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detection methods</title> <meta name="description" content="Search results for: exoplanet detection methods"> <meta name="keywords" content="exoplanet detection methods"> <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 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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="exoplanet detection methods"> <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> 17769</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: exoplanet detection methods</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17769</span> Discovery of Exoplanets in Kepler Data Using a Graphics Processing Unit Fast Folding Method and a Deep Learning Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Wang">Kevin Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Ge"> Jian Ge</a>, <a href="https://publications.waset.org/abstracts/search?q=Yinan%20Zhao"> Yinan Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Willis"> Kevin Willis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=astronomy%20data%20analysis" title=" astronomy data analysis"> astronomy data analysis</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=exoplanet%20detection%20methods" title=" exoplanet detection methods"> exoplanet detection methods</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20planets" title=" small planets"> small planets</a>, <a href="https://publications.waset.org/abstracts/search?q=habitable%20planets" title=" habitable planets"> habitable planets</a>, <a href="https://publications.waset.org/abstracts/search?q=transit%20%20photometry" title=" transit photometry"> transit photometry</a> </p> <a href="https://publications.waset.org/abstracts/140507/discovery-of-exoplanets-in-kepler-data-using-a-graphics-processing-unit-fast-folding-method-and-a-deep-learning-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140507.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">224</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">17768</span> AI/ML Atmospheric Parameters Retrieval Using the “Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN)”</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Monahan">Thomas Monahan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Gorius"> Nicolas Gorius</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanh%20Nguyen"> Thanh Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus. <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=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20computing" title=" edge computing"> edge computing</a>, <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20parameters%20retrieval" title=" atmospheric parameters retrieval"> atmospheric parameters retrieval</a> </p> <a href="https://publications.waset.org/abstracts/143382/aiml-atmospheric-parameters-retrieval-using-the-atmospheric-retrievals-conditional-generative-adversarial-network-arcgan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17767</span> Subjective Evaluation of Mathematical Morphology Edge Detection on Computed Tomography (CT) Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emhimed%20Saffor">Emhimed Saffor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection "> edge detection </a> </p> <a href="https://publications.waset.org/abstracts/44926/subjective-evaluation-of-mathematical-morphology-edge-detection-on-computed-tomography-ct-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44926.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">336</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">17766</span> A Framework for Review Spam Detection Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadali%20Tavakoli">Mohammadali Tavakoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Atefeh%20Heydari"> Atefeh Heydari</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuriati%20Ismail"> Zuriati Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Naomie%20Salim"> Naomie Salim </a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a high-quality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fake%20reviews" title="fake reviews">fake reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20collection" title=" feature collection"> feature collection</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20spam" title=" opinion spam"> opinion spam</a>, <a href="https://publications.waset.org/abstracts/search?q=spam%20detection" title=" spam detection"> spam detection</a> </p> <a href="https://publications.waset.org/abstracts/42751/a-framework-for-review-spam-detection-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42751.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">413</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">17765</span> Rapid and Culture-Independent Detection of Staphylococcus Aureus by PCR Based Protocols</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Verma">V. Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Riyaz-ul-Hassan"> Syed Riyaz-ul-Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Staphylococcus aureus is one of the most commonly found pathogenic bacteria and is hard to eliminate from the human environment. It is responsible for many nosocomial infections, besides being the main causative agent of food intoxication by virtue of its variety of enterotoxins. Routine detection of S. aureus in food is usually carried out by traditional methods based on morphological and biochemical characterization. These methods are time-consuming and tedious. In addition, misclassifications with automated susceptibility testing systems or commercially available latex agglutination kits have been reported by several workers. Consequently, there is a need for methods to specifically discriminate S. aureus from other staphylococci as quickly as possible. Data on protocols developed using molecular means like PCR technology will be presented for rapid and specific detection of this pathogen in food, clinical and environmental samples, especially milk. