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pre-processing</title> <meta name="description" content="Search results for: pre-processing"> <meta name="keywords" content="pre-processing"> <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="pre-processing"> <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> 154</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: pre-processing</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">154</span> Text Data Preprocessing Library: Bilingual Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kabil%20Boukhari">Kabil Boukhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on diﬀerent corpora and gave an interesting result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20preprocessing" title="text preprocessing">text preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</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=normalization" title=" normalization"> normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20generation" title=" text generation"> text generation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/150846/text-data-preprocessing-library-bilingual-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150846.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">153</span> LaPEA: Language for Preprocessing of Edge Applications in Smart Factory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masaki%20Sakai">Masaki Sakai</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsuyoshi%20Nakajima"> Tsuyoshi Nakajima</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuya%20Takahashi"> Kazuya Takahashi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to improve the productivity of a factory, it is often the case to create an inference model by collecting and analyzing operational data off-line and then to develop an edge application (EAP) that evaluates the quality of the products or diagnoses machine faults in real-time. To accelerate this development cycle, an edge application framework for the smart factory is proposed, which enables to create and modify EAPs based on prepared inference models. In the framework, the preprocessing component is the key part to make it work. This paper proposes a language for preprocessing of edge applications, called LaPEA, which can flexibly process several sensor data from machines into explanatory variables for an inference model, and proves that it meets the requirements for the preprocessing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20application%20framework" title="edge application framework">edge application framework</a>, <a href="https://publications.waset.org/abstracts/search?q=edgecross" title=" edgecross"> edgecross</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing%20language" title=" preprocessing language"> preprocessing language</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20factory" title=" smart factory"> smart factory</a> </p> <a href="https://publications.waset.org/abstracts/142882/lapea-language-for-preprocessing-of-edge-applications-in-smart-factory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142882.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">152</span> Optimized Preprocessing for Accurate and Efficient Bioassay Prediction with Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeff%20Clarine">Jeff Clarine</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Shyh%20Peng"> Chang-Shyh Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20Sang"> Daisy Sang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioassay" title="bioassay">bioassay</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=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20screen" title=" virtual screen"> virtual screen</a> </p> <a href="https://publications.waset.org/abstracts/77481/optimized-preprocessing-for-accurate-and-efficient-bioassay-prediction-with-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77481.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">274</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">151</span> The Implementation of the Javanese Lettered-Manuscript Image Preprocessing Stage Model on the Batak Lettered-Manuscript Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anastasia%20Rita%20Widiarti">Anastasia Rita Widiarti</a>, <a href="https://publications.waset.org/abstracts/search?q=Agus%20Harjoko"> Agus Harjoko</a>, <a href="https://publications.waset.org/abstracts/search?q=Marsono"> Marsono</a>, <a href="https://publications.waset.org/abstracts/search?q=Sri%20Hartati"> Sri Hartati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the results of a study to test whether the Javanese character manuscript image preprocessing model that have been more widely applied, can also be applied to segment of the Batak characters manuscripts. The treatment process begins by converting the input image into a binary image. After the binary image is cleaned of noise, then the segmentation lines using projection profile is conducted. If unclear histogram projection is found, then the smoothing process before production indexes line segments is conducted. For each line image which has been produced, then the segmentation scripts in the line is applied, with regard of the connectivity between pixels which making up the letters that there is no characters are truncated. From the results of manuscript preprocessing system prototype testing, it is obtained the information about the system truth percentage value on pieces of Pustaka Batak Podani Ma AjiMamisinon manuscript ranged from 65% to 87.68% with a confidence level of 95%. The value indicates the truth percentage shown the initial processing model in Javanese characters manuscript image can be applied also to the image of the Batak characters manuscript. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connected%20component" title="connected component">connected component</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=manuscript%20image" title=" manuscript image"> manuscript image</a>, <a href="https://publications.waset.org/abstracts/search?q=projection%20profiles" title=" projection profiles"> projection profiles</a> </p> <a href="https://publications.waset.org/abstracts/33231/the-implementation-of-the-javanese-lettered-manuscript-image-preprocessing-stage-model-on-the-batak-lettered-manuscript-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33231.