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检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

共 1949 条记录，以下是61-70 订阅

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Ensemble Two Stage machine learning for Network Abnormal Detection 23

Ensemble Two Stage Machine Learning for Network Abnormal Det...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Du, Runze Li, Runzhi Zhang, Zijiao School of Cyberspace Security Zhengzhou University Zhengzhou China Network Center Zhengzhou University Zhengzhou China

ISBN: (纸本)9781450398411

With the endless emergence of network security problems, it also brings the corresponding security threats to the society. IDS is an effective means to deal with network threats, but in the modern large-scale network environment, the traditional IDS has a high false positive rate. Researchers apply machine learning method to intrusion detection and get good results. In this work, based on the network traffic features, we proposed an ensemble two stage machine learning method CNN-CatBoost to detect network attacking behavior. To solve imbalance problem between normal and anomaly traffic, we construct a CNN model with multi-scale convolutional adopted to extract relations among traffic features for anomaly traffic recognition. Next for a multi-class classification problem, we use classic CatBoost model to identify different attacking types. In the experiments, datasets is from CICIDS2017. We deploy comparison experiments among proposed method and other methods. The experimental results show that CNN-CatBoost outperforms others in performance. Lastly, we give feature analysis by SHAP, understand how features influence on the model. © 2023 ACM.

关键词： machine learning

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Multi-missile Path Planning algorithm based on Reinforcement learning 23

Multi-missile Path Planning algorithm based on Reinforcement...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Ma, Xianlong Yin, Wei Gao, Zhiqiang Hu, Weijun Northwestern Polytechnical University China Shanghai Electro-Mechanical Engineering Institute China

ISBN: (纸本)9781450398411

Aiming at the problem that the on-line path planning of multiple targets hit by multiple missiles requires high real-time performance and optimal results of the algorithm, the Deep Q-network (DQN) algorithm is designed and used based on shared experience. Firstly, the Markov decision-making model of multi-missile path planning is established. Secondly, the DQN theory based on shared experience is established to design the multi-missile path planning algorithm. Finally, the DQN algorithm is simulated and verified in multiple scenarios. The simulation results show that the DQN algorithm can make multi-missile bypass the threat and get a better path to achieve online path planning. © 2023 ACM.

关键词： Reinforcement learning

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Enhanced Feature Importance learning for the Click-Through Rate Prediction 23

Enhanced Feature Importance Learning for the Click-Through R...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Sun, Hongbin Zeng, Wei University of Electronic Science and Technology of China China

ISBN: (纸本)9781450398411

The prediction of click-through rate (CTR) is critical in the recommender system, which aims to predict the probability of the user clicking on the recommended item. Considering the increasing number of features used by modern recommender systems, it is worth learning the feature importance. Inspired by FiBiNet's introduction of SENet into the recommendation system, we propose an Enhanced Feature Importance (EFI) learning framework for the embedding layer. EFI uses two feature importance vectors to reweight the original feature embedding vector, which can better describe the unique expression of the feature and improve the model's ability to learn the importance of features. Besides, EFI uses end-to-end training methods and can be a common part of most models in the task of CTR prediction. With extensive experiments on two public datasets, we verified the validity of EFI on the state-of-the-art models. © 2023 ACM.

关键词： Recommender systems

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Contrastive learning of Graph-Kernel-based Representations for Proteins 23

Contrastive Learning of Graph-Kernel-based Representations f...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Hu, Wei Zhang, Fan Feng, Yajuan School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781450398411

Protein property prediction is to infer protein properties by finding proteins with similar sequences, structures, surface clefts, and chemical properties. Since the acquisition of protein molecular property labels usually require complex and time-consuming experiments, protein property prediction based on supervised learning requires high costs. Through contrastive learning, our proposed model is capable of self-supervised learning on graphs kernels of proteins. It utilizes a strategy of subgraph removal for data augmentation, and its contrastive estimator maximizes the coherence of subgraphs from the same graphs of proteins while minimizing the coherence of subgraphs from different graphs of proteins. Experiments show that the framework can not only transform graph kernels of proteins into new graph-kernel-based representations for proteins, but also improve the performance of graph neural network encoders on protein property prediction. © 2023 ACM.

关键词： Proteins

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A Self-attention Feature Metric learning Method for Remote Sensing Image Retrieval 23

A Self-attention Feature Metric Learning Method for Remote S...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Wu, Jiahui Wang, Zhuowei Zhao, Genping Qu, Shuo School of Computer Science and Technology Guangdong University of Technology China

ISBN: (纸本)9781450398411

Remote sensing image retrieval (RSIR) is a challenging task due to the complex backgrounds of the image and the inclusion of various semantic objects. This paper presents a novel self-attention mechanism for RSIR using deep learning networks. We elaborate on the long-distance and the multi-layer dependence of image regions within a convolutional neural network to extract the salient features of remote sensing image against a complex background. We propose a new learning network structure of a multiple similarity (MS) loss function to further enhance the discriminability of features. The experiments on the typical datasets show that our method can significantly enhance the accuracy of RSIR. Moreover, when self-attention mechanism and MS loss function are simultaneously considered in the network, the retrieval precision is better than the results obtained by the previous methods. © 2023 ACM.

关键词： Semantics

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Multiclass Classification of Software Vulnerabilities with Deep learning 23

Multiclass Classification of Software Vulnerabilities with D...