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20Pathogens" title="food Pathogens">food Pathogens</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR%20technology" title=" PCR technology"> PCR technology</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20and%20specific%20detection" title=" rapid and specific detection"> rapid and specific detection</a>, <a href="https://publications.waset.org/abstracts/search?q=staphylococcus%20aureus" title=" staphylococcus aureus"> staphylococcus aureus</a> </p> <a href="https://publications.waset.org/abstracts/42644/rapid-and-culture-independent-detection-of-staphylococcus-aureus-by-pcr-based-protocols" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42644.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">513</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">17764</span> Detection of Parkinsonian Freezing of Gait</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sang-Hoon%20Park">Sang-Hoon Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeji%20Ho"> Yeji Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=Gwang-Moon%20Eom"> Gwang-Moon Eom</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fast and accurate detection of Freezing of Gait (FOG) is desirable for appropriate application of cueing which has been shown to ameliorate FOG. Utilization of frequency spectrum of leg acceleration to derive the freeze index requires much calculation and it would lead to delayed cueing. We hypothesized that FOG can be reasonably detected from the time domain amplitude of foot acceleration. A time instant was recognized as FOG if the mean amplitude of the acceleration in the time window surrounding the time instant was in the specific FOG range. Parameters required in the FOG detection was optimized by simulated annealing. The suggested time domain methods showed performances comparable to those of frequency domain methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=freezing%20of%20gait" title="freezing of gait">freezing of gait</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Parkinson%27s%20disease" title=" Parkinson's disease"> Parkinson's disease</a>, <a href="https://publications.waset.org/abstracts/search?q=time-domain%20method" title=" time-domain method"> time-domain method</a> </p> <a href="https://publications.waset.org/abstracts/4337/detection-of-parkinsonian-freezing-of-gait" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4337.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">444</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">17763</span> A Comparative Study of Virus Detection Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sulaiman%20Al%20amro">Sulaiman Al amro</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Alkhalifah"> Ali Alkhalifah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20viruses" title="computer viruses">computer viruses</a>, <a href="https://publications.waset.org/abstracts/search?q=virus%20detection" title=" virus detection"> virus detection</a>, <a href="https://publications.waset.org/abstracts/search?q=signature-based" title=" signature-based"> signature-based</a>, <a href="https://publications.waset.org/abstracts/search?q=behaviour-based" title=" behaviour-based"> behaviour-based</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic-based" title=" heuristic-based "> heuristic-based </a> </p> <a href="https://publications.waset.org/abstracts/28688/a-comparative-study-of-virus-detection-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28688.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">484</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">17762</span> Iris Detection on RGB Image for Controlling Side Mirror</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norzalina%20Othman">Norzalina Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Na%E2%80%99imy%20Wan"> Nurul Na’imy Wan</a>, <a href="https://publications.waset.org/abstracts/search?q=Azliza%20Mohd%20Rusli"> Azliza Mohd Rusli</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Noor%20Syahirah%20Meor%20Idris"> Wan Noor Syahirah Meor Idris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Iris detection is a process where the position of the eyes is extracted from the face images. It is a current method used for many applications such as for security purpose and drowsiness detection. This paper proposes the use of eyes detection in controlling side mirror of motor vehicles. The eyes detection method aims to make driver easy to adjust the side mirrors automatically. The system will determine the midpoint coordinate of eyes detection on RGB (color) image and the input signal from y-coordinate will send it to controller in order to rotate the angle of side mirror on vehicle. The eye position was cropped and the coordinate of midpoint was successfully detected from the circle of iris detection using Viola Jones detection and circular Hough transform methods on RGB image. The coordinate of midpoint from the experiment are tested using controller to determine the angle of rotation on the side mirrors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris%20detection" title="iris detection">iris detection</a>, <a href="https://publications.waset.org/abstracts/search?q=midpoint%20coordinates" title=" midpoint coordinates"> midpoint coordinates</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20images" title=" RGB images"> RGB images</a>, <a href="https://publications.waset.org/abstracts/search?q=side%20mirror" title=" side mirror"> side mirror</a> </p> <a href="https://publications.waset.org/abstracts/8133/iris-detection-on-rgb-image-for-controlling-side-mirror" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8133.