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A Genomic Signal Processing-Based Coronavirus Classification Model Using Deep learning with Web-Based Console 1

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14th international conference on soft computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Adetiba, Emmanuel Fayomi, Oluwatomilola Esther Ifijeh, Ayodele Abayomi, Abdultaofeek Adetiba, Joy Nwaogboko Thakur, Surendra Moyo, Sibusiso Department of Electrical and Information Engineering Covenant University Ota Nigeria Covenant Applied Informatics and Communication Africa Center of Excellence Covenant University Ota Nigeria Institute for Systems Science HRA Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Jacobs Durban4026 South Africa Department of Nursing Lagos State College of Nursing Igando Lagos Nigeria Department of Nursing University of KwaZulu-Natal Durban South Africa KZN E-Skills CoLab Durban University of Technology Durban South Africa Office of DVC Research Innovation and Postgraduate Studies Stellenbosch University Matieland Western Cape Stellenbosch7602 South Africa

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Various strains of Coronavirus have led to numerous deaths worldwide with CoViD-19 being the most recent. Hence, the need for various research studies to determine and develop technologies that would reduce the spread of this virus as well as aid in the early diagnosis of the disease. The Severe Acute Respiratory Syndrome CoV (SARS-CoV), which emerged in 2003, Middle East Respiratory Syndrome CoV (MERS-CoV) in 2012 and Severe Acute Respiratory Syndrome CoV 2 (SARS-CoV-2) which is generally regarded as CoViD-19, in 2019 have very similar symptoms and genetics. Without proper diagnosis of these strains, they may be mistaken for one another. Therefore, there is a need to distinguish CoViD-19 from the other two Coronaviruses to enhance prompt and specific treatment. In this study, we developed a deep learning model with a web console for the classification of genomic sequences of the three Coronavirus strains using genomic signal processing. The DNA sequences harvested from the Virus Pathogen Database and Analysis Resource (ViPR) was used as dataset and these sequences were transformed to RGB images using Voss and Z-curve encodings. A convolutional neural network (CNN) model was consequently used for classification and incorporated in a web application platform developed with the Django framework. The results of the transformation of the images highlights the similarities of the three coronaviruses in terms of visual and genetic characteristics with the CNN model distinctly classifying SARS-CoV-2, SARS-CoV and MERS-CoV with a training and validation accuracies of 95.58% and 85% respectively which compares favourably with other results in the literature. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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Robust learning-Augmented Caching: An Experimental Study 38

Robust Learning-Augmented Caching: An Experimental Study

引用

38th international conference on machine learning, ICML 2021

作者： Chlędowski, Jakub Polak, Adam Szabucki, Bartosz Żolna, Konrad Jagiellonian University Kraków Poland EPFL Lausanne Switzerland DeepMind London United Kingdom

ISBN: (纸本)9781713845065

Effective caching is crucial for the performance of modern-day computing systems. A key optimization problem arising in caching - which item to evict to make room for a new item - cannot be optimally solved without knowing the future. There are many classical approximation algorithms for this problem, but more recently researchers started to successfully apply machine learning to decide what to evict by discovering implicit input patterns and predicting the future. While machine learning typically does not provide any worst-case guarantees, the new field of learning-augmented algorithms proposes solutions that leverage classical online caching algorithms to make the machine-learned predictors robust. We are the first to comprehensively evaluate these learning-augmented algorithms on real-world caching datasets and state-of-the-art machine-learned predictors. We show that a straightforward method - blindly following either a predictor or a classical robust algorithm, and switching whenever one becomes worse than the other - has only a low overhead over a well-performing predictor, while competing with classical methods when the coupled predictor fails, thus providing a cheap worst-case insurance. Copyright © 2021 by the author(s)

关键词： machine learning

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On the Performance of Deep learning Models for Time Series Classification in Streaming 15th

On the Performance of Deep Learning Models for Time Series C...

