Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 19 - 34
Опубликована: Янв. 1, 2025
Язык: Английский
Information, Год журнала: 2024, Номер 15(10), С. 596 - 596
Опубликована: Сен. 30, 2024
This paper presents a novel framework, artificial intelligence-enabled intelligent assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI natural language processing (NLP) techniques to create an interactive engaging platform. platform is engineered reduce cognitive load on learners by providing easy access information, facilitating knowledge assessment, delivering support tailored individual needs styles. AIIA’s capabilities include understanding responding student inquiries, generating quizzes flashcards, offering pathways. research findings have the potential significantly impact design, implementation, evaluation of AI-enabled virtual teaching assistants (VTAs) education, informing development innovative educational tools that can enhance outcomes, engagement, satisfaction. methodology, architecture, services, integration with management systems (LMSs) while discussing challenges, limitations, future directions
Язык: Английский
Процитировано
86
Computers and Education Artificial Intelligence, Год журнала: 2023, Номер 5, С. 100183 - 100183
Опубликована: Янв. 1, 2023
In recent years, advancements in artificial intelligence (AI) have led to the development of large language models like GPT-4, demonstrating potential applications various fields, including education. This study investigates feasibility and effectiveness using ChatGPT, a GPT-4 based model, achieving satisfactory performance on Fundamentals Engineering (FE) Environmental Exam. further shows significant improvement model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating utility modification as viable approach enhance AI educational contexts. Furthermore, findings reflect remarkable improvements mathematical capabilities across successive iterations ChatGPT models, showcasing their solving complex engineering problems. Our paper also explores future research directions, emphasizing importance addressing challenges education, enhancing accessibility inclusion for diverse student populations, developing AI-resistant maintain examination integrity. By evaluating context Exam, this contributes valuable insights into limitations settings. As continues evolve, these offer foundation responsible effective integration disciplines, ultimately optimizing learning experience improving outcomes.
Язык: Английский
Процитировано
61
International Journal of Educational Technology in Higher Education, Год журнала: 2023, Номер 20(1)
Опубликована: Июль 23, 2023
Abstract Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due insecurities or scheduling conflicts, which can lead missed opportunities for questions. To support self-paced learning encourage creative thinking skills, academic institutions must redefine their approach education by offering flexible educational pathways that recognize continuous this end, we developed an AI-augmented intelligent assistance framework based on powerful language model (i.e., GPT-3) automatically generates course-specific assistants regardless of discipline level. The virtual teaching assistant (TA) system, at the core our framework, serves as voice-enabled helper capable answering wide range questions, from curriculum logistics course policies. By providing with easy access information, TA help improve engagement reduce barriers At same time, it also logistical workload TAs, freeing up time focus other aspects supporting students. Its GPT-3-based knowledge discovery component generalized system architecture are presented accompanied methodical evaluation system’s accuracy performance.
Язык: Английский
Процитировано
44
Remote Sensing, Год журнала: 2024, Номер 16(4), С. 656 - 656
Опубликована: Фев. 10, 2024
Floods are among the most severe and impacting natural disasters. Their occurrence rate intensity have been significantly increasing worldwide in last years due to climate change urbanization, bringing unprecedented effects on human lives activities. Hence, providing a prompt response flooding events is of crucial relevance for humanitarian, social economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers great deal support facing flood mitigating their global scale. As opposed multi-spectral sensors, SAR important advantages, as it enables Earth’s surface imaging regardless weather sunlight illumination conditions. In decade, availability data, even at no cost, thanks efforts international national space agencies, has deeply stimulating research activities every Earth observation field, including mapping monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, applied, demonstrating superiority with respect traditional classification strategies. However, fair assessment performance reliability techniques key importance an efficient disasters and, hence, should be addressed carefully quantitative basis trough quality metrics high-quality reference data. To this end, recent development open datasets specifically covering related ground-truth can thorough objective validation well reproducibility results. Notwithstanding, SAR-based monitoring still suffers from limitations, especially vegetated urban areas, complex scattering mechanisms impair accurate extraction water regions. All such aspects, methodologies, datasets, strategies, challenges future perspectives described discussed.
