List of Machine Language and Pattern Analysis IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Machine Language and Pattern Analysis for M.Phil Computer Science students.
Machine Language and Pattern Analysis IEEE 2015 ProjectsVijay Karan
List of Machine Language and Pattern Analysis IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Machine Language and Pattern Analysis for the year 2015
Machine Language and Pattern Analysis IEEE 2015 ProjectsVijay Karan
List of Machine Language and Pattern Analysis IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Machine Language and Pattern Analysis for the year 2015
In this paper we introduce an extension of FrameNet for structured and semantic modeling of factual claims and an adaptation of the frame detection algorithms in Open Sesame for identifying frames and extracting frame elements from text. This claim modeling capability can be leveraged in assisting a variety of steps for automating fact-checking, e.g., matching claims with fact-checks, translating claims to structured queries, and so on. Our preliminary results show that while many challenges remain, which we discuss, frames can potentially improve the aforementioned steps. Further studies will reveal the strength and weakness of this modeling approach in more detail, as well as how to incorporate it into the full pipeline of fact-checking automation.
An Empirical Comparison of Fast and Efficient Tools for Mining Textual Datavtunali
In order to effectively manage and retrieve the information comprised in vast amount of text documents, powerful text mining tools and techniques are essential. In this paper we evaluate and compare two state-of-the-art data mining tools for clustering high-dimensional text data, Cluto and Gmeans. Several experiments were conducted on three benchmark datasets, and results are analysed in terms of clustering quality, memory and CPU time consumption. We empirically show that Gmeans offers high scalability by sacrificing clustering quality while Cluto presents better clustering quality at the expense of memory and CPU time.
Presentation of the application EIRA which can be used to find relevant researchers for research projects within the medical field. The search input can be either a search string or a text file containing an RFA.
EIRA then finds articles related to the search and then ranks the authors of those articles.
Topic modeling of marketing scientific papers: An experimental surveyICDEcCnferenece
Malek Chebil, Rim Jallouli, Mohamed Anis Bach Tobji and Chiheb Eddine Ben Ncir. Topic modeling of marketing scientific papers: An experimental survey. (ICDEc 2021)
Nikos Koutsoupias / Kyriakos
Mikelis | nk mikelis }@uom.gr | Department of International & European Studies | University of Macedonia, Thessaloniki, GR
Abstract
We introduce the combined use of multiple correspondence analysis, metadata and term frequencies for clustering articles of a scientific journal. A period of five years (2010-2014) is covered, with approximately 125 articles. Through specific R packages for multidimensional data analysis and text mining, the approach links quantitative analysis of discourse to clustering documents considering both metadata and frequent terms.
Keywords document clustering; hierarchical clustering; multiple correspondence analysis, document metadata; text mining;
To appear in: Data Analysis and Rationality in a Complex World, Spinger, 2021, ISBN 978-3-030-60103-4.
See also:
=========================================
doi: https://dx.doi.org/10.4135/9781526486189
http://www.uom.gr/en/nk
http://gict.uom.gr
https://www.linkedin.com/in/nikos-koutsoupias/
https://www.researchgate.net/profile/Nikos_Koutsoupias
https://orcid.org/0000-0003-1664-5404
=============================================
SocialMedia
=============================================
https://www.facebook.com/nikos.koutsoupias/
https://twitter.com/nikoutsoupias
https://www.instagram.com/nkoutsoupias/
https://gr.pinterest.com/koutsoupiasn
=============================================
Sites
=============================================
Qdas- Qualitative Analysis - http://qdas.uom.gr
ArtAnalytics - http://qdas.uom.gr/index.php/artanalytics/
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
In this paper we introduce an extension of FrameNet for structured and semantic modeling of factual claims and an adaptation of the frame detection algorithms in Open Sesame for identifying frames and extracting frame elements from text. This claim modeling capability can be leveraged in assisting a variety of steps for automating fact-checking, e.g., matching claims with fact-checks, translating claims to structured queries, and so on. Our preliminary results show that while many challenges remain, which we discuss, frames can potentially improve the aforementioned steps. Further studies will reveal the strength and weakness of this modeling approach in more detail, as well as how to incorporate it into the full pipeline of fact-checking automation.
An Empirical Comparison of Fast and Efficient Tools for Mining Textual Datavtunali
In order to effectively manage and retrieve the information comprised in vast amount of text documents, powerful text mining tools and techniques are essential. In this paper we evaluate and compare two state-of-the-art data mining tools for clustering high-dimensional text data, Cluto and Gmeans. Several experiments were conducted on three benchmark datasets, and results are analysed in terms of clustering quality, memory and CPU time consumption. We empirically show that Gmeans offers high scalability by sacrificing clustering quality while Cluto presents better clustering quality at the expense of memory and CPU time.
Presentation of the application EIRA which can be used to find relevant researchers for research projects within the medical field. The search input can be either a search string or a text file containing an RFA.
EIRA then finds articles related to the search and then ranks the authors of those articles.
Topic modeling of marketing scientific papers: An experimental surveyICDEcCnferenece
Malek Chebil, Rim Jallouli, Mohamed Anis Bach Tobji and Chiheb Eddine Ben Ncir. Topic modeling of marketing scientific papers: An experimental survey. (ICDEc 2021)
Nikos Koutsoupias / Kyriakos
Mikelis | nk mikelis }@uom.gr | Department of International & European Studies | University of Macedonia, Thessaloniki, GR
Abstract
We introduce the combined use of multiple correspondence analysis, metadata and term frequencies for clustering articles of a scientific journal. A period of five years (2010-2014) is covered, with approximately 125 articles. Through specific R packages for multidimensional data analysis and text mining, the approach links quantitative analysis of discourse to clustering documents considering both metadata and frequent terms.
