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Information Technology To Enhance e-learning and Other Application (IT-ELA)

作者： Hussam Mohammed Jabir Mohammed Abdullah Naser Safaa O. Al-mamory University of Babylon Babylon Iraq College of Business Informatics University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9781728182339

ISBN: (纸本)9781728182346

Rumors can widely and rapidly diffuse in social networks than in offline platforms. This work was an attempt to address this challenge by introducing an approach for rumors detection, which learns from the Twitter dataset. In other words, this paper is going to suggest new features were extracted from Twitter datasets based on user behavior, the propagation features and the temporal features that representing the social context to reduce variation in features. Also, the proposed work involves adopting an ensemble classifier as a more appropriate approach and for achieving a better performance compared to existing individual-based classifiers. Moreover, the proposed method processes unbalanced data sets to reduce their impact on detection algorithms and improve the classification accuracy rate. Experimental results on the PHEME dataset showed that the new features made the classification process more effective and accurate compared to many related works of rumor classification that use the same data. Experimental results achieved 78.54% accuracy as an average of the events used.

关键词： Support vector machines Electronic learning Social networking (online) Blogs Feature extraction Information technology detection algorithms

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Community detection in Model-based Testing to Address Scalability: Study Design

Community Detection in Model-based Testing to Address Scalab...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Alper Silistre Onur Kilincceker Fevzi Belli Moharram Challenger Geylani Kardas International Computer Institute Ege University Izmir Turkey University of Paderborn Paderborn Germany Mugla Sitki Kocman University Mugla Turkey Izmir Institute of Technology Izmir Turkey University of Antwerp and Flanders Make Belgium

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently. In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms. A case study is presented as an example of possible improvements to make a model-based testing approach more efficient. We demonstrate layered ESG models as an example of our approach to consider the scalability problem. We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied.

关键词： detection algorithms Scalability Research Design graphical user interfaces Communities Public Speaking

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Disturbance Waveform detection Technology Based on Probability Density of Remaining Data

Disturbance Waveform Detection Technology Based on Probabili...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Shan Jiang Jiabi Liang Ke Zhu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9781728190266

ISBN: (纸本)9781728190273

With the increasing application of disturbance waveform data, a general disturbance detection technology faced with various transient waveforms has practical significance for simplifying the detection process of specific disturbances and improving the accuracy of the specific disturbance detection. To this end, a detection algorithm for transient disturbance waveforms based on the probability density of remaining data is proposed in this paper. Firstly, the method of obtaining the remaining waveform data and its probability density is presented. Then, the probability distribution law of noise in power waveform data is studied based on the field measured data. Finally, according to the probability density difference of the remaining waveform data in adjacent periods, a transient waveform detection algorithm based on Wasserstein distance between probability densities is established. The threshold is determined by the box plot. A large amount of field measured data verifies the effectiveness of the proposed disturbance detection algorithm.

关键词： Renewable energy sources Power measurement Gaussian distribution Probability distribution Noise measurement Transient analysis detection algorithms

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Bayesian occupancy grid for active sonar detection and localization of moving targets

Bayesian occupancy grid for active sonar detection and local...

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OCEANS

作者： Karl Thomas Hjelmervik Kristoffer Engedal Andreassen Elin M Bohler Jorn Inge Vestgården Henrik Berg Dan Henrik S Stender Norwegian Defence Research Establishment (FFI)

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

Active sonar performance is highly dependent on the surrounding environment. Conventional detection algorithms apply thresholds to acoustic data passed through a normalizer in order to detect targets. These methods fail to fully exploit available knowledge of the environment, which leads to higher false alarm rates than necessary, particularly in littoral environments. Furthermore, they do not exploit negative information, e.g. the significance of the repeated lack of threshold crossings in a given area. Sonar performance models may estimate both the probability of detection and false alarm in a known environment. Proper exploitation of this data allows for reduced false alarm rates in cluttering and reverberating environments. Furthermore, areas lacking threshold crossings and with high probability of detection, may be classified as target free. Bayesian occupancy grids is a probabilistic approach that takes into account both sensor information and prior information of surrounding walls or topography for localization of robots. Here we apply the method on synthetic sonar data combined with a sonar performance model. The synthetic sonar data contains moving and stationary targets in a littoral environment. The method's performance is compared to conventional algorithms, and its robustness to errors in the sound speed profile used in the modelling is assessed.

