相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Computer Science, Artificial Intelligence
Rafael Van Belle et al.
Summary: This paper proposes a network-based credit card fraud detection method called CATCHM, which leverages representation learning to improve fraud detection performance by avoiding manual feature engineering and explicitly considering the relational structure of transactions.
DECISION SUPPORT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Varada Vivek Khanna et al.
Summary: Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder characterized by hyperandrogenism, irregular menstruation, anovulation, and ovarian microcysts, leading to symptoms like obesity, acne, infertility, and hirsutism. This study utilizes AI and explainable machine learning tools to accurately predict PCOS in fertile patients, achieving a high accuracy rate of 98%. The aim is to assist medical professionals with automated screening and decision-making.
APPLIED SYSTEM INNOVATION
(2023)
Article
Computer Science, Cybernetics
Lina Ni et al.
Summary: This article proposes a credit card fraud detection model that incorporates a fraud feature-boosting mechanism and a spiral oversampling balancing technique. It improves data quality by excluding highly redundant and correlated features, and enhances the decision-making ability of each feature through a multifactor synchronous embedding mechanism. Additionally, it balances the ratio of legitimate to fraudulent transactions, improving the model's ability to distinguish between the two. Experimental results demonstrate that this method facilitates efficient credit card fraud detection and outperforms existing methods.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ebenezer Esenogho et al.
Summary: Recent advancements in electronic commerce and communication systems have led to an increase in credit card usage for both online and regular transactions. However, this has also resulted in a rise in fraudulent credit card transactions, causing significant financial losses for companies. To address this issue, the paper proposes an efficient approach using a neural network ensemble classifier and a hybrid data resampling method to detect credit card fraud. Experimental results show that the proposed method outperforms other algorithms, achieving a sensitivity and specificity of 0.996 and 0.998 respectively.
Article
Mathematics
Harsh Mankodiya et al.
Summary: A fall detection system is critical for the safety of older people. However, existing approaches have limitations. To address this, a new method using three sensors placed at different locations on the body is proposed, with data gathered for training purposes. The accuracy achieved by training five models and using a majority voting classifier is high, and an XAI technique called LIME is incorporated for improved interpretability.
Article
Computer Science, Information Systems
Deepti Saraswat et al.
Summary: Healthcare 5.0 is a transformative shift in the healthcare domain, leveraging digital wellness and patient-centric technologies. Explainable AI (EXAI) enhances the transparency and interpretability of traditional AI models, improving clinical practices and predictive analysis in healthcare.
Article
Computer Science, Information Systems
Fawaz Khaled Alarfaj et al.
Summary: The research focuses on credit card fraud detection, comparing and analyzing machine learning and deep learning algorithms. Through empirical analysis, it is found that deep learning algorithms have higher accuracy and are recommended to reduce fraud losses. Experiments were also conducted to minimize false alarm rate.
Article
Computer Science, Information Systems
Danilo Labanca et al.
Summary: This paper presents Amaretto, an active learning framework for money laundering detection that combines unsupervised and supervised learning techniques. Experimental evaluation shows that Amaretto outperforms state-of-the-art solutions by reducing money laundering risk and improving detection performance, while also reducing investigation costs.
Article
Computer Science, Information Systems
Wu Xiuguo et al.
Summary: This paper aims to develop an enhanced financial fraud detection system based on Chinese listed companies' annual reports. Deep learning models and text feature extraction methods are utilized, and the empirical results show significant performance improvement compared to traditional methods.
Article
Computer Science, Information Systems
Sahil Khatri et al.
Summary: The focus of Intelligent Transportation Systems is on low latency communication, smooth traffic flow, and road safety. Vehicular Ad hoc Network (VANET) has garnered attention from research communities, with Machine Learning algorithms being seen as effective in achieving VANET objectives. Future directions and challenges lie in applying these algorithms to address issues within VANET systems.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Computer Science, Cybernetics
Jipeng Cui et al.
Summary: Anomaly detection in online banking fraud detection faces challenges such as limited historical behavior data, heterogeneous attribute values, and underutilized label information. Our proposed ReMEMBeR model addresses these challenges by treating fraud detection as a pseudo-recommender system problem, utilizing collaborative filtering and embedding methods to improve performance.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Javad Forough et al.
