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1D CNN for Feature Reconstruction on Network Threat Detection 2021

1D CNN for Feature Reconstruction on Network Threat Detectio...

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2021 13th international conference on machine learning and computing, icmlc 2021

作者： Xu, Yanping Zhang, Xia Ye, Tingcong Qiu, Zhenliang Zhang, Lingjun Zhang, Hua Wu, Yifan School of Cyberspace Security Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450389310

machine learning algorithms for building network threat detection model are regarded as effective methods. Some big network security data has 1D characteristics. And 1D CNN can deal with 1D signal data well. Therefore, in this paper, we propose a network threat detection model based on 1D CNN and shallow machine learning algorithms. Firstly, 1D CNN deep model is constructed, including input layer, convolution layer, pooling layer, full connection layer, softmax layer and output layer. Combining the layers nonlinear learning, reconstructed feature data is generated, which reduces the dimension of the features comparing with that of original data. Secondly, the reconstructed data is input into the shallow machine learning algorithms to classify the network security data and detect the abnormal behavior in the security dataset. The experimental results show that the classification performance of 1D CNN is outstanding comparing with other dimension reduction algorithms. © 2021 ACM.

关键词： machine learning

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Applying Neural Network to Reconstruction of Phylogenetic Tree 2021

Applying Neural Network to Reconstruction of Phylogenetic Tr...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Zhu, Tujin Cai, Yunpeng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781450389310

Reconstruction of phylogenetic tree from biological sequences is a fundamental step in molecular biology, but it is computationally exhausting. Our goal is to use neural network to learn the heuristic strategy of phylogenetic tree reconstruction algorithm. We propose an attention model to learn heuristic strategies for constructing circular ordering related to phylogenetic trees. We use alignment-free K-mer frequency vector representation to represent biological sequences and use unlabeled sequence data sets to train attention model through reinforcement learning. Comparing with traditional methods, our approach is alignment-free and can be easily extended to large-scale data with computational efficiency. With the rapid growth of public biological sequence data, our method provides a potential way to reconstruct phylogenetic tree. © 2021 ACM.

关键词： Reinforcement learning

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Toward an Effective Analysis of COVID-19 Moroccan Business Survey Data using machine learning Techniques 2021

Toward an Effective Analysis of COVID-19 Moroccan Business S...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Lasri, Imane Riadsolh, Anouar El Belkacemi, Mourad Laboratory of Conception and Systems Electronics Signals and Informatics Faculty of Sciences Rabat Mohammed v University Rabat Morocco

ISBN: (纸本)9781450389310

COVID-19 pandemic has gravely affected our societies and economies with severe consequences. To contain the spread of the disease, most governments around the world authorized unprecedented measures, including Morocco, which has closed the borders and adopted full lockdown between March and June 2020. However, these measures have resulted in economic loss and have led to dramatic changes in how businesses act and consumers behave. The main focus of this study was to examine the impact of the full lockdown on Moroccan enterprises based on the COVID-19 Moroccan business survey carried out by the High Commission for Planning (HCP). A three-stage analysis method was employed. First, multiple correspondence analysis (MCA) was used to reduce the dimensionality of the categorical variables, and k-means clustering algorithm was used to cluster the data, then decision tree algorithm was performed in order to interpret each cluster and the maximum accuracy achieved is 84.45%. Compared with the decision tree algorithm, an artificial neural network (ANN) with stratified 10-fold cross-validation was applied to the dataset and has reached an accuracy of 83.4%. The simulation results confirm the effectiveness of the proposed techniques for analyzing survey data. © 2021 ACM.

关键词： COVID-19

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Predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive learning Framework 3rd

Predicting Falls Through Muscle Weakness from a Single Who...

