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learning</title> <meta name="description" content="Search results for: unsupervised learning"> <meta name="keywords" content="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 Excellence" /> </a> <button class="d-block d-lg-none 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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="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> 7254</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: 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">7254</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">7253</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">7252</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">7251</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">7250</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">7249</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">7248</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">7247</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">7246</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">7245</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">7244</span> Unsupervised Feature Learning by Pre-Route Simulation of Auto-Encoder Behavior Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youngjae%20Jin">Youngjae Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Daeshik%20Kim"> Daeshik Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-encoder" title="auto-encoder">auto-encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior%20model%20simulation" title=" behavior model simulation"> behavior model simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20hardware%20design" title=" digital hardware design"> digital hardware design</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-route%20simulation" title=" pre-route simulation"> pre-route simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=Unsupervised%20feature%20learning" title=" Unsupervised feature learning"> Unsupervised feature learning</a> </p> <a href="https://publications.waset.org/abstracts/4980/unsupervised-feature-learning-by-pre-route-simulation-of-auto-encoder-behavior-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4980.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">446</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">7243</span> Unsupervised Part-of-Speech Tagging for Amharic Using K-Means Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zelalem%20Fantahun">Zelalem Fantahun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Part-of-speech tagging is the process of assigning a part-of-speech or other lexical class marker to each word into naturally occurring text. Part-of-speech tagging is the most fundamental and basic task almost in all natural language processing. In natural language processing, the problem of providing large amount of manually annotated data is a knowledge acquisition bottleneck. Since, Amharic is one of under-resourced language, the availability of tagged corpus is the bottleneck problem for natural language processing especially for POS tagging. A promising direction to tackle this problem is to provide a system that does not require manually tagged data. In unsupervised learning, the learner is not provided with classifications. Unsupervised algorithms seek out similarity between pieces of data in order to determine whether they can be characterized as forming a group. This paper explicates the development of unsupervised part-of-speech tagger using K-Means clustering for Amharic language since large amount of data is produced in day-to-day activities. In the development of the tagger, the following procedures are followed. First, the unlabeled data (raw text) is divided into 10 folds and tokenization phase takes place; at this level, the raw text is chunked at sentence level and then into words. The second phase is feature extraction which includes word frequency, syntactic and morphological features of a word. The third phase is clustering. Among different clustering algorithms, K-means is selected and implemented in this study that brings group of similar words together. The fourth phase is mapping, which deals with looking at each cluster carefully and the most common tag is assigned to a group. This study finds out two features that are capable of distinguishing one part-of-speech from others these are morphological feature and positional information and show that it is possible to use unsupervised learning for Amharic POS tagging. In order to increase performance of the unsupervised part-of-speech tagger, there is a need to incorporate other features that are not included in this study, such as semantic related information. Finally, based on experimental result, the performance of the system achieves a maximum of 81% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=POS%20tagging" title="POS tagging">POS tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=Amharic" title=" Amharic"> Amharic</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=k-means" title=" k-means"> k-means</a> </p> <a href="https://publications.waset.org/abstracts/84660/unsupervised-part-of-speech-tagging-for-amharic-using-k-means-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84660.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">451</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">7242</span> Contrastive Learning for Unsupervised Object Segmentation in Sequential Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tian%20Zhang">Tian Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unsupervised object segmentation aims at segmenting objects in sequential images and obtaining the mask of each object without any manual intervention. Unsupervised segmentation remains a challenging task due to the lack of prior knowledge about these objects. Previous methods often require manually specifying the action of each object, which is often difficult to obtain. Instead, this paper does not need action information of objects and automatically learns the actions and relations among objects from the structured environment. To obtain the object segmentation of sequential images, the relationships between objects and images are extracted to infer the action and interaction of objects based on the multi-head attention mechanism. Three types of objects’ relationships in the object segmentation task are proposed: the relationship between objects in the same frame, the relationship between objects in two frames, and the relationship between objects and historical information. Based on these relationships, the proposed model (1) is effective in multiple objects segmentation tasks, (2) just needs images as input, and (3) produces better segmentation results as more relationships are considered. The experimental results on multiple datasets show that this paper’s method achieves state-of-art performance. The quantitative and qualitative analyses of the result are conducted. The proposed method could be easily extended to other similar applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20object%20segmentation" title="unsupervised object segmentation">unsupervised object segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</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=structured%20environment" title=" structured environment"> structured environment</a> </p> <a href="https://publications.waset.org/abstracts/148401/contrastive-learning-for-unsupervised-object-segmentation-in-sequential-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148401.