关键词： Location awareness Sonar Robot sensing systems Robustness Bayes methods detection algorithms Sonar detection

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Identifying Social Networks of Programmers using Text Mining for Code Similarity detection

Identifying Social Networks of Programmers using Text Mining...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Konstantinos F. Xylogiannopoulos Panagiotis Karampelas University of Calgary Calgary Canada Hellenic Air Force Academy Dekelia Greece

ISBN: (数字)9781728110561

ISBN: (纸本)9781728110578

The availability of code in many online repositories and collaborating platforms has posed new challenges in source code attribution not only for plagiarism detection but also in other settings such as in the use of insecure copied code in commercial application, etc. The sophistication of different type of attacks in the code sequence used especially by the students requires more effective code similarity detection algorithms. In this paper, a novel source code detection method is proposed that can identify programmers' social network based on advanced pattern detection text mining techniques. The proposed methodology has significant advantages against existing methods since ARPaD algorithm can detect all common patterns between all possible code sequences in one run. Therefore, the computational time is massively reduced to 0(mn log n). In order to assess the performance of the methodology, a new dataset was created by assigning to 46 students a code project with specific instructions. The assessment results have been visualized, producing the social network graphs of possible collaboration teams.

关键词： Text mining Visualization Social networking (online) Plagiarism Collaboration detection algorithms

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Robust QRS detection Using Combination of Three Independent Methods

Robust QRS Detection Using Combination of Three Independent ...

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Computers in Cardiology (CinC)

作者： Lukas Smital Lucie Marsanova Radovan Smisek Andrea Nemcova Martin Vitek Brno University of Technology Brno Czech Republic Institute of Scientific Instruments of the Czech Academy of Sciences Brno Czech Republic

ISBN: (数字)9781728173825

ISBN: (纸本)9781728111056

QRS detection is a fundamental step in ECG analysis. Although there are many algorithms reporting results close to 100%, this problem is still not resolved. The reported numbers are influenced by the quality of the detector, the quality of annotations and also by the chosen method of testing. In this study, we proposed and properly tested robust QRS detection algorithm based on a combination of three independent principles. For enhancement of QRS complexes there were developed three independent approaches based on continuous wavelet transform, Stockwell transform and phasor transform which are followed by individual adaptive thresholding. Each method produces candidates for QRS complexes which are further processed by cluster analysis resulting in final QRS positions. The proposed detection algorithm was tested on three complete standard ECG databases: MIT-BIH Arrhythmia Database, European ST-T Database and QT Database without any change in algorithm setting. We utilized complete data from mentioned databases including all provided leads and used original (not adjusted) reference positions of QRS complexes. Summarized detection accuracy for all three databases was expressed by sensitivity 99.16% and positive predictive value 98.99%.

关键词： Sensitivity Databases Detectors Electrocardiography detection algorithms Standards Testing

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Failure Prediction in Datacenters Using Unsupervised Multimodal Anomaly detection

Failure Prediction in Datacenters Using Unsupervised Multimo...

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IEEE International Conference on Big Data

作者： Minglu Zhao Reo Furuhata Mulya Agung Hiroyuki Takizawa Tomoya Soma Graduate School of Information Sciences Tohoku University Cyberscience Center Tohoku University Corporate Business Development Division NEC Corporation

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Predicting hard drive failures in datacenters can help avoid wasting resources and waiting time for recovery. Anomaly detection from sensing data is commonly used for predicting failures. Usually, conventional threshold-based anomaly detection methods consider each sensor independently. However, deciding an optimal threshold for each type of sensors is not trivial, especially for large-scale systems in datacenters. To detect failures that cannot conventionally be detected, multimodal anomaly detection becomes crucial integrating sensing data from different types of sensors. This work proposes a correlation-based multimodal anomaly detection approach. This approach is applied to a Network-Attached Storage (NAS) system with multiple hard disk drives (HDDs) and three sensors, which are a thermal camera, a microphone, and system performance logs. The unimodal results show that the auditory and system performance model can detect temporal anomalies, and the thermal model can detect spatial anomalies. The multimodal results show that even with a simple filter and detection algorithms, the multimodal approach was able to detect failure signs before the real failure and also earlier than the auditory unimodal approach.

