Decision Support System Based on Artificial Intelligence, Gis and Remote Sensing for Sustainable Public and Judicial Management

Kouziokas, Georgios N.; Perakis, Κonstantinos

doi:10.14207/ejsd.2017.v6n3p397

Cited by 22 publications

(3 citation statements)

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“…AI can augment decision-making, BC can enhance information management and 3DP can allow on-site production and mitigate inevitable congestions in the supply chain (Rodríguez-Espíndola, Chowdhury, Beltagui, & Albores, 2020). As mentioned above studies have also looked, with good promise, into the feasibility of integrating AI and remote sensing technologies in order to map poverty (Jean et al, 2016;Rigol et al, 2016;Steele et al, 2017), but also to develop decision support systems that can facilitate the adoption of sustainable management strategies (Kouziokas & Perakis, 2017). In sum, it is not only how AI as a standalone technology can contribute to development, but also how it can be combined with other technologies to deliver tailored solutions to the needs, tasks and objectives of development agencies and actors.…”

Section: Risks and Challengesmentioning

confidence: 99%

AI for development: implications for theory and practice

Bjola¹

2021

Oxford Development Studies

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The arrival of AI technology promises to add a fascinating new chapter to development theory and practice. Current studies have made good progress in examining the potential contributions of AI to achieving sustainable development goals and addressing challenges in specific development areas (poverty, global health, human rights, environment etc.). However, four lessons stand out when considering the impact of future research on the AI/development nexus: learning how to access and combine data from multiple sources, how to master AI techniques to extract analytical insight, how to build socially impactful AI solutions, and how to apply AI to development in an ethically responsible fashion. This paper makes the argument that AI could radically transform development theory and practice by prompting a rethinking of how data and algorithms come together to generate insights into the way in which development challenges are identified, studied, and managed. KEYWORDSArtificial Intelligence; SDGs; machine learning; AI4SG "AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. The rocket engine is the learning algorithms, but the fuel is the huge amounts of data we can feed to these algorithms"

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Section: Risks and Challengesmentioning

confidence: 99%

AI for development: implications for theory and practice

Bjola¹

2021

Oxford Development Studies

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“…With the progress of the times, there are more and more application scenarios of edge computing. Kouziokas and Perakis proposed a collaboration-based industrial intelligent perception framework that realizes intelligent and efcient industrial production services [6]. Liang et al designed an intelligent perception system for battlefeld intelligence, which can accurately analyze the military requirements, combat operations and mission requirements of the joint battlefeld [7].…”

Section: Introductionmentioning

confidence: 99%

IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge Computing

Liu

Zhao

2023

Mobile Information Systems

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The research on Internet of Things (IoT) network and edge computing has been a research hotspot in both industry and academia in recent years, especially for the ambient intelligence and massive communication. As a typical form of IoT network and edge computing, the intelligent perception vocal music singing learning system has attracted the attention of researchers in education and academia. Piano teaching is an important course for music majors in higher education. Strengthening piano teaching can cultivate outstanding piano talents for the country and promote the development of music art. This paper applies IoT perception technology to piano teaching, constructs an intelligent piano teaching system, and uses edge computing algorithms to accurately deploy sensors into the system by exploiting the ambient intelligence and massive communication. The system includes data acquisition, data perception, data monitoring, and other modules, making piano teaching more humanized and intelligent. Experiments show that the research in this paper provides important guidance for the application of IoT networks and edge computing, especially for the ambient intelligence and massive communication.

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“…Semantic segmentation of high-resolution remote sensing image (RSI) can help to extract the land use and land cover (LULC) information [1]- [5], and has diverse applications, e.g., monitoring [6], [7], planning [8], [9], and…”

Section: Introductionmentioning

confidence: 99%

RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation

He¹,

Lu²,

Tang³

et al. 2022

IEEE Access

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For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-ofthe-art RSI semantic segmentation methods.

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Decision Support System Based on Artificial Intelligence, Gis and Remote Sensing for Sustainable Public and Judicial Management

Cited by 22 publications

References 0 publications

AI for development: implications for theory and practice

AI for development: implications for theory and practice

IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge Computing

RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation

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