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">400</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">150</span> Heuristic to Generate Random X-Monotone Polygons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamaljit%20Pati">Kamaljit Pati</a>, <a href="https://publications.waset.org/abstracts/search?q=Manas%20Kumar%20Mohanty"> Manas Kumar Mohanty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjib%20Sadhu"> Sanjib Sadhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A heuristic has been designed to generate a random simple monotone polygon from a given set of ‘n’ points lying on a 2-Dimensional plane. Our heuristic generates a random monotone polygon in O(n) time after O(nℓogn) preprocessing time which is improved over the previous work where a random monotone polygon is produced in the same O(n) time but the preprocessing time is O(k) for n < k < n2. However, our heuristic does not generate all possible random polygons with uniform probability. The space complexity of our proposed heuristic is O(n). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sorting" title="sorting">sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=monotone%20polygon" title=" monotone polygon"> monotone polygon</a>, <a href="https://publications.waset.org/abstracts/search?q=visibility" title=" visibility"> visibility</a>, <a href="https://publications.waset.org/abstracts/search?q=chain" title=" chain"> chain</a> </p> <a href="https://publications.waset.org/abstracts/19252/heuristic-to-generate-random-x-monotone-polygons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19252.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">427</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">149</span> Arabic Text Representation and Classification Methods: Current State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rami%20Ayadi">Rami Ayadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Maraoui"> Mohsen Maraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have presented a brief current state of the art for Arabic text representation and classification methods. We decomposed Arabic Task Classification into four categories. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title="text classification">text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=impact%20of%20preprocessing" title=" impact of preprocessing"> impact of preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title=" classification algorithms"> classification algorithms</a> </p> <a href="https://publications.waset.org/abstracts/10277/arabic-text-representation-and-classification-methods-current-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10277.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">469</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">148</span> Classification of Cochannel Signals Using Cyclostationary Signal Processing and Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bryan%20Crompton">Bryan Crompton</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Giger"> Daniel Giger</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanay%20Mehta"> Tanay Mehta</a>, <a href="https://publications.waset.org/abstracts/search?q=Apurva%20Mody"> Apurva Mody</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title="signal processing">signal processing</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=cyclostationary%20signal%20processing" title=" cyclostationary signal processing"> cyclostationary signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20classification" title=" signal classification"> signal classification</a> </p> <a href="https://publications.waset.org/abstracts/164958/classification-of-cochannel-signals-using-cyclostationary-signal-processing-and-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164958.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">107</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">147</span> Video Heart Rate Measurement for the Detection of Trauma-Related Stress States</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jarek%20Krajewski">Jarek Krajewski</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Daxberger"> David Daxberger</a>, <a href="https://publications.waset.org/abstracts/search?q=Luzi%20Beyer"> Luzi Beyer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Finding objective and non-intrusive measurements of emotional and psychopathological states (e.g., post-traumatic stress disorder, PTSD) is an important challenge. Thus, the proposed approach here uses Photoplethysmographic imaging (PPGI) applying facial RGB Cam videos to estimate heart rate levels. A pipeline for the signal processing of the raw image has been proposed containing different preprocessing approaches, e.g., Independent Component Analysis, Non-negative Matrix factorization, and various other artefact correction approaches. Under resting and constant light conditions, we reached a sensitivity of 84% for pulse peak detection. The results indicate that PPGI can be a suitable solution for providing heart rate data derived from these indirectly post-traumatic stress states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20rate" title="heart rate">heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=PTSD" title=" PTSD"> PTSD</a>, <a href="https://publications.waset.org/abstracts/search?q=PPGI" title=" PPGI"> PPGI</a>, <a href="https://publications.waset.org/abstracts/search?q=stress" title=" stress"> stress</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a> </p> <a href="https://publications.waset.org/abstracts/153938/video-heart-rate-measurement-for-the-detection-of-trauma-related-stress-states" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153938.