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">109</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">7241</span> Data-Driven Market Segmentation in Hospitality 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=Rik%20van%20Leeuwen">Rik van Leeuwen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ger%20Koole"> Ger Koole</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20cluster%20analysis" title="hierarchical cluster analysis">hierarchical cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hospitality" title=" hospitality"> hospitality</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20segmentation" title=" market segmentation"> market segmentation</a> </p> <a href="https://publications.waset.org/abstracts/151668/data-driven-market-segmentation-in-hospitality-using-unsupervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151668.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">108</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">7240</span> Deep Learning Based Unsupervised Sport Scene Recognition and Highlights Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ksenia%20Meshkova">Ksenia Meshkova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</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=representation%20learning" title=" representation learning"> representation learning</a>, <a href="https://publications.waset.org/abstracts/search?q=autoencoders" title=" autoencoders"> autoencoders</a> </p> <a href="https://publications.waset.org/abstracts/126046/deep-learning-based-unsupervised-sport-scene-recognition-and-highlights-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126046.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">127</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">7239</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">7238</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">7237</span> Evolving Knowledge Extraction from Online Resources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhibo%20Xiao">Zhibo Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Tharini%20Nayanika%20de%20Silva"> Tharini Nayanika de Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Kezhi%20Mao"> Kezhi Mao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolving%20learning" title="evolving learning">evolving learning</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title=" knowledge graph"> knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/61571/evolving-knowledge-extraction-from-online-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61571.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">458</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">7236</span> Biofilm Text Classifiers Developed Using Natural Language Processing and Unsupervised Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kanika%20Gupta">Kanika Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar"> Ashok Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biofilms%20literature" title="biofilms literature">biofilms literature</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers%20development" title=" classifiers development"> classifiers development</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=unsupervised%20learning%20approach" title=" unsupervised learning approach"> unsupervised learning approach</a>, <a href="https://publications.waset.org/abstracts/search?q=unstructured%20data" title=" unstructured data"> unstructured data</a>, <a href="https://publications.waset.org/abstracts/search?q=relational%20database" title=" relational database"> relational database</a> </p> <a href="https://publications.waset.org/abstracts/86445/biofilm-text-classifiers-developed-using-natural-language-processing-and-unsupervised-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86445.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">170</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">7235</span> A Comparative Analysis of Clustering Approaches for Understanding Patterns in Health Insurance Uptake: Evidence from Sociodemographic Kenyan Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nelson%20Kimeli%20Kemboi%20Yego">Nelson Kimeli Kemboi Yego</a>, <a href="https://publications.waset.org/abstracts/search?q=Juma%20Kasozi"> Juma Kasozi</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Nkruzinza"> Joseph Nkruzinza</a>, <a href="https://publications.waset.org/abstracts/search?q=Francis%20Kipkogei"> Francis Kipkogei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=health%20insurance" title="health insurance">health insurance</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=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</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/164178/a-comparative-analysis-of-clustering-approaches-for-understanding-patterns-in-health-insurance-uptake-evidence-from-sociodemographic-kenyan-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164178.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">138</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">7234</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">7233</span> Domain Adaptive Dense Retrieval with Query Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Yin">Rui Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Haojie%20Wang"> Haojie Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xun%20Li"> Xun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then, the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. We also explore contrastive learning as a method for training domain-adapted dense retrievers and show that it leads to strong performance in various retrieval settings. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dense%20retrieval" title="dense retrieval">dense retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20generation" title=" query generation"> query generation</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=unsupervised%20training" title=" unsupervised training"> unsupervised training</a> </p> <a href="https://publications.waset.org/abstracts/168338/domain-adaptive-dense-retrieval-with-query-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168338.