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Yiheng Zhou, Kainan Ma, Qian Sun, Zhaoyuxuan Wang and Ming Liu
Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the sma...
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Thanda Shwe and Masayoshi Aritsugi
Intelligent applications in several areas increasingly rely on big data solutions to improve their efficiency, but the processing and management of big data incur high costs. Although cloud-computing-based big data management and processing offer a promi...
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Yushan Li and Satoshi Fujita
This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architec...
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Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disea...
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Xiaohui Yan, Tianqi Zhang, Wenying Du, Qingjia Meng, Xinghan Xu and Xiang Zhao
Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the a...
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Swati Kumari, Vatsal Tulshyan and Hitesh Tewari
Due to rising cyber threats, IoT devices? security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attac...
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Poornima Mahadevappa, Redhwan Al-amri, Gamal Alkawsi, Ammar Ahmed Alkahtani, Mohammed Fahad Alghenaim and Mohammed Alsamman
Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can ...
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Wenqi Lyu, Wei Ke, Hao Sheng, Xiao Ma and Huayun Zhang
In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved voxel...
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Sonia Díaz-Santos, Óscar Cigala-Álvarez, Ester Gonzalez-Sosa, Pino Caballero-Gil and Cándido Caballero-Gil
This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineate...
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Gulshan Saleem, Usama Ijaz Bajwa, Rana Hammad Raza and Fan Zhang
Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNe...
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