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">423</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">17761</span> Advancing in Cricket Analytics: Novel Approaches for Pitch and Ball Detection Employing OpenCV and YOLOV8</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratham%20Madnur">Pratham Madnur</a>, <a href="https://publications.waset.org/abstracts/search?q=Prathamkumar%20Shetty"> Prathamkumar Shetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneha%20Varur"> Sneha Varur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gouri%20Parashetti"> Gouri Parashetti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title="OpenCV">OpenCV</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title=" YOLOv8"> YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=cricket" title=" cricket"> cricket</a>, <a href="https://publications.waset.org/abstracts/search?q=custom%20dataset" title=" custom dataset"> custom dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=sports" title=" sports"> sports</a> </p> <a href="https://publications.waset.org/abstracts/182020/advancing-in-cricket-analytics-novel-approaches-for-pitch-and-ball-detection-employing-opencv-and-yolov8" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182020.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">80</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">17760</span> Protein Remote Homology Detection and Fold Recognition by Combining Profiles with Kernel Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu">Bin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=protein%20remote%20homology%20detection" title="protein remote homology detection">protein remote homology detection</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20fold%20recognition" title=" protein fold recognition"> protein fold recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=profile-based%20features" title=" profile-based features"> profile-based features</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machines%20%28SVMs%29" title=" Support Vector Machines (SVMs)"> Support Vector Machines (SVMs)</a> </p> <a href="https://publications.waset.org/abstracts/104054/protein-remote-homology-detection-and-fold-recognition-by-combining-profiles-with-kernel-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104054.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">161</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">17759</span> Flicker Detection with Motion Tolerance for Embedded Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianrong%20Wu">Jianrong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuan%20Fu"> Xuan Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Akihiro%20Higashi"> Akihiro Higashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiming%20Tan"> Zhiming Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=illumination%20flicker" title="illumination flicker">illumination flicker</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20camera" title=" embedded camera"> embedded camera</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20shutter" title=" rolling shutter"> rolling shutter</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/14449/flicker-detection-with-motion-tolerance-for-embedded-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14449.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">420</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">17758</span> Prevention of Road Accidents by Computerized Drowsiness Detection System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ujjal%20Chattaraj">Ujjal Chattaraj</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20C.%20Dasbebartta"> P. C. Dasbebartta</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Bhuyan"> S. Bhuyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to propose a method to detect the action of the driver’s eyes, using the concept of face detection. There are three major key contributing methods which can rapidly process the framework of the facial image and hence produce results which further can program the reactions of the vehicles as pre-programmed for the traffic safety. This paper compares and analyses the methods on the basis of their reaction time and their ability to deal with fluctuating images of the driver. The program used in this study is simple and efficient, built using the AdaBoost learning algorithm. Through this program, the system would be able to discard background regions and focus on the face-like regions. The results are analyzed on a common computer which makes it feasible for the end users. The application domain of this experiment is quite wide, such as detection of drowsiness or influence of alcohols in drivers or detection for the case of identification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AdaBoost%20learning%20algorithm" title="AdaBoost learning algorithm">AdaBoost learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</a>, <a href="https://publications.waset.org/abstracts/search?q=framework" title=" framework"> framework</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20safety" title=" traffic safety"> traffic safety</a> </p> <a href="https://publications.waset.org/abstracts/97960/prevention-of-road-accidents-by-computerized-drowsiness-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97960.