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Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine:a case study on resistant hypertension

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Journal of Traditional Chinese Medicine 2025年第1期45卷 192-200页

作者： CHE Qianzi LIU Dasheng XIANG Xinghua TIAN Yaxin XIE Feibiao XU Wenyuan LIU Jian WANG Xuejie WANG Liying BAI Weiguo HAN Xuejie YANG Wei Department of Medical Statistics Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Department of Science and Education Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Traditional Chinese Medicine Standards Research Center Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Computer Department Xiyuan Hospital of the China Academy of Chinese Medical SciencesBeijing 100091China

OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM ***:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)***,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of ***:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical ***:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.

关键词： machine learning human clinical experience personalized pharmacotherapy new drugs registration

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A survey on deep learning and machine learning techniques over histopathology image based Osteosarcoma Detection

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Multimedia Tools and Applications 2024年第16期84卷 1-24页

作者： Deepak, K.V. Bharanidharan, R. Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation Tamil Nadu Salem636308 India

Osteosarcoma is a common type of cancer that occurs in the cells and spreads to the bones. Osteosarcoma can develop due to genetic mutations, but most cases are not inherited. It often starts at the ends of long bones in the arms and legs during periods of rapid growth. Osteosarcoma can be diagnosed by Histopathological examination using microscopic images. In recent trends, pathological images are increasingly being examined by computer-intelligent approaches, also known as computational pathology. This field utilizes machine learning and deep learning algorithms to analyze digital pathology slides. Histopathology image based osteosarcoma detection is not the subject of any review papers. so, this study examines historical and current literature to present a concise review of Histopathology image based Osteosarcoma detection. This review discussed the types, clinical diagnosis, and modern and future treatment methods of Osteosarcoma. Also, a review of machine learning (ML) and deep learning (DL) approaches for histopathology image osteosarcoma detection is analyzed. sophisticated methods and the future scope of osteosarcoma detection have also been discussed. Comparative analysis and results of reviewed papers were also analyzed in this review study. This article aims to generate fresh concepts for creating more potent treatment alternatives. © The Author(s), under exclusive licence to springer science+Business Media, LLC, part of springer Nature 2024.

关键词： Pathology

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Applying Competition-based learning to stimulate students' Practical and Competitive AI Ability in a Machine learning Curriculum

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IEEE Transactions on Education 2024年第2期67卷 256-265页

作者： Chang, Hui-Tzu Lin, Chia-Yu National Yang Ming Chiao Tung University Center for Institutional Research and Data Analytics Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City320317 Taiwan

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students' AI practical and competitive abilities. © 1963-2012 IEEE.

关键词： Competition

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A Review of Relationship Extraction based on Deep learning 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Liao, Guolong Tang, Xiangyan Li, Tian Zhong, Li Zeng, Pengfan School of Computer Science and Technology Hainan University Haikou570228 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China Warrior Logistics 450 S Denton Tap Rd #2651 CoppellTX75019 United States

ISBN: (数字)9789819712779

ISBN: (纸本)9789819712762

Relation extraction is a key task in natural language processing. In recent years, deep learning techniques have been widely applied in relation extraction tasks. This paper systematically reviews relation extraction techniques based on deep learning, including the application of convolutional neural networks, recurrent neural networks and Transformer models. Firstly, it introduces the representative applications of these three models. Then, relation extraction methods based on the three deep learning models are comprehensively reviewed and compared. Finally, it discusses challenges faced by relation extraction tasks, including data sparsity, long-distance dependency, and outlooks new techniques like weakly supervised relation extraction. This paper provides a systematic overview of deep learning techniques for relation extraction, aiming to facilitate further research in this field. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

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computers, Materials & Continua 2023年第1期74卷 217-233页

作者： Mazen Mushabab Alqahtani Ashit Kumar Dutta sultan Almotairi M.Ilayaraja Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Physical Therapy Department Majmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhSaudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a1247Yemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Recent developments in digital cameras and electronic gadgets coupled with Machine learning(ML)and Deep learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection *** this background,the current paper devises an Effective sailfish Optimizer with EfficientNet-based Apple Leaf disease detection(EsFO-EALD)*** goal of the proposed EsFO-EALD technique is to identify the occurrence of plant leaf diseases *** this scenario,Median Filtering(MF)approach is utilized to boost the quality of apple plant leaf ***,sFO with Kapur’s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test ***,Adam optimizer with EfficientNet-based feature extraction and spiking Neural Network(sNN)-based classification are employed to detect and classify the apple plant leaf images.A wide range of simulations was conducted to ensure the effective outcomes of EsFO-EALD technique on benchmark *** results reported the supremacy of the proposed EsFO-EALD approach than the existing approaches.

