16
•Download as DOCX, PDF•
0 likes•17 views
For further details contact: N.RAJASEKARAN B.E M.S 9841091117,9840103301. IMPULSE TECHNOLOGIES, Old No 251, New No 304, 2nd Floor, Arcot road , Vadapalani , Chennai-26. www.impulse.net.in Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Report
Share
Report
Share
Recommended
SWiM – A Semantic Wiki for Mathematical Knowledge Management
SWiM is a semantic wiki for collaborative mathematical knowledge management. It uses IkeWiki combined with mathematical markup languages to allow users to formally edit content like proofs, formulas, and symbol dictionaries. The system extracts structured data from the wiki pages using an ontology and stores it as an RDF graph to power search and navigation services. SWiM aims to facilitate workflows like collaborative proof formalization and development of OpenMath content dictionaries. The approach is intended to be adaptable to other technical domains that use semantic markup.
A probabilistic source location privacy protection scheme in wireless sensor ...
This document summarizes a student project on developing a probabilistic source location privacy protection scheme for wireless sensor networks. The proposed scheme uses phantom nodes and fake sources to diversify routing paths and protect the location of the real data source. It defines two transmission modes for real and fake packets. Simulation results showed the scheme improves safety time without high energy consumption compared to other existing approaches. The system was developed using NS2 simulator on Ubuntu/Windows with OTCL as the front end language.
Text Mining: (Asynchronous Sequences)
In this paper we tried to correlate text sequences those provides common topics for semantic clues. We propose a two step method for asynchronous text mining. Step one check for the common topics in the sequences and isolates these with their timestamps. Step two takes the topic and tries to give the timestamp of the text document. After multiple repetitions of step two, we could give optimum result.
rnn_review.10.pdf
This document provides a critical review of recurrent neural networks for sequence learning. It begins with an abstract summarizing the paper. It then discusses why recurrent neural networks are well-suited for modeling sequential data compared to other models like feedforward neural networks and Markov models. Specifically, it notes that RNNs can capture long-range temporal dependencies, unlike models with a finite context window. It also explains that RNNs can represent a vast number of states using real-valued activations, unlike discrete state Markov models.
Concurrent Inference of Topic Models and Distributed Vector Representations
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Automatically Generating Wikipedia Articles: A Structure-Aware Approach
The document describes an approach for automatically generating Wikipedia-style articles by using the structure of existing human-authored articles as templates. It involves inducing templates by analyzing section headings across documents, retrieving relevant excerpts from the internet for each template topic, and jointly training extractors to select excerpts that optimize both local relevance and global coherence across the entire article. The results confirm the benefits of incorporating structural information into the content selection process.
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.PadmapriyaIJET - International Journal of Engineering and Techniques
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.12
The document presents a new approach called TSCAN for temporally summarizing topics from a collection of documents. TSCAN first derives the major themes of a topic from the eigenvectors of a temporal block association matrix. It then extracts significant events and their summaries for each theme by examining the eigenvectors. Finally, it associates the extracted events based on their temporal closeness and context similarity to form an evolution graph of the topic. Experiments on the TDT4 corpus show that temporal summaries generated by TSCAN present topics in a comprehensible form and are superior to existing summarization methods based on human references.
Recommended
SWiM – A Semantic Wiki for Mathematical Knowledge Management
SWiM is a semantic wiki for collaborative mathematical knowledge management. It uses IkeWiki combined with mathematical markup languages to allow users to formally edit content like proofs, formulas, and symbol dictionaries. The system extracts structured data from the wiki pages using an ontology and stores it as an RDF graph to power search and navigation services. SWiM aims to facilitate workflows like collaborative proof formalization and development of OpenMath content dictionaries. The approach is intended to be adaptable to other technical domains that use semantic markup.
A probabilistic source location privacy protection scheme in wireless sensor ...
This document summarizes a student project on developing a probabilistic source location privacy protection scheme for wireless sensor networks. The proposed scheme uses phantom nodes and fake sources to diversify routing paths and protect the location of the real data source. It defines two transmission modes for real and fake packets. Simulation results showed the scheme improves safety time without high energy consumption compared to other existing approaches. The system was developed using NS2 simulator on Ubuntu/Windows with OTCL as the front end language.
Text Mining: (Asynchronous Sequences)
In this paper we tried to correlate text sequences those provides common topics for semantic clues. We propose a two step method for asynchronous text mining. Step one check for the common topics in the sequences and isolates these with their timestamps. Step two takes the topic and tries to give the timestamp of the text document. After multiple repetitions of step two, we could give optimum result.
rnn_review.10.pdf
This document provides a critical review of recurrent neural networks for sequence learning. It begins with an abstract summarizing the paper. It then discusses why recurrent neural networks are well-suited for modeling sequential data compared to other models like feedforward neural networks and Markov models. Specifically, it notes that RNNs can capture long-range temporal dependencies, unlike models with a finite context window. It also explains that RNNs can represent a vast number of states using real-valued activations, unlike discrete state Markov models.
