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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是11-20 订阅

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A systematic review of deep learning and machine learning tools for predicting and classifying pediatric genetic disorders 4th

A systematic review of deep learning and machine learning to...

引用

4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Ishdeep Gagandeep Research Scholar Computer Science and Engineering IK Gujral Punjab Technical University Punjab Kapurthala India Punjab India

ISBN: (纸本)9781032911571

There are various hurdles in the field of Genetic ailments in children, particularly in the realms of timely identification and management. Conventional diagnostic techniques often encounter obstacles such as protracted methods and resource-demanding procedures, hampering timely diagnosis and treatment. This paper investigates the Capability of advanced computing, specifically machine learning and deep learning techniques, to address these challenges. By harnessing available AI (Artificial Intelligence) technologies, scholars and scientists aim to enhance the effectiveness and precision of paediatric genetic healthcare, ultimately enhancing outcomes for children with genetic disorders. Through a comprehensive review of existing literature, this paper review investigates the state-of-the-art AI techniques employed in the prediction and classification of pediatric genetic disorders. Despite challenges such as data availability and algorithm validation, AI presents promising opportunities for revolutionizing paediatric genetic healthcare, paving the way for prompt detection, personalized therapy, and improved recipients outcomes. © 2025 the Author(s).

关键词： Pediatrics

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Research on crime risk prediction model based on machine learning 24

Research on crime risk prediction model based on machine lea...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Hou, Xuehui Rexiti, Kudelaiti Aizezi, Yasen School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan China Faculty of Information Security and Engineering Xinjiang Police College Urumqi Xinjiang China

ISBN: (纸本)9798400717482

Crime prediction is an important method for public security departments to conduct crime early warning and investigation. According to the multidimensional characteristics of criminals, machine learning classification algorithm is used to establish a prediction model of whether a criminal commits a crime again, so as to quantitatively evaluate the possibility of the criminal commits a crime again, and provide a feasible scheme reference for preventing and reducing the recurrence of crime. Analysis of urban crime data in Boston, using Naive Bayes, Random Forest and XGboost algorithms to build a crime type prediction model, we can get the urban crime type tendency, help the public security department to predict the future crime, take effective measures to reduce the urban crime rate. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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A Byzantine Client Detection Method for Federated learning Based on Encrypted Repeated Median Linear Regression 24

A Byzantine Client Detection Method for Federated Learning B...

引用

5th international conference on Computer science and Management Technology, ICCSMT 2024

作者： Guo, Jinlong Yang, Ming Li, Huijuan Wu, Xiaoming He, Yunpeng Wang, Xin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Shandong Jinan China Shandong Evayinfo Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

Federated learning is an emerging distributed machine learning technology that allows participants to jointly train machine learning models locally without the need for large-scale data transmission and sharing, thereby greatly improving data security. However, some Byzantine clients may intentionally tamper with local model parameter updates with the intention of manipulating the global model's update direction, which can lead to a decline in the overall performance of federated learning. Besides, existing Byzantine client detection research is conducted in plain text, which will lead to the leakage of private information. To solve the above issues, we propose a Byzantine client detection method for federated learning based on encrypted repeated median linear regression. Specifically, it combines the repeated median linear regression algorithm with homomorphic encryption, which can detect and eliminate clients with Byzantine behavior in the ciphertext state, ensuring the security and reliability of the parameters in the entire federated learning process. Additionally, we also designed an adaptive gradient clipping method based on historical iteration to clip the gradient to a reasonable range, which can not only reduce communication and computing overhead, but also improve the convergence speed and stability of the model. Experiments show that the proposed method can maintain high accuracy under the influence of malicious behavior of Byzantine clients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Adversarial Attack Optimization and Evaluation for machine learning-Based Dark Web Traffic Analysis 33rd

Adversarial Attack Optimization and Evaluation for Machine L...

引用

33rd international conference on Software and data Engineering

作者： Harrison, Nyzaireyus Broome, Heather Shrestha, Yaju Robles, Alexander Gautam, Aayush Rahimi, Nick Univ Southern Mississippi Hattiesburg MS 39402 USA

ISBN: (纸本)9783031752001;9783031752018

machine learning (ML) is quickly becoming one of the most transformative technologies in the field of computing. Applications of MLare wide-spread and growing exponentially, revolutionizing the future of major industries such as finance, healthcare, automotives, and more. This has made it more necessary than ever to recognize the instability created by adversarial attacks-the deliberate manipulation of data to mislead ML models. This instability must be addressed through researching the effects of adversarial attacks and how they can be better recognized. Our research explored the use of adversarial attacks in dark web network traffic analysis by first improving our understanding of how adversarial attacks could be optimized. We manipulated a dataset of dark web traffic data through the analysis of confusion matrices and Euclidean distances, aiming to cause maximum confusion for each of our models. We then trained and tested each model in a variety of scenarios to further our understanding of weaknesses in both the traffic data and the machine learning techniques employed.

关键词： Dark web machine learning Adversarial attacks Network traffic analysis

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VISAGE: Video Synthesis Using Action Graphs for Surgery 9th

VISAGE: Video Synthesis Using Action Graphs for Surgery

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27th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Yeganeh, Yousef Lazuardi, Rachmadio Shamseddin, Amir Dari, Emine Thirani, Yash Navab, Nassir Farshad, Azade Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9783031776090;9783031776106

Surgical data science (SDS) is a field that analyzes patient data before, during, and after surgery to improve surgical outcomes and skills. However, surgical data is scarce, heterogeneous, and complex, which limits the applicability of existing machine learning methods. In this work, we introduce the novel task of future video generation in laparoscopic surgery. This task can augment and enrich the existing surgical data and enable various applications, such as simulation, analysis, and robot-aided surgery. Ultimately, it involves not only understanding the current state of the operation but also accurately predicting the dynamic and often unpredictable nature of surgical procedures. Our proposed method, VISAGE (VIdeo Synthesis using Action Graphs for Surgery), leverages the power of action scene graphs to capture the sequential nature of laparoscopic procedures and utilizes diffusion models to synthesize temporally coherent video sequences. VISAGE predicts the future frames given only a single initial frame, and the action graph triplets. By incorporating domain-specific knowledge through the action graph, VISAGE ensures the generated videos adhere to the expected visual and motion patterns observed in real laparoscopic procedures. The results of our experiments demonstrate high-fidelity video generation for laparoscopy procedures, which enables various applications in SDS.

