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A review of recent artificial intelligence for traditional medicine

Journal of Traditional and Complementary Medicine 2025年第3期15卷 215-228页

作者： Hou, Chengbin Gao, Yanzhuo Lin, Xinyu Wu, Jinchao Li, Ning Lv, Hairong Chu, William Cheng-Chung School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fujian 350109 China Maynooth International Engineering College Fuzhou University Fuzhou 350108 China MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China

Traditional Medicine (TM) has played a crucial role in global healthcare due to its long history and holistic approach. Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such as data mining, pattern recognition, and decision-making. The integration of Artificial Intelligence for Traditional Medicine (AITM) presents a promising frontier in advancing medicine and healthcare. In this review, we explore AITM from two perspectives: recent AI techniques and TM applications. Specifically, we investigate how machine learning, Deep learning, and Large Language Models are applied to TM, covering applications such as diagnosis (before, during, after) and research (drug research, structured knowledge, data analysis). By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. We anticipate this review can bridge the gap between AI and TM communities. And the goal is to foster collaboration and innovation between both communities, enabling them to exploit the state-of-the-art AI techniques to advance TM diagnosis and research, ultimately contributing to the enhancement of human health. © 2025 Center for Food and Biomolecules, National Taiwan University

关键词： AI for health AI for medicine Artificial intelligence Deep learning Large language models machine learning Traditional medicine

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Design and Implementation of a machine learning Model for Soccer Match Prediction Based on Player Statistics 17th

Design and Implementation of a Machine Learning Model for So...

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

作者： Barrientos, Antonio Muñoz Bautista, Humberto Muñoz Díaz, Alejandro Padilla Rodríguez, Francisco Javier Álvarez Universidad Autónoma de Aguascalientes AGS Aguascalientes20100 Mexico

ISBN: (纸本)9783031838781

Football generates vast amounts of data daily on players, teams, and leagues, used for performance analysis. However, advanced analysis technologies are often inaccessible to lower-budget teams. This study developed a predictive tool to estimate the number of goals a team might score in a match, focusing on the UEFA Champions League with data from 2005 to 2022, totaling 4237 records. The model development included three stages: database creation, data preprocessing and analysis, and model construction. data from the Fbref and FIFA Index, including player data from the EA FC video game, were collected using web scraping to build an expandable database. Additional variables, such as team origin league and momentum (average goals in the last five matches), were added to improve model accuracy. The model, a deep fully connected neural network evolved from a multi-layer perceptron, achieved 57% accuracy. The findings suggest that effective analysis tools can be created using data mining and machine learning, supporting lower-budget teams by providing more information and reducing competitive disparities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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An Adaptive CGAN-Enhanced Federated learning Model for Intrusion Detection in IoT 24

An Adaptive CGAN-Enhanced Federated Learning Model for Intru...

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2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Shen, Hao Cao, Yang Hai, Jingwei Yang, Yongli Guo, Jiaran Zhang, Shiqiang The School of Information Beijing Wuzi University Beijing China

ISBN: (纸本)9798400711237

In traditional IoT intrusion detection systems, data is typically stored and processed centrally, which can lead to privacy breaches and data security issues. In contrast, in conventional distributed IoT intrusion detection systems, the local traffic data and label distribution across different IoT devices are often imbalanced, which can result in suboptimal model training and, consequently, lower detection accuracy. To address the issue, our paper proposes an Adaptive Federated Intrusion Detection System based on Generative Adversarial Networks (AFCI). By combining Federated learning (FL) with Intrusion Detection Systems (IDS), the proposed approach effectively solves the privacy concerns associated with device data. Additionally, Generative Adversarial Networks (GANs) excel at data augmentation, which helps mitigate the imbalance in local data across IoT devices. Extensive experiments conducted on NSL-KDD assess the AFCI model, and the results demonstrate that it outperforms existing federated intrusion detection systems in terms of performance. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Enhancing Education Research: The Potential and Challenges of Incorporating AI into Qualitative data Analysis 14th

Enhancing Education Research: The Potential and Challenges o...

