| 1. |
- Azhari, Faris, et al.
(författare)
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Deep Learning Implementations in Mining Applications : a compact critical review
- 2023
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Ingår i: Artificial Intelligence Review. - : Springer Netherlands. - 0269-2821 .- 1573-7462. ; 56:12, s. 36
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.
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| 2. |
- Casas-Roma, Jordi, et al.
(författare)
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A survey of graph-modification techniques for privacy-preserving on networks
- 2017
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Ingår i: Artificial Intelligence Review. - : Springer. - 0269-2821 .- 1573-7462. ; 47:3, s. 341-366
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Tidskriftsartikel (refereegranskat)abstract
- Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users’ privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph’s structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction.
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| 3. |
- Farisco, Michele, et al.
(författare)
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A method for the ethical analysis of brain-inspired AI
- 2024
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Ingår i: Artificial Intelligence Review. - : Springer. - 0269-2821 .- 1573-7462. ; 57:6
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Tidskriftsartikel (refereegranskat)abstract
- Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to the underlying theoretical models, such as symbolic vs.connectionist), and operational (e.g., related to robustness and ability to generalize). Biologically inspired AI, and more specifically brain-inspired AI, promises to provide further biological aspects beyond those that are already traditionally included in AI, making it possible to assess and possibly overcome some of its present shortcomings. This article examines some conceptual, technical, and ethical issues raised by the development and use of brain-inspired AI. Against this background, the paper asks whether there is anything ethically unique about brain-inspired AI. The aim of the paper is to introduce a method that has a heuristic nature and that can be applied to identify and address the ethical issues arising from brain-inspired AI (and from AI more generally). The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
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| 4. |
- Fernandes, Sofia, et al.
(författare)
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Tensor decomposition for analysing time-evolving social networks : an overview
- 2021
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Ingår i: Artificial Intelligence Review. - Dordrecht : Springer Netherlands. - 0269-2821 .- 1573-7462. ; 54:5, s. 2891-2916
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Tidskriftsartikel (refereegranskat)abstract
- Social networks are becoming larger and more complex as new ways of collecting social interaction data arise (namely from online social networks, mobile devices sensors, ...). These networks are often large-scale and of high dimensionality. Therefore, dealing with such networks became a challenging task. An intuitive way to deal with this complexity is to resort to tensors. In this context, the application of tensor decomposition has proven its usefulness in modelling and mining these networks: it has not only been applied for exploratory analysis (thus allowing the discovery of interaction patterns), but also for more demanding and elaborated tasks such as community detection and link prediction. In this work, we provide an overview of the methods based on tensor decomposition for the purpose of analysing time-evolving social networks from various perspectives: from community detection, link prediction and anomaly/event detection to network summarization and visualization. In more detail, we discuss the ideas exploited to carry out each social network analysis task as well as its limitations in order to give a complete coverage of the topic. © 2020, Springer Nature B.V.
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| 5. |
- Flener, Pierre, et al.
(författare)
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An introduction to inductive programming
- 2008
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Ingår i: Artificial Intelligence Review. - : Springer Science and Business Media LLC. - 0269-2821 .- 1573-7462. ; 29:1, s. 45-62
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Tidskriftsartikel (refereegranskat)
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| 6. |
- Gerken, Jan, 1991, et al.
(författare)
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Geometric deep learning and equivariant neural networks
- 2023
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Ingår i: Artificial Intelligence Review. - : Springer Nature. - 1573-7462 .- 0269-2821. ; 56:12, s. 14605-14662
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Tidskriftsartikel (refereegranskat)abstract
- We survey the mathematical foundations of geometric deep learning, focusing on group equivariant and gauge equivariant neural networks. We develop gauge equivariant convolutional neural networks on arbitrary manifolds M using principal bundles with structure group K and equivariant maps between sections of associated vector bundles. We also discuss group equivariant neural networks for homogeneous spaces M= G/ K , which are instead equivariant with respect to the global symmetry G on M . Group equivariant layers can be interpreted as intertwiners between induced representations of G, and we show their relation to gauge equivariant convolutional layers. We analyze several applications of this formalism, including semantic segmentation and object detection networks. We also discuss the case of spherical networks in great detail, corresponding to the case M= S2= SO (3) / SO (2) . Here we emphasize the use of Fourier analysis involving Wigner matrices, spherical harmonics and Clebsch–Gordan coefficients for G= SO (3) , illustrating the power of representation theory for deep learning.
