Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.
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.
A rough set based hybrid method to text categorizationNinad Samel
This document summarizes a hybrid text categorization method that combines Latent Semantic Indexing (LSI) and Rough Sets theory to reduce the dimensionality of text data and generate classification rules. It introduces LSI to reduce the feature space of text documents represented as high-dimensional vectors. Then it applies Rough Sets theory to the reduced feature space to locate a minimal set of keywords that can distinguish document classes and generate multiple knowledge bases for classification instead of a single one. The method is tested on text categorization tasks and shown to improve accuracy over previous Rough Sets approaches.
The document proposes a new approach to compare stock market patterns to DNA sequences using compression techniques. Stock market data is converted to binary sequences representing increases and decreases, which are then encoded into DNA nucleotides. These nucleotide sequences are divided and matched against human genome sequences using BLAST. The analysis found certain sub-sequences of the stock market patterns matched 100% to the human genome, suggesting this approach could potentially predict stock market behavior.
Bioinformatics may be defined as the field of science
in which biology, computer science, and information
technology merge to form a single discipline. Its ultimate
goal is to enable the discovery of new biological insights as
well as to create a global perspective from which unifying
principles in biology can be discerned by means of
bioinformatics tools for storing, retrieving, organizing and
analyzing biological data. Also most of these tools possess
very distinct features and capabilities making a direct
comparison difficult to be done. In this paper we propose
taxonomy for characterizing bioinformatics tools and briefly
surveys major bioinformatics tools under each categories.
Hopefully this study will stimulate other designers
and
experienced end users understand the details of particular
tool categories/tools, enabling them to make the best choices
for their particular research interests.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
This document discusses the use of fuzzy queries to retrieve information from databases. Fuzzy queries allow for imprecise or vague terms to be used in queries, similar to natural language. The document first provides background on limitations of traditional database queries. It then discusses how fuzzy set theory and membership functions can be applied to queries and data to handle uncertain terms. The proposed approach applies fuzzy queries to a relational database, defining linguistic variables and membership functions. This allows information to be retrieved based on fuzzy criteria and improves the ability to query databases using human-like terms. Benefits of fuzzy queries include more natural interaction and accounting for real-world data imperfections.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Semantic similarity and semantic relatedness
measure in particular is very important in the current scenario
due to the huge demand for natural language processing based
applications such as chatbots and information retrieval systems
such as knowledge base based FAQ systems. Current approaches
generally use similarity measures which does not use the context
sensitive relationships between the words. This leads to erroneous
similarity predictions and is not of much use in real life
applications. This work proposes a novel approach that gives an
accurate relatedness measure of any two words in a sentence by
taking their context into consideration. This context correction
results in a more accurate similarity prediction which results in
higher accuracy of information retrieval systems.
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.
A rough set based hybrid method to text categorizationNinad Samel
This document summarizes a hybrid text categorization method that combines Latent Semantic Indexing (LSI) and Rough Sets theory to reduce the dimensionality of text data and generate classification rules. It introduces LSI to reduce the feature space of text documents represented as high-dimensional vectors. Then it applies Rough Sets theory to the reduced feature space to locate a minimal set of keywords that can distinguish document classes and generate multiple knowledge bases for classification instead of a single one. The method is tested on text categorization tasks and shown to improve accuracy over previous Rough Sets approaches.
The document proposes a new approach to compare stock market patterns to DNA sequences using compression techniques. Stock market data is converted to binary sequences representing increases and decreases, which are then encoded into DNA nucleotides. These nucleotide sequences are divided and matched against human genome sequences using BLAST. The analysis found certain sub-sequences of the stock market patterns matched 100% to the human genome, suggesting this approach could potentially predict stock market behavior.
Bioinformatics may be defined as the field of science
in which biology, computer science, and information
technology merge to form a single discipline. Its ultimate
goal is to enable the discovery of new biological insights as
well as to create a global perspective from which unifying
principles in biology can be discerned by means of
bioinformatics tools for storing, retrieving, organizing and
analyzing biological data. Also most of these tools possess
very distinct features and capabilities making a direct
comparison difficult to be done. In this paper we propose
taxonomy for characterizing bioinformatics tools and briefly
surveys major bioinformatics tools under each categories.
