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Detection</title> <meta name="description" content="Search results for: Forgery Detection"> <meta name="keywords" content="Forgery Detection"> <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="Forgery Detection"> <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> 3463</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Forgery Detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3433</span> Intrusion Detection and Prevention System (IDPS) in Cloud Computing Using Anomaly-Based and Signature-Based Detection Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Onyima">John Onyima</a>, <a href="https://publications.waset.org/abstracts/search?q=Ikechukwu%20Ezepue"> Ikechukwu Ezepue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly-based%20detection" title="anomaly-based detection">anomaly-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <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=intrusion%20prevention" title=" intrusion prevention"> intrusion prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=signature-based%20detection" title=" signature-based detection"> signature-based detection</a> </p> <a href="https://publications.waset.org/abstracts/89892/intrusion-detection-and-prevention-system-idps-in-cloud-computing-using-anomaly-based-and-signature-based-detection-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89892.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">305</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">3432</span> Survey on Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doaa%20Wael">Doaa Wael</a>, <a href="https://publications.waset.org/abstracts/search?q=Naswa%20Abdelbaky"> Naswa Abdelbaky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malware is malicious software that is built to cause destructive actions and damage information systems and networks. Malware infections increase rapidly, and types of malware have become more sophisticated, which makes the malware detection process more difficult. On the other side, the Internet of Things IoT technology is vulnerable to malware attacks. These IoT devices are always connected to the internet and lack security. This makes them easy for hackers to access. These malware attacks are becoming the go-to attack for hackers. Thus, in order to deal with this challenge, new malware detection techniques are needed. Currently, building a blockchain solution that allows IoT devices to download any file from the internet and to verify/approve whether it is malicious or not is the need of the hour. In recent years, blockchain technology has stood as a solution to everything due to its features like decentralization, persistence, and anonymity. Moreover, using blockchain technology overcomes some difficulties in malware detection and improves the malware detection ratio over-than the techniques that do not utilize blockchain technology. In this paper, we study malware detection models which are based on blockchain technology. Furthermore, we elaborate on the effect of blockchain technology in malware detection, especially in the android environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20analysis" title="malware analysis">malware analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=blockchain" title=" blockchain"> blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20attacks" title=" malware attacks"> malware attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection%20approaches" title=" malware detection approaches"> malware detection approaches</a> </p> <a href="https://publications.waset.org/abstracts/164823/survey-on-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164823.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">87</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3431</span> A Study of Effective Stereo Matching Method for Long-Wave Infrared Camera Module</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Koo%20Kim">Hyun-Koo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonghun%20Kim"> Yonghun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Hoon%20Kim"> Yong-Hoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Hee%20Lee"> Ju Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Myungho%20Song"> Myungho Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20driver%20assistance%20system" title="advanced driver assistance system">advanced driver assistance system</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title=" pedestrian detection"> pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20matching%20method" title=" stereo matching method"> stereo matching method</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20long-wave%20IR%20camera" title=" stereo long-wave IR camera"> stereo long-wave IR camera</a> </p> <a href="https://publications.waset.org/abstracts/58413/a-study-of-effective-stereo-matching-method-for-long-wave-infrared-camera-module" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58413.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">3430</span> mKDNAD: A Network Flow Anomaly Detection Method Based On Multi-teacher Knowledge Distillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yang">Yang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Liu"> Dan Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20flow%20anomaly%20detection%20%28NAD%29" title="network flow anomaly detection (NAD)">network flow anomaly detection (NAD)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-teacher%20knowledge%20distillation" title=" multi-teacher knowledge distillation"> multi-teacher knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/156811/mkdnad-a-network-flow-anomaly-detection-method-based-on-multi-teacher-knowledge-distillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156811.