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and unsupervised learning</title> <meta name="description" content="Search results for: supervised and unsupervised learning"> <meta name="keywords" content="supervised and unsupervised learning"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research 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method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="supervised and unsupervised learning"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 7401</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: supervised and unsupervised learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7401</span> Adapted Intersection over Union: A Generalized Metric for Evaluating Unsupervised Classification Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prajwal%20Prakash%20Vasisht">Prajwal Prakash Vasisht</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharath%20Rajamurthy"> Sharath Rajamurthy</a>, <a href="https://publications.waset.org/abstracts/search?q=Nishanth%20Dara"> Nishanth Dara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a supervised machine learning approach, metrics such as precision, accuracy, and coverage can be calculated using ground truth labels to help in model tuning, evaluation, and selection. In an unsupervised setting, however, where the data has no ground truth, there are few interpretable metrics that can guide us to do the same. Our approach creates a framework to adapt the Intersection over Union metric, referred to as Adapted IoU, usually used to evaluate supervised learning models, into the unsupervised domain, which solves the problem by factoring in subject matter expertise and intuition about the ideal output from the model. This metric essentially provides a scale that allows us to compare the performance across numerous unsupervised models or tune hyper-parameters and compare different versions of the same model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20metric" title="general metric">general metric</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=intersection%20over%20union" title=" intersection over union"> intersection over union</a> </p> <a href="https://publications.waset.org/abstracts/185432/adapted-intersection-over-union-a-generalized-metric-for-evaluating-unsupervised-classification-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185432.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7400</span> Modern Machine Learning Conniptions for Automatic Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Jagadeesh%20Kumar">S. Jagadeesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20methods" title=" deep learning methods"> deep learning methods</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20archetypes" title=" machine learning archetypes"> machine learning archetypes</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning" title=" Bayesian learning"> Bayesian learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20and%20unsupervised%20learning" title=" supervised and unsupervised learning"> supervised and unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/71467/modern-machine-learning-conniptions-for-automatic-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7399</span> Unsupervised Images Generation Based on Sloan Digital Sky Survey with Deep Convolutional Generative Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guanghua%20Zhang">Guanghua Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fubao%20Wang"> Fubao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Weijun%20Duan"> Weijun Duan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network" title="convolution neural network">convolution neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminator" title=" discriminator"> discriminator</a>, <a href="https://publications.waset.org/abstracts/search?q=generator" title=" generator"> generator</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/89010/unsupervised-images-generation-based-on-sloan-digital-sky-survey-with-deep-convolutional-generative-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89010.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">268</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7398</span> Unsupervised Learning of Spatiotemporally Coherent Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ross%20Goroshin">Ross Goroshin</a>, <a href="https://publications.waset.org/abstracts/search?q=Joan%20Bruna"> Joan Bruna</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Tompson"> Jonathan Tompson</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Eigen"> David Eigen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yann%20LeCun"> Yann LeCun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20clustering" title=" pattern clustering"> pattern clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=pooling" title=" pooling"> pooling</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification "> classification </a> </p> <a href="https://publications.waset.org/abstracts/29488/unsupervised-learning-of-spatiotemporally-coherent-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29488.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">456</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7397</span> Double Clustering as an Unsupervised Approach for Order Picking of Distributed Warehouses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hsin-Yi%20Huang">Hsin-Yi Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming-Sheng%20Liu"> Ming-Sheng Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiun-Yan%20Shiau"> Jiun-Yan Shiau</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=order%20picking" title="order picking">order picking</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse" title=" warehouse"> warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/136656/double-clustering-as-an-unsupervised-approach-for-order-picking-of-distributed-warehouses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136656.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7396</span> Unsupervised Echocardiogram View Detection via Autoencoder-Based Representation Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Trevi%C3%B1o%20Gavito">Andrea Treviño Gavito</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Klabjan"> Diego Klabjan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjiv%20J.%20Shah"> Sanjiv J. Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we introduce a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, and 25.9% in accuracy and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=echocardiographic%20view%20detection" title=" echocardiographic view detection"> echocardiographic view detection</a>, <a href="https://publications.waset.org/abstracts/search?q=echocardiography" title=" echocardiography"> echocardiography</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20representation%20learning" title=" self-supervised representation learning"> self-supervised representation learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/189008/unsupervised-echocardiogram-view-detection-via-autoencoder-based-representation-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">32</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7395</span> Self-Supervised Learning for Hate-Speech Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shrabani%20Ghosh">Shrabani Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention%20learning" title="attention learning">attention learning</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</a>, <a href="https://publications.waset.org/abstracts/search?q=offensive%20language%20detection" title=" offensive language detection"> offensive language detection</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title=" self-supervised learning"> self-supervised learning</a> </p> <a href="https://publications.waset.org/abstracts/147950/self-supervised-learning-for-hate-speech-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147950.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7394</span> Human Digital Twin for Personal Conversation Automation Using Supervised Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aya%20Salama">Aya Salama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20digital%20twin" title="human digital twin">human digital twin</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20extraction" title=" topic extraction"> topic extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20machine%20learning" title=" unsupervised machine learning"> unsupervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a> </p> <a href="https://publications.waset.org/abstracts/152736/human-digital-twin-for-personal-conversation-automation-using-supervised-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152736.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">87</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7393</span> Large-Scale Electroencephalogram Biometrics through Contrastive Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20%E2%80%98Neo%E2%80%99%20Mohsenvand">Mostafa ‘Neo’ Mohsenvand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Rasool%20Izadi"> Mohammad Rasool Izadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Pattie%20Maes"> Pattie Maes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> EEG-based biometrics (user identification) has been explored on small datasets of no more than 157 subjects. Here we show that the accuracy of modern supervised methods falls rapidly as the number of users increases to a few thousand. Moreover, supervised methods require a large amount of labeled data for training which limits their applications in real-world scenarios where acquiring data for training should not take more than a few minutes. We show that using contrastive learning for pre-training, it is possible to maintain high accuracy on a dataset of 2130 subjects while only using a fraction of labels. We compare 5 different self-supervised tasks for pre-training of the encoder where our proposed method achieves the accuracy of 96.4%, improving the baseline supervised models by 22.75% and the competing self-supervised model by 3.93%. We also study the effects of the length of the signal and the number of channels on the accuracy of the user-identification models. Our results reveal that signals from temporal and frontal channels contain more identifying features compared to other channels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brainprint" title="brainprint">brainprint</a>, <a href="https://publications.waset.org/abstracts/search?q=contrastive%20learning" title=" contrastive learning"> contrastive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalo-gram" title=" electroencephalo-gram"> electroencephalo-gram</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title=" self-supervised learning"> self-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20identification" title=" user identification"> user identification</a> </p> <a href="https://publications.waset.org/abstracts/135468/large-scale-electroencephalogram-biometrics-through-contrastive-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135468.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7392</span> Unsupervised Learning with Self-Organizing Maps for Named Entity Recognition in the CONLL2003 Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Assel%20Jaxylykova">Assel Jaxylykova</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexnder%20Pak"> Alexnder Pak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=named%20entity%20recognition" title="named entity recognition">named entity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=CONLL-2003" title=" CONLL-2003"> CONLL-2003</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a> </p> <a href="https://publications.waset.org/abstracts/188422/unsupervised-learning-with-self-organizing-maps-for-named-entity-recognition-in-the-conll2003-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188422.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">45</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7391</span> Supervised Learning for Cyber Threat Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jihen%20Bennaceur">Jihen Bennaceur</a>, <a href="https://publications.waset.org/abstracts/search?q=Wissem%20Zouaghi"> Wissem Zouaghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Mabrouk"> Ali Mabrouk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=threat%20information%20sharing" title="threat information sharing">threat information sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20classification" title=" data classification"> data classification</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20evaluation" title=" performance evaluation"> performance evaluation</a> </p> <a href="https://publications.waset.org/abstracts/162756/supervised-learning-for-cyber-threat-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162756.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7390</span> Combining Shallow and Deep Unsupervised Machine Learning Techniques to Detect Bad Actors in Complex Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Ming%20Moey">Jun Ming Moey</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiyaun%20Chen"> Zhiyaun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Nicholson"> David Nicholson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection" title="detection">detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised" title=" unsupervised"> unsupervised</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier%20analysis" title=" outlier analysis"> outlier analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud" title=" fraud"> fraud</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crime" title=" financial crime"> financial crime</a> </p> <a href="https://publications.waset.org/abstracts/153061/combining-shallow-and-deep-unsupervised-machine-learning-techniques-to-detect-bad-actors-in-complex-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153061.