Hopefully this study will stimulate other designers
and
experienced end users understand the details of particular
tool categories/tools, enabling them to make the best choices
for their particular research interests.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
This document discusses the use of fuzzy queries to retrieve information from databases. Fuzzy queries allow for imprecise or vague terms to be used in queries, similar to natural language. The document first provides background on limitations of traditional database queries. It then discusses how fuzzy set theory and membership functions can be applied to queries and data to handle uncertain terms. The proposed approach applies fuzzy queries to a relational database, defining linguistic variables and membership functions. This allows information to be retrieved based on fuzzy criteria and improves the ability to query databases using human-like terms. Benefits of fuzzy queries include more natural interaction and accounting for real-world data imperfections.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Semantic similarity and semantic relatedness
measure in particular is very important in the current scenario
due to the huge demand for natural language processing based
applications such as chatbots and information retrieval systems
such as knowledge base based FAQ systems. Current approaches
generally use similarity measures which does not use the context
sensitive relationships between the words. This leads to erroneous
similarity predictions and is not of much use in real life
applications. This work proposes a novel approach that gives an
accurate relatedness measure of any two words in a sentence by
taking their context into consideration. This context correction
results in a more accurate similarity prediction which results in
higher accuracy of information retrieval systems.
Full-Text Retrieval in Unstructured P2P Networks using Bloom Cast Efficientlyijsrd.com
Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O (N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. Results show that BloomCast achieves an average query recall, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.
IRJET - Deep Collaborrative Filtering with Aspect InformationIRJET Journal
This document discusses a proposed system for deep collaborative filtering with aspect information. The system aims to help web users efficiently locate relevant information on unfamiliar topics to increase their knowledge. It utilizes techniques like multi-keyword search, synonym matching, and ontology mapping to return relevant web links, images, and news articles to the user based on their search terms. The proposed system architecture includes an index structure to efficiently search and rank results based on similarity to the search query terms. The implementation and evaluation of the proposed system are also discussed.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Analysis of Opinionated Text for Opinion Miningmlaij
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONIJDKP
This article will introduce some approaches for improving text categorization models by integrating
previously imported ontologies. From the Reuters Corpus Volume I (RCV1) dataset, some categories very
similar in content and related to telecommunications, Internet and computer areas were selected for models
experiments. Several domain ontologies, covering these areas were built and integrated to categorization
models for their improvements.
ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVALijaia
This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.
Spam filtering poses a critical problem in
text categorization as the features of text is
continuously changing. Spam evolves continuously and
makes it difficult for the filter to classify the evolving
and evading new feature patterns. Most practical
applications are based on online user feedback, the
task calls for fast, incremental and robust learning
algorithms. This paper presents a system for
automatically detection and filtering of unsolicited
electronic messages. In this paper, we have developed
a content-based classifier, which uses two topic models
LSI and PLSA complemented with a text patternmatching
based natural language approach. By
combining these powerful statistical and NLP
techniques we obtained a parallel content based Spam
filter, which performs the filtration in two stages. In
the first stage each model generates its individual
predictions, which are combined by a voting
mechanism as the second stage.
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.
This document discusses using automatic text analysis techniques to streamline the process of multi-dimensional analysis of collaborative learning discussions. It describes a tool called TagHelper that was evaluated against a hand-coded corpus with a 7-dimensional coding scheme. TagHelper achieved a Cohen's Kappa agreement of over 0.7 for 6 of the 7 dimensions when considering only the text segments it was most confident about, and was confident in its coding for at least 88% of the corpus for 5 of those dimensions. The document motivates the need for such automatic analysis to reduce the time and effort required for manual coding of collaborative learning data.
