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Online Phase Detection Algorithms

P. Nagpurkar, C. Krintz, M. Hind, P.F. Sweeney, V.T. Rajan
International Symposium on Code Generation and Optimization (CGO'06)  
More specifically, we contribute (a) a novel framework for online phase detection, (b) multiple instantiations of the framework that produce novel online phase detection algorithms, (c) a novel client-and  ...  In this work, we focus on the enabling technology of online phase detection.  ...  Moreover, our algorithms are online; they detect phases while they occur.  ... 
doi:10.1109/cgo.2006.26 dblp:conf/cgo/NagpurkarKHSR06 fatcat:jgbkyr35djd2hppt66botqcdj4

Supervision and Control of Students during Online Assessments Applying Computer Vision Techniques: A Systematic Literature Review

Maritza G. Méndez-Ortega, Erick P. Herrera-Granda, Adriana E. Prado Malte, Rodolfo B. Heredia Enríquez
2021 Universal Journal of Educational Research  
, region-based algorithms, grid-based algorithms, face detection, detection of gestures and object detection.  ...  What techniques have been used to detect plagiarism in online evaluations? What machine vision algorithms are used?  ...  object detection)) ((-copying online exam‖ OR -copying online courses‖ OR -online evaluation plagiarism‖) AND (-computer vision phase detection‖ OR -computer vision gesture detection  ... 
doi:10.13189/ujer.2021.090513 fatcat:j4pytewiojegthcn2msprtjmja

Novelty detection algorithm for data streams multi-class problems

Elaine R. Faria, João Gama, André C. P. L. F. Carvalho
2013 Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13  
This work presents a new algorithm to address novelty detection in data streams multi-class problems, the MINAS algorithm.  ...  The examples not explained by the model are detected as belonging to a class named novelty.  ...  Online phase The online phase (see Algorithm 2 and 3) receives as input an unlabeled data stream. MINAS checks each new example to verify if it can be explained by the current model.  ... 
doi:10.1145/2480362.2480515 dblp:conf/sac/FariaGC13 fatcat:au23wrd7mbatxaujtfwn2swb44

Online Seizure Detection In Adults With Temporal Lobe Epilepsy Using Single-Lead Ecg

Paul Boon, Evelien Carrette, Thomas De Cooman, Alfred Meurs, S. Van Huffel
2014 Zenodo  
Therefore, an online patient-independent seizure detection algorithm for TLE patients will be proposed in this paper.  ...  The seizure onset is typically around the start of the linear phase. The number of extensively tested algorithms for online ECG seizure detection in adults is rather limited.  ... 
doi:10.5281/zenodo.43975 fatcat:gvy2rz46mbbirl7ljuh6km77hy

Use of Stylometry and Outlier Detection Algorithm in Online Writing Sample to Detect Outliers

Sonia Sharma
2014 IOSR Journal of Engineering  
Outlier detection contains a broad spectrum of techniques to detect outliers. [1] Here, we are going to propose an algorithm which detects outliers (unmatched sample) in online writing sample.  ...  The online writing sample is analysed firstly by using the well known concept "Stylometry". Stylometry is the study which helps to distinguish between the writing style of two persons.  ...  So, In this paper, we purposed a model which will detect the outliers (unmatched sample) in any online samples by using stylometry to analyze the writing sample and then an algorithm. II.  ... 
doi:10.9790/3021-04422730 fatcat:fyju6xlbc5as7gof7yd6tbthgq

Use of Decision Trees and Attributional Rules in Incremental Learning of an Intrusion Detection Model

2014 International journal of computer networks and communications security  
The model is intensively tested online and its evaluation showed promising results.  ...  In this paper, we propose a Learnable Model for Anomaly Detection (LMAD), as an ensemble real-time intrusion detection model using incremental supervised machine learning techniques.  ...  RELATED WORK Many data mining algorithms have been applied to intrusion detection, which can be divided into typical offline algorithms and incremental online algorithms.  ... 
doi:10.47277/ijcncs/2(7)1 fatcat:ha6agyvg5jcwtdneemdasyrjsq

Towards an outlier detection model in text data stream

Awab Noori, Universiti Utara Malaysia, Malaysia
2019 International Journal of Advanced Trends in Computer Science and Engineering  
An online feature selection will be improved on phase number three. Finally, in the fourth phase, one of the swarm intelligence techniques will be improved to detect outlier in the text stream.  ...  The model contains four main phases namely pre-processing, text representation, feature selection, and outlier detection phase.  ...  Outlier detection step is the last phase in the proposed model. In this phase an improved SI algorithm that will be able to detect outlier in text stream data is proposed.  ... 
doi:10.30534/ijatcse/2019/47862019 fatcat:hn7crfcls5eafi3ck6dxvc6pxi

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

Fangzheng Wang, Lei Yan, Jiang Xiao
2019 Sensors and materials  
of gait phase detection.  ...  The N-HMM was used for gait phase detection and the detection accuracy of the N-HMM was compared with that of the HMM, support vector machine (SVM), decision tree, and back propagation (BP) network algorithms  ...  Fig. 13 . 13 (Color online) Gait phase identification algorithm diagram of N-HMM. Fig. 14 . 14 (Color online) Four-phase gait model structure.  ... 
doi:10.18494/sam.2019.2431 fatcat:bv7lbkqg7bhhlpioazhykvcfha

