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Annabel Whipp, Nicolas Malleson, Jonathan Ward and Alison Heppenstall
This paper will critically assess the utility of conventional and novel data sources for building fine-scale spatio-temporal estimates of the ambient population. It begins with a review of data sources employed in existing studies of the ambient populati...
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Mahmoud Elkhodr, Samiya Khan and Ergun Gide
In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring dat...
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Ismail Shahin, Ali Bou Nassif, Rameena Thomas and Shibani Hamsa
Modern developments in machine learning methodology have produced effective approaches to speech emotion recognition. The field of data mining is widely employed in numerous situations where it is possible to predict future outcomes by using the input se...
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Xue Yang, Jingkai Zhi, Wenjun Zhang, Sheng Xu and Xiangkun Meng
Arctic navigation faces numerous challenges, including uncertain ice conditions, rapid weather changes, limited communication capabilities, and lack of search and rescue infrastructure, all of which increase the risks involved. According to an Arctic Cou...
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Jianfei Wang and Wen Cao
In the era of big data, a significant volume of spatiotemporal data exists in a multiscale format, describing diverse phenomena in the objective world across different spatial and temporal scales. While existing methods focus on analyzing the features an...
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Gaurav Narkhede, Anil Hiwale, Bharat Tidke and Chetan Khadse
Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world?s u...
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Bowen Yang, Zunhao Liu, Zhi Cai, Dongze Li, Xing Su, Limin Guo and Zhiming Ding
In order to improve the effect of path planning in emergencies, the missing position imputation and velocity restoration in vehicle trajectory provide data support for emergency path planning and analysis. At present, there are many methods to fill in th...
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Wenxiang Zhou, Sangwei Lu, Jinquan Huang, Muxuan Pan and Zhongguang Chen
Accurate component maps, which can significantly affect the efficiency, reliability and availability of aero-engines, play a critical role in aero-engine performance simulation. Unfortunately, the information of component maps is insufficient, leading to...
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Behnam Mobaraki, Seyedmilad Komarizadehasl, Francisco Javier Castilla Pascual, José Antonio Lozano-Galant and Rocio Porras Soriano
Owing to the high energy consumption in the building sector, appraising the thermal performance of building envelopes is an increasing concern. Recently, a few in situ methodologies to diagnose the thermal parameters of buildings have been considered. Ho...
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Jibo Liu, Xiaoyu Liu, Xieyu Lv, Bo Wang and Xugang Lian
Addressing the problem that traditional methods cannot reliably monitor surface subsidence in coal mining, a novel method has been developed for monitoring subsidence in mining areas using time series unmanned aerial vehicle (UAV) photogrammetry in combi...
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Mahbubun Nahar, A. H. M. Kamal, Md Rafiul Hassan and Mohammad Ali Moni
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Shruti Bharadwaj, Rakesh Dubey, Md Iltaf Zafar, Saurabh Kr Tiwary, Rashid Aziz Faridi and Susham Biswas
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Minghai Luo, Sixian Qin, Bo Tan, Mingming Cai, Yufeng Yue and Qiangqiang Xiong
At the beginning of 2020, a suddenly appearing novel coronavirus (COVID-19) rapidly spread around the world. The outbreak of the COVID-19 pandemic in China occurred during the Spring Festival when a large number of migrants traveled between cities, which...
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Bhargav Prakash, Gautam Kumar Baboo and Veeky Baths
Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by tr...
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Mimi Mukherjee and Matloob Khushi
Real-world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these underrepresented instances. To solv...
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Manuel Lopez-Martin, Antonio Sanchez-Esguevillas, Luis Hernandez-Callejo, Juan Ignacio Arribas and Belen Carro
This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different cla...
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Kai Zhou, Yan Xie, Zhan Gao, Fang Miao and Lei Zhang
Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we ...
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Zhihui Hu, Tianrui Zhou, Mohd Tarmizi Osman, Xiaohe Li, Yongxin Jin and Rong Zhen
Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based o...
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Jeong-Seok Lee, Woo-Ju Son, Hyeong-Tak Lee and Ik-Soon Cho
A maritime route is used by sea transportation vessels to access the trading ports, and route design standards for the safety of maritime traffic have been established in various countries and organizations. However, no quantitative safety verification m...
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Omid Bazgir, Eric Walden, Brian Nutter and Sunanda Mitra
Developing tools for precise quantification of brain metabolites using magnetic resonance spectroscopy (MRS) is an active area of research with broad application in non-invasive neurodegenerative disease studies. The tools are mainly developed based on b...
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