New prediction method for data spreading in social networks based on machine ...TELKOMNIKA JOURNAL
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Effective Data Retrieval System with Bloom in a Unstructured p2p NetworkUvaraj Shan
1) Bloomcast is an efficient and effective full-text retrieval scheme for unstructured peer-to-peer (P2P) networks. It guarantees perfect recall rates with high probability while reducing communication costs.
2) Bloomcast replicates documents across the P2P network using Bloom filters rather than the raw data. This reduces storage and communication costs for replication.
3) By leveraging a hybrid P2P protocol, Bloomcast uniformly distributes items randomly across the network. This allows for random node sampling and network size estimation while guaranteeing recall at a communication cost of O(√N), where N is the network size.
- The document proposes a multi-view stacking ensemble method for drug-target interaction (DTI) prediction that combines predictions from multiple machine learning models trained on different drug and target feature view combinations.
- It generates 126 view combination datasets from 14 drug views and 9 target views, then trains extra trees, random forest, and XGBoost classifiers on each view combination. Predictions from these base models are then combined using a stacking ensemble with an extra trees meta-learner.
- The method is shown to outperform single models and voting ensembles, and calibration of the meta-learner and use of local imbalance measures provide further improvements to predictive performance on DTI prediction tasks.
A QUERY LEARNING ROUTING APPROACH BASED ON SEMANTIC CLUSTERSijait
Peer-to-peer systems have recently a remarkable success in the social, academic, and commercial communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers to answer a specific query (Query Routing Problem). A lot of approaches have been carried out to enhance search result quality as well as to reduce network overhead. Recently, researches focus on methods based on query-oriented routing indices. These methods utilize the historical information of past queries and query hits to build a local knowledge base per peer, which represents the user's interests or profile. When a peer forwards a given query, it evaluates the query against its local knowledge base in order to select a set of relevant peers to whom the query will be routed. Usually, an insufficient number of relevant peers is selected from the current peer's local knowledge base thus a broadcast search is investigated which badly affects the approach efficiency. To tackle this problem, we introduce a novel method that clusters peers having similar interests. It exploits not only the current peer's knowledge base but also that of the others in
the cluster to extract relevant peers. We implemented the proposed approach, and tested (i) its retrieval effectiveness in terms of recall and precision, (ii) its search cost in terms of messages traffic and visited peers number. Experimental results show that our approach improves the recall and precision metrics while reducing dramatically messages traffic.
AUTOMATED INFORMATION RETRIEVAL MODEL USING FP GROWTH BASED FUZZY PARTICLE SW...ijcseit
To mine out relevant facts at the time of need from web has been a tenuous task. Research on diverse fields
are fine tuning methodologies toward these goals that extracts the best of information relevant to the users
search query. In the proposed methodology discussed in this paper find ways to ease the search complexity
tackling the severe issues hindering the performance of traditional approaches in use. The proposed
methodology find effective means to find all possible semantic relatable frequent sets with FP Growth
algorithm. The outcome of which is the further source of fuel for Bio inspired Fuzzy PSO to find the optimal
attractive points for the web documents to get clustered meeting the requirement of the search query
without losing the relevance. On the whole the proposed system optimizes the objective function of
minimizing the intra cluster differences and maximizes the inter cluster distances along with retention of all
possible relationships with the search context intact. The major contribution being the system finds all
possible combinations matching the user search transaction and thereby making the system more
meaningful. These relatable sets form the set of particles for Fuzzy Clustering as well as PSO and thus
being unbiased and maintains a innate behaviour for any number of new additions to follow the herd
behaviour’s evaluations reveals the proposed methodology fares well as an optimized and effective
enhancements over the conventional approaches.
MMP-TREE FOR SEQUENTIAL PATTERN MINING WITH MULTIPLE MINIMUM SUPPORTS IN PROG...IJCSEA Journal
The document proposes a new algorithm called MS-PISA for mining sequential patterns from progressive databases that have multiple minimum support thresholds. MS-PISA uses a tree structure called MMP-tree to store information about the database and discovered patterns. The MMP-tree tracks the percentage of participation of each itemset based on the minimum support thresholds. MS-PISA progressively updates the MMP-tree as new data arrives to find sequential patterns that satisfy the varying minimum support requirements.