Язык: Английский
Процитировано
28
International Journal of River Basin Management, Год журнала: 2024, Номер unknown, С. 1 - 18
Опубликована: Фев. 13, 2024
Given the growing climate variability, quantifying droughts has gained significant importance, particularly in agriculturally concentrated areas such as Iowa. This study presents a novel approach for evaluating risk of agricultural drought, which combines geospatial methods with fuzzy logic algorithm. The integrates diverse array meteorological, physical, and social factors, yielding more comprehensive nuanced understanding impacts drought. covered sector within Corn Belt region Iowa formulated maps illustrating vulnerability drought timeframe spanning from 2015 to 2021. illustrate progress analysis, fully representing spatial temporal dimensions uniqueness this is ascribed its methodological framework, thorough assessment prior research inform assignment weights parameters logic-based index. findings demonstrate notable increase proportion Iowa's land area classified at a'very high' risk, rising 0.66% 5.39% 2018. upward trend suggests an escalating susceptibility conditions. Mid-Iowa western portion state exhibited increased 'high' 'extremely threats during period. accuracy our was validated using Kappa coefficient 75%. indicator potential be utilized context mitigation program monitoring. Moreover, methodology can modified implementation geographical across globe.
Язык: Английский
Процитировано
11
Journal of Hydroinformatics, Год журнала: 2024, Номер 26(3), С. 589 - 607
Опубликована: Март 1, 2024
Abstract The significance of improving rainfall prediction methods has escalated due to climate change-induced flash floods and severe flooding. In this study, nowcasting been studied utilizing NASA Giovanni satellite-derived precipitation products the convolutional long short-term memory (ConvLSTM) approach. goal study is assess impact data augmentation on flood nowcasting. Due requirements deep learning-based methods, performed using eight different interpolation techniques. Spatial, temporal, spatio-temporal interpolated are used conduct a comparative analysis results obtained through rainfall. This research examines two catastrophic that transpired in Türkiye Marmara Region 2009 Central Black Sea 2021, which selected as focal case studies. regions prone frequent flooding, which, dense population, devastating consequences. Furthermore, these exhibit distinct topographical characteristics patterns, frontal systems them also dissimilar. nowcast for significant difference. Although significantly reduced error values by 59% one region, it did not yield same effectiveness other region.
Язык: Английский
Процитировано
9
Remote Sensing, Год журнала: 2024, Номер 16(16), С. 3032 - 3032
Опубликована: Авг. 18, 2024
Rapid urbanization and climate change exacerbate the urban heat island effect, increasing vulnerability of residents to extreme heat. Although many studies have assessed vulnerability, there is a significant lack standardized criteria references for selecting indicators, building models, validating those models. Many existing approaches do not adequately meet planning needs due insufficient spatial resolution, temporal coverage, accuracy. To address this gap, paper introduces U-HEAT framework, conceptual model analyzing vulnerability. The primary objective outline theoretical foundations potential applications U-HEAT, emphasizing its nature. This framework integrates machine learning (ML) with remote sensing (RS) identify at both long-term detailed levels. It combines retrospective forward-looking mapping continuous monitoring assessment, providing essential data developing comprehensive strategies. With active capacity, enables refinement evaluation policy impacts. presented in offers sustainable approach, aiming enhance practical analysis tools. highlights importance interdisciplinary research bolstering resilience stresses need ecosystems capable addressing complex challenges posed by increased study provides valuable insights researchers, administrators, planners effectively combat challenges.
Язык: Английский
Процитировано
9
Water Resources Management, Год журнала: 2025, Номер unknown
Опубликована: Янв. 11, 2025
Язык: Английский
Процитировано
1
Journal of Hydrology, Год журнала: 2024, Номер 633, С. 130988 - 130988
Опубликована: Март 1, 2024
Язык: Английский
Процитировано
7
International Journal of Digital Earth, Год журнала: 2023, Номер 16(1), С. 2522 - 2554
Опубликована: Июль 4, 2023
Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack available data in actual needs. In this paper, we propose high-resolution multi-source dataset area extraction: GF-FloodNet. GF-FloodNet contains 13388 samples from Gaofen-3 (GF-3) and Gaofen-2 (GF-2) images. We use multi-level sample selection interactive annotation strategy based active to construct it. Compare with other flood-related datasets, not only has spatial resolution up 1.5 m provides pixel-level labels, but also consists thoroughly validate evaluate the using several deep models, including quantitative analysis, qualitative validation large-scale real scenes. Experimental results reveal that significant advantages by It can support different models training extract areas. There should be potential optimal boundary model any dataset. The seems close 4824 provide at https://www.kaggle.com/datasets/pengliuair/gf-floodnet https://pan.baidu.com/s/11yx5ERsGkkfUQXPYn34KkQ?pwd=yh47.
Язык: Английский
Процитировано
15