关键词： Surgical Video Synthesis Diffusion Models Surgical Scene Graphs Surgical data science

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A Comparative Study of Short Text Classification Methods based on Word Vector Fusion 24

A Comparative Study of Short Text Classification Methods bas...

引用

8th international conference on computing and data Analysis, ICCDA 2024

作者： Yan, Duanwu He, Junjie Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400710445

Currently, there are many algorithms that can be used for text multicategorization, but each algorithm has its own specific assumptions, advantages, and disadvantages. Aiming at the multiclassification problem of short text, this paper crawls the title and abstract text of blogs on 51CTO blog website and its domain classification as a dataset, proposes a feature representation method of fusing Word2Vec word vectors with Bert characterised text vectors after weighting them with TF-IDF, based on the three commonly used machine learning base classifiers and their deep learning models, namely, Decision Tree, Random Forest and KNN BERT, and introduce the integration algorithm for the calculation of the F1 value of the model, which is used as an evaluation index of the merits of text classification algorithms, and compared with the experimental results of the single text feature representation method, with a view to improving the performance of the classification model through the fusion of word vectors. The experiment shows that compared with the single text feature representation method, the model performance is improved under the fusion feature vector representation, and its F1 value reaches 86.6% on the integration algorithm in traditional machine learning, which is improved by 0.6%∼1% compared with the original one;the splicing of fused features with BERT makes its F1 value also improved by about 2%, which indicates that the fusion features proposed in this experiment can be more comprehensive, accurately describe the semantic information of text, and can more effectively characterize short texts. © 2024 Copyright held by the owner/author(s).

关键词： Vectors

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

This study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. The paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. The research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. This study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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New era of gene classification methods using machine learning 4th

New era of gene classification methods using machine learnin...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kaur, Amanpreet Kaur, Harpreet Singh, Amandeep Punjabi University Patiala Department of Information Technology Chandigarh Engg. College-CGC India Punjabi University Patiala India Department of Mechanical Engineering BBSBEC Fatehgarh Sahib India

ISBN: (纸本)9781032911571

New diagnostic technologies like DNA microarray technology have changed illness detection in the fast-changing technological age. This work focuses DNA microarray technology in gene expression research to find and understand genetic anomalies that cause illnesses, including cancer. Gene expression analysis is critical for cancer diagnosis to uncover underlying problems and create novel treatments. The number and size of data make human analysis difficult, requiring automation. machine learning and statistics are used for data categorization, focusing on cancer prediction. This article discusses various feature selection approaches for large data sets. The related work section discusses KNN-based, MLDA, signal-to-noise ratio, and ensemble algorithms. Sample size, computing costs, and classification accuracy are addressed by each technique. The study emphasizes feature selection and the range of machine learning algorithms. This article proposes a gene classification research, encompassing current gene categorization approaches, feature extraction methods, datasets, result analysis, and conclusion. The article seeks to improve gene expression analysis for illness diagnosis and therapy. © 2025 the Author(s).

关键词： Microarrays

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SparkKG-ML: A Library to Facilitate End–to–End Large–Scale machine learning Over Knowledge Graphs in Python 23rd

SparkKG-ML: A Library to Facilitate End–to–End Large–Sca...

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23rd international Semantic Web conference, ISWC 2024

作者： Gergin, Bedirhan Chelmis, Charalampos University at Albany SUNY AlbanyNY12222 United States

ISBN: (纸本)9783031778469

This paper presents SparkKG–ML, the first open–source library for machine learning at scale over semantic data stored in Knowledge Graphs directly in Python. SparkKG–ML serves as a bridge between (i) the Semantic Web data model, (ii) the distributed computing capabilities of Apache Spark, and (iii) the Python ecosystem. By harnessing the flexibility of Python and the scalability of Spark, SparkKG–ML reduces the barriers for data Scientists and machine learning researchers to work with semantic data, and for Semantic Web experts to develop machine learning models. Resource Type: Software Repository:https://***/IDIASLab/SparkKG-ML License: Apache License 2.0 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Handwritten digit recognition based on SVM 24

Handwritten digit recognition based on SVM

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Tang, Wendi Fei, Li Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400710681

Handwritten digit recognition (HDR) is one of the most classic application scenarios in machine learning and is often employed to validate algorithms and techniques. The task poses multiple challenges due to variations in writing styles and angles. Traditional feature extraction methods are intricate, and machine learning algorithms have difficulty handling complex nonlinear relationships and rely on features. Deep learning algorithms require a considerable amount of data and computing resources, and there exist problems such as gradient disappearance, explosion, and poor interpretability. In this paper, an algorithm model consisting of an encoder, a decoder, and an image classifier is proposed. Features are extracted through convolutional neural networks, image representation is optimized, and multi-classification support vector machines are utilized for classification. In light of the lack of challenge in MNIST datasets, variational autoencoders are utilized to generate numerically extended datasets in diverse styles. Experiments demonstrate that the proposed method outperforms the classical CNN algorithm by nearly 10% on EMNIST datasets, and the accuracy of the generated datasets is 99.4%. At the same time, the influence of different kernel functions on the performance of the model is also explored. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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