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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge 27th

Hierarchical Graph Neural Network for Patient Treatment Pref...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Li, Quan Chen, Lingwei Cai, Yong Wu, Dinghao Penn State Univ University Pk PA USA Wright State Univ Dayton OH USA Calif State Univ Monterey CA 93955 USA IQVIA Inc Wayne PA 19087 USA

ISBN: (纸本)9783031333798;9783031333804

The healthcare industry has a wealth of data that can be used by researchers and medical professionals to infer a patient's condition and intention to receive treatment using machine learning models. However, this line of research generally suffers from some limitations: (1) struggling to leverage structural interactions among patients;(2) attending to learn patient representations from electronic medical records (EMRs) but rarely considering supplementary contexts;and (3) overlooking EMR data imbalance issue. To address these limitations, in this paper, we propose a hierarchical graph neural network for patient treatment preference prediction. Doctors' information and their viewing activities are first integrated as external knowledge with EMRs to construct the hierarchical graph, where a dual message passing paradigm is then devised to perform intra- and inter-subgraph aggregation to enrich patient representations and advance label propagation. To mitigate patient data imbalance issue, a community detection method is further designed to better prediction. Our experimental results demonstrate the state-of-the-art performance on patient treatment preference prediction.

关键词： Hierarchical graph neural network Oncology treatments Preference prediction Healthcare Community detection

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Dynamic Signature Verification Using Selected Regions 21st

Dynamic Signature Verification Using Selected Regions

引用

21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Zalasinski, Marcin Duda, Piotr Lota, Stanislaw Cpalka, Krzysztof Univ Lower Silesia Wroclaw Poland Cz estochowa Univ Technol Dept Computat Intelligence Cz estochowa Poland

ISBN: (纸本)9783031234798;9783031234804

Identity verification takes into account biometric attributes. Behavioral ones are particularly important. One of them is a dynamic signature. An analysis of such type of a signature uses signals describing the signing process. In this paper, we consider the velocity signal and propose a new method for dividing the dynamic signature into groups. In this case, a group is a subset of consecutive discretization points corresponding to similar velocity values. We have also assumed here that the signature fragments characterized by the highest pen velocity are the most characteristic of each user, therefore we reject partitions related to medium and low velocity values. As a result, we individually create a unique set of partitions of different sizes for each user. We do not use skilled forgeries, which is an additional advantage of our approach. The proposed method has been tested using the BioSecure dynamic signature database. The obtained results have confirmed the effectiveness of the proposed approach.

关键词： Behavioral biometrics Identity verification Dynamic signature Signature partitioning Signature groups

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Employing Convolutional Neural Networks for Continual Learning 21st

Employing Convolutional Neural Networks for Continual Learni...

引用

21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Jasinski, Marcin Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783031234910;9783031234927

The main motivation for the presented research was to investigate the behavior of different convolutional neural network architectures in the analysis of non-stationary data streams. Learning a model on continuously incoming data is different from learning where a complete learning set is immediately available. However, streaming data is definitely closer to reality, as nowadays, most data needs to be analyzed as soon as it arrives (e.g., in the case of anti-fraud systems, cybersecurity, and analysis of images from on-board cameras and other sensors). Besides the vital aspect related to the limitations of computational and memory resources that the proposed algorithms must consider, one of the critical difficulties is the possibility of concept drift. This phenomenon means that the probabilistic characteristics of the considered task change, and this, in consequence, may lead to a significant decrease in classification accuracy. This paper pays special attention to models of convolutional neural networks based on probabilistic methods: Monte Carlo dropout and Bayesian convolutional neural networks. Of particular interest was the aspect related to the uncertainty of predictions returned by the model. Such a situation may occur mainly during the classification of drifting data streams. Under such conditions, the prediction system should be able to return information about the high uncertainty of predictions and the need to take action to update the model used. This paper aims to study the behavior of the network of the models mentioned above in the task of classification of non-stationary data streams and to determine the impact of the occurrence of a sudden drift on the accuracy and uncertainty of the predictions.

关键词： Continous learning Concept drift Bayesian convolutional neural network Deep learning

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Minimizing Cross Intersections in Graph Drawing via Linear Splines 10th

Minimizing Cross Intersections in Graph Drawing via Linear S...