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">3429</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">3428</span> Application of Laser Spectroscopy for Detection of Actinides and Lanthanides in Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Igor%20Izosimov">Igor Izosimov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is devoted to applications of the Time-resolved laser-induced luminescence (TRLIF) spectroscopy and time-resolved laser-induced chemiluminescence spectroscopy for detection of lanthanides and actinides. Results of the experiments on Eu, Sm, U, and Pu detection in solutions are presented. The limit of uranyl detection (LOD) in urine in our TRLIF experiments was up to 5 pg/ml. In blood plasma LOD was 0.1 ng/ml and after mineralization was up to 8pg/ml – 10pg/ml. In pure solution, the limit of detection of europium was 0.005ng/ml and samarium, 0.07ng/ml. After addition urine, the limit of detection of europium was 0.015 ng/ml and samarium, 0.2 ng/ml. Pu, Np, and some U compounds do not produce direct luminescence in solutions, but when excited by laser radiation, they can induce chemiluminescence of some chemiluminogen (luminol in our experiments). It is shown that multi-photon scheme of chemiluminescence excitation makes chemiluminescence not only a highly sensitive but also a highly selective tool for the detection of lanthanides/actinides in solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=actinides%2Flanthanides%20detection" title="actinides/lanthanides detection">actinides/lanthanides detection</a>, <a href="https://publications.waset.org/abstracts/search?q=laser%20spectroscopy%20with%20time%20resolution" title=" laser spectroscopy with time resolution"> laser spectroscopy with time resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=luminescence%2Fchemiluminescence" title=" luminescence/chemiluminescence"> luminescence/chemiluminescence</a>, <a href="https://publications.waset.org/abstracts/search?q=solutions" title=" solutions"> solutions</a> </p> <a href="https://publications.waset.org/abstracts/61605/application-of-laser-spectroscopy-for-detection-of-actinides-and-lanthanides-in-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61605.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">333</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">3427</span> Improvements in OpenCV's Viola Jones Algorithm in Face Detection–Skin Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Bharti">Jyoti Bharti</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Gupta"> M. K. Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Astha%20Jain"> Astha Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a new improved approach for false positives filtering of detected face images on OpenCV’s Viola Jones Algorithm In this approach, for Filtering of False Positives, Skin Detection in two colour spaces i.e. HSV (Hue, Saturation and Value) and YCrCb (Y is luma component and Cr- red difference, Cb- Blue difference) is used. As a result, it is found that false detection has been reduced. Our proposed method reaches the accuracy of about 98.7%. Thus, a better recognition rate is achieved. <p class="card-text"><strong>Keywords:</strong> <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=Viola%20Jones" title=" Viola Jones"> Viola Jones</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title=" OpenCV"> OpenCV</a> </p> <a href="https://publications.waset.org/abstracts/48849/improvements-in-opencvs-viola-jones-algorithm-in-face-detection-skin-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48849.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">406</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">3426</span> Change Detection Method Based on Scale-Invariant Feature Transformation Keypoints and Segmentation for Synthetic Aperture Radar Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lan%20Du">Lan Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Dai"> Hui Dai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title="change detection">change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Synthetic%20Aperture%20Radar%20%28SAR%29" title=" Synthetic Aperture Radar (SAR)"> Synthetic Aperture Radar (SAR)</a>, <a href="https://publications.waset.org/abstracts/search?q=Scale-Invariant%20Feature%20Transformation%20%28SIFT%29" title=" Scale-Invariant Feature Transformation (SIFT)"> Scale-Invariant Feature Transformation (SIFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/66992/change-detection-method-based-on-scale-invariant-feature-transformation-keypoints-and-segmentation-for-synthetic-aperture-radar-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66992.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">386</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">3425</span> Optimized Road Lane Detection Through a Combined Canny Edge Detection, Hough Transform, and Scaleable Region Masking Toward Autonomous Driving</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samane%20Sharifi%20Monfared">Samane Sharifi Monfared</a>, <a href="https://publications.waset.org/abstracts/search?q=Lavdie%20Rada"> Lavdie Rada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, autonomous vehicles are developing rapidly toward facilitating human car driving. One of the main issues is road lane detection for a suitable guidance direction and car accident prevention. This paper aims to improve and optimize road line detection based on a combination of camera calibration, the Hough transform, and Canny edge detection. The video processing is implemented using the Open CV library with the novelty of having a scale able region masking. The aim of the study is to introduce automatic road lane detection techniques with the user’s minimum manual intervention. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hough%20transform" title="hough transform">hough transform</a>, <a href="https://publications.waset.org/abstracts/search?q=canny%20edge%20detection" title=" canny edge detection"> canny edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisation" title=" optimisation"> optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=scaleable%20masking" title=" scaleable masking"> scaleable masking</a>, <a href="https://publications.waset.org/abstracts/search?q=camera%20calibration" title=" camera calibration"> camera calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=improving%20the%20quality%20of%20image" title=" improving the quality of image"> improving the quality of image</a>, <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=video%20processing" title=" video processing"> video processing</a> </p> <a href="https://publications.waset.org/abstracts/156139/optimized-road-lane-detection-through-a-combined-canny-edge-detection-hough-transform-and-scaleable-region-masking-toward-autonomous-driving" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156139.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">3424</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">3423</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">3422</span> Design and Implementation of an Image Based System to Enhance the Security of ATM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Nima%20Tayarani%20Bathaie">Seyed Nima Tayarani Bathaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20detection%20algorithm" title="face detection algorithm">face detection algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Haar%20features" title=" Haar features"> Haar features</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20of%20ATM" title=" security of ATM"> security of ATM</a> </p> <a href="https://publications.waset.org/abstracts/3011/design-and-implementation-of-an-image-based-system-to-enhance-the-security-of-atm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3011.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">419</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">3421</span> Use of Hierarchical Temporal Memory Algorithm in Heart Attack Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tesnim%20Charrad">Tesnim Charrad</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaouther%20Nouira"> Kaouther Nouira</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Ferchichi"> Ahmed Ferchichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiac%20anomalies" title="cardiac anomalies">cardiac anomalies</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=HTM" title=" HTM"> HTM</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20anomaly%20detection" title=" real time anomaly detection"> real time anomaly detection</a> </p> <a href="https://publications.waset.org/abstracts/104419/use-of-hierarchical-temporal-memory-algorithm-in-heart-attack-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104419.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">228</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">3420</span> Design of a New Architecture of IDS Called BiIDS (IDS Based on Two Principles of Detection)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Farhaoui">Yousef Farhaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An IDS is a tool which is used to improve the level of security.In this paper we present different architectures of IDS. We will also discuss measures that define the effectiveness of IDS and the very recent works of standardization and homogenization of IDS. At the end, we propose a new model of IDS called BiIDS (IDS Based on the two principles of detection). <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=architectures" title=" architectures"> architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=characteristic" title=" characteristic"> characteristic</a>, <a href="https://publications.waset.org/abstracts/search?q=tools" title=" tools"> tools</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/12298/design-of-a-new-architecture-of-ids-called-biids-ids-based-on-two-principles-of-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12298.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">462</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">3419</span> Proposed Anticipating Learning Classifier System for Cloud Intrusion Detection (ALCS-CID)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wafa%27%20Slaibi%20Alsharafat">Wafa' Slaibi Alsharafat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=anticipating%20classifier%20system" title=" anticipating classifier system"> anticipating classifier system</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a> </p> <a href="https://publications.waset.org/abstracts/18240/proposed-anticipating-learning-classifier-system-for-cloud-intrusion-detection-alcs-cid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18240.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">474</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">3418</span> Crater Detection Using PCA from Captured CMOS Camera Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tatsuya%20Takino">Tatsuya Takino</a>, <a href="https://publications.waset.org/abstracts/search?q=Izuru%20Nomura"> Izuru Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuji%20Kageyama"> Yuji Kageyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin%20Nagata"> Shin Nagata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroyuki%20Kamata"> Hiroyuki Kamata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a method of detecting the craters from the image of the lunar surface. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) working group aiming at the pinpoint landing on the lunar surface and investigating scientific research. It is difficult to equip and use high-performance computers for the small space probe. So, it is necessary to use a small computer with an exclusive hardware such as FPGA. We have studied the crater detection using principal component analysis (PCA), In this paper, We implement detection algorithm into the FPGA, and the detection is performed on the data that was captured from the CMOS camera. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crater%20detection" title="crater detection">crater detection</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</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/19003/crater-detection-using-pca-from-captured-cmos-camera-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19003.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">549</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">3417</span> On-Road Text Detection Platform for Driver Assistance Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guezouli%20Larbi">Guezouli Larbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Soundes"> Belkacem Soundes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title="text detection">text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=PZM" title=" PZM"> PZM</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/161507/on-road-text-detection-platform-for-driver-assistance-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161507.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">83</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">3416</span> A Paper Based Sensor for Mercury Ion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emine%20G.%20Cansu%20Ergun">Emine G. Cansu Ergun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugated system based sensors for selective detection of metal ions have been taking attention during last two decades. Fluorescent sensors are the promising candidates for ion detection due to their high selectivity towards metal ions, and rapid response times. Detection of mercury in an environmenet is important since mercury is a toxic element for human. Beyond the maximum allowable limit, mercury may cause serious problems in human health by spreading into the atmosphere, water and the food chain. In this study, a quinoxaline and 3,4-ethylenedioxy thiophene based donor-acceptor-donor type conjugated molecule used as a fluorescent sensor for detecting the mercury ion in aqueous medium. Among other various cations, existence of mercury resulted in a full quenching of the fluorescence signal. Then, a paper based sensor is constructed and used for mercury detection. As a result it is concluded that the offering sensor is a good candidate for selective mercury detection in aqueous media both in solution and paper based forms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Conjugated%20molecules" title="Conjugated molecules ">Conjugated molecules </a>, <a href="https://publications.waset.org/abstracts/search?q=fluorescence%20quenching" title=" fluorescence quenching"> fluorescence quenching</a>, <a href="https://publications.waset.org/abstracts/search?q=metal%20ion%20detection" title=" metal ion detection "> metal ion detection </a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a> </p> <a href="https://publications.waset.org/abstracts/128523/a-paper-based-sensor-for-mercury-ion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128523.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">158</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">3415</span> Automated Pothole Detection Using Convolution Neural Networks and 3D Reconstruction Using Stereovision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eshta%20Ranyal">Eshta Ranyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamal%20Jain"> Kamal Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Vikrant%20Ranyal"> Vikrant Ranyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Potholes are a severe threat to road safety and a major contributing factor towards road distress. In the Indian context, they are a major road hazard. Timely detection of potholes and subsequent repair can prevent the roads from deteriorating. To facilitate the roadway authorities in the timely detection and repair of potholes, we propose a pothole detection methodology using convolutional neural networks. The YOLOv3 model is used as it is fast and accurate in comparison to other state-of-the-art models. You only look once v3 (YOLOv3) is a state-of-the-art, real-time object detection system that features multi-scale detection. A mean average precision(mAP) of 73% was obtained on a training dataset of 200 images. The dataset was then increased to 500 images, resulting in an increase in mAP. We further calculated the depth of the potholes using stereoscopic vision by reconstruction of 3D potholes. This enables calculating pothole volume, its extent, which can then be used to evaluate the pothole severity as low, moderate, high. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=pothole%20detection" title=" pothole detection"> pothole detection</a>, <a href="https://publications.waset.org/abstracts/search?q=pothole%20severity" title=" pothole severity"> pothole severity</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLO" title=" YOLO"> YOLO</a>, <a href="https://publications.waset.org/abstracts/search?q=stereovision" title=" stereovision"> stereovision</a> </p> <a href="https://publications.waset.org/abstracts/131553/automated-pothole-detection-using-convolution-neural-networks-and-3d-reconstruction-using-stereovision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131553.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">136</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">3414</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">3413</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">3412</span> Music Note Detection and Dictionary Generation from Music Sheet Using Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ammar">Muhammad Ammar</a>, <a href="https://publications.waset.org/abstracts/search?q=Talha%20Ali"> Talha Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Basit"> Abdul Basit</a>, <a href="https://publications.waset.org/abstracts/search?q=Bakhtawar%20Rajput"> Bakhtawar Rajput</a>, <a href="https://publications.waset.org/abstracts/search?q=Zobia%20Sohail"> Zobia Sohail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music%20note" title="music note">music note</a>, <a href="https://publications.waset.org/abstracts/search?q=sheet%20music" title=" sheet music"> sheet music</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20music%20recognition" title=" optical music recognition"> optical music recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=blob%20detection" title=" blob detection"> blob detection</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20generation" title=" dictionary generation"> dictionary generation</a> </p> <a href="https://publications.waset.org/abstracts/133670/music-note-detection-and-dictionary-generation-from-music-sheet-using-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133670.