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15th international conference on machine learning and computing, icmlc 2023

作者： Contreras, Crystal Dokic, Hristina Huang, Zhen Stan Raicu, Daniela Furst, Jacob Tchoua, Roselyne *** Inc. East Palo AltoCA United States DePaul University ChicagoIL United States

ISBN: (纸本)9781450398411

Detecting software vulnerabilities has been a challenge for decades. Many techniques have been developed to detect vulnerabilities by reporting whether a vulnerability exists in the code of software. But few of them have the capability to categorize the types of detected vulnerabilities, which is crucial for human developers or other tools to analyze and address vulnerabilities. In this paper, we present our work on identifying the types of vulnerabilities using deep learning. Our data consists of code slices parsed in a manner that captures the syntax and semantics of a vulnerability, sourced from prior work. We train deep neural networks on these features to perform multiclass classification of software vulnerabilities in the dataset. Our experiments show that our models can effectively identify the vulnerability classes of the vulnerable functions in our dataset. © 2023 ACM.

关键词： Semantics

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A Hybrid Binary Neural Tree for Malicious URL Detection 23

A Hybrid Binary Neural Tree for Malicious URL Detection

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Hu, Zhiqi Xu, Guoliang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450398411

Criminals use malicious URLs to distribute unsolicited content as a primary distribution method. Aiming at the problems of low detection accuracy caused by insufficient representation of URL character-level local features in the character-level embedded malicious URL detection, an algorithm based on a Hybrid Binary Neural Tree (HBNT) is proposed. HBNT combines the decision tree and neural networks to form an interpretable model of character features. It performs branching decisions and local semantic enhancement from top to bottom on nodes and connected edges of the tree. The URL is judged to have malicious attributes by merging the output results of each leaf node detection function. Experimental results on URL datasets obtained from public websites show that the detection accuracy of HBNT is higher than that of both traditional machine learning methods and deep learning methods. In addition, HBNT eliminates the unsupervised language modelling step for URL datasets and achieves comparable detection accuracy of over 99% when compared to advanced large-scale pre-trained language models. Finally, the effectiveness of each node module of HBNT is evaluated by ablation experiments. © 2023 ACM.

关键词： Deep learning

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IoT based monitoring model to identifying and classifying heart disease through machine learning

IoT based monitoring model to identifying and classifying he...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Parisha Kumar, Santosh Shrivastava, Gaurav Kumar Department of Computer Science & Engineering Babu Banarasi Das University Lucknow India School of Computing & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

Healthcare research has advanced thanks to artificial intelligence. Because there is free access to healthcare information, researchers have developed methods to help with the heart disease diagnosis and prognosis. For such difficult illnesses, deep learning and machine learning models offer a reliable, quick, and efficient solution. The articles from Web of Science, EBSCO, and EMBASE that were released between 2009 and 2021 were chosen using PRISMA criteria. A successful search method was used to discover the research papers for this study that used AI-based learning techniques for heart disease prediction. There are 185 studies in all that conventional machine learning-based classifications and deep learning-based classifiers rank as having a significant impact on heart disease prediction. The survey also analysed the research done by various researchers and drew attention to the flaws in the earlier literature. Several metrics, including prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area covered, precision, recall, and F1-score, were utilized to compare the findings. The solutions to the five scheduled investigations have been examined. Even though several of the methods suggested in the literature have excellent predicative accuracy, heart disease mortality has not decreased. As a result, further study is needed to address the problems with heart disease prediction. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Forecasting

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Preserving model privacy for Federated Reinforcement learning in Complementary Environments 23

Preserving model privacy for Federated Reinforcement Learnin...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Xuan, Pengyu Chen, Aiguo Sha, Zexin University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781450398411

Federated reinforcement learning (FRL) uses data from multiple partners interacting with the environment to train a global decision model while maintaining data privacy. In specific situations, it is necessary to protect not only data privacy but also model privacy. That is, our objective is to collaboratively train a global model that can be used privately and independently by the initiator. We refer to this need for model privacy preservation as model monopolization. It allows the task initiator to ensure the privacy of the model during the training process and to own the trained model alone. To the best of our knowledge, model monopolization protection in Federated Reinforcement learning has not been studied. We propose monopolized federated reinforcement learning in complementary environments via function transformation. We also provide contribution metrics to ensure the sustainability of monopolized federated reinforcement learning and to provide a quantitative basis for task initiators to give back to task participants. The experimental results show that our method achieved model monopolization by training agents in the Grid-world. © 2023 ACM.

关键词： Reinforcement learning

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Prediction and Detection of Cancer Through machine learning: A review 1

Prediction and Detection of Cancer Through Machine Learning:...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Saxena, Nikita Yadav, Sudeept Singh Dujawara, Asmita Kumar, Avneesh Galgotias University School of Computing and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India Galgotias University School of Allied Health Sciences Greater Noida India

ISBN: (纸本)9798350356816

Expertise in the medical field is growing thanks to artificial intelligence. Free health data has led to the development of ways by experts to help in tumor detection and prediction. For those difficult illnesses, deep learning and machine learning models offer a trustworthy, quick, and efficient solution. Dissertations via Web of Science, EBSCO, and EMBASE that became available between 2009 and 2021 were selected using PRISMA criteria. Leveraging a useful discovery technique, the scientific publications for this investigation that employed AI-based learning algorithms for tumor forecasting were located. A collection of 185 studies demonstrate the substantial influence of neural network-based processors and conventional neural network-based taxonomies on carcinoma forecasting. The survey also looked at earlier studies and pointed out flaws in those studies. Forecasting rate, preciseness, specificity, sensitivity, rolling rating, recognition velocity, territory illustrated accuracy, recall, and F1-score were among the metrics utilized to gauge the results. The five scheduled examinations' remedies have been looked into. Considering the high likelihood of success for multiple of the treatments suggested in studies, the death rate from carcinoma has not dropped. Therefore, more investigation is needed to address the problems with carcinoma forecast. © 2024 IEEE.

关键词： Adversarial machine learning

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