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10th IAPR TC3 workshop on artificial Neural Networks for Pattern Recognition (ANNPR)

作者： Hussain, Rida Ghafoor Tiezzi, Matteo Ciravegna, Gabriele Gori, Marco Univ Florence Florence Italy Univ Siena Siena Italy Univ Cote Azur Nice France

ISBN: (纸本)9783031206498;9783031206504

The generation of aesthetically pleasing graph layouts is the main purpose of Graph Drawing techniques. Recent contributions delved into the usage of Gradient-descent (GD) based schemes to optimize differentiable loss functions, built to measure the graph layout adherence to given layout characteristics. However, some properties cannot be easily expressed via differentiable functions. In this direction, the recently proposed Graph Neural Drawer (GND) framework proposes to exploit the representational capability of neural models in order to be able to express differentiable losses, specifically for edge intersection, that can be subsequently optimized via GD. In this paper, we propose to improve graph layout readability leveraging linear splines. We exploit the principles behind GND and use a neural model both to identify crossing edges and to optimize their relative position. We split crossing edges introducing linear splines, and threat the control points as novel "fake" vertices that can be optimized via the underlying layout optimization process. We provide qualitative and quantitative analysis over multiple graphs and optimizing different aesthetic losses, that show how the proposed method is a viable solution.

关键词： Graph Drawing Gradient Descent Graph Neural Drawers Splines

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Learning Distinct Features Helps, Provably

Learning Distinct Features Helps, Provably

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Laakom, Firas Raitoharju, Jenni Iosifidis, Alexandros Gabbouj, Moncef Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland Finnish Environm Inst Programme Environm Informat Jyvaskyla Finland Aarhus Univ Dept Elect & Comp Engn Aarhus Denmark

ISBN: (纸本)9783031434143;9783031434150

We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average L-2-distance between the hidden-layer features and theoretically investigate how learning non-redundant distinct features affects the performance of the network. To do so, we derive novel generalization bounds depending on feature diversity based on Rademacher complexity for such networks. Our analysis proves that more distinct features at the network's units within the hidden layer lead to better generalization. We also show how to extend our results to deeper networks and different losses.

关键词： Neural Networks Generalization Theory Feature Diversity

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Therapist Perceptions of Automated Deception Detection in Mental Health Applications 4th

Therapist Perceptions of Automated Deception Detection in Me...

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4th International Conference on artificial intelligence in HCI (AI-HCI) Held as Part of the 25th International Conference on Human-Computer Interaction (HCII)

作者： King, Sayde L. Johnson, Najare Kosyluk, Kristin Neal, Tempestt Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Nova Southeastern Univ Coll Psychol Ft Lauderdale FL 33314 USA Univ S Florida Dept Mental Hlth Law & Policy Tampa FL 33620 USA

ISBN: (纸本)9783031358906;9783031358913

This paper discusses the results of a qualitative study which assessed the perceptions of mental health professionals (N = 15) on the use of artificial intelligence for deception detection in therapy sessions. Four themes emerged from coding analysis of the interview data, including Functional Components of the Computer Science Implementation, Perceptions of the Computer Science Implementation, Integration of the Computer Science Implementation, and Suggestions. These themes encompass feedback from practicing mental health professionals suggesting a potential use case for automated deception detection in mental health, albeit considerations for confidentiality, client autonomy, data access, and therapist-client trust.

关键词： qualitative analysis AI and mental health deception detection research

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Low-Resolution Sensing for Sim-to-Real Complex Terrain Robots 1

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24th Annual Conference on Towards Autonomous Robotic Systems (TAROS)

作者： Shepherd, Dexter R. Knight, James C. Univ Sussex Brighton England

ISBN: (数字)9783031433603

ISBN: (纸本)9783031433597;9783031433603

Here we employ evolutionary approaches to enable robotic agents to learn strategies for energy-efficient navigation through complex terrain, consisting of water and different heights. Simulated agents, equipped with a low-resolution depth sensor, must learn how to navigate between randomly chosen locations in a procedurally generated world, along a path which minimises energy usage. The solution that consistently emerged, was an agent that followed the contours of the map, resulting in near-optimal performance in little evolutionary time. Furthermore, initial experiments showed that neural networks trained in simulation were able to successfully predict the movements required to follow contours from real Kinect sensor data. This demonstrates that this approach is able to transfer from simulation to the real world (known as the 'reality gap') and suggests that the evolved strategies are robust to noise. We suggest that this robustness is due to the use of a low-resolution sensor.

关键词： Robotics low-resolution evolution

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Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction

Multivariate Time-Series Anomaly Detection with Temporal Sel...