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">124</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">146</span> Automated End-to-End Pipeline Processing Solution for Autonomous Driving</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Kumar">Ashish Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Munesh%20Raghuraj%20Varma"> Munesh Raghuraj Varma</a>, <a href="https://publications.waset.org/abstracts/search?q=Nisarg%20Joshi"> Nisarg Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gujjula%20Vishwa%20Teja"> Gujjula Vishwa Teja</a>, <a href="https://publications.waset.org/abstracts/search?q=Srikanth%20Sambi"> Srikanth Sambi</a>, <a href="https://publications.waset.org/abstracts/search?q=Arpit%20Awasthi"> Arpit Awasthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=augmentation" title="augmentation">augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20driving" title=" autonomous driving"> autonomous driving</a>, <a href="https://publications.waset.org/abstracts/search?q=camera" title=" camera"> camera</a>, <a href="https://publications.waset.org/abstracts/search?q=custom%20end-to-end%20pipeline" title=" custom end-to-end pipeline"> custom end-to-end pipeline</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20unification" title=" data unification"> data unification</a>, <a href="https://publications.waset.org/abstracts/search?q=lidar" title=" lidar"> lidar</a>, <a href="https://publications.waset.org/abstracts/search?q=post-processing" title=" post-processing"> post-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a> </p> <a href="https://publications.waset.org/abstracts/162488/automated-end-to-end-pipeline-processing-solution-for-autonomous-driving" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162488.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">123</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">145</span> Preprocessing and Fusion of Multiple Representation of Finger Vein patterns using Conventional and Machine Learning techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Trainys">Tomas Trainys</a>, <a href="https://publications.waset.org/abstracts/search?q=Algimantas%20Venckauskas"> Algimantas Venckauskas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-cryptography" title="bio-cryptography">bio-cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=biometrics" title=" biometrics"> biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptographic%20key%20generation" title=" cryptographic key generation"> cryptographic key generation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20security" title=" information security"> information security</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=finger%20vein%20method." title=" finger vein method."> finger vein method.</a> </p> <a href="https://publications.waset.org/abstracts/97366/preprocessing-and-fusion-of-multiple-representation-of-finger-vein-patterns-using-conventional-and-machine-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97366.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">150</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">144</span> Improving the Performance of Deep Learning in Facial Emotion Recognition with Image Sharpening</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ksheeraj%20Sai%20Vepuri">Ksheeraj Sai Vepuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Nada%20Attar"> Nada Attar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=facial%20expression%20recognittion" title="facial expression recognittion">facial expression recognittion</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20preprocessing" title=" image preprocessing"> image preprocessing</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=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/130679/improving-the-performance-of-deep-learning-in-facial-emotion-recognition-with-image-sharpening" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130679.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">143</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">143</span> Mean Shift-Based Preprocessing Methodology for Improved 3D Buildings Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikolaos%20Vassilas">Nikolaos Vassilas</a>, <a href="https://publications.waset.org/abstracts/search?q=Theocharis%20Tsenoglou"> Theocharis Tsenoglou</a>, <a href="https://publications.waset.org/abstracts/search?q=Djamchid%20Ghazanfarpour"> Djamchid Ghazanfarpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we explore the capability of the mean shift algorithm as a powerful preprocessing tool for improving the quality of spatial data, acquired from airborne scanners, from densely built urban areas. On one hand, high resolution image data corrupted by noise caused by lossy compression techniques are appropriately smoothed while at the same time preserving the optical edges and, on the other, low resolution LiDAR data in the form of normalized Digital Surface Map (nDSM) is upsampled through the joint mean shift algorithm. Experiments on both the edge-preserving smoothing and upsampling capabilities using synthetic RGB-z data show that the mean shift algorithm is superior to bilateral filtering as well as to other classical smoothing and upsampling algorithms. Application of the proposed methodology for 3D reconstruction of buildings of a pilot region of Athens, Greece results in a significant visual improvement of the 3D building block model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20buildings%20reconstruction" title="3D buildings reconstruction">3D buildings reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20upsampling" title=" data upsampling"> data upsampling</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20shift" title=" mean shift"> mean shift</a> </p> <a href="https://publications.waset.org/abstracts/23279/mean-shift-based-preprocessing-methodology-for-improved-3d-buildings-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23279.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">315</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">142</span> Estimation of Chronic Kidney Disease Using Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilker%20Ali%20Ozkan">Ilker Ali Ozkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation" title="estimation">estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=chronic%20kidney%20failure%20disease" title=" chronic kidney failure disease"> chronic kidney failure disease</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20diagnosis" title=" disease diagnosis"> disease diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/38766/estimation-of-chronic-kidney-disease-using-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38766.