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A Machine learning Filter for Relation Extraction

Kevin Lange Di Cesare, Michel Gagnon, Amal Zouaq, Ludovic Jean-Louis
2016 Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion  
In this work, we present a machine learning filter whose aim is to enhance the precision of relation extractors while minimizing the impact on recall.  ...  The results show that our filter is able to improve the precision of the best 2013 system by nearly 20% and improve the F1score for 17 relations out of 33 considered.  ...  METHODOLOGY Our approach consists in using a machine learning filter trained on the output of the ESF participating systems.  ... 
doi:10.1145/2872518.2889397 dblp:conf/www/CesareGZJ16 fatcat:p76722fvcjf2bmvfqtccxq6oxm

Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case [article]

Nutta Homdee, John Lach
2020 arXiv   pre-print
Machine learning has shown successes for complex learning problems in which data/parameters can be multidimensional and too complex for a first-principles based analysis.  ...  Some applications that utilize machine learning require human interpretability, not just to understand a particular result (classification, detection, etc.) but also for humans to take action based on  ...  Many of the widely-used machine-learning algorithms are considered black-box; Rudin explains that a black-box model could be a function that is too complicated for a human to understand or a proprietary  ... 
arXiv:2009.05097v1 fatcat:zst6dy3z3vevdc6brt7wcbwsya

Offline strategies for online question answering

Michael Fleischman, Eduard Hovy, Abdessamad Echihabi
2003 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03  
We present an alternative strategy in which patterns are used to extract highly precise relational information offline, creating a data repository that is used to efficiently answer questions.  ...  Results indicate that the extracted relations answer 25% more questions correctly and do so three orders of magnitude faster than the state of the art system.  ...  The authors would like to thank Miruna Ticrea for her valuable help with training the classifier.  ... 
doi:10.3115/1075096.1075097 dblp:conf/acl/FleischmanHE03 fatcat:24ynvt5gazethlzw643tynuzvi

WeSeqMiner: A Weka package for building machine-learning models for sequence data [article]

Daniel Hogan, Bharathikumar Vellalore Maruthachalam, Ronald C. Geyer, Anthony Kusalik
2017 bioRxiv   pre-print
To this end, we propose a Weka package called WeSeqMiner, which provides several useful filters for extracting numerical features from sequence data for use in the Weka machine learning workbench.  ...  The application of machine learning techniques to biological sequence data typically requires a vector representation of the sequences.  ...  The large volume of data produced by high-throughput technologies like antibody phage display and peptide microarrays makes machine learning a viable option for discovering functions relating sequence  ... 
doi:10.1101/217802 fatcat:m42virm3xfdibpkv2sq62glb3q

Deep Packet Filtering Mechanism for Secure Internetworks

Hyun Woo Kim and Eun
2021 Turkish Journal of Computer and Mathematics Education  
In this paper, we propose a Deep Packet Filtering Mechanism (DPFM) to analyze and filter malicious data packets moving between network environments.  ...  DPFM analyzes the behavior of malicious packets on the network and extracts information about the network as a sequence.  ...  log files for network-related activities.  ... 
doi:10.17762/turcomat.v12i6.1956 fatcat:cpvos7ec55ghvba5yf6fz2p47u

Elastic Cloud Logs Traces, Storing and Replaying for Deep Machine Learning

Tariq Daradkeh, Anjali Agarwal, Nishith Goel, Jim Kozlowski
2020 Procedia Computer Science  
Storing logs and retrieving it in cloud computing environment is a critical task for Deep Machine Learning models.  ...  Abstract Storing logs and retrieving it in cloud computing environment is a critical task for Deep Machine Learning models.  ...  These logs data were analyzed for deep machine learning model by making data cleaning and correlating to produce input and output relation as supervised machine learning model requires (cause and result  ... 
doi:10.1016/j.procs.2020.04.011 fatcat:ehfmytdyyraoji6ourijcla7ey


S. M. Ayazi, M. Saadat Seresht
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Today, the use of machine learning techniques has improved the results of classification, which has led to significant results, especially when data can be labelled at the presence of training data.  ...  Point cloud filtering techniques can be divided into two general rule-based and novel methods.  ...  The extraction of high-performance effective features for use in machine learning methods leads to better results in the classification of ground and non-ground points.  ... 
doi:10.5194/isprs-archives-xlii-4-w18-141-2019 fatcat:laoo5z7u6fbtddim5eayifn5xu

Lung cancer detection with prediction employing machine learning algorithms

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The proposed system employs machine learning algorithms like support vector machine (SVM) and deep learning algorithm like convolutional neural network (CNN), to perform the classification, using an extensive  ...  Lung cancer-related deaths are increasing globally every year.  ...  Classification Support Vector Machine (SVM) is a machine learning algorithm used for classification.  ... 
doi:10.30534/ijatcse/2020/97952020 fatcat:ozfh3hsq7re7bg7dhoe6shl3q4

Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning [article]

Christoph Stanik, Marlo Haering, Walid Maalej
2019 arXiv   pre-print
Our results show that using traditional machine learning, we can still achieve comparable results to deep learning, although we collected thousands of labels.  ...  In this work, we compare the results of traditional machine learning and deep learning in classifying user feedback in English and Italian into problem reports, inquiries, and irrelevant.  ...  Researchers have applied supervised machine learning to filter noisy, irrelevant feedback and to extract requirements related information [27] , [14] .  ... 
arXiv:1909.05504v1 fatcat:ig7drjbrcfecbmo7cztqlyh7ay

A Two-Step Unsupervised Learning Approach to Diagnose Machine Fault Using Big Data

V. J. Sharmila, D. Jemi Florinabel
2022 Information Technology and Control  
As a first step, we used a two-layer neural network sparse filteringprocedure to extract vibration signals' features.  ...  , we encapsulate the two-stage learning technique such as sparse filtering andRectified Linear Unit (ReLU) regression function.  ...  It was applicable for a small dataset only. Kuncan [10] discussed the combination of local binary patterns with a gray relational model for feature extraction and classifying bearing faults.  ... 
doi:10.5755/j01.itc.51.1.29686 fatcat:lbs2ck4qrbb6rf7kujyvtwugbm

E-Mail Spam Detection using Machine Learning and Deep Learning

Shivam Pandey
2020 International Journal for Research in Applied Science and Engineering Technology  
Our focus is primarily on machine learning-based spam filters and variants that are inspired by them. We report on related ideas, techniques, major efforts and cutting-edge art in the field.  ...  Here we present an inclusive review of recent and successful content-based e-mail spam filtering techniques.  ...  Machine learning approaches have a wide range of Importance and a lot of algorithms can be used for e-mail filtering and classification. These include Support Vector Machine, Naïve Bayes. II.  ... 
doi:10.22214/ijraset.2020.6159 fatcat:kmn33grvjrcwxek5amfiqi2xvu

DeepImageSpam: Deep Learning based Image Spam Detection [article]

Amara Dinesh Kumar, Vinayakumar R, Soman KP
2018 arXiv   pre-print
This paper proposes a deep learning based approach for image spam detection using the convolutional neural networks which uses a dataset with 810 natural images and 928 spam images for classification achieving  ...  an accuracy of 91.7% outperforming the existing image processing and machine learning techniques  ...  Using machine learning techniques For performing classification using the machine learning algorithms the features are to be selected and extracted manually.Their are two types of features in the image  ... 
arXiv:1810.03977v1 fatcat:kky6mgmlajbv7ky4hoy6omgp34


Mijodrag Milošević, University of Novi Sad, Faculty of Technical Sciences, Department of Production Engineering, Novi Sad, Serbia, Dejan Lukić, Gordana Ostojić, Milovan Lazarević, Aco Antić, University of Novi Sad, Faculty of Technical Sciences, Department of Production Engineering, Novi Sad, Serbia, University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia, University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia, University of Novi Sad, Faculty of Technical Sciences, Department of Production Engineering, Novi Sad, Serbia
2022 Journal of Production Engineering  
This system applies a machine learning method that is integrated within the MS Azure cloud system.  ...  In this context, the paper present a developed cloud-based system for monitoring the condition of cutting tool wear by measuring vibration.  ...  CLOUD-BASED MACHINE LEARNING Azure Machine Learning Studio allows create and test different machine learning models for a some data set.  ... 
doi:10.24867/jpe-2022-01-020 fatcat:v5qv3ip3wnf7vosg5me7gcvosq

Identification of SNARE Proteins Through a Novel Hybrid Model

Guilin Li
2020 IEEE Access  
Some researchers attempt to identify the SNARE proteins by the machine learning algorithms. A deep learning model called SNARE-CNN is proposed to predict SNARE proteins.  ...  In this paper, a novel hybrid model, that combines the random forest algorithm with the oversampling filter and 188D feature extraction method, is proposed.  ...  To evaluate the performance of a particular machine learning algoirhtm, we calculate the average SN of all three filtering methods for each machine learning algorithm.  ... 
doi:10.1109/access.2020.3004446 fatcat:cmqo4ayryzbyhcygrc66ilmkai


Darshan A Khade, Ilakiyaselvan N
2017 Asian Journal of Pharmaceutical and Clinical Research  
Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data.  ...  This technique is useful in forensic as well as in artificial intelligence for developing future technology.  ...  After extracting the epochs, a reference point was used to check with time. Interval from the filtered dataset.  ... 
doi:10.22159/ajpcr.2017.v10s1.19495 fatcat:kmeuugpuxffjhfore5ocvntsgm
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