关键词： Temperature sensors Correlation System performance Filtering algorithms Big Data detection algorithms Anomaly detection

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Fall detection algorithm for the elderly based on human posture estimation

Fall detection algorithm for the elderly based on human post...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Guangmin Sun Zhongqi Wang Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

With the acceleration of the global aging process, the safety and health of the elderly has become a widespread concern, and falls have become the main health threat of the elderly. In this paper, a fall detection model based on OpenPose human posture estimation algorithm is proposed by using the fall detection method based on machine vision. On the basis of OpenPose human key point detection, combined with SSD MobileNet object detection framework can remove the non human key points detected by OpenPose algorithm, reduce the false detection rate of the algorithm, improve the robustness of the algorithm in complex environment, and then extract the features of human joint points, use SVDD classification algorithm to classify, experiments show that this method can effectively detect the occurrence of falls, and the accuracy rate can reach more than 93%.

关键词： Human posture estimation OpenPose object detection Object detection falls aged person Human Posture Fall accuracy rate detection rate (DR) algorithms robot vision detection algorithms

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detection of Spinal Fracture Lesions based on Improved Yolov2

Detection of Spinal Fracture Lesions based on Improved Yolov...

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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Gang Sha Junsheng Wu Bin Yu School of Computer Science Northwestern Polytechnical University Xi'an Xi'an Shaanxi China School of Software and Microelectronics Northwestern Polytechnical University Xi'an Xi'an Shaanxi China School of Computer Science and Technology Xidian University Xi'an Xi'an Shaanxi China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

Yolo algorithm has a good detection effect in target detection. Because of its high detection accuracy and fast detection speed, it is widely used in practice. Because of the problem that the complexity of spine CT images, the irregular shape of vertebral boundary, meanwhile there are artificial deviations and low efficiencies in clinic, which needs doctors' prior knowledge and clinical experience to determine lesions location in CT images, so it can not meet the clinical real-time needs. In this paper, We use deep learning to process the CT images of spine, and to detect and locate lesion of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by the improved YOLOv2. Through using lesions bounding box dimensional cluster and multiscale transformation of input CT images to improve detection efficiency and accuracy. The experiment shows the results are more accurate, and mAP (mean average precision) of detection algorithm is 75.30%, detection rate is 0.027 seconds per detection, and IOU((Intersection-over-Union) is 77.3, which can basically meet the clinical real-time needs.

关键词： Lesions Computed tomography Object detection Training Hospitals Machine learning detection algorithms

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An Instrument for Accurate and Non-Invasive Screening of Skin Cancer Based on Multimodal Imaging

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IEEE ACCESS 2019年 7卷 176646-176657页

作者： Diaz, Silvana Krohmer, Thomas Moreira, Alvaro Godoy, Sebastian E. Figueroa, Miguel Sicom Elect Int SA Concepcion 1234 Chile Univ Concepcion Dept Elect Engn Coneepcion 4030000 Chile

We present an instrument based on commodity embedded hardware, that implements an automatic procedure for early skin-cancer screening using dynamic thermal imaging. The procedure leverages image segmentation in the visible range and real-time multimodal registration to compute the temperature recovery curve (TRC) of suspicious skin lesions using thermal infrared video. The instrument implements two algorithms that infer the malignancy of the lesion from the computed TRCs. The first algorithm assumes that the TRCs are deterministic and infers the malignancy from the distance between the TRC of the suspicious lesion and its surrounding skin, which is assumed to be healthy tissue. The second algorithm models the TRC of the lesion as a random process and uses detection theory to statistically infer its malignancy from the eigenfunctions and corresponding eigenvalues of its covariance function. We built a prototype of the instrument using a Raspberry Pi 3 model B+ board, which acquires a visible-range image of the lesion at the beginning of the procedure and performs image segmentation in 62ms. Operating on a 400 x 400-pixel region-of-interest within the infrared video, the board performs frame-by-frame multimodal image registration and generates the TRCs in real time at more than 37 frames per second, thus eliminating the need to store video data for off-line processing. The statistical detection algorithm, which yields the best results, runs in 1.07s at the end of the procedure, and achieves a sensitivity of 98% and a specificity of 95% on a dataset of 116 volunteer subjects.

关键词： Skin cancer screening dynamic thermal imaging infrared imaging multimodal registration image segmentation classification detection algorithms embedded software

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