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">7232</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">7231</span> Exploring Antimicrobial Resistance in the Lung Microbial Community 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=Camilo%20Cerda%20Sarabia">Camilo Cerda Sarabia</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernanda%20Bravo%20Cornejo"> Fernanda Bravo Cornejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Santibanez%20Oyarce"> Diego Santibanez Oyarce</a>, <a href="https://publications.waset.org/abstracts/search?q=Hugo%20Osses%20Prado"> Hugo Osses Prado</a>, <a href="https://publications.waset.org/abstracts/search?q=Esteban%20G%C3%B3mez%20Ter%C3%A1n"> Esteban Gómez Terán</a>, <a href="https://publications.waset.org/abstracts/search?q=Bel%C3%A9n%20Diaz%20Diaz"> Belén Diaz Diaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%BAl%20Caulier-Cisterna"> Raúl Caulier-Cisterna</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Vergara-Quezada"> Jorge Vergara-Quezada</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Moya-Beltr%C3%A1n"> Ana Moya-Beltrán</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antibiotic%20resistance" title="antibiotic resistance">antibiotic resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20community" title=" microbial community"> microbial community</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=sequences%20of%20%20AMR%20gene" title=" sequences of AMR gene"> sequences of AMR gene</a> </p> <a href="https://publications.waset.org/abstracts/190239/exploring-antimicrobial-resistance-in-the-lung-microbial-community-using-unsupervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190239.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">23</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">7230</span> Feature Evaluation Based on Random Subspace and Multiple-K Ensemble</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaehong%20Yu">Jaehong Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20analysis" title="clustering analysis">clustering analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple-k%20ensemble" title=" multiple-k ensemble"> multiple-k ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20subspace-based%20feature%20evaluation" title=" random subspace-based feature evaluation"> random subspace-based feature evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20feature%20ranking" title=" unsupervised feature ranking"> unsupervised feature ranking</a> </p> <a href="https://publications.waset.org/abstracts/52081/feature-evaluation-based-on-random-subspace-and-multiple-k-ensemble" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52081.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">339</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">7229</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">7228</span> Unsupervised Domain Adaptive Text Retrieval with Query Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Yin">Rui Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Haojie%20Wang"> Haojie Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xun%20Li"> Xun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dense%20retrieval" title="dense retrieval">dense retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20generation" title=" query generation"> query generation</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20training" title=" unsupervised training"> unsupervised training</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20retrieval" title=" text retrieval"> text retrieval</a> </p> <a href="https://publications.waset.org/abstracts/173903/unsupervised-domain-adaptive-text-retrieval-with-query-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173903.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">7227</span> Identifying Autism Spectrum Disorder Using Optimization-Based Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharifah%20Mousli">Sharifah Mousli</a>, <a href="https://publications.waset.org/abstracts/search?q=Sona%20Taheri"> Sona Taheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiayuan%20He"> Jiayuan He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autism%20spectrum%20disorder" title="autism spectrum disorder">autism spectrum disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20machine%20learning" title=" unsupervised machine learning"> unsupervised machine learning</a> </p> <a href="https://publications.waset.org/abstracts/173901/identifying-autism-spectrum-disorder-using-optimization-based-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173901.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">115</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">7226</span> Optimal Pricing Based on Real Estate Demand Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vanessa%20Kummer">Vanessa Kummer</a>, <a href="https://publications.waset.org/abstracts/search?q=Maik%20Meusel"> Maik Meusel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demand%20estimate" title="demand estimate">demand estimate</a>, <a href="https://publications.waset.org/abstracts/search?q=missing-data%20imputation" title=" missing-data imputation"> missing-data imputation</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20estate" title=" real estate"> real estate</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/63354/optimal-pricing-based-on-real-estate-demand-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63354.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">285</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">7225</span> Component Based Testing Using Clustering and Support Vector Machine </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iqbaldeep%20Kaur">Iqbaldeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Amarjeet%20Kaur"> Amarjeet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software Reusability is important part of software development. So component based software development in case of software testing has gained a lot of practical importance in the field of software engineering from academic researcher and also from software development industry perspective. Finding test cases for efficient reuse of test cases is one of the important problems aimed by researcher. Clustering reduce the search space, reuse test cases by grouping similar entities according to requirements ensuring reduced time complexity as it reduce the search time for retrieval the test cases. In this research paper we proposed approach for re-usability of test cases by unsupervised approach. In unsupervised learning we proposed k-mean and Support Vector Machine. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20testing" title="software testing">software testing</a>, <a href="https://publications.waset.org/abstracts/search?q=reusability" title=" reusability"> reusability</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=k-mean" title=" k-mean"> k-mean</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM "> SVM </a> </p> <a href="https://publications.waset.org/abstracts/32164/component-based-testing-using-clustering-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32164.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">430</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning&page=5">5</a></li> <li class="page-item"><a class="page-link" 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