Summary: The rapid development of e-commerce technologies in recent years has provided more choices for people, but also increased the risk of fraud. This paper proposes an ensemble model based on deep learning and artificial neural networks to detect fraudulent actions, showing its advantage in real-time performance.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Rajesh Gupta et al.
Summary: This paper introduces a secure UAV communication scheme based on the InterPlanetary File System and Blockchain, aiming to address data security and privacy issues, reduce data storage costs, and enhance network performance. It also outlines research challenges and future directions for further improvement of the proposed system.
IET COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Erico Tjoa et al.
Summary: Artificial intelligence and machine learning have shown remarkable performances in various fields, but the challenge of interpretability remains. The medical sector requires higher levels of interpretability to ensure the reliability of machine decisions, and a deeper understanding of the mechanisms behind machine algorithms is needed to advance medical practices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Rajesh Gupta et al.
Summary: The BATS system proposed in this paper is a remote surgery system based on blockchain and AI, which can classify diseases and transport medical supplies using drones in emergency situations, demonstrating better performance compared to traditional schemes.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Cybernetics
Ruihao Cao et al.
Summary: The article proposes a novel method combining two modules to learn hidden information at different levels of data for fraud detection, achieving effective results in experiments.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2021)
Article
Computer Science, Information Systems
Emmanuel Ileberi et al.
Summary: The research develops a machine learning framework for credit card fraud detection using real-world imbalanced datasets from European credit cardholders, addressing class imbalance with the Synthetic Minority over-sampling TEchnique (SMOTE). AdaBoost technique improves classification quality, with results showing superior performance compared to existing methods.
Article
Computer Science, Theory & Methods
Ibtissam Benchaji et al.
Summary: This paper introduces a novel credit card fraud detection system model based on sequential modeling and deep neural networks, which not only considers the sequential nature of transaction data but also identifies important transactions to improve accuracy. Experimental results show that the model is highly efficient and effective.
JOURNAL OF BIG DATA
(2021)
Proceedings Paper
Engineering, Multidisciplinary
E. M. S. W. Balagolla et al.
Summary: The article introduces a solution to reduce credit card fraud using blockchain technology and smart contracts. This solution can increase transaction security and transparency, while also reducing the amount of money lost by banks due to fraud.
2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT)
(2021)
Article
Engineering, Multidisciplinary
Shivani Bharatbhai Patel et al.
Summary: In this paper, a deep-learning based credit-recommender scheme called KiRTi is proposed for public blockchain to facilitate smart lending operations. It stores transaction data in blockchain, uses LSTM model to generate credit score for loan recommendations, and employs smart contracts for automatic loan repayments between borrowers and lenders. KiRTi achieves high accuracy and low computation and communication costs when compared to other approaches.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Review
Computer Science, Information Systems
Fernando G. D. C. Ferreira et al.
Summary: The application of Artificial Intelligence in financial investment has attracted extensive research attention since the 1990s, and the literature is increasingly becoming more specific and thorough. The reviewed papers on AI in investments in the stock market were categorized into four main areas, showing a development path from initial research to state-of-the-art applications over time.
Article
Computer Science, Information Systems
Weiwei Fang et al.
Summary: This paper explores the potential of applying deep neural networks for fraud detection in Internet finance, demonstrating through experiments the superiority of deep neural networks over traditional models, potentially bringing new opportunities for application in Internet loans.
Article
Computer Science, Cybernetics
Lutao Zheng et al.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2020)
Article
Computer Science, Information Systems
Huang Tingfei et al.
Article
Computer Science, Information Systems
Guangsheng Luo et al.
Article
Computer Science, Information Systems
Sara Makki et al.
Article
Computer Science, Information Systems
Changjun Jiang et al.
IEEE INTERNET OF THINGS JOURNAL
(2018)
Article
Computer Science, Information Systems
Dahee Choi et al.
SECURITY AND COMMUNICATION NETWORKS
(2018)
Article
Computer Science, Information Systems
Kai Fan et al.
TSINGHUA SCIENCE AND TECHNOLOGY
(2018)