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7389</span> Identification of Biological Pathways Causative for Breast Cancer Using Unsupervised Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karthik%20Mittal">Karthik Mittal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20biology" title=" computational biology"> computational biology</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20machine%20learning" title=" unsupervised machine learning"> unsupervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a> </p> <a href="https://publications.waset.org/abstracts/148748/identification-of-biological-pathways-causative-for-breast-cancer-using-unsupervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148748.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7388</span> Feature Based Unsupervised Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deeman%20Yousif%20Mahmood">Deeman Yousif Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Abdullah%20Hussein"> Mohammed Abdullah Hussein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20gain%20%28IG%29" title="information gain (IG)">information gain (IG)</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system%20%28IDS%29" title=" intrusion detection system (IDS)"> intrusion detection system (IDS)</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=Weka" title=" Weka"> Weka</a> </p> <a href="https://publications.waset.org/abstracts/5974/feature-based-unsupervised-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5974.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7387</span> Self-Supervised Pretraining on Sequences of Functional Magnetic Resonance Imaging Data for Transfer Learning to Brain Decoding Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sean%20Paulsen">Sean Paulsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Casey"> Michael Casey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised" title=" self-supervised"> self-supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20decoding" title=" brain decoding"> brain decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=multitask%20training" title=" multitask training"> multitask training</a> </p> <a href="https://publications.waset.org/abstracts/165380/self-supervised-pretraining-on-sequences-of-functional-magnetic-resonance-imaging-data-for-transfer-learning-to-brain-decoding-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7386</span> PatchMix: Learning Transferable Semi-Supervised Representation by Predicting Patches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arpit%20Rai">Arpit Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we propose PatchMix, a semi-supervised method for pre-training visual representations. PatchMix mixes patches of two images and then solves an auxiliary task of predicting the label of each patch in the mixed image. Our experiments on the CIFAR-10, 100 and the SVHN dataset show that the representations learned by this method encodes useful information for transfer to new tasks and outperform the baseline Residual Network encoders by on CIFAR 10 by 12% on ResNet 101 and 2% on ResNet-56, by 4% on CIFAR-100 on ResNet101 and by 6% on SVHN dataset on the ResNet-101 baseline model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title="self-supervised learning">self-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=representation%20learning" title=" representation learning"> representation learning</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=generalization" title=" generalization"> generalization</a> </p> <a href="https://publications.waset.org/abstracts/150013/patchmix-learning-transferable-semi-supervised-representation-by-predicting-patches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150013.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7385</span> A Family of Distributions on Learnable Problems without Uniform Convergence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C%C3%A9sar%20Garza">César Garza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In supervised binary classification and regression problems, it is well-known that learnability is equivalent to a uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20learning%20theory" title="statistical learning theory">statistical learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=learnability" title=" learnability"> learnability</a>, <a href="https://publications.waset.org/abstracts/search?q=uniform%20convergence" title=" uniform convergence"> uniform convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=stability" title=" stability"> stability</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20loss%20minimization" title=" regularized loss minimization"> regularized loss minimization</a> </p> <a href="https://publications.waset.org/abstracts/151038/a-family-of-distributions-on-learnable-problems-without-uniform-convergence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151038.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7384</span> Evaluation Metrics for Machine Learning Techniques: A Comprehensive Review and Comparative Analysis of Performance Measurement Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed-Ali%20Sadegh-Zadeh">Seyed-Ali Sadegh-Zadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaveh%20Kavianpour"> Kaveh Kavianpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Atashbar"> Hamed Atashbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Heidari"> Elham Heidari</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Shiry%20Ghidary"> Saeed Shiry Ghidary</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20M.%20Hajiyavand"> Amir M. Hajiyavand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evaluation%20metrics" title="evaluation metrics">evaluation metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20measurement" title=" performance measurement"> performance measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20robustness%20and%20stability" title=" model robustness and stability"> model robustness and stability</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title=" comparative analysis"> comparative analysis</a> </p> <a href="https://publications.waset.org/abstracts/184552/evaluation-metrics-for-machine-learning-techniques-a-comprehensive-review-and-comparative-analysis-of-performance-measurement-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7383</span> Unsupervised Assistive and Adaptative Intelligent Agent in Smart Enviroment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sebasti%C3%A3o%20Pais">Sebastião Pais</a>, <a href="https://publications.waset.org/abstracts/search?q=Jo%C3%A3o%20Casal"> João Casal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Ponciano"> Ricardo Ponciano</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C3%A9rgio%20Loren%C3%A7o"> Sérgio Lorenço</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The adaptation paradigm is a basic defining feature for pervasive computing systems. Adaptation systems must work efficiently in a smart environment while providing suitable information relevant to the user system interaction. The key objective is to deduce the information needed information changes. Therefore relying on