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MC-LSTM: Mass-Conserving LSTM 38

MC-LSTM: Mass-Conserving LSTM

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international conference on machine learning (ICML)

作者： Hoedt, Pieter-Jan Kratzert, Frederik Klotz, Daniel Halmich, Christina Holzleitner, Markus Nearing, Grey Hochreiter, Sepp Klambauer, Gunter Johannes Kepler Univ Linz Inst Machine Learning LIT AI Lab ELLIS Unit Linz Linz Austria Google Res Mountain View CA USA Inst Adv Res Artificial Intelligence IARAI Linz Austria

ISBN: (纸本)9781713845065

The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related tasks with random weights, meaning without learning. Similarly, Long Short-Term Memory (LSTM) has a strong inductive bias toward storing information over time. However, many real-world systems are governed by conservation laws, which lead to the redistribution of particular quantities - e.g. in physical and economical systems. Our novel Mass-Conserving LSTM (MC-LSTM) adheres to these conservation laws by extending the inductive bias of LSTM to model the redistribution of those stored quantities. MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant over time. Further, MC-LSTM is applied to traffic forecasting, modeling a damped pendulum, and a large benchmark dataset in hydrology, where it sets a new state-of-the-art for predicting peak flows. In the hydrology example, we show that MC-LSTM states correlate with real world processes and are therefore interpretable.

关键词： Long short-term memory

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Predicting Cardiovascular Disease Through IoT and Deep learning Methods

Predicting Cardiovascular Disease Through IoT and Deep Learn...

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Sustainable Expert Systems (ICSES), international conference on

作者： Sakshi Shukla S. Neduncheliyan Bharath Institute of Higher Education and Research Chennai India School of Computing Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Today, heart disease affects many people and is a leading cause of death worldwide. Cardiovascular disease, also known as CVD, is the leading cause of morbidity and mortality, accounting for more than 70% of all deaths. According to the 2017 Global Burden of Disease Study, CVD causes 43% of all deaths. According to the World Health Organization (WHO), heart disease and stroke will kill 23.6 million people worldwide by 2030. This situation is difficult to predict when there are no symptoms and requires prior knowledge of the patient. A brilliant method called the Internet of Things captures the information that patients need and stores it in cloud servers. Many studies have been done on heart diseases; however, the accuracy of the prediction is very low. To solve this problem, we have created a revolutionary IoT-smart solution. Current healthcare systems are based on IoT devices and distributed systems, and they face serious problems due to insufficient distribution of patient information, which can lead to wrong diagnosis and treatment decisions. This paper uses a combination of ResNet197 and Gradient Boosting machine (GBM). ResNet197 is a deep residual neural network with 197 layers for CVD diagnosis. This technology outperforms existing models for predicting heart disease with 99.25% accuracy, 96.68% accuracy, 93.97 sensitivity, 96.96 F1 score, 98.69% accuracy, and 97.8% return.

关键词： Heart Deep learning Accuracy Sensitivity Transfer learning Medical services Predictive models Internet of Things Cardiovascular diseases Strain

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A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

A Data-Driven Wall-Shear Stress Model for LES Using Gradient...

引用

35th international ISC High Performance conference

作者： Radhakrishnan, Sarath Adu Gyamfi, Lawrence Miro, Arnau Font, Bernat Calafell, Joan Lehmkuhl, Oriol Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9783030905392;9783030905385

With the recent advances in machine learning, data-driven strategies could augment wall modeling in large eddy simulation (LES). In this work, a wall model based on gradient boosted decision trees is presented. The model is trained to learn the boundary layer of a turbulent channel flow so that it can be used to make predictions for significantly different flows where the equilibrium assumptions are valid. The methodology of building the model is presented in detail. The experiment conducted to choose the data for training is described. The trained model is tested a posteriori on a turbulent channel flow and the flow over a wall-mounted hump. The results from the tests are compared with that of an algebraic equilibrium wall model, and the performance is evaluated. The results show that the model has succeeded in learning the boundary layer, proving the effectiveness of our methodology of data-driven model development, which is extendable to complex flows.

