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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="events 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> 5474</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: events detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5474</span> Learning Grammars for Detection of Disaster-Related Micro Events</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josef%20Steinberger">Josef Steinberger</a>, <a href="https://publications.waset.org/abstracts/search?q=Vanni%20Zavarella"> Vanni Zavarella</a>, <a href="https://publications.waset.org/abstracts/search?q=Hristo%20Tanev"> Hristo Tanev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20%20news" title="online news">online news</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20%20language%20%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=event%20extraction" title=" event extraction"> event extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=crisis%20computing" title=" crisis computing"> crisis computing</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20effects" title=" disaster effects"> disaster effects</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/58693/learning-grammars-for-detection-of-disaster-related-micro-events" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58693.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">478</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">5473</span> Automatic Motion Trajectory Analysis for Dual Human Interaction Using Video Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-Hsiang%20Chang">Yuan-Hsiang Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pin-Chi%20Lin"> Pin-Chi Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Der%20Jeng"> Li-Der Jeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20detection" title="motion detection">motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20tracking" title=" motion tracking"> motion tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20analysis" title=" trajectory analysis"> trajectory analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/13650/automatic-motion-trajectory-analysis-for-dual-human-interaction-using-video-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13650.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">548</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">5472</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">5471</span> Labview-Based System for Fiber Links Events Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Liu">Bo Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingshan%20Kong"> Qingshan Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Weiqing%20Huang"> Weiqing Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title="empirical mode decomposition">empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=events%20detection" title=" events detection"> events detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20transform" title=" Gabor transform"> Gabor transform</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20time%20domain%20reflectometer" title=" optical time domain reflectometer"> optical time domain reflectometer</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20threshold%20denoising" title=" wavelet threshold denoising"> wavelet threshold denoising</a> </p> <a href="https://publications.waset.org/abstracts/105512/labview-based-system-for-fiber-links-events-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5470</span> Detection of Voltage Sag and Voltage Swell in Power Quality Using Wavelet Transforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nor%20Asrina%20Binti%20Ramlee">Nor Asrina Binti Ramlee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title="power quality">power quality</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20sag" title=" voltage sag"> voltage sag</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20swell" title=" voltage swell"> voltage swell</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/68643/detection-of-voltage-sag-and-voltage-swell-in-power-quality-using-wavelet-transforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68643.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">372</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">5469</span> An Architectural Model for APT Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nam-Uk%20Kim">Nam-Uk Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Hwan%20Kim"> Sung-Hwan Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Tai-Myoung%20Chung"> Tai-Myoung Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Typical security management systems are not suitable for detecting APT attack, because they cannot draw the big picture from trivial events of security solutions. Although SIEM solutions have security analysis engine for that, their security analysis mechanisms need to be verified in academic field. Although this paper proposes merely an architectural model for APT detection, we will keep studying on correlation analysis mechanism in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20persistent%20threat" title="advanced persistent threat">advanced persistent threat</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining "> data mining </a> </p> <a href="https://publications.waset.org/abstracts/23009/an-architectural-model-for-apt-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23009.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">528</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">5468</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">5467</span> The Impact of Cognitive Load on Deceit Detection and Memory Recall in Children’s Interviews: A Meta-Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sevilay%20%C3%87ankaya">Sevilay Çankaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detection of deception in children’s interviews is essential for statement veracity. The widely used method for deception detection is building cognitive load, which is the logic of the cognitive interview (CI), and its effectiveness for adults is approved. This meta-analysis delves into the effectiveness of inducing cognitive load as a means of enhancing veracity detection during interviews with children. Additionally, the effectiveness of cognitive load on children's total number of events recalled is assessed as a second part of the analysis. The current meta-analysis includes ten effect sizes from search using databases. For the effect size calculation, Hedge’s g was used with a random effect model by using CMA version 2. Heterogeneity