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15th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Lara-Benítez, Pedro Carranza-García, Manuel Martínez-Álvarez, Francisco Riquelme, José C. Division of Computer Science University of Sevilla Seville41012 Spain Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783030578015

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, there have been recent efforts to adapt complex deep learning models for streaming tasks by reducing their processing rate. The design of the asynchronous dual-pipeline deep learning framework allows to predict over incoming instances and update the model simultaneously using two separate layers. The aim of this work is to assess the performance of different types of deep architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time-series datasets that are simulated as streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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AI-Powered Financial Fraud Detection System

AI-Powered Financial Fraud Detection System

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Prabha M Sowmiya Sunder Rajasreenithaa Kumarabaabu Rachel Davis Saraswathi P Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Credit card blackmail may be a major issue, and numerous innocents are mishandled with their private pieces of data. Since credit cards are both available and commendable, they have been a well-known outline installment elective. Their wide utilization comes with the natural issue of securing individual and installment information against blackmail or unauthorized get-to. This makes a distinction in keeping the belief and certainty of the clients while premonition security hones are crucial. To address this fundamental issue, we have utilized the display day concepts of credit card extortion disclosure and we'll propose coordinates metaphysics for a computer-aided handle in which property imprisonments are characterized by interior reification components by utilizing progressed twins as well as blockchain. That exploration besides prescribes that understanding and combatting credit card blackmail are basic for making strides in the welfare of buyers as well as budgetary teach. This consider gives a comprehensive see of credit card blackmail examination counting classification of unmistakable sorts of it to show the chance scene. It presents a new computerized twin strategy, which contrasts with prevalent extortion discovery. In the slightest complex terms, a computerized twin is the virtual adjustment of something honest to goodness. The foremost critical issues for financial specialists and clients are hacking, scamming, etc. People do not have appropriate mindfulness of this issue. In this issue, the most challenging task is recognizing. To overcome this issue afterward considers multi-source content information to overhaul the blackmail area. This investigates a more significant consideration of interior organization and outside media which contrasts recognizing the precision for location of cash-related blackmail revelation systems. This method of multi-source substance data procedure is utilized in the Chinese promotion system between 2017 and 2021.

关键词： Logistic regression Heuristic algorithms Finance Organizations machine learning Media Credit cards Fraud Security Particle swarm optimization

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XSS Detection Using Hybrid machine learning Methods

XSS Detection Using Hybrid Machine Learning Methods

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Data Science and Intelligent computing (ICDSIC), international conference on

作者： Nagham Kamil Albusalih Rana Jumaa Aljanabi Dept. Computer science University of al-qadisiyah Qadisiyah Iraq

Nowadays, the increased use of Web services has resulted in a rise in the danger of web application attacks. These types of attacks are become more severe by the day. Intrusion detection systems (IDS) are essential in proactively detecting Web threats. The network traffic contains a significant number of characteristics. Identification of irrelevant and essential features is a primary task in machine learning. Pre-processing, feature selection, and naive Bayes (NB) classifier proposed in this study as part of IDS for Web application attack. The system is evaluated on the CICIDS-2017 dataset and employs a random forest to select significant features for cross-site scripting (XSS) attack classification. Experimental results show that the feature selection using random forest enhanced the classifier's performance in terms of accuracy, detection rate and execution time, are also reducing false *** comparison with using all features in the classification method.

关键词： Web services Cross-site scripting Intrusion detection Telecommunication traffic Forestry Data science Feature extraction

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machine learning Approach for Air Shower Recognition in EUSO-SPB Data 36

Machine Learning Approach for Air Shower Recognition in EUSO...

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36th international Cosmic Ray conference (ICRC)

作者： Vrabel, Michel Genci, Jan Bobik, Pavol Bisconti, Francesca Tech Univ Kosice Fac Elect Engn & Informat Kosice Slovakia Slovak Acad Sci Inst Expt Phys Bratislava Slovakia INFN Sec Turin Turin Italy

The main goal of The Extreme Universe Space Observatory on a Super Pressure Balloon (EUSO-SPB1) was to observe from above extensive air showers caused by ultra-high energy cosmic rays. EUSO-SPB1 uses a fluorescence detector that observes the atmosphere in a nadir observation mode from a near space altitude. During the 12-day flight, an onboard first level trigger detected more than 175 000 candidate events. This paper presents an approach to recognize air showers in this dataset. The approach uses a feature extraction method to create a simpler representation of an event and then it uses established machine learning techniques to classify data into at least two classes - shower and noise. The machine learning models are trained on a set of air shower simulations put on top of the background observed during the flight and a set of events from the flight. We present the efficiency of the method on datasets of simulated events. The flight data events are also used in unsupervised learning methods to identify groups of events with similar features. The presented methods allow us to shorten the candidate events list and, thanks to the groups of similar events identified by the unsupervised methods, the classification of the triggered events is made simpler.

