and homogeneity, to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.
Scopus ID: 85171791734
Expert Systems with Applications, (2024), Vol. 237, pp. 1-9
Journal Article | Published: March 1, 2024
Abstract
Carbonation depth involves complex physical process and interactions of multiple variables and is thus extremely complicated to predict in concrete structures. It is imperative to quantify this depth due to its vital role in the corrosion of rebars in recycled aggregate concrete (RAC). This paper developed a novel carbonation depth prediction model from a large database of 445 experimental results using artificial neural network (ANN). The relative importance and effect of the independent parameters in the carbonation depth are identified using Garson index and parametric analysis, respectively. Among all the architectures considered, the N 8-10-1 having 10 nodes in the hidden layer provided the best prediction in good agreement with experimental results. The model demonstrated superior performance relative to existing carbonation depth equations in the literature. Despite the presence of fuzziness in the data, the effect of each variable in the development of carbonation is explored in great detail. The model proposed here can provide a robust prediction of carbonation depth that can be used as a basis for assessing the structural health of recycled aggregate concrete structures.
Scopus ID: 85186088509
IEEE Transactions on Consumer Electronics, (2024), Vol. 70, No. 1, pp. 1672-1684
Tanveer Ahmad, Muhammad Usman, ... Essam A. Al-Ammar
Journal Article | Published: February 1, 2024
Abstract
Location information is the most crucial information used in context-aware applications, e-commerce and IoT-based consumer applications. Traditional methods doesn’t focus on network coverage, accuracy, hardware cost, and noise in dense environment. To defeat these issues, this paper presents a novel localization algorithm for UWB nodes adopting self-calibration and ToA measurement for context-aware applications. The Link quality induction values are used instead of RSSI for distance estimation by costing technique. A calibration factor (CF) is further introduce to automatically update the location information in mobility. As the signal strength can be distorted heavily due to shadowing and multi-path fading, the localization is estimated in noisy condition and extended Kalman filtering (EKF) is applied to refine the node coordinates. Simulation results shows that the positioning error is decreased with an overall accuracy of 0.23m and standard-deviation of 0.76m.
Scopus ID: 85163001229
Annals of Biomedical Engineering, (2024), Vol. 52, No. 2, pp. 139-140
Letter to the Editor | Published: February 1, 2024
Abstract
This letter highlights a pressing issue regarding the absence of established editorial policies for the utilization of AI tools (e.g., ChatGPT) in the peer review process. The increasing adoption of AI tools in academic publishing necessitates the formulation of standardized guidelines to ensure fairness, transparency, and accountability. Without clear editorial policies, there is a threat of compromising the integrity of the peer review process and undermining the credibility of academic publications. Urgent attention is needed to address this gap and establish robust protocols that govern the use of AI tools in peer review.
Scopus ID: 86000023442
2024 9th International Conference on Business and Industrial Research (ICBIR), (2024), pp. 0841-0846
Conference Paper | Published: January 1, 2024
Abstract
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.
Scopus ID: 86000010175
2024 9th International Conference on Business and Industrial Research (ICBIR), (2024), pp. 0823-0828
Conference Paper | Published: January 1, 2024
Abstract
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.
Scopus ID: 86000025454
2024 9th International Conference on Business and Industrial Research (ICBIR), (2024), pp. 0835-0840
Alexander A. Hernandez, Victor James C. Escolano, ... Darrel Cardaña
Conference Paper | Published: January 1, 2024
Abstract
Sustainability in transportation technologies is growing in all parts of the world through electric and micro-mobility sharing services. As such, there is a need to explore the factors that influence its adoption and use behavior. However, this is relatively underexamined in many developing countries. This study attempts to understand the intention and use behavior of micro-mobility as a service (MaaS) in the Philippines, a developing country. This study used survey data, and analysis was performed using partial least squares and structural equation modeling (PLS-SEM). Results show that performance expectancy is the strongest predictor of intention, while satisfaction is the least significant predictor. Factors such as social influence, price value, and habit have a positive effect on intention. Overall, the predictive model is explained by the coefficient of determination, revealing that behavior intention, satisfaction, and use behavior have large predictive relevance. This study provides theoretical and practical implications for further micro-mobility research in the future.
Scopus ID: 85194769387
Creativity Research Journal, (2024), pp. 1-14
Journal Article | Published: January 1, 2024
Abstract
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.