analysis was conducted to detect potential moderators. The overall result indicated that cognitive load had no significant effect on veracity outcomes (g =0.052, 95% CI [-.006,1.25]). However, a high level of heterogeneity was found (I² = 92%). Age, participants’ characteristics, interview setting, and characteristics of the interviewer were coded as possible moderators to explain variance. Age was significant moderator (β = .021; p = .03, R2 = 75%) but the analysis did not reveal statistically significant effects for other potential moderators: participants’ characteristics (Q = 0.106, df = 1, p = .744), interview setting (Q = 2.04, df = 1, p = .154), and characteristics of interviewer (Q = 2.96, df = 1, p = .086). For the second outcome, the total number of events recalled, the overall effect was significant (g =4.121, 95% CI [2.256,5.985]). The cognitive load was effective in total recalled events when interviewing with children. All in all, while age plays a crucial role in determining the impact of cognitive load on veracity, the surrounding context, interviewer attributes, and inherent participant traits may not significantly alter the relationship. These findings throw light on the need for more focused, age-specific methods when using cognitive load measures. It may be possible to improve the precision and dependability of deceit detection in children's interviews with the help of more studies in this field. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deceit%20detection" title="deceit detection">deceit detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20load" title=" cognitive load"> cognitive load</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20recall" title=" memory recall"> memory recall</a>, <a href="https://publications.waset.org/abstracts/search?q=children%20interviews" title=" children interviews"> children interviews</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-analysis" title=" meta-analysis"> meta-analysis</a> </p> <a href="https://publications.waset.org/abstracts/177530/the-impact-of-cognitive-load-on-deceit-detection-and-memory-recall-in-childrens-interviews-a-meta-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177530.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">55</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">5466</span> The Impact of Recurring Events in Fake News Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Raza">Ali Raza</a>, <a href="https://publications.waset.org/abstracts/search?q=Shafiq%20Ur%20Rehman%20Khan"> Shafiq Ur Rehman Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Raja%20Sher%20Afgun%20Usmani"> Raja Sher Afgun Usmani</a>, <a href="https://publications.waset.org/abstracts/search?q=Asif%20Raza"> Asif Raza</a>, <a href="https://publications.waset.org/abstracts/search?q=Basit%20Umair"> Basit Umair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection of Fake news and missing information is gaining popularity, especially after the advancement in social media and online news platforms. Social media platforms are the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to identify whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use two models BERT and Bi-LSTM to investigate, and it is concluded from BERT we get better results and 70% of true news are recurring and rest of 30% are non-recurring. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=fake%20news%20detection" title=" fake news detection"> fake news detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title=" Bi-LSTM"> Bi-LSTM</a> </p> <a href="https://publications.waset.org/abstracts/190551/the-impact-of-recurring-events-in-fake-news-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190551.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">22</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">5465</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">5464</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">5463</span> Intrusion Detection Based on Graph Oriented Big Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahlem%20Abid">Ahlem Abid</a>, <a href="https://publications.waset.org/abstracts/search?q=Farah%20%20Jemili"> Farah Jemili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark%20Streaming" title="Apache Spark Streaming">Apache Spark Streaming</a>, <a href="https://publications.waset.org/abstracts/search?q=Graph" title=" Graph"> Graph</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=k2%20algorithm" title=" k2 algorithm"> k2 algorithm</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=MAWILab" title=" MAWILab"> MAWILab</a>, <a href="https://publications.waset.org/abstracts/search?q=Microsoft%20Azure%20Cloud" title=" Microsoft Azure Cloud"> Microsoft Azure Cloud</a> </p> <a href="https://publications.waset.org/abstracts/127073/intrusion-detection-based-on-graph-oriented-big-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127073.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">147</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">5462</span> Implementation of Environmental Sustainability into Event Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C3%96zlem%20K%C3%BC%C3%A7%C3%BCkak%C3%A7a">Özlem Küçükakça</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The world population is rapidly growing. In the last few decades, environmental protection and climate change have been remarked as a global concern. All events have their own ecological footprint. Therefore, all participants who take part in the events, from event organizer to audience should be responsible for reducing carbon emissions. Currently, there is a literature gap which investigates the relationship between events and environment. Hence, this study is conducted to investigate how to implement environmental sustainability in the event management. Therefore, a wide literature and also the UK festivals database have been investigated. Finally, environmental effects and the solution of reducing impacts at events were discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ecological%20footprint" title="ecological footprint">ecological footprint</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20sustainability" title=" environmental sustainability"> environmental sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=events" title=" events"> events</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability"> sustainability</a> </p> <a href="https://publications.waset.org/abstracts/72164/implementation-of-environmental-sustainability-into-event-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72164.