引用

作者： Hojjati, Hadi Sadeghi, Mohammadreza Armanfard, Narges McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9783031434297;9783031434303

Failure prediction is key to ensuring the reliable operation of vehicles, especially for organizations that depend on a fleet of vehicles. However, traditional approaches often rely on rule-based or heuristic methods that may not be effective in detecting subtle anomalies, rare events, or in more modern vehicles containing a complex sensory network. This paper presents a novel approach to vehicle failure prediction, called mVSG-VFP, which employs self-supervised learning and graphbased techniques. The proposed method realizes the failure prediction task by exploring information hidden in the time-series data recorded through the sensors embedded in the vehicle. mVSG-VFP includes two main components: a graph-based autoencoder that learns representations of normal data while considering the relationship between different sensors and a self-supervised component that maps temporally-adjacent data to similar representations. We propose a novel approach to define the notion of adjacency in vehicle temporal data. To evaluate mVSG-VFP, we apply it to a dataset comprised of vehicle sensor recordings to identify the abnormal data samples that signal a potential future failure. We performed a flurry of experiments to verify the accuracy of our model and demonstrate it outperforms state-of-the-art models in this task. Overall, the method is robust and intuitive, making it a useful tool for real-world applications.

关键词： Self-Supervised Learning Failure Prediction Time-Series Anomaly Detection Graph Neural Networks Predictive Maintenance

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Emotion Detection from Text in Social Networks 15th

Emotion Detection from Text in Social Networks

引用

15th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Probierz, Barbara Kozak, Jan Juszczuk, Przemyslaw Univ Econ Katowice Dept Machine Learning 1 Maja 50 PL-40287 Katowice Poland Inst Innovat Technol EMAG Lukasiewicz Res Network Leopolda 31 PL-40189 Katowice Poland

ISBN: (纸本)9789819958337;9789819958344

Emotion detection from text in social networks is an important tool for monitoring and analyzing discussions on social networks. However, the difficulty of the problem related to emotional analysis from texts is caused by the lack of additional features such as facial expressions or tone of voice, and the use of colloquial language and various signs and symbols in the texts contributes to misinterpretation of the content of the statement. For this reason, this article proposes an approach to automatically detect emotions from real text data from social networking sites and classify them into appropriate emotion categories. The aim of the article is to develop a model for detecting and classifying emotions through the use of natural language processing techniques and machine learning algorithms. In addition, it was decided to analyze the impact of the number of types of emotions on the classification results. Our research was tested for six classifiers (CART, SVM, AdaBoost, Bagging, Random Forest and K-NN) and three word weighting measures (TF, TF-IDF and Binary). Four measures of classification quality were used to evaluate the classifiers, i.e. accuracy, precision, recall and F1-score. Two datasets from different social networking sites were used for the research - one is a collection of comments from a social networking site and the other is tweets from Twitter. The analysis of the results confirmed that the proposed solution allows detecting emotions from the actual content of tweets, and the results obtained are better for a smaller number of emotion categories.

关键词： emotion detection natural language processing classification machine learning social networks

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A New Rebinning Reconstruction Method for the Low Dose CT Scanners with Flying Focal Spot 22nd

A New Rebinning Reconstruction Method for the Low Dose CT Sc...

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Pluta, Piotr Cierniak, Robert Czestochowa Tech Univ Dept Intelligent Comp Syst Czestochowa Poland

ISBN: (纸本)9783031425073;9783031425080

This paper presents an original approach to the image reconstruction problem for spiral CT scanners where the multi-source and/or the Flying Focal Spot (FFS) technology is implemented. The geometry of those scanners causes problems for computed tomography systems based on traditional (FDK) reconstruction methods. Therefore, we propose an original rebinning strategy, where does not occur the problem of non-equiangular X-rays. It is possible to implement this approach in all three types of CT scanners (only Multi-Source, only Flying Focal Spot, mixed Multi-Source with Flying Focal Spot). This approach is divided into two blocks (rebinning and iterative reconstruction procedure). This method is based on statistical model-based iterative reconstruction (MBIR), where the reconstruction problem is formulated as a shift-invariant system (a continuous-to-continuous data model). Our method allows for reducing the X-ray dose absorbed by patients during examinations. The most significant feature of the proposed method is the possibility of parallel implementation using the various GPU graphic card - what we have done for the NVIDIA graphic card. This fact resulted in the acceleration of the calculation and the significantly shortened time for the first reconstructed image.

关键词： X-ray computed tomography multi source tomography flying focal spot statistical method image reconstruction from projections rebinning method

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