fixed operational models would be inappropriate. This paper presents a study on developing an Intelligent Personal Assistant to assist the user in interacting with their Smart Environment. We propose an Unsupervised and Language-Independent Adaptation through Intelligent Speech Interface and a set of methods of Acquiring Knowledge, namely Semantic Similarity and Unsupervised Learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20personal%20assistants" title="intelligent personal assistants">intelligent personal assistants</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20speech%20interface" title=" intelligent speech interface"> intelligent speech interface</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=language-independent" title=" language-independent"> language-independent</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20acquisition" title=" knowledge acquisition"> knowledge acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=association%20measures" title=" association measures"> association measures</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetric%20word%20similarities" title=" symmetric word similarities"> symmetric word similarities</a>, <a href="https://publications.waset.org/abstracts/search?q=attributional%20word%20similarities" title=" attributional word similarities"> attributional word similarities</a> </p> <a href="https://publications.waset.org/abstracts/21135/unsupervised-assistive-and-adaptative-intelligent-agent-in-smart-enviroment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7382</span> Unsupervised Assistive and Adaptive Intelligent Agent in Smart Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sebasti%C3%A3o%20Pais">Sebastião Pais</a>, <a href="https://publications.waset.org/abstracts/search?q=Jo%C3%A3o%20Casal"> João Casal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Ponciano"> Ricardo Ponciano</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C3%A9rgio%20Louren%C3%A7o"> Sérgio Lourenço</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The adaptation paradigm is a basic defining feature for pervasive computing systems. Adaptation systems must work efficiently in smart environment while providing suitable information relevant to the user system interaction. The key objective is to deduce the information needed information changes. Therefore, relying on fixed operational models would be inappropriate. This paper presents a study on developing a Intelligent Personal Assistant to assist the user in interacting with their Smart Environment. We propose a Unsupervised and Language-Independent Adaptation through Intelligent Speech Interface and a set of methods of Acquiring Knowledge, namely Semantic Similarity and Unsupervised Learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20personal%20assistants" title="intelligent personal assistants">intelligent personal assistants</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20speech%20interface" title=" intelligent speech interface"> intelligent speech interface</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=language-independent" title=" language-independent"> language-independent</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20acquisition" title=" knowledge acquisition"> knowledge acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=association%20measures" title=" association measures"> association measures</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetric%20word%20similarities" title=" symmetric word similarities"> symmetric word similarities</a>, <a href="https://publications.waset.org/abstracts/search?q=attributional%20word%20similarities" title=" attributional word similarities"> attributional word similarities</a> </p> <a href="https://publications.waset.org/abstracts/21136/unsupervised-assistive-and-adaptive-intelligent-agent-in-smart-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21136.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">643</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7381</span> Real-Time Network Anomaly Detection Systems Based on Machine-Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Ramezanpanah">Zahra Ramezanpanah</a>, <a href="https://publications.waset.org/abstracts/search?q=Joachim%20Carvallo"> Joachim Carvallo</a>, <a href="https://publications.waset.org/abstracts/search?q=Aurelien%20Rodriguez"> Aurelien Rodriguez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temporal%20graph%20network" title="temporal graph network">temporal graph network</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20security" title=" cyber security"> cyber security</a>, <a href="https://publications.waset.org/abstracts/search?q=IDS" title=" IDS"> IDS</a> </p> <a href="https://publications.waset.org/abstracts/150847/real-time-network-anomaly-detection-systems-based-on-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150847.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7380</span> Visualization-Based Feature Extraction for Classification in Real-Time Interaction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C3%81goston%20Nagy">Ágoston Nagy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title="gesture recognition">gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20interaction" title=" real-time interaction"> real-time interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/68382/visualization-based-feature-extraction-for-classification-in-real-time-interaction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7379</span> Investigation of Topic Modeling-Based Semi-Supervised Interpretable Document Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dasom%20Kim">Dasom Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20Xiu%20Shun%20Wong"> William Xiu Shun Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoonjin%20Hyun"> Yoonjin Hyun</a>, <a href="https://publications.waset.org/abstracts/search?q=Donghoon%20Lee"> Donghoon Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Minji%20Paek"> Minji Paek</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungho%20Byun"> Sungho Byun</a>, <a href="https://publications.waset.org/abstracts/search?q=Namgyu%20Kim"> Namgyu Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20classifier" title=" document classifier"> document classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a> </p> <a href="https://publications.waset.org/abstracts/48985/investigation-of-topic-modeling-based-semi-supervised-interpretable-document-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48985.