关键词： Cosmology

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Generalized Intersection over Union Based Online Weighted Multiple Instance learning Algorithm for Object Tracking 2021

Generalized Intersection over Union Based Online Weighted Mu...

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

作者： Lu, Xiaoshun Chen, Si Zheng, Zhuoyuan Weng, Chenyu Xu, Rui School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450389310

The traditional weighted multiple instance learning based online object tracking methods often use the Euclidean distance between the centers of the bounding boxes of the target and the instance to weight the instances in the positive bag, which can not effectively measure the contribution degree of the instances of the positive and negative bags and easily causes the object drifting problem. This paper proposes a generalized intersection over union based online weighted multiple instance learning algorithm (named GIoU-WMIL) for object tracking. This algorithm introduces a novel generalized intersection over union (GIoU) to calculate the overlap degree between the bounding boxes of the target and each instance in the bags, in order to effectively measure the contribution of the different instances. Furthermore, a new objective function is designed by employing the GIoU-based weights of all the instances in the positive and negative bags. Experiments show that the proposed algorithm has the good robustness and accuracy on several challenging video sequences. © 2021 ACM.

关键词： learning algorithms

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Grammar Inference Based on Passive learning and Genetic Algorithm 2019

Grammar Inference Based on Passive Learning and Genetic Algo...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Grachev, Petr ITMO Univ 49 Kronverksky Pr St Petersburg Russia

ISBN: (纸本)9781450366120

The mathematical model of a deterministic finite automaton has a wide potential of application, for instance, in control systems. Some of that systems are not trivial and can be defined only in terms of formal language theory. In this paper, we propose a new model for grammar inference, i.e. synthesizing of a deterministic finite automaton by a list of positive and negative examples. We present the results of testing developed model on formal grammars of various complexity.

关键词： deterministic finite automaton passive learning genetic algorithm grammar inference formal language

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learning Subject-independent Representation for EEG-based Drowsy Driving Detection 9

Learning Subject-independent Representation for EEG-based Dr...

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9th IEEE international Winter conference on Brain-Computer Interface (BCI)

作者： Hwang, Sunhee Lee, Pilhyeon Park, Sungho Byun, Hyeran Yonsei Univ Dept Comp Sci Seoul South Korea Yonsei Univ Grad Program Cognit Sci Seoul South Korea Yonsei Univ Grad Sch AI Seoul South Korea

ISBN: (纸本)9781728184852

Training a subject-independent Electroencephalography (EEG) classification model is challenging since there are large variations in EEG signals between different subjects. To this end, existing works adopt the subject-dependent training scheme to reduce the individual variations, but training one model per each subject raises expensive costs, especially when the number of subjects is large. In this work, we aim to learn a subject-independent EEG classification model that predicts target labels independent of subjects, which avoids the cost issue. Specifically, we prevent the model from learning subject dependency via minimizing the mutual information between target and subject labels. Our model consists of a feature embedding module, followed by two branches for target and subject label prediction. The subject prediction module is trained adversarially against the feature embedding module, which encourages the feature representation to be encoded invariant to the subjects. To evaluate our method, we conduct experiments on the EEG-based drowsy driving detection task, requiring consistent performances among different subjects to be adapted in real-world applications. Through the analysis on SEED-VIG dataset, we demonstrate that our method achieves meaningful performance in terms of both accuracy and individual differences.

关键词： subject independent EEG classification drowsy driving detection adversarial learning mutual information

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MFI: Multi-range Feature Interchange for Video Action Recognition 25

MFI: Multi-range Feature Interchange for Video Action Recogn...

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25th international conference on Pattern Recognition (ICPR)

作者： Bai, Sikai Wang, Qi Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781728188089

Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

关键词： Computer vision

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