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">141</span> An Efficient Machine Learning Model to Detect Metastatic Cancer in Pathology Scans Using Principal Component Analysis Algorithm, Genetic Algorithm, and Classification Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bliss%20Singhal">Bliss Singhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer. <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=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors" title=" k-nearest neighbors"> k-nearest neighbors</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/164933/an-efficient-machine-learning-model-to-detect-metastatic-cancer-in-pathology-scans-using-principal-component-analysis-algorithm-genetic-algorithm-and-classification-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164933.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">82</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">140</span> Clothes Identification Using Inception ResNet V2 and MobileNet V2</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subodh%20Chandra%20Shakya">Subodh Chandra Shakya</a>, <a href="https://publications.waset.org/abstracts/search?q=Badal%20Shrestha"> Badal Shrestha</a>, <a href="https://publications.waset.org/abstracts/search?q=Suni%20Thapa"> Suni Thapa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Chauhan"> Ashutosh Chauhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Saugat%20Adhikari"> Saugat Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inception%20ResNet" title="inception ResNet">inception ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20net" title=" convolutional neural net"> convolutional neural net</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=confusion%20matrix" title=" confusion matrix"> confusion matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20preprocessing" title=" data preprocessing"> data preprocessing</a> </p> <a href="https://publications.waset.org/abstracts/129604/clothes-identification-using-inception-resnet-v2-and-mobilenet-v2" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129604.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">187</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">139</span> Email Phishing Detection Using Natural Language Processing and Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Hilani">M. Hilani</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Nassih"> B. Nassih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Phishing is one of the oldest and best known scams on the Internet. It can be defined as any type of telecommunications fraud that uses social engineering tricks to obtain confidential data from its victims. It’s a cybercrime aimed at stealing your sensitive information. Phishing is generally done via private email, so scammers impersonate large companies or other trusted entities to encourage victims to voluntarily provide information such as login credentials or, worse yet, credit card numbers. The COVID-19 theme is used by cybercriminals in multiple malicious campaigns like phishing. In this environment, messaging filtering solutions have become essential to protect devices that will now be used outside of the secure perimeter. Despite constantly updating methods to avoid these cyberattacks, the end result is currently insufficient. Many researchers are looking for optimal solutions to filter phishing emails, but we still need good results. In this work, we concentrated on solving the problem of detecting phishing emails using the different steps of NLP preprocessing, and we proposed and trained a model using one-dimensional CNN. Our study results show that our model obtained an accuracy of 99.99%, which demonstrates how well our model is working. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phishing" title="phishing">phishing</a>, <a href="https://publications.waset.org/abstracts/search?q=e-mail" title=" e-mail"> e-mail</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP%20preprocessing" title=" NLP preprocessing"> NLP preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=e-mail%20filtering" title=" e-mail filtering"> e-mail filtering</a> </p> <a href="https://publications.waset.org/abstracts/157571/email-phishing-detection-using-natural-language-processing-and-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157571.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">126</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">138</span> Sentiment Analysis: An Enhancement of Ontological-Based Features Extraction Techniques and Word Equations </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Ridzwan%20Yaakub">Mohd Ridzwan Yaakub</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Iqbal%20Abu%20Latiffi"> Muhammad Iqbal Abu Latiffi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion" title=" opinion"> opinion</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing%20data" title=" preprocessing data"> preprocessing data</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis "> sentiment analysis </a> </p> <a href="https://publications.waset.org/abstracts/88316/sentiment-analysis-an-enhancement-of-ontological-based-features-extraction-techniques-and-word-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88316.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">200</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">137</span> Optimizing Machine Learning Through Python Based Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinidhi.%20A">Srinidhi. A</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Twinkle%20Hareendran"> Twinkle Hareendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Vriksha%20Prakash"> Vriksha Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20applications" title=" machine learning applications"> machine learning applications</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20matching" title=" template matching"> template matching</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a> </p> <a href="https://publications.waset.org/abstracts/193107/optimizing-machine-learning-through-python-based-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193107.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">15</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">136</span> Microarray Data Visualization and Preprocessing Using R and Bioconductor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruchi%20Yadav">Ruchi Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivani%20Pandey"> Shivani Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Prachi%20Srivastava"> Prachi Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microarrays provide a rich source of data on the molecular working of cells. Each microarray reports on the abundance of tens of thousands of mRNAs. Virtually every human disease is being studied using microarrays with the hope of finding the molecular mechanisms of disease. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation. A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project based on the R programming language. This paper describes specific procedures for conducting quality assessment, visualization and preprocessing of Affymetrix Gene Chip and also details the different bioconductor packages used to analyze affymetrix microarray data and describe the analysis and outcome of each plots. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microarray%20analysis" title="microarray analysis">microarray analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20language" title=" R language"> R language</a>, <a href="https://publications.waset.org/abstracts/search?q=affymetrix%20visualization" title=" affymetrix visualization"> affymetrix visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=bioconductor" title=" bioconductor "> bioconductor </a> </p> <a href="https://publications.waset.org/abstracts/20198/microarray-data-visualization-and-preprocessing-using-r-and-bioconductor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20198.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">480</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">135</span> A New Approach of Preprocessing with SVM Optimization Based on PSO for Bearing Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tawfik%20Thelaidjia">Tawfik Thelaidjia</a>, <a href="https://publications.waset.org/abstracts/search?q=Salah%20Chenikher"> Salah Chenikher </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title="condition monitoring">condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=kurtosis" title=" kurtosis"> kurtosis</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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=roller%20bearing" title=" roller bearing"> roller bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=rotating%20machines" title=" rotating machines"> rotating machines</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20measurement" title=" vibration measurement "> vibration measurement </a> </p> <a href="https://publications.waset.org/abstracts/2554/a-new-approach-of-preprocessing-with-svm-optimization-based-on-pso-for-bearing-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2554.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">437</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">134</span> High-Resolution ECG Automated Analysis and Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayad%20Dalloo">Ayad Dalloo</a>, <a href="https://publications.waset.org/abstracts/search?q=Sulaf%20Dalloo"> Sulaf Dalloo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20diagnostic%20system" title="ECG diagnostic system">ECG diagnostic system</a>, <a href="https://publications.waset.org/abstracts/search?q=QRS%20detection" title=" QRS detection"> QRS detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20baseline%20removal" title=" ECG baseline removal"> ECG baseline removal</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiovascular%20diseases" title=" cardiovascular diseases"> cardiovascular diseases</a> </p> <a href="https://publications.waset.org/abstracts/64108/high-resolution-ecg-automated-analysis-and-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64108.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">297</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">133</span> Brain Tumor Segmentation Based on Minimum Spanning Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simeon%20Mayala">Simeon Mayala</a>, <a href="https://publications.waset.org/abstracts/search?q=Ida%20Herdlev%C3%A6r"> Ida Herdlevær</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonas%20Bull%20Haugs%C3%B8en"> Jonas Bull Haugsøen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamundeeswari%20Anandan"> Shamundeeswari Anandan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonia%20Gavasso"> Sonia Gavasso</a>, <a href="https://publications.waset.org/abstracts/search?q=Morten%20Brun"> Morten Brun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the standard gold segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title="brain tumor">brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor%20segmentation" title=" brain tumor segmentation"> brain tumor segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20spanning%20tree" title=" minimum spanning tree"> minimum spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/148591/brain-tumor-segmentation-based-on-minimum-spanning-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148591.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">132</span> Principle Component Analysis on Colon Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20K.%20Caecar%20Pratiwi">N. K. Caecar Pratiwi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunendah%20Nur%20Fuadah"> Yunendah Nur Fuadah</a>, <a href="https://publications.waset.org/abstracts/search?q=Rita%20Magdalena"> Rita Magdalena</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20D.%20Atmaja"> R. D. Atmaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Saidah"> Sofia Saidah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ocky%20Tiaramukti"> Ocky Tiaramukti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=carcinoma" title="carcinoma">carcinoma</a>, <a href="https://publications.waset.org/abstracts/search?q=colorectal%20cancer" title=" colorectal cancer"> colorectal cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbor" title=" k-nearest neighbor"> k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=lymphoma" title=" lymphoma"> lymphoma</a>, <a href="https://publications.waset.org/abstracts/search?q=principle%20component%20analysis" title=" principle component analysis"> principle component analysis</a> </p> <a href="https://publications.waset.org/abstracts/105607/principle-component-analysis-on-colon-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105607.