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7378</span> Methods for Distinction of Cattle Using Supervised Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radoslav%20%C5%BDidek">Radoslav Židek</a>, <a href="https://publications.waset.org/abstracts/search?q=Veronika%20%C5%A0idlov%C3%A1"> Veronika Šidlová</a>, <a href="https://publications.waset.org/abstracts/search?q=Radovan%20Kasarda"> Radovan Kasarda</a>, <a href="https://publications.waset.org/abstracts/search?q=Birgit%20Fuerst-Waltl"> Birgit Fuerst-Waltl</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20data" title="genetic data">genetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=Pinzgau%20cattle" title=" Pinzgau cattle"> Pinzgau cattle</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/9320/methods-for-distinction-of-cattle-using-supervised-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">550</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7377</span> Semi-Supervised Learning Using Pseudo F Measure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahesh%20Balan%20U">Mahesh Balan U</a>, <a href="https://publications.waset.org/abstracts/search?q=Rohith%20Srinivaas%20Mohanakrishnan"> Rohith Srinivaas Mohanakrishnan</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkat%20Subramanian"> Venkat Subramanian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PU%20learning" title="PU learning">PU learning</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20learning" title=" semi-supervised learning"> semi-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudo%20f%20measure" title=" pseudo f measure"> pseudo f measure</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/141550/semi-supervised-learning-using-pseudo-f-measure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141550.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7376</span> Experiments on Weakly-Supervised Learning on Imperfect Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Cheng">Yan Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yijun%20Shao"> Yijun Shao</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Rudolph"> James Rudolph</a>, <a href="https://publications.waset.org/abstracts/search?q=Charlene%20R.%20Weir"> Charlene R. Weir</a>, <a href="https://publications.waset.org/abstracts/search?q=Beth%20Sahlmann"> Beth Sahlmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing%20Zeng-Treitler"> Qing Zeng-Treitler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weakly-supervised%20learning" title="weakly-supervised learning">weakly-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=delirium" title=" delirium"> delirium</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/99362/experiments-on-weakly-supervised-learning-on-imperfect-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99362.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7375</span> Efficient Schemes of Classifiers for Remote Sensing Satellite Imageries of Land Use Pattern Classifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20S.%20Patil">S. S. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachidanand%20Kini"> Sachidanand Kini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title="Mahalanobis distance">Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20distance" title=" minimum distance"> minimum distance</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised" title=" supervised"> supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised" title=" unsupervised"> unsupervised</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20classification%20accuracy" title=" user classification accuracy"> user classification accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=producer%27s%20classification%20accuracy" title=" producer's classification accuracy"> producer's classification accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=kappa%20coefficient" title=" kappa coefficient"> kappa coefficient</a> </p> <a href="https://publications.waset.org/abstracts/103621/efficient-schemes-of-classifiers-for-remote-sensing-satellite-imageries-of-land-use-pattern-classifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103621.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7374</span> An Embarrassingly Simple Semi-supervised Approach to Increase Recall in Online Shopping Domain to Match Structured Data with Unstructured Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sachin%20Nagargoje">Sachin Nagargoje</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Complete labeled data is often difficult to obtain in a practical scenario. Even if one manages to obtain the data, the quality of the data is always in question. In shopping vertical, offers are the input data, which is given by advertiser with or without a good quality of information. In this paper, an author investigated the possibility of using a very simple Semi-supervised learning approach to increase the recall of unhealthy offers (has badly written Offer Title or partial product details) in shopping vertical domain. The author found that the semisupervised learning method had improved the recall in the Smart Phone category by 30% on A=B testing on 10% traffic and increased the YoY (Year over Year) number of impressions per month by 33% at production. This also made a significant increase in Revenue, but that cannot be publicly disclosed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20learning" title="semi-supervised learning">semi-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=recall" title=" recall"> recall</a>, <a href="https://publications.waset.org/abstracts/search?q=coverage" title=" coverage"> coverage</a> </p> <a href="https://publications.waset.org/abstracts/125267/an-embarrassingly-simple-semi-supervised-approach-to-increase-recall-in-online-shopping-domain-to-match-structured-data-with-unstructured-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125267.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7373</span> Incorporating Multiple Supervised Learning Algorithms for Effective Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Albalawi">Umar Albalawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20C.%20Suh"> Sang C. Suh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinoh%20Kim"> Jinoh Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20classification" title=" traffic classification"> traffic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20networks" title=" computer networks"> computer networks</a> </p> <a href="https://publications.waset.org/abstracts/5421/incorporating-multiple-supervised-learning-algorithms-for-effective-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7372</span> Identification of Hepatocellular Carcinoma Using Supervised Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagri%20Sharma">Sagri Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=biomarker" title=" biomarker"> biomarker</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20datasets" title=" gene expression datasets"> gene expression datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=hepatocellular%20carcinoma" title=" hepatocellular carcinoma"> hepatocellular carcinoma</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning%20algorithms" title=" supervised learning algorithms"> supervised learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/19034/identification-of-hepatocellular-carcinoma-using-supervised-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19034.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">429</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li 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