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">181</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">3411</span> Efficient Iterative V-BLAST Detection Technique in Wireless Communication System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hwan-Jun%20Choi">Hwan-Jun Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Bok%20Choi"> Sung-Bok Choi</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> Recently, among the MIMO-OFDM detection techniques, a lot of papers suggested V-BLAST scheme which can achieve high data rate. Therefore, the signal detection of MIMOOFDM system is important issue. In this paper, efficient iterative VBLAST detection technique is proposed in wireless communication system. The proposed scheme adjusts the number of candidate symbol and iterative scheme based on channel state. According to the simulation result, the proposed scheme has better BER performance than conventional schemes and similar BER performance of the QRD-M with iterative scheme. Moreover complexity of proposed scheme has 50.6 % less than complexity of QRD-M detection with iterative scheme. Therefore the proposed detection scheme can be efficiently used in wireless communication. <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=V-BLAST" title=" V-BLAST"> V-BLAST</a>, <a href="https://publications.waset.org/abstracts/search?q=QR-decomposition" title=" QR-decomposition"> QR-decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=QRDM" title=" QRDM"> QRDM</a>, <a href="https://publications.waset.org/abstracts/search?q=DFE" title=" DFE"> DFE</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20scheme" title=" iterative scheme"> iterative scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20condition" title=" channel condition"> channel condition</a> </p> <a href="https://publications.waset.org/abstracts/3522/efficient-iterative-v-blast-detection-technique-in-wireless-communication-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3522.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">530</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">3410</span> Combination between Intrusion Systems and Honeypots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majed%20Sanan">Majed Sanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Rammal"> Mohammad Rammal</a>, <a href="https://publications.waset.org/abstracts/search?q=Wassim%20Rammal"> Wassim Rammal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, security is a major concern. Intrusion Detection, Prevention Systems and Honeypot can be used to moderate attacks. Many researchers have proposed to use many IDSs ((Intrusion Detection System) time to time. Some of these IDS’s combine their features of two or more IDSs which are called Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS, if an attacker attacks slowly and in organized way, the attack may go undetected through the IDS, as signatures include factors based on duration of the events but the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. So there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we propose a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed: both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed Hybrid Intrusion Detection System (HIDS) is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment, we ran both the Intrusion Detection System individually first and then together and recorded the data from time to time. From the data we can conclude that the resulting IDS are much better in detecting intrusions from the existing IDSs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=security" title="security">security</a>, <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=intrusion%20prevention" title=" intrusion prevention"> intrusion prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=honeypot" title=" honeypot"> honeypot</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly-based%20detection" title=" anomaly-based detection"> anomaly-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=signature-based%20detection" title=" signature-based detection"> signature-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=kfsensor" title=" kfsensor"> kfsensor</a> </p> <a href="https://publications.waset.org/abstracts/40174/combination-between-intrusion-systems-and-honeypots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40174.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">382</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">3409</span> Mosaic Augmentation: Insights and Limitations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olivia%20A.%20Kjorlien">Olivia A. Kjorlien</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Asghari"> Maryam Asghari</a>, <a href="https://publications.waset.org/abstracts/search?q=Farshid%20Alizadeh-Shabdiz"> Farshid Alizadeh-Shabdiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/abstracts/search?q=mosaic%20augmentation" title=" mosaic augmentation"> mosaic augmentation</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=YOLOV4" title=" YOLOV4"> YOLOV4</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOV4-Tiny" title=" YOLOV4-Tiny"> YOLOV4-Tiny</a> </p> <a href="https://publications.waset.org/abstracts/162634/mosaic-augmentation-insights-and-limitations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162634.