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- Carrasco Limeros, Sandra, et al.
(författare)
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Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
- 2024
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Ingår i: CAAI Transactions on Intelligence Technology. - : WILEY. - 2468-6557 .- 2468-2322. ; 9:3, s. 557-572
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Tidskriftsartikel (refereegranskat)abstract
- Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can be considerable (multi-modal). Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpretability. Moreover, the metrics used in current benchmarks do not evaluate all aspects of the problem, such as the diversity and admissibility of the output. The authors aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. The focus is on evaluation criteria, robustness, and interpretability of outputs. First, the evaluation metrics are comprehensively analysed, the main gaps of current benchmarks are identified, and a new holistic evaluation framework is proposed. Then, a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system. To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework, an intent prediction layer that can be attached to multi-modal motion prediction models is proposed. The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions. The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autonomous vehicles, advancing the field towards greater safety and reliability.
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| 2. |
- Ma, Liyao, et al.
(författare)
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Apple grading method based on neural network with ordered partitions and evidential ensemble learning
- 2022
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Ingår i: CAAI Transactions on Intelligence Technology. - : John Wiley & Sons. - 2468-6557 .- 2468-2322. ; 7:4, s. 561-569
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Tidskriftsartikel (refereegranskat)abstract
- In order to improve the performance of the automatic apple grading and sorting system, in this paper, an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed. As a non-destructive grading method, apples are graded into three grades based on the Soluble Solids Content value, with features extracted from the preprocessed near-infrared spectrum of apple serving as model inputs. Considering the uncertainty in grading labels, mass generation approach and evidential encoding scheme for ordinal label are proposed, with uncertainty handled within the framework of Dempster–Shafer theory. Constructing neural network with ordered partitions as the base learner, the learning procedure of the Bagging-based ensemble model is detailed. Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification. © 2022 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.
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| 3. |
- Mohammed, Mazin Abed, et al.
(författare)
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Adaptive secure malware efficient machine learning algorithm for healthcare data
- 2023
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Ingår i: CAAI Transactions on Intelligence Technology. - : Wiley-Blackwell. - 2468-2322 .- 2468-6557.
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Tidskriftsartikel (refereegranskat)abstract
- Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT-based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content-aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content-aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre- and post-train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes. © 2023 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.
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| 4. |
- Sun, Lilei, et al.
(författare)
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Two-view attention-guided convolutional neural network for mammographic image classification
- 2022
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Ingår i: CAAI Transactions on Intelligence Technology. - : Institution of Engineering and Technology (IET). - 2468-6557 .- 2468-2322.
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature extraction. However, general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take the specific traits of these images into account. To exploit the essential discriminant information of mammographic images, we propose a novel classification method based on a convolutional neural network. Specifically, the proposed method designs two branches to extract the discriminative features from mammographic images from the mediolateral oblique and craniocaudal (CC) mammographic views. The features extracted from the two-view mammographic images contain complementary information that enables breast cancer to be more easily distinguished. Moreover, the attention block is introduced to capture the channel-wise information by adjusting the weight of each feature map, which is beneficial to emphasising the important features of mammographic images. Furthermore, we add a penalty term based on the fuzzy cluster algorithm to the cross-entropy function, which improves the generalisation ability of the classification model by maximising the interclass distance and minimising the intraclass distance of the samples. The experimental results on The Digital database for Screening Mammography INbreast and MIAS mammography databases illustrate that the proposed method achieves the best classification performance and is more robust than the compared state-of-the-art classification methods.
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| 5. |
- Zheng, X., et al.
(författare)
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Short-time wind speed prediction based on Legendre multi-wavelet neural network
- 2023
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Ingår i: CAAI Transactions on Intelligence Technology. - : John Wiley & Sons. - 2468-6557 .- 2468-2322. ; 8:3, s. 946-962
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Tidskriftsartikel (refereegranskat)abstract
- As one of the most widespread renewable energy sources, wind energy is now an important part of the power system. Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation. However, due to the stochastic and uncertain nature of wind energy, more accurate forecasting is necessary for its more stable and safer utilisation. This paper proposes a Legendre multiwavelet-based neural network model for non-linear wind speed prediction. It combines the excellent properties of Legendre multi-wavelets with the self-learning capability of neural networks, which has rigorous mathematical theory support. It learns input-output data pairs and shares weights within divided subintervals, which can greatly reduce computing costs. We explore the effectiveness of Legendre multi-wavelets as an activation function. Meanwhile, it is successfully being applied to wind speed prediction. In addition, the application of Legendre multi-wavelet neural networks in a hybrid model in decomposition-reconstruction mode to wind speed prediction problems is also discussed. Numerical results on real data sets show that the proposed model is able to achieve optimal performance and high prediction accuracy. In particular, the model shows a more stable performance in multi-step prediction, illustrating its superiority.
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