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">304</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">5461</span> Anomaly Detection Based on System Log Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kamel">M. Kamel</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hoayek"> A. Hoayek</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Batton-Hubert"> M. Batton-Hubert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=logs" title="logs">logs</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=scoring" title=" scoring"> scoring</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a> </p> <a href="https://publications.waset.org/abstracts/162951/anomaly-detection-based-on-system-log-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162951.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">5460</span> Timely Detection and Identification of Abnormalities for Process Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Woo%20Cho">Hyun-Woo Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection" title="detection">detection</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20data" title=" measurement data"> measurement data</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20techniques" title=" multivariate techniques"> multivariate techniques</a> </p> <a href="https://publications.waset.org/abstracts/79287/timely-detection-and-identification-of-abnormalities-for-process-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79287.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">236</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">5459</span> Chinese Event Detection Technique Based on Dependency Parsing and Rule Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weitao%20Lin">Weitao Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinese%20event%20detection" title=" Chinese event detection"> Chinese event detection</a>, <a href="https://publications.waset.org/abstracts/search?q=rules%20matching" title=" rules matching"> rules matching</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20parsing" title=" dependency parsing"> dependency parsing</a> </p> <a href="https://publications.waset.org/abstracts/158129/chinese-event-detection-technique-based-on-dependency-parsing-and-rule-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158129.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">141</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">5458</span> Real-Time Monitoring of Drinking Water Quality Using Advanced Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amani%20Abdallah">Amani Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Isam%20Shahrour"> Isam Shahrour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distribution%20system" title="distribution system">distribution system</a>, <a href="https://publications.waset.org/abstracts/search?q=drinking%20water" title=" drinking water"> drinking water</a>, <a href="https://publications.waset.org/abstracts/search?q=refraction%20index" title=" refraction index"> refraction index</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time" title=" real-time "> real-time </a> </p> <a href="https://publications.waset.org/abstracts/33144/real-time-monitoring-of-drinking-water-quality-using-advanced-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33144.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">5457</span> Continuous Land Cover Change Detection in Subtropical Thicket Ecosystems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Craig%20Mahlasi">Craig Mahlasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Subtropical Thicket Biome has been in peril of transformation. Estimates indicate that as much as 63% of the Subtropical Thicket Biome is severely degraded. Agricultural expansion is the main driver of transformation. While several studies have sought to document and map the long term transformations, there is a lack of information on disturbance events that allow for timely intervention by authorities. Furthermore, tools that seek to perform continuous land cover change detection are often developed for forests and thus tend to perform poorly in thicket ecosystems. This study investigates the utility of Earth Observation data for continuous land cover change detection in Subtropical Thicket ecosystems. Temporal Neural Networks are implemented on a time series of Sentinel-2 observations. The model obtained 0.93 accuracy, a recall score of 0.93, and a precision score of 0.91 in detecting Thicket disturbances. The study demonstrates the potential of continuous land cover change in Subtropical Thicket ecosystems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover%20change%20detection" title=" land cover change detection"> land cover change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=subtropical%20thickets" title=" subtropical thickets"> subtropical thickets</a>, <a href="https://publications.waset.org/abstracts/search?q=near-real%20time" title=" near-real time"> near-real time</a> </p> <a href="https://publications.waset.org/abstracts/144799/continuous-land-cover-change-detection-in-subtropical-thicket-ecosystems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144799.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">162</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">5456</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">5455</span> Reduced Complexity of ML Detection Combined with DFE</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Hyun%20Ro">Jae-Hyun Ro</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Jun%20Kim"> Yong-Jun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Bin%20Ha"> Chang-Bin Ha</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 multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection" title="detection">detection</a>, <a href="https://publications.waset.org/abstracts/search?q=DFE" title=" DFE"> DFE</a>, <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=ML" title=" ML"> ML</a> </p> <a href="https://publications.waset.org/abstracts/42215/reduced-complexity-of-ml-detection-combined-with-dfe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42215.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">610</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">5454</span> Generation of Automated Alarms for Plantwide Process Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Woo%20Cho">Hyun-Woo Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection" title="detection">detection</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20data" title=" process data"> process data</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a> </p> <a href="https://publications.waset.org/abstracts/72518/generation-of-automated-alarms-for-plantwide-process-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72518.