引用

3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Zhang, Xia Saleem, Afsah Ilyas, Zaid Suter, David Nadeem, Uzair Prince, Richard L. Zhu, Kun Lewis, Joshua R. Sim, Marc Gilani, Syed Zulqarnain Centre for AI and ML School of Science Edith Cowan University Joondalup Australia Nutrition and Health Innovation Research Institute Edith Cowan University Joondalup Australia Computer Science and Software Engineering The University of Western Australia Perth Australia Medical School The University of Western Australia Perth Australia Department of Endocrinology and Diabetes Sir Charles Gairdner Hospital PerthWA Australia

ISBN: (纸本)9783031820069

Falls are often attributed to poor muscle function, with weak hand grip strength clinically recognized as a major risk factor. However, grip strength is rarely assessed clinically. Low radiation dual-energy X-ray absorptiometry (DXA) whole-body scans, that can be obtained during routine osteoporosis screening, offer a comprehensive overview of body composition, thereby providing valuable information for musculoskeletal health. Here, we propose a machine learning technique, exploiting image and clinical data to classify weak grip strength ( © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Using Principal Component Analysis and Online Sequential Extreme learning machine Approach for Transient Electromagnetic Nonlinear Inversion: TEM-Inversion-based-on-PCA-OSELM 2021

Using Principal Component Analysis and Online Sequential Ext...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Li, Ruiyou The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9781450389310

The traditional artificial neural network based on gradient descent method result in low computational efficiency and local convergence for transient electromagnetic inversion. To solve the these problems, a hybrid approach combining principal component analysis (PCA) and online sequential extreme learning machine (OSELM) is proposed in this paper (PCA-OSELM) and is applied in the transient electromagnetic inversion. First, a principal component analysis method is introduced to reduce the dimension of vertical magnetic field data and improves the computational efficiency. Then, the new samples obtained from the data sets are added to the training samples as the next update information to establish the OSELM prediction models, so that improve the inversion accuracy. Finally, the inversion results of the two typical layered geoelectric models and a quasi two-dimensional geoelectric model show that the proposed approach can well solve the modeling nonlinear problem that high-dimensional data generated by transient electromagnetic method. Moreover, compared with other nonlinear inversion methods (OSELM, ELM), the PCA-OSELM achieves more accurate, better generalization ability and higher computational efficiency, which can provide new ideas for the application of neural networks in geophysical inversion. © 2021 ACM.

关键词： Principal component analysis

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Inter-domain Link Inference with Confidence Using Naïve Bayes Classifier 2021

Inter-domain Link Inference with Confidence Using Naïve Bay...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Zhao, Yi Liu, Yan Guo, XiaoYu Sui, ZhongHang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450389310

Inter-domain link inference is not only important for network security and fault diagnosis, but also helps to conduct research on inter-domain congestion detection and network resilience assessment. Current researches on this issue lack confidence analysis of the inferred results. In this paper, the IP link types (i.e., intra-domain link and inter-domain link) are considered as the latent variable in probability model, while the parameters are probabilities of different link types with particular features. The expectation maximization algorithm is applied to estimate parameters of the model. In each iteration of EM algorithm, Naïve Bayes is used for classification. The final result is determined according to the probability, and the probability is the confidence of the result. The experimental results show that our method can achieve better precision and recall on the validation set than two existing general methods. © 2021 ACM.

关键词： Network security

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Stock Price Prediction by Using machine learning Techniques: A Study of TCS Ltd

Stock Price Prediction by Using Machine Learning Techniques:...

引用

Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Yogesh Kumar Jakhar Pawan Sharma Bilal Ahmed Department of Computer Application University of Engineering & Management Jaipur India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Prediction of stock prices is challenging, but now stock price prediction is becoming very popular among researchers. Exact prediction of the stock market is not feasible, but the present study is an attempt to understand stock price prediction with special attention to TCS Ltd. The key objective of the present study is to forecast the high price of TCS stock on a daily basis. To achieve the objective successfully, this study comprises the use of historical time-based data for 15 years, from 2009 to 2023. (12 years of data for training purposes and 3 years of data for testing purposes are significantly categorized). Day-wise high prices have been significantly predicted by using open prices, low prices, and close prices. This study comprises the use of Multi Linear Regression (MLR), Support Vector machine (SVM) with Linear Kernel, Ploy Kernel, and RBF Kernels, Multilayer Perceptron (MLP 2, 2), and XGBOOST algorithms. To access the accuracy of results from different models, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) also been significantly calculated. Multi Linear Regression (MLR) model showed more accurate results for the prediction of day-wise high prices as compared to other algorithms. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of MLR found 11.57 and 14.69 respectively, the lowest of all the algorithms. Traditional regression tools are not as effective as these algorithms because these algorithms can provide more accurate and precise results to serve the interests of investors. This study concluded with suggestions to the investors while deciding whether to hold the stocks or sell them on a particular day.

