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Filters
A Machine learning Filter for Relation Extraction
2016
Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion
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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]
2020
arXiv
pre-print
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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
2003
Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03
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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]
2017
bioRxiv
pre-print
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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
2021
Turkish Journal of Computer and Mathematics Education
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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
2020
Procedia Computer Science
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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
COMPARISON OF TRADITIONAL AND MACHINE LEARNING BASE METHODS FOR GROUND POINT CLOUD LABELING
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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
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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]
2019
arXiv
pre-print
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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
2022
Information Technology and Control
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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
2020
International Journal for Research in Applied Science and Engineering Technology
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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]
2018
arXiv
pre-print
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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
APPLICATION OF CLOUD-BASED MACHINE LEARNING IN CUTTING TOOL CONDITION MONITORING
2022
Journal of Production Engineering
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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
2020
IEEE Access
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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
SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL
2017
Asian Journal of Pharmaceutical and Clinical Research
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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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