An Improved Similarity Matching based Clustering Framework for Short and Sent...IJECEIAES
Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods.
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITIONcscpconf
The document describes a semantic network-based algorithm for knowledge acquisition from text. The algorithm uses the WiSENet semantic network to generate rules representing lexical relationships between concepts. It then applies these rules to text data as a finite state automaton to identify matches and acquire new concepts and relationships for expanding the semantic network. The algorithm tolerates variations in word order through its use of a "bag of concepts" approach during rule matching. Experiments showed the algorithm was effective at knowledge acquisition from text in a flexible manner.
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.
Presented at the Global Pharma R&D Informatics Congress. To find out more, visit:
www.global-engage.com
Text mining extracts complex information from text (entities, events and epistemic knowledge). It can be used to support pathway construction and the design of experiments by extracting evidence from literature. In this presentation, Sophia Ananiadou, Director of the National Centre for Text Mining, discusses bridging the gap between knowledge and text in cancer biology.
Eat it, Review it: A New Approach for Review Predictionvivatechijri
Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays,
Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on
Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely
adopted by organizations around the world. A basic task in deep learning is classification be it image or text.
Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis.
Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis.
Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural
Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system
focuses on context based review prediction and will provide full length sentence. This will help to write a proper
reviews by understanding the context of user.
Writing long sentences is bit boring, but with text prediction in the keyboard technology has made
this simple. Learning technology behind the keyboard is developing fast and has become more accurate.
Learning technologies such as machine learning, deep learning here play an important role in predicting the
text. Current trending techniques in deep learning has opened door for data analysis. Emerging technologies
such has Region CNN, Recurrent CNN have been under consideration for the analysis. Many techniques have
been used for text sequence prediction such as Convolutional Neural Networks (CNN), Recurrent Neural
Networks (RNN), and Recurrent Convolution Neural Networks (RCNN). This paper aims to provide a
comparative study of different techniques used for text prediction.
Full-Text Retrieval in Unstructured P2P Networks using Bloom Cast Efficientlyijsrd.com
Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O (N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. Results show that BloomCast achieves an average query recall, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.
IRJET - Deep Collaborrative Filtering with Aspect InformationIRJET Journal
This document discusses a proposed system for deep collaborative filtering with aspect information. The system aims to help web users efficiently locate relevant information on unfamiliar topics to increase their knowledge. It utilizes techniques like multi-keyword search, synonym matching, and ontology mapping to return relevant web links, images, and news articles to the user based on their search terms. The proposed system architecture includes an index structure to efficiently search and rank results based on similarity to the search query terms. The implementation and evaluation of the proposed system are also discussed.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Analysis of Opinionated Text for Opinion Miningmlaij
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONIJDKP
This article will introduce some approaches for improving text categorization models by integrating
previously imported ontologies. From the Reuters Corpus Volume I (RCV1) dataset, some categories very
similar in content and related to telecommunications, Internet and computer areas were selected for models
experiments. Several domain ontologies, covering these areas were built and integrated to categorization
models for their improvements.
ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVALijaia
This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.
Spam filtering poses a critical problem in
text categorization as the features of text is
continuously changing. Spam evolves continuously and
makes it difficult for the filter to classify the evolving
and evading new feature patterns. Most practical
applications are based on online user feedback, the
task calls for fast, incremental and robust learning
algorithms. This paper presents a system for
automatically detection and filtering of unsolicited
electronic messages. In this paper, we have developed
a content-based classifier, which uses two topic models
LSI and PLSA complemented with a text patternmatching
based natural language approach. By
combining these powerful statistical and NLP
techniques we obtained a parallel content based Spam
filter, which performs the filtration in two stages. In
the first stage each model generates its individual
predictions, which are combined by a voting
mechanism as the second stage.
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.
