Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-...
详细信息
Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment ...
详细信息
“TourChennai” is a customized Tourism Suggestion Android application designed to provide a seamless and user-friendly experience for exploring the city of Chennai. Developed using Android Studio, Java, and XML, the ...
详细信息
Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. There were specific tools to determine the risk of cardiovascular disease. A...
详细信息
Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and...
详细信息
Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa
Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** ...
详细信息
Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.
Effective student management is crucial for fostering productive learning environments. This study presents a hybrid framework integrating machine learning (ML) techniques with rough set theory to enhance student mana...
详细信息
Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digi...
详细信息
The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational im...
详细信息
The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments.
暂无评论