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3408</span> Real Time Video Based Smoke Detection Using Double Optical Flow Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anton%20Stadler">Anton Stadler</a>, <a href="https://publications.waset.org/abstracts/search?q=Thorsten%20Ike"> Thorsten Ike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20density" title="low density">low density</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow" title=" optical flow"> optical flow</a>, <a href="https://publications.waset.org/abstracts/search?q=upward%20smoke%20motion" title=" upward smoke motion"> upward smoke motion</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20based%20smoke%20detection" title=" video based smoke detection"> video based smoke detection</a> </p> <a href="https://publications.waset.org/abstracts/49542/real-time-video-based-smoke-detection-using-double-optical-flow-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49542.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">354</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">3407</span> Active Islanding Detection Method Using Intelligent Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kuang-Hsiung%20Tan">Kuang-Hsiung Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Chan%20Hu"> Chih-Chan Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chien-Wu%20Lan"> Chien-Wu Lan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shih-Sung%20Lin"> Shih-Sung Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Te-Jen%20Chang"> Te-Jen Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the <em>d</em>-axis current which leads to a frequency deviation at the terminal of the <em>RLC</em> load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generators" title="distributed generators">distributed generators</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20fuzzy%20neural%20network" title=" probabilistic fuzzy neural network"> probabilistic fuzzy neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=islanding%20detection" title=" islanding detection"> islanding detection</a>, <a href="https://publications.waset.org/abstracts/search?q=non-detection%20zone" title=" non-detection zone"> non-detection zone</a> </p> <a href="https://publications.waset.org/abstracts/39253/active-islanding-detection-method-using-intelligent-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39253.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">389</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">3406</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">3405</span> GPU Based Real-Time Floating Object Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jie%20Yang">Jie Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian-Min%20Meng"> Jian-Min Meng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme. <p class="card-text"><strong>Keywords:</strong> <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=GPU" title=" GPU"> GPU</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title=" motion estimation"> motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20processing" title=" parallel processing"> parallel processing</a> </p> <a href="https://publications.waset.org/abstracts/54425/gpu-based-real-time-floating-object-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54425.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">474</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">3404</span> Thermal Neutron Detection Efficiency as a Function of Film Thickness for Front and Back Irradiation Detector Devices Coated with ¹⁰B, ⁶LiF, and Pure Li Thin Films</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vedant%20Subhash">Vedant Subhash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the physics of the detection of thermal neutrons using thin-film coated semiconductor detectors. The thermal neutron detection efficiency as a function of film thickness is calculated for the front and back irradiation detector devices coated with ¹⁰B, ⁶LiF, and pure Li thin films. The detection efficiency for back irradiation devices is 4.15% that is slightly higher than that for front irradiation detectors, 4.0% for ¹⁰B films of thickness 2.4μm. The theoretically calculated thermal neutron detection efficiency using ¹⁰B film thickness of 1.1 μm for the back irradiation device is 3.0367%, which has an offset of 0.0367% from the experimental value of 3.0%. The detection efficiency values are compared and proved consistent with the given calculations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection%20efficiency" title="detection efficiency">detection efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=neutron%20detection" title=" neutron detection"> neutron detection</a>, <a href="https://publications.waset.org/abstracts/search?q=semiconductor%20detectors" title=" semiconductor detectors"> semiconductor detectors</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20neutrons" title=" thermal neutrons"> thermal neutrons</a> </p> <a href="https://publications.waset.org/abstracts/133906/thermal-neutron-detection-efficiency-as-a-function-of-film-thickness-for-front-and-back-irradiation-detector-devices-coated-with-1b-6lif-and-pure-li-thin-films" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133906.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">132</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Forgery%20Detection&page=1" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Forgery%20Detection&page=1">1</a></li> <li class="page-item active"><span class="page-link">2</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Forgery%20Detection&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Forgery%20Detection&page=4">4</a></li> <li class="page-item"><a class="page-link" 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