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17757</span> Rapid Detection System of Airborne Pathogens</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shigenori%20Togashi">Shigenori Togashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kei%20Takenaka"> Kei Takenaka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We developed new processes which can collect and detect rapidly airborne pathogens such as the avian flu virus for the pandemic prevention. The fluorescence antibody technique is known as one of high-sensitive detection methods for viruses, but this needs up to a few hours to bind sufficient fluorescence dyes to viruses for detection. In this paper, we developed a mist-labeling can detect substitution viruses in a short time to improve the binding rate of fluorescent dyes and substitution viruses by the micro reaction process. Moreover, we developed the rapid detection system with the above 'mist labeling'. The detection system set with a sampling bag collecting patient’s breath and a cartridge can detect automatically pathogens within 10 minutes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=viruses" title="viruses">viruses</a>, <a href="https://publications.waset.org/abstracts/search?q=sampler" title=" sampler"> sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=mist" title=" mist"> mist</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fluorescent%20dyes" title=" fluorescent dyes"> fluorescent dyes</a>, <a href="https://publications.waset.org/abstracts/search?q=microreaction" title=" microreaction"> microreaction</a> </p> <a href="https://publications.waset.org/abstracts/2700/rapid-detection-system-of-airborne-pathogens" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2700.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">475</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">17756</span> Development of Cost-effective Sensitive Methods for Pathogen Detection in Community Wastewater for Disease Surveillance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jesmin%20Akter">Jesmin Akter</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang%20Hyuk%20Ahn"> Chang Hyuk Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilho%20Kim"> Ilho Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaiyeop%20Lee"> Jaiyeop Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rapid%20detection" title="rapid detection">rapid detection</a>, <a href="https://publications.waset.org/abstracts/search?q=SARS-CoV-2" title=" SARS-CoV-2"> SARS-CoV-2</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitive%20detection" title=" sensitive detection"> sensitive detection</a>, <a href="https://publications.waset.org/abstracts/search?q=wastewater%20surveillance" title=" wastewater surveillance"> wastewater surveillance</a> </p> <a href="https://publications.waset.org/abstracts/167037/development-of-cost-effective-sensitive-methods-for-pathogen-detection-in-community-wastewater-for-disease-surveillance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167037.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">85</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">17755</span> Attention-Based Spatio-Temporal Approach for Fire and Smoke Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Mirrashid">Alireza Mirrashid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Khoshbin"> Mohammad Khoshbin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Atghaei"> Ali Atghaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Shahbazi"> Hassan Shahbazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention" title="attention">attention</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20detection" title=" fire detection"> fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=smoke%20detection" title=" smoke detection"> smoke detection</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal" title=" spatio-temporal"> spatio-temporal</a> </p> <a href="https://publications.waset.org/abstracts/153248/attention-based-spatio-temporal-approach-for-fire-and-smoke-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153248.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">203</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">17754</span> Bayesian Prospective Detection of Small Area Health Anomalies Using Kullback Leibler Divergence </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chawarat%20Rotejanaprasert">Chawarat Rotejanaprasert</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Lawson"> Andrew Lawson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title="Bayesian">Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial" title=" spatial"> spatial</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal" title=" temporal"> temporal</a>, <a href="https://publications.waset.org/abstracts/search?q=surveillance" title=" surveillance"> surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=prospective" title=" prospective"> prospective</a> </p> <a href="https://publications.waset.org/abstracts/52142/bayesian-prospective-detection-of-small-area-health-anomalies-using-kullback-leibler-divergence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52142.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">311</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">17753</span> Cross Site Scripting (XSS) Attack and Automatic Detection Technology Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tao%20Feng">Tao Feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Wei%20Zhang"> Wei-Wei Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Ming%20Ding"> Chang-Ming Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cross-site scripting (XSS) is one of the most popular WEB Attacking methods at present, and also one of the most risky web attacks. Because of the population of JavaScript, the scene of the cross site scripting attack is also gradually expanded. However, since the web application developers tend to only focus on functional testing and lack the awareness of the XSS, which has made the on-line web projects exist many XSS vulnerabilities. In this paper, different various techniques of XSS attack are analyzed, and a method automatically to detect it is proposed. It is easy to check the results of vulnerability detection when running it as a plug-in. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=XSS" title="XSS">XSS</a>, <a href="https://publications.waset.org/abstracts/search?q=no%20target%20attack%20platform" title=" no target attack platform"> no target attack platform</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20detection%EF%BC%8CXSS%20detection" title=" automatic detection，XSS detection"> automatic detection，XSS detection</a> </p> <a href="https://publications.waset.org/abstracts/41829/cross-site-scripting-xss-attack-and-automatic-detection-technology-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41829.