关键词： Support vector machines Training machine learning algorithms Accuracy Linear regression machine learning Prediction algorithms

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Factors Affecting Accuracy of Genotype Imputation Using Neural Networks in Deep learning 2021

Factors Affecting Accuracy of Genotype Imputation Using Neur...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Shi, Tianfeng Peng, Jing School of Computer Science and Technology Wuhan University of Technology

ISBN: (纸本)9781450389310

The genotype imputation is an important topic in the field of genomics. Many genome analyses require data without missing values, which requires to impute the missing data. In recent years, deep learning has become hot, and it is more suitable for text sequence type problems, which may fit with the genotype imputation problem. Based on the recurrent neural network and convolutional neural network in deep learning, our study proposes and constructs five model combinations, imputes and compares the results under different missing rate scenarios. And on the basis of the basic model, a higher imputation accuracy is obtained by tuning the model hyperparameters. The results indicated that on all the data sets with various levels of missing rates, the CNN1D-RNNM with tuned hyperparameters well has obtained the best results. The combination of a one-dimensional convolutional neural network and a recurrent neural network with tuned hyperparameters can beat a single convolutional network or a recurrent network at various levels of missing rates. This research provides new solutions for genotype imputation by using the deep learning to build complex neural networks. © 2021 ACM.

关键词： Recurrent neural networks

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Conservative generalisation for small data analytics-an extended lattice machine approach 19

Conservative generalisation for small data analytics-an exte...

引用

19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Kong, Shuangshuang Wang, Hui Wang, Kaijun Fujian Normal University School of Mathematics and Informatics China Ulster University School of Computing United Kingdom

ISBN: (纸本)9780738124261

Small data analytics is to tackle the data analysis challenges such as overfitting when the data set is small. There are different approaches to small data analytics, including knowledge-based learning, but most of these approaches need experience to use. In this paper we consider another approach, lattice machine. Lattice machine is a conservative generalisation based learning algorithm. It is a learning paradigm that "learns"by generalising data in a consistent, conservative and parsimonious way. A lattice machine model built from a dataset is a set of hyper tuples that tightly "wraps around"clusters of data, each of which is a conservative generalisation of the underlying cluster. A key feature of lattice machine, indeed any conservative generalisation based learning algorithm, is that it has high precision and low recall, limiting its applications as high recall is needed in some applications such as disease (e.g. covid-19) screening. It is thus necessary to improve lattice machine's recall whilst retaining his high precision. In this paper, we present a study on how to achieve this for lattice machine. © 2020 IEEE.

关键词： Data Analytics

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Distant Supervision for Relation Extraction via Noise Filtering 2021

Distant Supervision for Relation Extraction via Noise Filter...

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2021 13th international conference on machine learning and computing, icmlc 2021

作者： Chen, Jing Guo, Zhiqiang Yang, Jie School of Information Engineering Wuhan University of Technology Hubei Wuhan China Hubei Provincial Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9781450389310

As a widely used method in relation extraction at the present stage suggests, distant supervision is affected by label noise. The data noise is introduced artificially due to the theory and the performance of distant supervision will be restricted during the modeling process. To solve this problem on the sentence level, the task of relation extraction in our project is modeled with two parts: sentence selector and relation extractor. Sentence selector, based on the theory of reinforcement learning, processes the corpus in units of entity pairs. The training corpus is divided into three parts including selected sentences, discarded sentences, and unlabeled sentences. We try to obtain more semantic information of the training corpus by introducing the intra-class attention and inter-class similarity. To make the operation of filtering noise data more accurate, this model evaluates the predicted value produced by the relation extractor between the selected and discarded sentences in the sentence package. The result shows that the redesigned reinforcement learning algorithm WPR-RL in this study can significantly improve the deficiencies of the existing approach. At the same time, we also carry out a number of composite tests to discuss the impact of each improvement on the performance of the model. © 2021 ACM.

关键词： Reinforcement learning

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