关键词： machine learning XGBoost Turbulence Wall models

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Semi-supervised Few-shot Network Intrusion Detection based on Meta-learning

Semi-supervised Few-shot Network Intrusion Detection based o...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Yao Liu Le Zhou Qiao Liu Tian Lan Xiaoyu Bai Tinghao Zhou School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Due to the rapid development of the Internet, the scale of networks is growing exponentially, and new types of attacks continue to emerge. These new attacks are becoming more threatening, and the means of attack are changing with days. Most network intrusion detection models fail to achieve the same level of detection performance in real-world network environments as in laboratory environments. This is because different types of intrusion traffic evolve over time, and new intrusion attacks continue to emerge, rendering the sample data used for model training gradually obsolete. Real-time and reliable sample data is always scarce. How to achieve accurate and effective network intrusion detection in few-shot or zero-shot scenarios is a significant challenge for researchers. To address the problem of lacking real-time labeled data for new or variant attacks in real networks, this paper proposes a few-shot intrusion detection model called SPN based on semi-supervised prototype network. In this model, the original classification problem is transformed into a metric learning problem by using prototype network in meta-learning. By redefining the construction process of prototype points through the newly designed soft K-means and soft masking mechanisms, the SPN model can incorporate a large amount of unlabeled data in training based on a small amount of labeled data. This enables the SPN model to achieve outstanding intrusion detection performance in few-shot scenarios, while also having the ability to identify unknown intrusion attacks in zero-shot scenarios. A series of experimental results on datasets CICIDS2017 and UNSW-NB15 demonstrate that the proposed SPN model can accurately detect known attacks in few-shot scenarios, achieving an accuracy rate of 96%. Furthermore, it can effectively detect unknown intrusion attacks in zero-shot scenarios with a detection rate of over 93%.

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Defense against Power System Time Delay Attacks via Attention-based Multivariate Deep learning

Defense against Power System Time Delay Attacks via Attentio...

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2021 IEEE international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2021

作者： Ghahremani, Shahram Sidhu, Rajvir Yau, David K.Y. Cheung, Ngai-Man Albrethsen, Justin Singapore

ISBN: (纸本)9781665415026

Time delay attacks pose a threat to power systems that conventional cybersecurity methods do not adequately address. Conventional methods analyze the contents of network packets to identify threats;this is not effective against time delay attacks, which do not alter packet contents. To detect and identify time delay attacks, a new method is needed. In this paper, a novel and data-driven deep learning (DL) approach is developed to detect time delay attacks on power systems and simultaneously identify both the time of attack and attack magnitude. While conventional DL networks struggle with multivariate long time series data generated by power systems, this can be improved using attention mechanisms. In this paper, dual attention mechanisms (DA) are used to focus and improve a gated recurrent unit (GRU) network for detecting and identifying time delay attacks. A comparative analysis shows the proposed GRU-DA approach outperforms conventional DL, machine learning (ML), and statistical methods while maintaining low model complexity. © 2021 IEEE.

关键词： Time delay

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JUWELS Booster - A Supercomputer for Large-Scale AI Research

JUWELS Booster - A Supercomputer for Large-Scale AI Research

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35th international ISC High Performance conference

作者： Kesselheim, Stefan Herten, Andreas Krajsek, Kai Ebert, Jan Jitsev, Jenia Cherti, Mehdi Langguth, Michael Gong, Bing Stadtler, Scarlet Mozaffari, Amirpasha Cavallaro, Gabriele Sedona, Rocco Schug, Alexander Strube, Alexandre Kamath, Roshni Schultz, Martin G. Riedel, Morris Lippert, Thomas Forschungszentrum Julich Julich Supercomp Ctr Julich Germany Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland Univ Duisburg Essen Duisburg Germany

ISBN: (纸本)9783030905392;9783030905385

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the Julich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligence (AI) research and applications. We detail its system architecture, parallel, distributed model training, and benchmarks indicating its outstanding performance. We exemplify its potential for research application by presenting large-scale AI research highlights from various scientific fields that require such a facility.

关键词： Artificial Intelligence Deep learning GPU High-performance computing machine learning Mich supercomputing centre

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Sentiment Analysis and machine learning Algorithm Implementation on Flipkart Product Customer Reviews

Sentiment Analysis and Machine Learning Algorithm Implementa...

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Advanced computing Technologies and Applications (ICACTA), international conference on

作者： R Nancy Deborah S Alwyn Rajiv A Vinora A Gobinath S Vigneswaran K V Shiivadhanush Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu Department of Electronics and Communication Engineering Kamaraj College of Engineering and Technology Viruthunagar Tamilnadu

Sentiment analysis is a vital aspect of data mining that aims to prize private information from textual data. With the adding fashionability of online shopping platforms like Flipkart, assaying client feedback through product reviews have become a pivotal task for businesses to ameliorate their products and services. In recent times, researchers have explored the operation of machine knowledge algorithms for sentiment analysis of Flipkart product reviews. The results of this approach have demonstrated great promisein directly classifying the opposition of reviews as positive, negative, or neutral. colorful ways similar as Naive Bayes, SVM, Random Forest, Decision Trees, deep literacy, and rule-grounded approaches have been used for sentiment analysis on Flipkart reviews. also, preprocessing ways like Bag of Words, TF-IDF, Word Embeddings, and N-grams have been employed for point birth. This literature check highlights ten exploration papers published from 2017 to 2022, which bandy the perpetration of sentiment analysis and machine literacy algorithms on Flipkart product reviews. The findings of these studies give perceptivity into the effectiveness of different approaches and ways for sentiment analysis on Flipkart product reviews, and can guide unborn exploration in this field. We proposed a model called “GRUUSE” (Gated Recurrent Unit - Universal Sentence Encoder), which can directly classify the product reviews into any one of the three orders positive, negative or neutral. It is a neural network model that converts variable-length words into numerical representations of a fixed length using GRU cells. Tasks like phrase similarity or grouping are made possible by these representations, which capture semantic meaning.