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">403</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">17752</span> Refined Edge Detection Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Elharrouss">Omar Elharrouss</a>, <a href="https://publications.waset.org/abstracts/search?q=Youssef%20Hmamouche"> Youssef Hmamouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Assia%20Kamal%20Idrissi"> Assia Kamal Idrissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Btissam%20El%20Khamlichi"> Btissam El Khamlichi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20El%20Fallah-Seghrouchni"> Amal El Fallah-Seghrouchni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</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=scale-representation" title=" scale-representation"> scale-representation</a>, <a href="https://publications.waset.org/abstracts/search?q=backbone" title=" backbone"> backbone</a> </p> <a href="https://publications.waset.org/abstracts/150865/refined-edge-detection-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150865.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">102</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">17751</span> Mutiple Medical Landmark Detection on X-Ray Scan Using Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vijaya%20Yuvaram%20Singh%20V%20M">Vijaya Yuvaram Singh V M</a>, <a href="https://publications.waset.org/abstracts/search?q=Kameshwar%20Rao%20J%20V"> Kameshwar Rao J V</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20landmark%20detection" title=" medical landmark detection"> medical landmark detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20target%20detection" title=" multi target detection"> multi target detection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a> </p> <a href="https://publications.waset.org/abstracts/127710/mutiple-medical-landmark-detection-on-x-ray-scan-using-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127710.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">142</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">17750</span> HRV Analysis Based Arrhythmic Beat Detection Using kNN Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Onder%20Yakut">Onder Yakut</a>, <a href="https://publications.waset.org/abstracts/search?q=Oguzhan%20Timus"> Oguzhan Timus</a>, <a href="https://publications.waset.org/abstracts/search?q=Emine%20Dogru%20Bolat"> Emine Dogru Bolat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmic%20beat%20detection" title="arrhythmic beat detection">arrhythmic beat detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=HRV" title=" HRV"> HRV</a>, <a href="https://publications.waset.org/abstracts/search?q=kNN%20classifier" title=" kNN classifier"> kNN classifier</a> </p> <a href="https://publications.waset.org/abstracts/41219/hrv-analysis-based-arrhythmic-beat-detection-using-knn-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41219.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">352</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">17749</span> Fully Automated Methods for the Detection and Segmentation of Mitochondria in Microscopy Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Blessing%20Ojeme">Blessing Ojeme</a>, <a href="https://publications.waset.org/abstracts/search?q=Frederick%20Quinn"> Frederick Quinn</a>, <a href="https://publications.waset.org/abstracts/search?q=Russell%20Karls"> Russell Karls</a>, <a href="https://publications.waset.org/abstracts/search?q=Shannon%20Quinn"> Shannon Quinn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=2D" title="2D">2D</a>, <a href="https://publications.waset.org/abstracts/search?q=binarization" title=" binarization"> binarization</a>, <a href="https://publications.waset.org/abstracts/search?q=CLAHE" title=" CLAHE"> CLAHE</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fluorescence%20microscopy" title=" fluorescence microscopy"> fluorescence microscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=mitochondria" title=" mitochondria"> mitochondria</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/153306/fully-automated-methods-for-the-detection-and-segmentation-of-mitochondria-in-microscopy-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153306.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">357</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">17748</span> Concealed Objects Detection in Visible, Infrared and Terahertz Ranges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kowalski">M. Kowalski</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Kastek"> M. Kastek</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Szustakowski"> M. Szustakowski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multispectral screening systems are becoming more popular because of their very interesting properties and applications. One of the most significant applications of multispectral screening systems is prevention of terrorist attacks. There are many kinds of threats and many methods of detection. Visual detection of objects hidden under clothing of a person is one of the most challenging problems of threats detection. There are various solutions of the problem; however, the most effective utilize multispectral surveillance imagers. The development of imaging devices and exploration of new spectral bands is a chance to introduce new equipment for assuring public safety. We investigate the possibility of long lasting detection of potentially dangerous objects covered with various types of clothing. In the article we present the results of comparative studies of passive imaging in three spectrums – visible, infrared and terahertz <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=terahertz" title="terahertz">terahertz</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared" title=" infrared"> infrared</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=screening%20camera" title=" screening camera"> screening camera</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/6914/concealed-objects-detection-in-visible-infrared-and-terahertz-ranges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6914.