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| 7. |
- Jia, Heming, et al.
(författare)
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Modified crayfish optimization algorithm for solving multiple engineering application problems
- 2024
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Ingår i: Artificial Intelligence Review. - : SPRINGER. - 0269-2821 .- 1573-7462. ; 57:5
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Tidskriftsartikel (refereegranskat)abstract
- Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish, MCOA proposes an environmental renewal mechanism that uses water quality factors to guide crayfish to seek a better environment. In addition, integrating a learning strategy based on ghost antagonism into MCOA enhances its ability to evade local optimality. To evaluate the performance of MCOA, tests were performed using the IEEE CEC2020 benchmark function and experiments were conducted using four constraint engineering problems and feature selection problems. For constrained engineering problems, MCOA is improved by 11.16%, 1.46%, 0.08% and 0.24%, respectively, compared with COA. For feature selection problems, the average fitness value and accuracy are improved by 55.23% and 10.85%, respectively. MCOA shows better optimization performance in solving complex spatial and practical application problems. The combination of the environment updating mechanism and the learning strategy based on ghost antagonism significantly improves the performance of MCOA. This discovery has important implications for the development of the field of optimization.
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| 8. |
- Jiang, Wei, et al.
(författare)
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Modeling, reasoning, and application of fuzzy Petri net model : a survey
- 2022
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Ingår i: Artificial Intelligence Review. - : Springer. - 0269-2821 .- 1573-7462. ; 55, s. 6567-6605
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Tidskriftsartikel (refereegranskat)abstract
- A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the 'state-explosion problem' are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions.
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| 9. |
- Meyers, Charles, et al.
(författare)
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Safety-critical computer vision : an empirical survey of adversarial evasion attacks and defenses on computer vision systems
- 2023
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Ingår i: Artificial Intelligence Review. - : Elsevier. - 0269-2821 .- 1573-7462. ; 56, s. 217-251
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Tidskriftsartikel (refereegranskat)abstract
- Considering the growing prominence of production-level AI and the threat of adversarial attacks that can poison a machine learning model against a certain label, evade classification, or reveal sensitive data about the model and training data to an attacker, adversaries pose fundamental problems to machine learning systems. Furthermore, much research has focused on the inverse relationship between robustness and accuracy, raising problems for real-time and safety-critical systems particularly since they are governed by legal constraints in which software changes must be explainable and every change must be thoroughly tested. While many defenses have been proposed, they are often computationally expensive and tend to reduce model accuracy. We have therefore conducted a large survey of attacks and defenses and present a simple and practical framework for analyzing any machine-learning system from a safety-critical perspective using adversarial noise to find the upper bound of the failure rate. Using this method, we conclude that all tested configurations of the ResNet architecture fail to meet any reasonable definition of ‘safety-critical’ when tested on even small-scale benchmark data. We examine state of the art defenses and attacks against computer vision systems with a focus on safety-critical applications in autonomous driving, industrial control, and healthcare. By testing a combination of attacks and defenses, their efficacy, and their run-time requirements, we provide substantial empirical evidence that modern neural networks consistently fail to meet established safety-critical standards by a wide margin.
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| 10. |
- Mumtaz, Nadia, et al.
(författare)
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An overview of violence detection techniques : current challenges and future directions
- 2023
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Ingår i: Artificial Intelligence Review. - Dordrecht : Springer Nature. - 0269-2821 .- 1573-7462. ; 56, s. 4641-4666
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Tidskriftsartikel (refereegranskat)abstract
- The Big Video Data generated in today’s smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis adifcult task in terms of computation and preciseness. Violence detection (VD), broadly plunging under action and activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally basedon manually engineered features, though advancements to deep learning based standalone models are developed for real-time VD analysis. This paper focuses on overview of deepsequence learning approaches along with localization strategies of the detected violence.This overview also dives into the initial image processing and machine learning-based VD literature and their possible advantages such as efciency against the current complex models. Furthermore,the datasets are discussed, to provide an analysis of the current models, explaining their pros and cons with future directions in VD domain derived from anin-depth analysis of the previous methods. © The Author(s), under exclusive licence to Springer Nature B.V. 2022.
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