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">252</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">5453</span> Cigarette Smoke Detection Based on YOLOV3</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Li">Wei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tuo%20Yang"> Tuo Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes. <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=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=cigarette%20smoke%20detection" title=" cigarette smoke detection"> cigarette smoke detection</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOV3" title=" YOLOV3"> YOLOV3</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20feature%20extraction" title=" color feature extraction"> color feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/159151/cigarette-smoke-detection-based-on-yolov3" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159151.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">5452</span> Hybrid Anomaly Detection Using Decision Tree and Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Serkani">Elham Serkani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Gharaee%20Garakani"> Hossein Gharaee Garakani</a>, <a href="https://publications.waset.org/abstracts/search?q=Naser%20Mohammadzadeh"> Naser Mohammadzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaheh%20Vaezpour"> Elaheh Vaezpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/90456/hybrid-anomaly-detection-using-decision-tree-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90456.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">265</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">5451</span> An Architecture for New Generation of Distributed Intrusion Detection System Based on Preventive Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Benmoussa">H. Benmoussa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20El%20Kalam"> A. A. El Kalam</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ait%20Ouahman"> A. Ait Ouahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The design and implementation of intrusion detection systems (IDS) remain an important area of research in the security of information systems. Despite the importance and reputation of the current intrusion detection systems, their efficiency and effectiveness remain limited as they should include active defense approach to allow anticipating and predicting intrusions before their occurrence. Consequently, they must be readapted. For this purpose we suggest a new generation of distributed intrusion detection system based on preventive detection approach and using intelligent and mobile agents. Our architecture benefits from mobile agent features and addresses some of the issues with centralized and hierarchical models. Also, it presents advantages in terms of increasing scalability and flexibility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intrusion%20Detection%20System%20%28IDS%29" title="Intrusion Detection System (IDS)">Intrusion Detection System (IDS)</a>, <a href="https://publications.waset.org/abstracts/search?q=preventive%20detection" title=" preventive detection"> preventive detection</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agents" title=" mobile agents"> mobile agents</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20architecture" title=" distributed architecture"> distributed architecture</a> </p> <a href="https://publications.waset.org/abstracts/18239/an-architecture-for-new-generation-of-distributed-intrusion-detection-system-based-on-preventive-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18239.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">583</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">5450</span> Video Based Ambient Smoke Detection By Detecting Directional Contrast Decrease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omair%20Ghori">Omair Ghori</a>, <a href="https://publications.waset.org/abstracts/search?q=Anton%20Stadler"> Anton Stadler</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Wilk"> Stefan Wilk</a>, <a href="https://publications.waset.org/abstracts/search?q=Wolfgang%20Effelsberg"> Wolfgang Effelsberg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fire-related incidents account for extensive loss of life and material damage. Quick and reliable detection of occurring fires has high real world implications. Whereas a major research focus lies on the detection of outdoor fires, indoor camera-based fire detection is still an open issue. Cameras in combination with computer vision helps to detect flames and smoke more quickly than conventional fire detectors. In this work, we present a computer vision-based smoke detection algorithm based on contrast changes and a multi-step classification. This work accelerates computer vision-based fire detection considerably in comparison with classical indoor-fire detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contrast%20analysis" title="contrast analysis">contrast analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20fire%20detection" title=" early fire detection"> early fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20smoke%20detection" title=" video smoke detection"> video smoke detection</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/52006/video-based-ambient-smoke-detection-by-detecting-directional-contrast-decrease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52006.