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">357</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">17747</span> Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Waqqas-ur-Rehman%20Butt">Waqqas-ur-Rehman Butt</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Servin"> Martin Servin</a>, <a href="https://publications.waset.org/abstracts/search?q=Marion%20Pause"> Marion Pause</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods. <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=illumination%20equalization" title=" illumination equalization"> illumination equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=shadow%20filtering" title=" shadow filtering"> shadow filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a> </p> <a href="https://publications.waset.org/abstracts/77157/object-detection-in-digital-images-under-non-standardized-conditions-using-illumination-and-shadow-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77157.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">216</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">17746</span> Performance Evaluation of Contemporary Classifiers for Automatic Detection of Epileptic EEG</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20E.%20Ch.%20Vidyasagar">K. E. Ch. Vidyasagar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Moghavvemi"> M. Moghavvemi</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20S.%20S.%20T.%20Prabhat"> T. S. S. T. Prabhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy. <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=seizure" title=" seizure"> seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN" title=" KNN"> KNN</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA" title=" LDA"> LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a> </p> <a href="https://publications.waset.org/abstracts/14692/performance-evaluation-of-contemporary-classifiers-for-automatic-detection-of-epileptic-eeg" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14692.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">520</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">17745</span> Detection of Clipped Fragments in Speech Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergei%20Aleinik">Sergei Aleinik</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Matveev"> Yuri Matveev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel method for the detection of clipping in speech signals is described. It is shown that the new method has better performance than known clipping detection methods, is easy to implement, and is robust to changes in signal amplitude, size of data, etc. Statistical simulation results are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clipping" title="clipping">clipping</a>, <a href="https://publications.waset.org/abstracts/search?q=clipped%20signal" title=" clipped signal"> clipped signal</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20signal%20processing" title=" speech signal processing"> speech signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20signal%20processing" title=" digital signal processing"> digital signal processing</a> </p> <a href="https://publications.waset.org/abstracts/4816/detection-of-clipped-fragments-in-speech-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4816.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">392</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">17744</span> Structural Damage Detection Using Sensors Optimally Located</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Alberto%20Riveros">Carlos Alberto Riveros</a>, <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Fabi%C3%A1n%20Garc%C3%ADa"> Edwin Fabián García</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Enrique%20Rivero"> Javier Enrique Rivero</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The measured data obtained from sensors in continuous monitoring of civil structures are mainly used for modal identification and damage detection. Therefore when modal identification analysis is carried out the quality in the identification of the modes will highly influence the damage detection results. It is also widely recognized that the usefulness of the measured data used for modal identification and damage detection is significantly influenced by the number and locations of sensors. The objective of this study is the numerical implementation of two widely known optimum sensor placement methods in beam-like structures <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimum%20sensor%20placement" title="optimum sensor placement">optimum sensor placement</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20damage%20detection" title=" structural damage detection"> structural damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20identification" title=" modal identification"> modal identification</a>, <a href="https://publications.waset.org/abstracts/search?q=beam-like%20structures." title=" beam-like structures. "> beam-like structures. </a> </p> <a href="https://publications.waset.org/abstracts/15240/structural-damage-detection-using-sensors-optimally-located" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15240.