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">205</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">131</span> Assessment of Pre-Processing Influence on Near-Infrared Spectra for Predicting the Mechanical Properties of Wood</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aasheesh%20Raturi">Aasheesh Raturi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vimal%20Kothiyal"> Vimal Kothiyal</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20D.%20Semalty"> P. D. Semalty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FT-NIR" title="FT-NIR">FT-NIR</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20properties" title=" mechanical properties"> mechanical properties</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-processing" title=" pre-processing"> pre-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=PLS" title=" PLS"> PLS</a> </p> <a href="https://publications.waset.org/abstracts/62047/assessment-of-pre-processing-influence-on-near-infrared-spectra-for-predicting-the-mechanical-properties-of-wood" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62047.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">362</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">130</span> Deep Learning for Qualitative and Quantitative Grain Quality Analysis Using Hyperspectral Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ole-Christian%20Galbo%20Engstr%C3%B8m">Ole-Christian Galbo Engstrøm</a>, <a href="https://publications.waset.org/abstracts/search?q=Erik%20Schou%20Dreier"> Erik Schou Dreier</a>, <a href="https://publications.waset.org/abstracts/search?q=Birthe%20M%C3%B8ller%20Jespersen"> Birthe Møller Jespersen</a>, <a href="https://publications.waset.org/abstracts/search?q=Kim%20Steenstrup%20Pedersen"> Kim Steenstrup Pedersen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type. <p class="card-text"><strong>Keywords:</strong> <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=grain%20analysis" title=" grain analysis"> grain analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20imaging" title=" hyperspectral imaging"> hyperspectral imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing%20techniques" title=" preprocessing techniques"> preprocessing techniques</a> </p> <a href="https://publications.waset.org/abstracts/157339/deep-learning-for-qualitative-and-quantitative-grain-quality-analysis-using-hyperspectral-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157339.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">99</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">129</span> Selecting the Best Sub-Region Indexing the Images in the Case of Weak Segmentation Based on Local Color Histograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mawloud%20Mosbah">Mawloud Mosbah</a>, <a href="https://publications.waset.org/abstracts/search?q=Bachir%20Boucheham"> Bachir Boucheham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color Histogram is considered as the oldest method used by CBIR systems for indexing images. In turn, the global histograms do not include the spatial information; this is why the other techniques coming later have attempted to encounter this limitation by involving the segmentation task as a preprocessing step. The weak segmentation is employed by the local histograms while other methods as CCV (Color Coherent Vector) are based on strong segmentation. The indexation based on local histograms consists of splitting the image into N overlapping blocks or sub-regions, and then the histogram of each block is computed. The dissimilarity between two images is reduced, as consequence, to compute the distance between the N local histograms of the both images resulting then in N*N values; generally, the lowest value is taken into account to rank images, that means that the lowest value is that which helps to designate which sub-region utilized to index images of the collection being asked. In this paper, we make under light the local histogram indexation method in the hope to compare the results obtained against those given by the global histogram. We address also another noteworthy issue when Relying on local histograms namely which value, among N*N values, to trust on when comparing images, in other words, which sub-region among the N*N sub-regions on which we base to index images. Based on the results achieved here, it seems that relying on the local histograms, which needs to pose an extra overhead on the system by involving another preprocessing step naming segmentation, does not necessary mean that it produces better results. In addition to that, we have proposed here some ideas to select the local histogram on which we rely on to encode the image rather than relying on the local histogram having lowest distance with the query histograms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CBIR" title="CBIR">CBIR</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20global%20histogram" title=" color global histogram"> color global histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20local%20histogram" title=" color local histogram"> color local histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20segmentation" title=" weak segmentation"> weak segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20distance" title=" Euclidean distance"> Euclidean distance</a> </p> <a href="https://publications.waset.org/abstracts/14435/selecting-the-best-sub-region-indexing-the-images-in-the-case-of-weak-segmentation-based-on-local-color-histograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14435.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">359</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">128</span> An Adaptive Oversampling Technique for Imbalanced Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaukat%20Ali%20Shahee">Shaukat Ali Shahee</a>, <a href="https://publications.waset.org/abstracts/search?q=Usha%20Ananthakumar"> Usha Ananthakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20dataset" title=" imbalanced dataset"> imbalanced dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=Lowner-John%20ellipsoid" title=" Lowner-John ellipsoid"> Lowner-John ellipsoid</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20based%20clustering" title=" model based clustering"> model based clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=oversampling" title=" oversampling"> oversampling</a> </p> <a href="https://publications.waset.org/abstracts/83833/an-adaptive-oversampling-technique-for-imbalanced-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83833.