This paper presents an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three ma...
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The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML...
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The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.
The introduction of smart grids allows utility providers to collect detailed data about consumers, which can be utilized to enhance grid efficiency and reliability. However, this data collection also raises privacy co...
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This paper presents a cutting-edge data mining approach to investigate user trust in decentralized applications (dApps) on public blockchains. Our innovative data analysis methodology enables a comprehensive explorati...
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This paper introduces a novel residual-based model to identify households with Battery Electric Vehicles (EVs) under high Air Conditioning (AC) load. The considerable energy demands of AC units can obscure charging ev...
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Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered ...
Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.
A rapid technology change has significant opportunities for almost every industry, especially for healthcare. By using advanced network technologies and Internet of Medical Things (IoMT) devices, health professionals ...
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Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model compl...
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Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.
Federated Learning (FL) for household-level Short-Term Load Forecasting (STLF) has emerged as a solution to privacy concerns in smart grids, enabling clients to collaboratively train models without sharing their consu...
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Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved d...
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