This document discusses using automatic text analysis techniques to streamline the process of multi-dimensional analysis of collaborative learning discussions. It describes a tool called TagHelper that was evaluated against a hand-coded corpus with a 7-dimensional coding scheme. TagHelper achieved a Cohen's Kappa agreement of over 0.7 for 6 of the 7 dimensions when considering only the text segments it was most confident about, and was confident in its coding for at least 88% of the corpus for 5 of those dimensions. The document motivates the need for such automatic analysis to reduce the time and effort required for manual coding of collaborative learning data.
New prediction method for data spreading in social networks based on machine ...TELKOMNIKA JOURNAL
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Effective Data Retrieval System with Bloom in a Unstructured p2p NetworkUvaraj Shan
1) Bloomcast is an efficient and effective full-text retrieval scheme for unstructured peer-to-peer (P2P) networks. It guarantees perfect recall rates with high probability while reducing communication costs.
2) Bloomcast replicates documents across the P2P network using Bloom filters rather than the raw data. This reduces storage and communication costs for replication.
3) By leveraging a hybrid P2P protocol, Bloomcast uniformly distributes items randomly across the network. This allows for random node sampling and network size estimation while guaranteeing recall at a communication cost of O(√N), where N is the network size.
- The document proposes a multi-view stacking ensemble method for drug-target interaction (DTI) prediction that combines predictions from multiple machine learning models trained on different drug and target feature view combinations.
- It generates 126 view combination datasets from 14 drug views and 9 target views, then trains extra trees, random forest, and XGBoost classifiers on each view combination. Predictions from these base models are then combined using a stacking ensemble with an extra trees meta-learner.
- The method is shown to outperform single models and voting ensembles, and calibration of the meta-learner and use of local imbalance measures provide further improvements to predictive performance on DTI prediction tasks.
A QUERY LEARNING ROUTING APPROACH BASED ON SEMANTIC CLUSTERSijait
Peer-to-peer systems have recently a remarkable success in the social, academic, and commercial communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers to answer a specific query (Query Routing Problem). A lot of approaches have been carried out to enhance search result quality as well as to reduce network overhead. Recently, researches focus on methods based on query-oriented routing indices. These methods utilize the historical information of past queries and query hits to build a local knowledge base per peer, which represents the user's interests or profile. When a peer forwards a given query, it evaluates the query against its local knowledge base in order to select a set of relevant peers to whom the query will be routed. Usually, an insufficient number of relevant peers is selected from the current peer's local knowledge base thus a broadcast search is investigated which badly affects the approach efficiency. To tackle this problem, we introduce a novel method that clusters peers having similar interests. It exploits not only the current peer's knowledge base but also that of the others in
the cluster to extract relevant peers. We implemented the proposed approach, and tested (i) its retrieval effectiveness in terms of recall and precision, (ii) its search cost in terms of messages traffic and visited peers number. Experimental results show that our approach improves the recall and precision metrics while reducing dramatically messages traffic.
AUTOMATED INFORMATION RETRIEVAL MODEL USING FP GROWTH BASED FUZZY PARTICLE SW...ijcseit
To mine out relevant facts at the time of need from web has been a tenuous task. Research on diverse fields
are fine tuning methodologies toward these goals that extracts the best of information relevant to the users
search query. In the proposed methodology discussed in this paper find ways to ease the search complexity
tackling the severe issues hindering the performance of traditional approaches in use. The proposed
methodology find effective means to find all possible semantic relatable frequent sets with FP Growth
algorithm. The outcome of which is the further source of fuel for Bio inspired Fuzzy PSO to find the optimal
attractive points for the web documents to get clustered meeting the requirement of the search query
without losing the relevance. On the whole the proposed system optimizes the objective function of
minimizing the intra cluster differences and maximizes the inter cluster distances along with retention of all
possible relationships with the search context intact. The major contribution being the system finds all
possible combinations matching the user search transaction and thereby making the system more
meaningful. These relatable sets form the set of particles for Fuzzy Clustering as well as PSO and thus
being unbiased and maintains a innate behaviour for any number of new additions to follow the herd
behaviour’s evaluations reveals the proposed methodology fares well as an optimized and effective
enhancements over the conventional approaches.
