Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into the decision-making process of AI systems. In recent years, most efforts were made to build XAI algorithms that are a...
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ISBN:
(纸本)9783031087578;9783031087561
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into the decision-making process of AI systems. In recent years, most efforts were made to build XAI algorithms that are able to explain black-box models. However, in many cases, including medical and industrial applications, the explanation of a decision may be worth equally or even more than the decision itself. This imposes a question about the quality of explanations. In this work, we aim at investigating how the explanations derived from black-box models combined with XAI algorithms differ from those obtained from inherently interpretable glass-box models. We also aim at answering the question whether there are justified cases to use less accurate glass-box models instead of complex black-box approaches. We perform our study on publicly available datasets.
This study addresses the detection of DoS (Denial of Service) attacks in WSNs (Wireless Sensor Networks), which are constrained by limited resources and node vulnerabilities. The primary goal is to improve the precisi...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
This study addresses the detection of DoS (Denial of Service) attacks in WSNs (Wireless Sensor Networks), which are constrained by limited resources and node vulnerabilities. The primary goal is to improve the precision and reliability of IDSs through advanced machine learning techniques. Firstly, data preprocessing involved handling missing values, applying feature scaling (standardization and min-max normalization), encoding labels, and balancing the dataset using the SMOTE (Synthetic Minority Over-sampling Technique). Secondly, feature selection was conducted using Recursive Feature Elimination (RFE) to ensure high-quality input data. The study applies a range of machine learning algorithms, including MLP Classifier, Decision Tree, K-Nearest Neighbors, Gaussian Naive Bayes, SGD Classifier, Logistic Regression, Random Forest, and Voting Classifier, to assess their performance for the classification task. WSN-DS and WUSTL-EHMS-2020datasets are used to evaluate effective intrusion detection systems (IDSs) for WSNs. Results showed that the Ensembles Learning methods achieved the highest accuracy, while tree-based models were particularly effective in detecting DoS attacks. These findings highlight the need for further research on real-time and hybrid detection strategies to enhance IDS performance in WSNs.
Recent advancements in distributed data storage, colloborative processing capabilities, and their architectural design have a profound influence on our daily lives, simplifying the process of hosting computation-inten...
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Existing supervised methods for error detection require access to clean labels in order to train the classification models. This is difficult to achieve in practical scenarios. While the majority of the error detectio...
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Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is c...
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ISBN:
(纸本)9783031349522;9783031349539
Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.
This study explores the significant impact of corporate financial information disclosure on investor decision-making and economic policy-making. Financial fraud may lead to information distortion and disrupt market or...
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ISBN:
(数字)9798331529246
ISBN:
(纸本)9798331529253
This study explores the significant impact of corporate financial information disclosure on investor decision-making and economic policy-making. Financial fraud may lead to information distortion and disrupt market order, therefore the identification of financial fraud has always been a research focus. Previous studies have mainly relied on financial and non-financial data disclosed by enterprises, while Internet information is more indicative in identifying financial fraud. However, using Internet data will face copyright problems, and crawler technology is not the optimal solution. Information disclosure and transaction costs also limit its economic feasibility. To address these issues, this article adopts privacy preserving machine learning technology, which avoids legal, technical, and economic barriers by generating model parameters instead of using raw data. Based on 16112 samples from 2012 to 2020, this paper collects financial, non-financial and Internet information, and constructs three models: Model 1 is only based on financial and non-financial data, Model 2 adds Internet information on this basis, and Model 3 combines two privacy protection algorithms - SecureBoost and vertical neural network. The experimental results show that Model 2 improves accuracy by 7% to 10% compared to Model 1, while Model 3 further optimizes model performance while ensuring data privacy. This paper theoretically and empirically verifies the necessity of introducing Internet information, and the application potential of privacy protection machine learning technology in financial fraud detection.
BACKGROUND: Disability, especially in children, is a very important and current problem. Lack of proper diagnosis and care increases the difficulty for children to adapt to disabilities. Disabled children have many pr...
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BACKGROUND: Disability, especially in children, is a very important and current problem. Lack of proper diagnosis and care increases the difficulty for children to adapt to disabilities. Disabled children have many problems with basic activities of daily living. Therefore, it is very important to support diagnosticians and physiotherapists in recognizing self-care problems in children. OBJECTIVE: The aim of this paper is to extract classification and action rules, useful for those who work with children with disabilities. METHODS: First, features and their impact on the accuracy of classification are determined. Then, two models are built: one with all features and one with selected ones. For these models the classification rules are extracted. Finally, action rules are mined and the next step in treatment process is predicted. RESULTS: Seventeen features with the greatest impact on classifying a child into a particular group of self-care problems were identified. Based on the implemented algorithms, decision and action rules were obtained. CONCLUSIONS: The obtained model, selected attributes and extracted classification and action rules can support the work of therapists and direct their work to those areas of disability where even a minimal reduction of features would be of great benefit to the children.
With the development of computer software and hardware system, machine learning methods are more and more used in various industries of social development. In the aspect of stock index prediction, the current predicti...
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Large Language models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Their profound capabilities in processing and interpreting complex ...
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A significant number of recent advancements in Deep Learning have significantly benefited from training sets that are both larger and more diversified. Nevertheless, the collection of huge datasets for medical imaging...
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ISBN:
(数字)9798350379945
ISBN:
(纸本)9798350379952
A significant number of recent advancements in Deep Learning have significantly benefited from training sets that are both larger and more diversified. Nevertheless, the collection of huge datasets for medical imaging continues to be a challenge due to issues around privacy and the expenses associated with labelling. Through the use of data augmentation, it is feasible to significantly increase the quantity and variety of data that is accessible for training purposes without actually collecting additional samples. data augmentation techniques span from straightforward changes like cropping, padding, and flipping to more complicated generative models. These transformations are surprisingly powerful despite their apparent simplicity. Different data augmentation procedures are likely to function differently depending on the nature of the input and the visual task that is being performed. As a result of this, it is probable that medical imaging calls for particular augmentation algorithms that are capable of producing believable data samples and enabling the successful regularization of deep neural networks. This paper reviews different data augmentation techniques.
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