Keywords document clustering; hierarchical clustering; multiple correspondence analysis, document metadata; text mining;
To appear in: Data Analysis and Rationality in a Complex World, Spinger, 2021, ISBN 978-3-030-60103-4.
See also:
=========================================
doi: https://dx.doi.org/10.4135/9781526486189
http://www.uom.gr/en/nk
http://gict.uom.gr
https://www.linkedin.com/in/nikos-koutsoupias/
https://www.researchgate.net/profile/Nikos_Koutsoupias
https://orcid.org/0000-0003-1664-5404
=============================================
SocialMedia
=============================================
https://www.facebook.com/nikos.koutsoupias/
https://twitter.com/nikoutsoupias
https://www.instagram.com/nkoutsoupias/
https://gr.pinterest.com/koutsoupiasn
=============================================
Sites
=============================================
Qdas- Qualitative Analysis - http://qdas.uom.gr
ArtAnalytics - http://qdas.uom.gr/index.php/artanalytics/
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A Formal Machine Learning or Multi Objective Decision Making System for Deter...Editor IJCATR
Decision-making typically needs the mechanisms to compromise among opposing norms. Once multiple objectives square measure is concerned of machine learning, a vital step is to check the weights of individual objectives to the system-level performance. Determinant, the weights of multi-objectives is associate in analysis method, associated it's been typically treated as a drawback. However, our preliminary investigation has shown that existing methodologies in managing the weights of multi-objectives have some obvious limitations like the determination of weights is treated as one drawback, a result supporting such associate improvement is limited, if associated it will even be unreliable, once knowledge concerning multiple objectives is incomplete like an integrity caused by poor data. The constraints of weights are also mentioned. Variable weights square measure is natural in decision-making processes. Here, we'd like to develop a scientific methodology in determinant variable weights of multi-objectives. The roles of weights in a creative multi-objective decision-making or machine-learning of square measure analyzed, and therefore the weights square measure determined with the help of a standard neural network.
Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
Rank based similarity search reducing the dimensional dependenceShakas Technologies
This paper introduces a data structure for k-NN search, the Rank Cover Tree (RCT), whose pruning tests rely solely on the comparison of similarity values; other properties of the underlying space, such as the triangle inequality, are not employed.
Vchunk join an efficient algorithm for edit similarity joinsVijay Koushik
Similarity join is most important technique to
involve many applications such as data integration, record
linkage and pattern recognition. Here we introduce new
algorithm for similarity join with edit distance constraints.
Currently extracting overlapping grams from string and consider
only string that share certain gram as candidate. Now we propose
extracting non-overlapping substring or chunk from string.
Chunk scheme based on tail-restricted chunk boundary
dictionary (CBD). This approach integrated existing approach
for calculating similarity with several new filters unique to chunk
based method. Greedy algorithm automatically select good
chunking scheme from given data set. Then show the result our
method occupies less space and faster performance to compute
the value
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
M.Phil Computer Science Wireless Communication ProjectsVijay Karan
List of Wireless Communication IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Wireless Communication for M.Phil Computer Science students.
M.E Computer Science Wireless Communication ProjectsVijay Karan
List of Wireless Communication IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Wireless Communication for M.E Computer Science students.
M.Phil Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.Phil Computer Science students.
M.E Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.E Computer Science students.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
M phil-computer-science-machine-language-and-pattern-analysis-projects
1. M.Phil Computer Science Machine Language and Pattern
Analysis Projects
Web : www.kasanpro.com Email : sales@kasanpro.com
List Link : http://kasanpro.com/projects-list/m-phil-computer-science-machine-language-and-pattern-analysis-projects
Title :Rank-Based Similarity Search: Reducing the Dimensional Dependence
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/rank-based-similarity-search
Abstract : This paper introduces a data structure for k-NN search, the Rank Cover Tree (RCT), whose pruning tests
rely solely on the comparison of similarity values; other properties of the underlying space, such as the triangle
inequality, are not employed. Objects are selected according to their ranks with respect to the query object, allowing
much tighter control on the overall execution costs. A formal theoretical analysis shows that with very high probability,
the RCT returns a correct query result in time that depends very competitively on a measure of the intrinsic
dimensionality of the data set. The experimental results for the RCT show that non-metric pruning strategies for
similarity search can be practical even when the representational dimension of the data is extremely high. They also
show that the RCT is capable of meeting or exceeding the level of performance of state-of-the-art methods that make
use of metric pruning or other selection tests involving numerical constraints on distance values.
Title :Rank-Based Similarity Search: Reducing the Dimensional Dependence
Language : Java
Project Link : http://kasanpro.com/p/java/rank-based-similarity-search-reducing-dimensional-dependence
Abstract : This paper introduces a data structure for k-NN search, the Rank Cover Tree (RCT), whose pruning tests
rely solely on the comparison of similarity values; other properties of the underlying space, such as the triangle
inequality, are not employed. Objects are selected according to their ranks with respect to the query object, allowing
much tighter control on the overall execution costs. A formal theoretical analysis shows that with very high probability,
the RCT returns a correct query result in time that depends very competitively on a measure of the intrinsic
dimensionality of the data set. The experimental results for the RCT show that non-metric pruning strategies for
similarity search can be practical even when the representational dimension of the data is extremely high. They also
show that the RCT is capable of meeting or exceeding the level of performance of state-of-the-art methods that make
use of metric pruning or other selection tests involving numerical constraints on distance values.