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">418</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">127</span> Determination of Physical Properties of Crude Oil Distillates by Near-Infrared Spectroscopy and Multivariate Calibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayten%20Ekin%20Me%C5%9Fe">Ayten Ekin Meşe</a>, <a href="https://publications.waset.org/abstracts/search?q=Selahattin%20%C5%9Eent%C3%BCrk"> Selahattin Şentürk</a>, <a href="https://publications.waset.org/abstracts/search?q=Melike%20Duvano%C4%9Flu"> Melike Duvanoğlu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Petroleum refineries are a highly complex process industry with continuous production and high operating costs. Physical separation of crude oil starts with the crude oil distillation unit, continues with various conversion and purification units, and passes through many stages until obtaining the final product. To meet the desired product specification, process parameters are strictly followed. To be able to ensure the quality of distillates, routine analyses are performed in quality control laboratories based on appropriate international standards such as American Society for Testing and Materials (ASTM) standard methods and European Standard (EN) methods. The cut point of distillates in the crude distillation unit is very crucial for the efficiency of the upcoming processes. In order to maximize the process efficiency, the determination of the quality of distillates should be as fast as possible, reliable, and cost-effective. In this sense, an alternative study was carried out on the crude oil distillation unit that serves the entire refinery process. In this work, studies were conducted with three different crude oil distillates which are Light Straight Run Naphtha (LSRN), Heavy Straight Run Naphtha (HSRN), and Kerosene. These products are named after separation by the number of carbons it contains. LSRN consists of five to six carbon-containing hydrocarbons, HSRN consist of six to ten, and kerosene consists of sixteen to twenty-two carbon-containing hydrocarbons. Physical properties of three different crude distillation unit products (LSRN, HSRN, and Kerosene) were determined using Near-Infrared Spectroscopy with multivariate calibration. The absorbance spectra of the petroleum samples were obtained in the range from 10000 cm⁻¹ to 4000 cm⁻¹, employing a quartz transmittance flow through cell with a 2 mm light path and a resolution of 2 cm⁻¹. A total of 400 samples were collected for each petroleum sample for almost four years. Several different crude oil grades were processed during sample collection times. Extended Multiplicative Signal Correction (EMSC) and Savitzky-Golay (SG) preprocessing techniques were applied to FT-NIR spectra of samples to eliminate baseline shifts and suppress unwanted variation. Two different multivariate calibration approaches (Partial Least Squares Regression, PLS and Genetic Inverse Least Squares, GILS) and an ensemble model were applied to preprocessed FT-NIR spectra. Predictive performance of each multivariate calibration technique and preprocessing techniques were compared, and the best models were chosen according to the reproducibility of ASTM reference methods. This work demonstrates the developed models can be used for routine analysis instead of conventional analytical methods with over 90% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crude%20distillation%20unit" title="crude distillation unit">crude distillation unit</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20calibration" title=" multivariate calibration"> multivariate calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=near%20infrared%20spectroscopy" title=" near infrared spectroscopy"> near infrared spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20preprocessing" title=" data preprocessing"> data preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=refinery" title=" refinery"> refinery</a> </p> <a href="https://publications.waset.org/abstracts/153031/determination-of-physical-properties-of-crude-oil-distillates-by-near-infrared-spectroscopy-and-multivariate-calibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153031.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">131</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">126</span> A Comparison of Convolutional Neural Network Architectures for the Classification of Alzheimer’s Disease Patients Using MRI Scans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Premoli">Tomas Premoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Sareh%20Rowlands"> Sareh Rowlands</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%E2%80%99s%20disease" title="Alzheimer’s disease">Alzheimer’s disease</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</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=medical%20imaging" title=" medical imaging"> medical imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a> </p> <a href="https://publications.waset.org/abstracts/176580/a-comparison-of-convolutional-neural-network-architectures-for-the-classification-of-alzheimers-disease-patients-using-mri-scans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176580.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">125</span> Agile Software Effort Estimation Using Regression Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mikiyas%20Adugna">Mikiyas Adugna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agile%20software%20development" title="agile software development">agile software development</a>, <a href="https://publications.waset.org/abstracts/search?q=effort%20estimation" title=" effort estimation"> effort estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=elastic%20net%20regression" title=" elastic net regression"> elastic net regression</a>, <a href="https://publications.waset.org/abstracts/search?q=LASSO" title=" LASSO"> LASSO</a> </p> <a href="https://publications.waset.org/abstracts/182726/agile-software-effort-estimation-using-regression-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182726.pdf" target="_blank" class="btn btn-primary 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