Concurrent Inference of Topic Models and Distributed Vector Representations
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Automatically Generating Wikipedia Articles: A Structure-Aware Approach
The document describes an approach for automatically generating Wikipedia-style articles by using the structure of existing human-authored articles as templates. It involves inducing templates by analyzing section headings across documents, retrieving relevant excerpts from the internet for each template topic, and jointly training extractors to select excerpts that optimize both local relevance and global coherence across the entire article. The results confirm the benefits of incorporating structural information into the content selection process.
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.PadmapriyaIJET - International Journal of Engineering and Techniques
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.12
The document presents a new approach called TSCAN for temporally summarizing topics from a collection of documents. TSCAN first derives the major themes of a topic from the eigenvectors of a temporal block association matrix. It then extracts significant events and their summaries for each theme by examining the eigenvectors. Finally, it associates the extracted events based on their temporal closeness and context similarity to form an evolution graph of the topic. Experiments on the TDT4 corpus show that temporal summaries generated by TSCAN present topics in a comprehensible form and are superior to existing summarization methods based on human references.
12
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Dr31564567
This document discusses integrating natural language processing and parse tree query language with text mining and topic summarization methods to more efficiently extract relevant content from documents. It presents an approach that uses natural language processing to automatically generate queries from sentences, and then applies a topic summarization method called TSCAN to identify themes, segment events, and construct an evolution graph to show relationships between events. The integrated system aims to make content extraction more effective and easier to use for real-time applications. Evaluation of the methods showed benefits for tasks like information extraction.
Knowledge Representation on the Web
This document outlines a course on Knowledge Representation (KR) on the Web. The course aims to expose students to challenges of applying traditional KR techniques to the scale and heterogeneity of data on the Web. Students will learn about representing Web data through formal knowledge graphs and ontologies, integrating and reasoning over distributed datasets, and how characteristics such as volume, variety and veracity impact KR approaches. The course involves lectures, literature reviews, and milestone projects where students publish papers on building semantic systems, modeling Web data, ontology matching, and reasoning over large knowledge graphs.
G04124041046
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
The International Journal of Engineering and Science (IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Neural Models for Information Retrieval
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items, and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task.
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A systematic review on sequence-to-sequence learning with neural network and ...
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Semantic IoT Semantic Inter-Operability Practices - Part 1
G. Cassar Semantic IoT Semantic Inter-Operability Practices-Part1 presented at the IERC AC4 IoT Semantic Interoperability workshop, Guildford, UK, 15 April 2013
Ginix generalized inverted index for keyword search
1. The document presents Ginix, a generalized inverted index structure that improves keyword search efficiency. It merges consecutive IDs in inverted lists into intervals to reduce storage space.
2. Ginix algorithms for union and intersection operations on interval lists do not require decompression, improving efficiency. Performance is also enhanced by reordering documents.
3. Experiments show Ginix requires less storage than traditional inverted indexes while improving search performance on real datasets.
JAVA 2013 IEEE DATAMINING PROJECT Ginix generalized inverted index for keywor...
JAVA 2013 IEEE DATAMINING PROJECT Ginix generalized inverted index for keywor...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.orgSeattle Scalability Meetup 6-26-13
Amdahl's Law Redux - Using Concurix's Node.js Analytics Tools
Presentation by Alexander Gounares to Seattle Scalability Meetup Group on June 26, 2013
Ir 02
This document discusses building an inverted index to efficiently support information retrieval on large document collections. It describes tokenizing documents, building a dictionary of normalized terms, and creating postings lists that map each term to the documents it appears in. Inverted indexes allow skipping linear scanning and support flexible queries by indexing term locations. The document also covers calculating precision and recall to measure system effectiveness.
Concurrency Issues in Object-Oriented Modeling
This document discusses concurrency issues in object-oriented modeling. It begins with an abstract that introduces the topic of finding a synthesis between concurrency and object models by analyzing representative concurrent object-oriented languages. The document then provides background on concurrency and object-oriented programming individually before discussing how they intersect and the issues that arise when combining them. Key concepts of concurrency like activities, parallelism, and communication are defined. Common language constructs for concurrency like co-routines and threads are also introduced.
Linked Open Data: Combining Data for the Social Sciences and Humanities (and ...
A glimpse of how we are used to connecting datasets on our laptops and how, imho, need to move to the Web of Data, including a demo connecting various sources all from your(!) machine.