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5449</span> Big Data and Cardiovascular Healthcare Management: Recent Advances, Future Potential and Pitfalls</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maariyah%20Irfan">Maariyah Irfan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intro: Current cardiovascular (CV) care faces challenges such as low budgets and high hospital admission rates. This review aims to evaluate Big Data in CV healthcare management through the use of wearable devices in atrial fibrillation (AF) detection. AF may present intermittently, thus it is difficult for a healthcare professional to capture and diagnose a symptomatic rhythm. Methods: The iRhythm ZioPatch, AliveCor portable electrocardiogram (ECG), and Apple Watch were chosen for review due to their involvement in controlled clinical trials, and their integration with smartphones. The cost-effectiveness and AF detection of these devices were compared against the 12-lead ambulatory ECG (Holter monitor) that the NHS currently employs for the detection of AF. Results: The Zio patch was found to detect more arrhythmic events than the Holter monitor over a 2-week period. When patients presented to the emergency department with palpitations, AliveCor portable ECGs detected 6-fold more symptomatic events compared to the standard care group over 3-months. Based off preliminary results from the Apple Heart Study, only 0.5% of participants received irregular pulse notifications from the Apple Watch. Discussion: The Zio Patch and AliveCor devices have promising potential to be implemented into the standard duty of care offered by the NHS as they compare well to current routine measures. Nonetheless, companies must address the discrepancy between their target population and current consumers as those that could benefit the most from the innovation may be left out due to cost and access. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=atrial%20fibrillation" title="atrial fibrillation">atrial fibrillation</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiovascular%20healthcare%20management" title=" cardiovascular healthcare management"> cardiovascular healthcare management</a>, <a href="https://publications.waset.org/abstracts/search?q=wearable%20devices" title=" wearable devices"> wearable devices</a> </p> <a href="https://publications.waset.org/abstracts/125524/big-data-and-cardiovascular-healthcare-management-recent-advances-future-potential-and-pitfalls" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125524.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5448</span> Intrusion Detection Techniques in NaaS in the Cloud: A Review </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashid%20Mahmood">Rashid Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The network as a service (NaaS) usage has been well-known from the last few years in the many applications, like mission critical applications. In the NaaS, prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in NaaS. The authentication and encryption are considered the first solution of the NaaS problem whereas now these are not sufficient as NaaS use is increasing. In this paper, we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in NaaS and aim to compare in some important fields. <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" title=" cloud"> cloud</a>, <a href="https://publications.waset.org/abstracts/search?q=naas" title=" naas"> naas</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/36475/intrusion-detection-techniques-in-naas-in-the-cloud-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36475.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">320</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">5447</span> Anomaly Detection in a Data Center with a Reconstruction Method Using a Multi-Autoencoders Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Victor%20Breux">Victor Breux</a>, <a href="https://publications.waset.org/abstracts/search?q=J%C3%A9r%C3%B4me%20Boutet"> Jérôme Boutet</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20Goret"> Alain Goret</a>, <a href="https://publications.waset.org/abstracts/search?q=Viviane%20Cattin"> Viviane Cattin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title="anomaly detection">anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=autoencoder" title=" autoencoder"> autoencoder</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20centers" title=" data centers"> data centers</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/137944/anomaly-detection-in-a-data-center-with-a-reconstruction-method-using-a-multi-autoencoders-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137944.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">194</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">5446</span> Detecting Anomalous Matches: An Empirical Study from National Basketball Association</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jacky%20Liu">Jacky Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dulani%20Jayasuriya"> Dulani Jayasuriya</a>, <a href="https://publications.waset.org/abstracts/search?q=Ryan%20Elmore"> Ryan Elmore</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly%20match%20detection" title="anomaly match detection">anomaly match detection</a>, <a href="https://publications.waset.org/abstracts/search?q=match%20fixing" title=" match fixing"> match fixing</a>, <a href="https://publications.waset.org/abstracts/search?q=match%20outcome%20forecasting" title=" match outcome forecasting"> match outcome forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=problematic%20players%20identification" title=" problematic players identification"> problematic players identification</a> </p> <a href="https://publications.waset.org/abstracts/169744/detecting-anomalous-matches-an-empirical-study-from-national-basketball-association" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169744.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">79</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">5445</span> Fake Accounts Detection in Twitter Based on Minimum Weighted Feature Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20ElAzab">Ahmed ElAzab</a>, <a href="https://publications.waset.org/abstracts/search?q=Amira%20M.%20Idrees"> Amira M. Idrees</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20A.%20Mahmoud"> Mahmoud A. Mahmoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Hesham%20Hefny"> Hesham Hefny</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fake%20accounts%20detection" title="fake accounts detection">fake accounts detection</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title=" classification algorithms"> classification algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter%20accounts%20analysis" title=" twitter accounts analysis"> twitter accounts analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20based%20techniques" title=" features based techniques"> features based techniques</a> </p> <a href="https://publications.waset.org/abstracts/41564/fake-accounts-detection-in-twitter-based-on-minimum-weighted-feature-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41564.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">416</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=events%20detection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=events%20detection&page=3">3</a></li> <li class="page-item"><a class="page-link" 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