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Dynamic Ensemble Selection for Imbalanced Data Stream Classification with Limited Label Access 20th

Dynamic Ensemble Selection for Imbalanced Data Stream Classi...

引用

20th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Zyblewski, Pawel Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783030878979;9783030878962

Real data streams often, in addition to the possibility of concept drift occurrence, can display a high imbalance ratio. Another important problem with real classification tasks, often overlooked in the literature, is the cost of obtaining labels. This work aims to connect three rarely combined research directions i.e., data stream classification, imbalanced data classification, and limited access to labels. For this purpose, the behavior of the DESISC-SB framework proposed by the authors in earlier works for the classification of highly imbalanced data stream was examined under the scenario of limited label access. Experiments conducted on synthetic and real streams confirmed the potential of using DESISC-SB to classify highly imbalanced data streams even in the case of low label availability.

关键词： Data stream Classifier selection Active learning

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ML-Based Intrusion Detection Systems in IoT Networks: A Survey

ML-Based Intrusion Detection Systems in IoT Networks: A Surv...

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IEEE international conference on Intelligent computing and Information Systems (ICICIS)

作者： Dina Mohamed Abdelhameed Abdel-Rahman Hedar Basem Mohamed Elomda Hesham Ahmed Hassan Egyptian E-Learning University Assuit Egypt Department of Computer Science Faculty of Computers and Information Assuit University Assuit Egypt Agriculture Research Center (ARC) Egyptian E-Learning University Cairo Egypt Faculty of Computers and Information and Artificial Intelligence Egyptian E-Learning University Cairo Egypt

The Internet of Things (IoT) is a network that connects numerous autonomous and heterogeneous smart nodes, enabling them to interact with each other autonomously, without requiring human intervention. In recent times, there has been a growing use of IoT systems to augment the overall user experience in multiple industries such as education, transportation, traffic management, monitoring, and utility services. However, the presence of node heterogeneity has given rise to security concerns, which pose significant challenges within the realm of the IoT. The utilization of intrusion detection systems (IDS) has significantly contributed to the enhancement of network and information system security. However, the IoT presents several distinctive characteristics, such as resource limitations in devices, diverse protocol stacks, and varying standards. Consequently, the application of a conventional IDS strategy to the IoT context becomes a tough task. This paper presents the different types of IoT and related networks and discusses the characteristics of IoT network types, structures, types of attacks, and how to prevent attacks. Moreover, the paper discusses the advantages and disadvantages of IoT architectures, IoT networks’ cyber-attack vulnerabilities, and how to secure the IoT. Characteristics detection methods, IDS deployment strategies, security threats, and validation strategies are also categorized as suggested in the literature. The paper also discusses a range of potential outcomes for IoT characteristics and provides in-depth analysis of studies that either propose particular IDS frameworks for IoT or devise ways for detecting IoT threats that IDSs may obscure. The results obtained include an assessment of multiple machine learning algorithms on the CICIDS2017 dataset, with a specific emphasis on comparing their accuracy and investigating innovative combinations of algorithms for the purpose of intrusion detection.

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Cardiac Transmembrane Potential Imaging with GCN Based Iterative soft Threshold Network 24th

Cardiac Transmembrane Potential Imaging with GCN Based Itera...

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

作者： Mu, Lide Liu, Huafeng Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China

ISBN: (纸本)9783030872311;9783030872304

Accurate reconstruction and imaging of cardiac transmembrane potential through body surface ECG signals can provide great help for the diagnosis of heart disease. In this paper, a cardiac transmembrane potential reconstruction method (GISTA-Net) based on graph convolutional neural network and iterative soft threshold algorithm is proposed. It fully combines the rigor of mathematical derivation of traditional iterative threshold shrinkage algorithm and the powerful expression ability of deep learning method, as well as the characterization ability of graph convolutional neural network for non-Euclidean space data. We used this algorithm to simulate ectopic pacing data and simulated myocardial infarction data. The experimental results show that this algorithm can not only accurately locate the ectopic pacing point, but also accurately reconstruct the edge details of myocardial infarction scar while graph convolution makes full use of the connection information between the nodes on the heart surface.

关键词： Cardiac electrophysiological reconstruction ISTA Graph convolution neural network

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