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">431</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">17743</span> Efficient Signal Detection Using QRD-M Based on Channel Condition in MIMO-OFDM System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Jeong%20Kim">Jae-Jeong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki-Ro%20Kim"> Ki-Ro Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyoung-Kyu%20Song"> Hyoung-Kyu Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an efficient signal detector that switches M parameter of QRD-M detection scheme is proposed for MIMO-OFDM system. The proposed detection scheme calculates the threshold by 1-norm condition number and then switches M parameter of QRD-M detection scheme according to channel information. If channel condition is bad, the parameter M is set to high value to increase the accuracy of detection. If channel condition is good, the parameter M is set to low value to reduce complexity of detection. Therefore, the proposed detection scheme has better trade off between BER performance and complexity than the conventional detection scheme. The simulation result shows that the complexity of proposed detection scheme is lower than QRD-M detection scheme with similar BER performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MIMO-OFDM" title="MIMO-OFDM">MIMO-OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=QRD-M" title=" QRD-M"> QRD-M</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20condition" title=" channel condition"> channel condition</a>, <a href="https://publications.waset.org/abstracts/search?q=BER" title=" BER"> BER</a> </p> <a href="https://publications.waset.org/abstracts/3518/efficient-signal-detection-using-qrd-m-based-on-channel-condition-in-mimo-ofdm-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3518.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">370</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">17742</span> Design an Intelligent Fire Detection System Based on Neural Network and Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Arvan">Majid Arvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Beygi"> Peyman Beygi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sina%20Rokhsati"> Sina Rokhsati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20fire%20detection" title="intelligent fire detection">intelligent fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</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=fire%20sensor%20network" title=" fire sensor network"> fire sensor network</a> </p> <a href="https://publications.waset.org/abstracts/55735/design-an-intelligent-fire-detection-system-based-on-neural-network-and-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55735.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">380</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">17741</span> Incorporating Multiple Supervised Learning Algorithms for Effective Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Albalawi">Umar Albalawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20C.%20Suh"> Sang C. Suh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinoh%20Kim"> Jinoh Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20classification" title=" traffic classification"> traffic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20networks" title=" computer networks"> computer networks</a> </p> <a href="https://publications.waset.org/abstracts/5421/incorporating-multiple-supervised-learning-algorithms-for-effective-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17740</span> Comparison of Sensitivity and Specificity of Pap Smear and Polymerase Chain Reaction Methods for Detection of Human Papillomavirus: A Review of Literature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Malekian">M. Malekian</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Heydari"> M. E. Heydari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Irani%20Estyar"> M. Irani Estyar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human papillomavirus (HPV) is one of the most common sexually transmitted infection, which may lead to cervical cancer as the main cause of it. With early diagnosis and treatment in health care services, cervical cancer and its complications are considered to be preventable. This study was aimed to compare the efficiency, sensitivity, and specificity of Pap smear and polymerase chain reaction (PCR) in detecting HPV. A literature search was performed in Google Scholar, PubMed and SID databases using the keywords 'human papillomavirus', 'pap smear' and 'polymerase change reaction' to identify studies comparing Pap smear and PCR methods for the detection. No restrictions were considered.10 studies were included in this review. All samples that were positive by pop smear were also positive by PCR. However, there were positive samples detected by PCR which was negative by pop smear and in all studies, many positive samples were missed by pop smear technique. Although The Pap smear had high specificity, PCR based HPV detection was more sensitive method and had the highest sensitivity. In order to promote the quality of detection and high achievement of the maximum results, PCR diagnostic methods in addition to the Pap smear are needed and Pap smear method should be combined with PCR techniques according to the high error rate of Pap smear in detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20papillomavirus" title="human papillomavirus">human papillomavirus</a>, <a href="https://publications.waset.org/abstracts/search?q=cervical%20cancer" title=" cervical cancer"> cervical cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=pap%20smear" title=" pap smear"> pap smear</a>, <a href="https://publications.waset.org/abstracts/search?q=polymerase%20chain%20reaction" title=" polymerase chain reaction"> polymerase chain reaction</a> </p> <a href="https://publications.waset.org/abstracts/111248/comparison-of-sensitivity-and-specificity-of-pap-smear-and-polymerase-chain-reaction-methods-for-detection-of-human-papillomavirus-a-review-of-literature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111248.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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=exoplanet%20detection%20methods&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=exoplanet%20detection%20methods&page=3">3</a></li> <li class="page-item"><a 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