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14th international conference on Methodologies and Intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Costa, António Pedro Bryda, Grzegorz Research Centre on Didactics and Technology in the Education of Trainers Department of Education and Psychology University of Aveiro Aveiro3810-193 Portugal IInstitute of Sociology CAQDAS TM Lab Jagiellonian University Grodzka 52 Kraków30-962 Poland

ISBN: (纸本)9783031855603

This reflection examines the integration of artificial intelligence (AI) in computer-assisted qualitative data analysis (CAQDAS) in education research. It highlights the benefits, challenges, and ethical implications of AI, natural language processing (NLP), and machine learning technologies. The paper discusses the reliability, validity, and potential loss of context in analysis. It calls for a balanced approach, balancing technological advancements with the nuanced nature of qualitative analysis. It emphasises the importance of ongoing dialogue, ethical protocols, and comprehensive training to ensure the mindful use of AI technologies in qualitative research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Research of Aspects of Omnicanal Approach in the Industry of Digital learning Technologies of Organizations

Research of Aspects of Omnicanal Approach in the Industry of...

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international conference on Innovative Approaches to the Application of Digital Technologies in Education and Research, SLET 2022

作者： Olifirov, Alexander Makoveichuk, Krystina Petrenko, Sergei V.I. Vernadsky Crimean Federal University Simferopol Russia Financial University Under the Government of the Russian Federation Moscow Russia Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031787751

The article proposes an approach to education with omnichannel aspects that will help to achieve success. Based on the study of international experience of leading universities and the experience gained in the process of training and retraining of bachelors and masters in the field of economics, finance and information technology in leading Russian universities, the omnichannel revolution in higher education in the conditions of digital transformation is investigated. The stages of omnichannel strategy of higher education organization development in the conditions of digital transformation are defined. Aspects of the omnichannel approach in the digital learning technology industry are investigated using data from university websites. The first aspect is defined as promoting the use of integrated channels in education. The second aspect is declared as the need to apply tools and technologies to support omnichannel educational activities. The third aspect is specified as the possibility of measuring the omnichannel learning trajectory, collecting online statistics by an educational organization. The following directions of further implementation of omnichannel educational activity are identified: creation of an online master's degree program, in which all courses are read in MOOC format, and the use of machine learning for mining and analysis of data generated by programs based on digital learning technologies. It is shown that the proposed omnichannel approach can be applied to any organisation operating in the market of educational services. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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9th international Workshop on machine learning and data Mining for Sports Analytics, MLSA 2022, co-located with the 21st Joint European conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 202

9th International Workshop on Machine Learning and Data Mini...

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9th international Workshop on machine learning and data Mining for Sports Analytics, MLSA 2022, co-located with the 21st Joint European conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 202

ISBN: (纸本)9783031275265

The proceedings contain 9 papers. The special focus in this conference is on machine learning and data Mining for Sports Analytics. The topics include: Analyzing Passing Sequences for the Prediction of Goal-Scoring Opportunities;let’s Penetrate the Defense: A machine learning Model for Prediction and Valuation of Penetrative Passes;evaluation of Creating Scoring Opportunities for Teammates in Soccer via Trajectory Prediction;Cost-Efficient and Bias-Robust Sports Player Tracking by Integrating GPS and Video;predicting Tennis Serve Directions with machine learning;discovering and Visualizing Tactics in a Table Tennis Game Based on Subgroup Discovery;athlete Monitoring in Professional Road Cycling Using Similarity Search on Time Series data.

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A Divide-and-Conquer Approach for Container License Plate Detection Using Multi-frame Analysis 25th

A Divide-and-Conquer Approach for Container License Plate De...