Scientific Publication Retrieval in Linked Data
The document describes a prototype that retrieves related scientific publications from different linked datasets through thesaurus alignment. It introduces several linked datasets, including Agrovoc, OpenAgris, STW and EconStor. The prototype matches concepts from a user query to concepts in the linked datasets' thesauri to identify related publications. Pseudocode is provided to illustrate the process of concept mapping and querying multiple datasets. The goal is to retrieve relevant publications from different sources through a single interface.
Summary Of Thesis
This dissertation examines using neural networks to predict financial time series, specifically the S&P Mib Index of the Milan stock exchange. The document provides background on neural networks, including their history and development from simple linear models to modern multi-layer models. It describes supervised neural networks and their components like activation functions and weights. The dissertation then details training neural network weights using methods like backpropagation and techniques to prevent overfitting. Finally, it applies these concepts in a case study using neural networks to forecast changes in the S&P Mib Index.
Software tools to facilitate materials science research
The document discusses software tools to facilitate materials science research, noting that the author's group works to standardize and automate computational methods for high-throughput calculations and discovery of new functional materials. It advocates for developing automated workflows and analysis frameworks to reduce errors, improve efficiency, and enable non-experts to easily conduct complex simulations and analyses through intuitive online interfaces. The goal is to make advanced computational materials science accessible to a wider audience.
Automated identification of sensitive information
October 21, 1999: "Using Ultra-Structure for Automated Identification of Sensitive Information in Documents". Presented at the 20th annual conference of the American Society for Engineering Management. Paper published in conference proceedings.
IEEE Datamining 2016 Title and Abstract
For Details, Contact TSYS Academic Projects.
Ph: 9841103123, 044-42607879
Website: http://www.tsys.co.in/
Mail Id: tsysglobalsolutions2014@gmail.com.
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
More Related Content
Similar to 16
12
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Dr31564567
This document discusses integrating natural language processing and parse tree query language with text mining and topic summarization methods to more efficiently extract relevant content from documents. It presents an approach that uses natural language processing to automatically generate queries from sentences, and then applies a topic summarization method called TSCAN to identify themes, segment events, and construct an evolution graph to show relationships between events. The integrated system aims to make content extraction more effective and easier to use for real-time applications. Evaluation of the methods showed benefits for tasks like information extraction.
Knowledge Representation on the Web
This document outlines a course on Knowledge Representation (KR) on the Web. The course aims to expose students to challenges of applying traditional KR techniques to the scale and heterogeneity of data on the Web. Students will learn about representing Web data through formal knowledge graphs and ontologies, integrating and reasoning over distributed datasets, and how characteristics such as volume, variety and veracity impact KR approaches. The course involves lectures, literature reviews, and milestone projects where students publish papers on building semantic systems, modeling Web data, ontology matching, and reasoning over large knowledge graphs.
G04124041046
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
The International Journal of Engineering and Science (IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Neural Models for Information Retrieval
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items, and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task.
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A systematic review on sequence-to-sequence learning with neural network and ...
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Semantic IoT Semantic Inter-Operability Practices - Part 1
G. Cassar Semantic IoT Semantic Inter-Operability Practices-Part1 presented at the IERC AC4 IoT Semantic Interoperability workshop, Guildford, UK, 15 April 2013
Ginix generalized inverted index for keyword search
1. The document presents Ginix, a generalized inverted index structure that improves keyword search efficiency. It merges consecutive IDs in inverted lists into intervals to reduce storage space.
2. Ginix algorithms for union and intersection operations on interval lists do not require decompression, improving efficiency. Performance is also enhanced by reordering documents.
3. Experiments show Ginix requires less storage than traditional inverted indexes while improving search performance on real datasets.
JAVA 2013 IEEE DATAMINING PROJECT Ginix generalized inverted index for keywor...
JAVA 2013 IEEE DATAMINING PROJECT Ginix generalized inverted index for keywor...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.orgSeattle Scalability Meetup 6-26-13
Amdahl's Law Redux - Using Concurix's Node.js Analytics Tools
Presentation by Alexander Gounares to Seattle Scalability Meetup Group on June 26, 2013
Ir 02
This document discusses building an inverted index to efficiently support information retrieval on large document collections. It describes tokenizing documents, building a dictionary of normalized terms, and creating postings lists that map each term to the documents it appears in. Inverted indexes allow skipping linear scanning and support flexible queries by indexing term locations. The document also covers calculating precision and recall to measure system effectiveness.
Concurrency Issues in Object-Oriented Modeling
This document discusses concurrency issues in object-oriented modeling. It begins with an abstract that introduces the topic of finding a synthesis between concurrency and object models by analyzing representative concurrent object-oriented languages. The document then provides background on concurrency and object-oriented programming individually before discussing how they intersect and the issues that arise when combining them. Key concepts of concurrency like activities, parallelism, and communication are defined. Common language constructs for concurrency like co-routines and threads are also introduced.