关键词： Agriculture computer vision image processing deep learning metaheuristics image segmentation

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A novel attention based deep learning model for software defect prediction with bidirectional word embedding system

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soft Computing 2025年第4期29卷 2171-2188页

作者： Devi, M. Chitra Rajkumar, T. Dhiliphan Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil626126 India

software defect prediction (sDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. several artificial intelligence-based methods were available to predict these software defects. However, the detection accuracy is still low due to imbalanced datasets, poor feature learning, and tuning of the model's parameters. This paper proposes a novel attention-included Deep learning (DL) model for sDP with effective feature learning and dimensionality reduction mechanisms. The system mainly comprises ‘6’ phases: dataset balancing, source code parsing, word embedding, feature extraction, dimensionality reduction, and classification. First, dataset balancing was performed using the density peak based k-means clustering (DPKMC) algorithm, which prevents the model from having biased outcomes. Then, the system parses the source code into abstract syntax trees (AsTs) that capture the structure and relationship between different elements of the code to enable type checking and the representative nodes on AsTs are selected to form token vectors. Then, we use bidirectional encoder representations from transformers (BERT), which converts the token vectors into numerical vectors and extracts semantic features from the data. We then input the embedded vectors to multi-head attention incorporated bidirectional gated recurrent unit (MHBGRU) for contextual feature learning. After that, the dimensionality reduction is performed using kernel principal component analysis (KPCA), which transforms the higher dimensional data into lower dimensions and removes irrelevant features. Finally, the system used a deep, fully connected network-based softMax layer for defect prediction, in which the cross-entropy loss is utilized to minimize the prediction loss. The experiments on the National Aeronautics and space Administration (NAsA) and AEEEM show that the system achieves better outcomes than the existing state-of-the-art models f

关键词： Dimensionality reduction

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Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation

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soft Computing 2024年第20期28卷 11873-11894页

作者： Rezvani, salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan Department of Computer Science Ryerson University TorontoON Canada Department of Electrical and Computer Engineering University of Windsor WindsorON Canada Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC Australia

This paper presents a review on methods for class-imbalanced learning with the support Vector Machine (sVM) and its variants. We first explain the structure of sVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of sVM-based models with respect to class-imbalanced learning. specifically, we categorize sVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative sVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided. © The Author(s), under exclusive licence to springer-Verlag GmbH Germany, part of springer Nature 2024.

关键词： support vector machines

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FedTC:A Personalized Federated learningMethod with Two Classifiers

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computers, Materials & Continua 2023年第9期76卷 3013-3027页

作者： Yang Liu Jiabo Wang Qinbo Liu Mehdi Gheisari Wanyin Xu Zoe L.Jiang Jiajia Zhang School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055China Research Center for Cyberspace Security Peng Cheng LaboratoryShenzhen518055China

Centralized training of deep learning models poses privacy risks that hinder their *** learning(FL)has emerged as a solution to address these risks,allowing multiple clients to train deep learning models collaborativelywithout sharing ***,FL is vulnerable to the impact of heterogeneous distributed data,which weakens convergence stability and suboptimal performance of the trained model on local *** is due to the discarding of the old local model at each round of training,which results in the loss of personalized information in the model critical for maintaining model accuracy and ensuring *** this paper,we propose FedTC,a personalized federated learning method with two classifiers that can retain personalized information in the local model and improve the model’s performance on local *** divides the model into two parts,namely,the extractor and the classifier,where the classifier is the last layer of the model,and the extractor consists of other *** classifier in the local model is always retained to ensure that the personalized information is not *** receiving the global model,the local extractor is overwritten by the globalmodel’s extractor,and the classifier of the globalmodel serves as anadditional classifier of the localmodel toguide local *** FedTCintroduces a two-classifier training strategy to coordinate the two classifiers for local model *** results on Cifar10 and Cifar100 datasets demonstrate that FedTC performs better on heterogeneous data than current studies,such as FedAvg,FedPer,and local training,achieving a maximum improvement of 27.95%in model classification test accuracy compared to FedAvg.

关键词： Distributed machine learning federated learning data hetero-geneity non-independent identically distributed

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Robust Machine learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in Engineering & sciences 2024年第2期138卷 1981-2004页

作者： shaik Mahaboob Basha Victor Hugo Cde Albuquerque samia Allaoua Chelloug Mohamed Abd Elaziz shaik Hashmitha Mohisin suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A short Review 1st

Generative Methods for Out-of-distribution Prediction and Ap...

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1st International Workshop on Digital sovereignty in Cyber security: New Challenges in Future Vision, Cybersec4Europe 2022

作者： Coppolillo, Erica Liguori, Angelica Guarascio, Massimo Pisani, Francesco sergio Manco, Giuseppe Department of Mathematics and Computer Science University of Calabria Rende Italy Department of Computer Engineering Modeling Electronics an d Systems University of Calabria Rende Italy Institute for High Performance Computing and Networking of Italian National Research Council Rende Italy

ISBN: (纸本)9783031360954

In recent times, Machine learning has played an important role in developing novel advanced tools for threat detection and mitigation. Intrusion Detection, Misinformation, Malware, and Fraud Detection are just some examples of cybersecurity fields in which Machine learning techniques are used to reveal the presence of malicious behaviors. However, Out-of-Distribution, i.e., the potential distribution gap between training and test set, can heavily affect the performances of the traditional Machine learning based methods. Indeed, they could fail in identifying out-of-samples as possible threats, therefore devising robust approaches to cope with this issue is a crucial and relevant challenge to mitigate the risk of undetected attacks. Moreover, a recent emerging line proposes to use generative models to yield synthetic likely examples to feed the learning algorithms. In this work, we first survey recent Machine learning and Deep learning based solutions to face both the problems, i.e., outlier detection and generation;then we illustrate the main cybersecurity application scenarios in which these approaches have been adopted successfully. © The Author(s), under exclusive license to springer Nature switzerland AG 2023.

关键词： learning systems

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