Online Anomaly Detection of Transformer Vibration Based on SVDD Incremental Learning

Wei XU, Bin ZHONG, Wen-zhang XIAO, Hong HU, Nai-hui WANG, Yang JING, Jian LUO, Zhou-rui YAN, Jia-bo SONG
2019 DEStech Transactions on Computer Science and Engineering  
In this paper, an incremental learning algorithm based on support vector data description was proposed for on-line anomaly detection of transformer vibration.  ...  The anomaly detection model was established by using one-class support vector data description, the incremental data were reduced based on the Quickhull algorithm to achieve quick incremental learning.  ...  In terms of detection accuracy, Q-SVDD algorithm had 4 best AUC values among 5 anomaly detection experiments, indicating that its detection accuracy was higher than that of SVDD algorithm and Online SVDD  ... 
doi:10.12783/dtcse/icaic2019/29402 fatcat:l4aep4vg65g5th6nlmkdgcc35q

A novel hybrid artificial immune inspired approach for online break-in fraud detection

R. Huang, H. Tawfik, A.K. Nagar
2010 Procedia Computer Science  
This paper proposes a new hybrid model for online fraud detection of the Video-on-Demand System, which is aimed to improve the current Risk Management Pipeline (RMP) by adding Artificial Immune System  ...  The AIS based model combines two artificial immune system algorithms with behavior based intrusion detection using Classification and Regression trees (CART).  ...  Fig 5 . 5 Finite State Machine Fig 6 . 6 Phase 1: CSPRA training phase for break-in fraud detection Fig 7 . 7 Phase 2: CSPRA detection phase for break-in fraud detection Fig 8 . 8 Sampling phase  ... 
doi:10.1016/j.procs.2010.04.307 fatcat:bhstgetoe5cs5luv5ltfetsyv4

Self-adaptive and dynamic clustering for online anomaly detection

Seungmin Lee, Gisung Kim, Sehun Kim
2011 Expert systems with applications  
In this study, a novel framework is developed for fully unsupervised training and online anomaly detection.  ...  In the framework, a self-organizing map (SOM) that is seamlessly combined with K-means clustering is transformed into an adaptive and dynamic algorithm suitable for real-time processing.  ...  Online anomaly detection An initial learning model obtained from the SOM and K-means algorithms is stationary.  ... 
doi:10.1016/j.eswa.2011.05.058 fatcat:ygiftoenv5h7hl5scvhx4utlqq

XNDDF: Towards a Framework for Flexible Near-Duplicate Document Detection Using Supervised and Unsupervised Learning

Lavanya Pamulaparty, C.V. Guru Rao, M. Sreenivasa Rao
2015 Procedia Computer Science  
and online processing required by a search engine.  ...  The first algorithm is meant for unsupervised probabilistic clustering of documents while the second algorithm is to detect near duplicates that can handle in offline processing of search engines.  ...  Second one is the near-duplicate detection algorithm which has two phases, namely training phase and testing phase.  ... 
doi:10.1016/j.procs.2015.04.175 fatcat:nssoxjvixzdedbpgyapf343rhm

Machine Learning System for Malicious Website Detection: A Literature Review

Chaitanya R. Vyawhare, Reshma Y. Totare, Prashant S. Sonawane, Purva B. Deshmukh
2022 International Journal for Research in Applied Science and Engineering Technology  
Different machine learning algorithms used for such detection is also discuss in this paper.  ...  This review paper studies the different phases such as feature extraction phase and feature representation phase of machine learning techniques for detecting malicious URLs.  ...  MALICIOUS URL DETECTION: ALGORITHMS AND PHASES A.  ... 
doi:10.22214/ijraset.2022.42050 fatcat:fpzg3ymysvfehnzttmstg5snem

Research on Hall Sensor Fault Diagnosis and Compensation Method to Improve Motor Control Reliability

Jae-Yong Lee, Dong-Yeol Lee, Myung-Sik Jeong, Dong-Woo Kang
2018 Journal of Magnetics  
Unlike existing algorithms, it has higher reliability than existing fault detection algorithms, because it detects faults in real time.  ...  In this paper, we study the fault detection algorithm and the fault compensation method in case of hall sensor fault of BLDC motor.  ...  Fig. 1 . 1 (Color online) Hall sensor fault diagnosis algorithm (a) Original algorithm, (b) Proposed algorithm.  ... 
doi:10.4283/jmag.2018.23.4.648 fatcat:g3bvcfd4izdtvlgixrmu3w6fte

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Jinping Sui, Zhen Liu, Li Liu, Alexander Jung, Xiang Li
2020 IEEE Transactions on Cybernetics  
The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering.  ...  In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted.  ...  EDSSC is a two-phase algorithm, including a static learning phase and an online clustering phase. Compared with the state-ofthe-art algorithms, EDSSC satisfactorily handles the following issues.  ... 
doi:10.1109/tcyb.2020.3023973 pmid:33232249 fatcat:jqjwgmevkffpzcn2gqtvla2ome
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