Linked Open Data: Combining Data for the Social Sciences and Humanities (and ...
A glimpse of how we are used to connecting datasets on our laptops and how, imho, need to move to the Web of Data, including a demo connecting various sources all from your(!) machine.
Scientific Publication Retrieval in Linked Data
The document describes a prototype that retrieves related scientific publications from different linked datasets through thesaurus alignment. It introduces several linked datasets, including Agrovoc, OpenAgris, STW and EconStor. The prototype matches concepts from a user query to concepts in the linked datasets' thesauri to identify related publications. Pseudocode is provided to illustrate the process of concept mapping and querying multiple datasets. The goal is to retrieve relevant publications from different sources through a single interface.
Summary Of Thesis
This dissertation examines using neural networks to predict financial time series, specifically the S&P Mib Index of the Milan stock exchange. The document provides background on neural networks, including their history and development from simple linear models to modern multi-layer models. It describes supervised neural networks and their components like activation functions and weights. The dissertation then details training neural network weights using methods like backpropagation and techniques to prevent overfitting. Finally, it applies these concepts in a case study using neural networks to forecast changes in the S&P Mib Index.
Software tools to facilitate materials science research
The document discusses software tools to facilitate materials science research, noting that the author's group works to standardize and automate computational methods for high-throughput calculations and discovery of new functional materials. It advocates for developing automated workflows and analysis frameworks to reduce errors, improve efficiency, and enable non-experts to easily conduct complex simulations and analyses through intuitive online interfaces. The goal is to make advanced computational materials science accessible to a wider audience.
Automated identification of sensitive information
October 21, 1999: "Using Ultra-Structure for Automated Identification of Sensitive Information in Documents". Presented at the 20th annual conference of the American Society for Engineering Management. Paper published in conference proceedings.
IEEE Datamining 2016 Title and Abstract
For Details, Contact TSYS Academic Projects.
Ph: 9841103123, 044-42607879
Website: http://www.tsys.co.in/
Mail Id: tsysglobalsolutions2014@gmail.com.
Similar to 16 (20)
The International Journal of Engineering and Science (IJES)
The International Journal of Engineering and Science (IJES)
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI)
A systematic review on sequence-to-sequence learning with neural network and ...
A systematic review on sequence-to-sequence learning with neural network and ...
Semantic IoT Semantic Inter-Operability Practices - Part 1
Semantic IoT Semantic Inter-Operability Practices - Part 1
Ginix generalized inverted index for keyword search
Ginix generalized inverted index for keyword search
JAVA 2013 IEEE DATAMINING PROJECT Ginix generalized inverted index for keywor...
JAVA 2013 IEEE DATAMINING PROJECT Ginix generalized inverted index for keywor...
Linked Open Data: Combining Data for the Social Sciences and Humanities (and ...
Linked Open Data: Combining Data for the Social Sciences and Humanities (and ...
Software tools to facilitate materials science research
Software tools to facilitate materials science research
More from Technology_solution
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
25
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
24
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
23
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
22
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
21
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
20
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
19
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
14
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
13
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
11
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
10
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
9
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
8
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Recently uploaded
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف أحرف الخلاف للقراء العشرةأعد أحرف الخلاف بالتلوين وصلا سمير بسيوني غفر الله له
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
BBR 2024 Summer Sessions Interview Training
Qualitative research interview training by Professor Katrina Pritchard and Dr Helen Williams
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdA Visual Guide to 1 Samuel | A Tale of Two Hearts
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
How to deliver Powerpoint Presentations.pptx
"How to make and deliver dynamic presentations by making it more interactive to captivate your audience attention"
How to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Recently uploaded (20)
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
16
- 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in Topic-Mining-over-Asynchronous-Text-Sequences Abstract Time stamped texts, or text sequences, are ubiquitous in real-world applications. Multiple text sequences are often related to each other by sharing common topics. The correlation among these sequences provides more meaningful and comprehensive clues for topic mining than those from each individual sequence. However, it is nontrivial to explore the correlation with the existence of asynchronism among multiple sequences, i.e., documents from different sequences about the same topic may have different time stamps. In this paper, we formally address this problem and put forward a novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step extracts common topics from multiple sequences based on the adjusted time stamps provided by the second step; the second step adjusts the time stamps of the documents according to the time distribution of the topics discovered by the first step. We perform these two steps alternately and after iterations a monotonic convergence of our objective function can be guaranteed. The effectiveness and advantage of our approach were justified through extensive empirical studies on two real data sets consisting of six research paper repositories and two news article feeds, respectively. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1