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25th international conference on Intelligent data engineering and Automated learning

作者： Geirinhas, Joao Pedro Estima, Jacinto Silva, Catarina Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9783031777370;9783031777387

The surge in global trade necessitates enhanced efficiency in logistics operations, particularly in container management. Current manual identification methods for container license plates and labels are prone to errors, causing significant delays and increased emissions. This paper presents a divide-and-conquer approach for detecting container license plates, distinguishing between the identification of numbers and letters, and leveraging multiple video frames to improve detection performance. We designed a set of experiments to define which models are optimized for each task, namely we assessed the performance of models to identify the whole license plate and models specifically for numbers and letters. Results on a real dataset of shipping containers entering dry ports show that the divide-and-conquer approach surpasses the holistic approach. Additionally, experiments show that for different contrasting colors analyzing multiple frames can improve the identification performance.

关键词： Container logistics License plate recognition Deep learning Logistics supply chain Dry ports

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RPPFL: Robust and Privacy-Preserving Federated learning via Trusted Execution Environments

RPPFL: Robust and Privacy-Preserving Federated Learning via ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xiaolei Chen, Zhaoyu Chen, Guangpu Feng, Xinyu Shen, Qingni Wu, Zhonghai School of Computer Science Peking University China School of Software and Microelectronics Peking University China National Engineering Research Center for Software Engineering Peking University China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University China

ISBN: (纸本)9798350368741

Federated learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10×, with less than a 9% loss in model performance under poisoning attacks. © 2025 IEEE.

关键词： data Obfuscation Federated learning Privacy Preservation Robustness Trusted Execution Environments

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Optimize Deep Neural-Fuzzy System Algorithms for Regression Problems

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international JOURNAL OF FUZZY SYSTEMS 2025年 1-20页

作者： Huang, Yunhu Lin, Geng Chen, Dewang Zhao, Wendi Fu, Mingjian MinJiang Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350118 Peoples R China Fujian BeiDou Nav & Intelligent Transportat Collab Fuzhou 350118 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China

To enhance the performance of machine learning algorithms, overcome the curse of dimensionality, and maintain model interpretability, there are significant challenges that continue to confront fuzzy systems (FS). Mini-batch Gradient Descent (MBGD) is characterized by its fast convergence and strong generalization performance. However, its applications have been generally restricted to the low-dimensional problems with small datasets. In this paper, we propose a novel deep-learning-based prediction method. This method optimizes deep neural-fuzzy systems (ODNFS) by considering the essential correlations of external and internal factors. Specifically, the Maximal Information Coefficient (MIC) is used to sort features based on their significance and eliminate the least relevant ones, and then the uniform regularization is introduced, which enforces consistency in the average normalized activation levels across rules. An improved novel MBGD technique with DropRule and AdaBound (MBGD-RDA) is put forward to train deep fuzzy systems for the training of each sub-FS in a fashion of layer by layer. Experiments on several datasets show that the ODNFS can effectively balance the efficiency, accuracy, and stability within the system, which can be used for training datasets of any size. The proposed ODNFS outperforms MBGD-RDA and the state-of-the-art methods in terms of accuracy and generalization, with fewer parameters and rules.

关键词： Maximum information coefficient ANFIS Mini-batch gradient descent Deep neural-fuzzy system

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Optimal Scheduling for Integrated Energy System via Model-Based Reinforcement learning 24

Optimal Scheduling for Integrated Energy System via Model-Ba...

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2024 international conference on Artificial Intelligence of Things and Computing, AITC 2024

作者： Cheng, Kexin Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao Zhang, Huaqin School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

The Integrated Energy System (IES) integrates energy consumption, conversion, and storage to increase the utilization of renewable energy sources and fossil fuels. When using heuristic algorithms and mathematical optimization methods for optimal scheduling of IES, these methods often face challenges of high computational cost and insufficient model accuracy, for which Reinforcement learning (RL) offers a promising solution. However, most studies have used Model-Free Reinforcement learning (MFRL) for optimal scheduling, which is limited by problems of low sampling efficiency and long training time. Model-Based Reinforcement learning (MBRL) is an effective alternative to accelerate learning and facilitate RL applications. Therefore, this paper compares the performance gap between MFRL and MBRL in the optimal scheduling problem of IES to determine the superiority of MBRL. Experimental results show that the MBPO algorithm significantly improves sampling efficiency and learning speed at high prediction accuracy, outperforming the Soft Actor-Critic (SAC) algorithm. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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