MMP-TREE FOR SEQUENTIAL PATTERN MINING WITH MULTIPLE MINIMUM SUPPORTS IN PROG...IJCSEA Journal
The document proposes a new algorithm called MS-PISA for mining sequential patterns from progressive databases that have multiple minimum support thresholds. MS-PISA uses a tree structure called MMP-tree to store information about the database and discovered patterns. The MMP-tree tracks the percentage of participation of each itemset based on the minimum support thresholds. MS-PISA progressively updates the MMP-tree as new data arrives to find sequential patterns that satisfy the varying minimum support requirements.
An Improved Similarity Matching based Clustering Framework for Short and Sent...IJECEIAES
Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods.
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITIONcscpconf
The document describes a semantic network-based algorithm for knowledge acquisition from text. The algorithm uses the WiSENet semantic network to generate rules representing lexical relationships between concepts. It then applies these rules to text data as a finite state automaton to identify matches and acquire new concepts and relationships for expanding the semantic network. The algorithm tolerates variations in word order through its use of a "bag of concepts" approach during rule matching. Experiments showed the algorithm was effective at knowledge acquisition from text in a flexible manner.
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.
Presented at the Global Pharma R&D Informatics Congress. To find out more, visit:
www.global-engage.com
Text mining extracts complex information from text (entities, events and epistemic knowledge). It can be used to support pathway construction and the design of experiments by extracting evidence from literature. In this presentation, Sophia Ananiadou, Director of the National Centre for Text Mining, discusses bridging the gap between knowledge and text in cancer biology.
Eat it, Review it: A New Approach for Review Predictionvivatechijri
Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays,
Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on
Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely
adopted by organizations around the world. A basic task in deep learning is classification be it image or text.
Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis.
Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis.
Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural
Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system
focuses on context based review prediction and will provide full length sentence. This will help to write a proper
reviews by understanding the context of user.
Writing long sentences is bit boring, but with text prediction in the keyboard technology has made
this simple. Learning technology behind the keyboard is developing fast and has become more accurate.
Learning technologies such as machine learning, deep learning here play an important role in predicting the
text. Current trending techniques in deep learning has opened door for data analysis. Emerging technologies
such has Region CNN, Recurrent CNN have been under consideration for the analysis. Many techniques have
been used for text sequence prediction such as Convolutional Neural Networks (CNN), Recurrent Neural
Networks (RNN), and Recurrent Convolution Neural Networks (RCNN). This paper aims to provide a
comparative study of different techniques used for text prediction.
Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...ijtsrd
This document presents a method for generating suggestions for specific erroneous parts of sentences in Indian languages like Malayalam using deep learning. The method uses recurrent neural networks with long short-term memory layers to train a model on input-output examples of sentences and their corrections. The model takes in preprocessed sentence data and generates a set of possible corrections for erroneous parts through multiple network layers. An analysis of the model shows that it can accurately generate suggestions for word length of three, but requires more data and study to handle the complex morphology and symbols of Malayalam. The performance of the method is limited by the hardware used and it could be improved with a more powerful system and additional training data.
Discover How Scientific Data is Used for the Public Good with Natural Languag...BaoTramDuong2
This document discusses using natural language processing techniques like n-grams, deep learning models, and named entity recognition to analyze scientific publications and identify references to datasets. It evaluates classifiers like recurrent neural networks and convolutional neural networks to perform sequence labeling and extract dataset citations. The goal is to help government agencies and researchers quickly find datasets, measures, and experts by automating the analysis of research articles.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r
Taxonomy extraction from automotive natural language requirements using unsup...ijnlc
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural
language requirements of the automotive industry. The approach is based on the distributional hypothesis
and the special characteristics of domain-specific German compounds. We extract taxonomies by using
clustering techniques in combination with general thesauri. Such a taxonomy can be used to support
requirements engineering in early stages by providing a common system understanding and an agreedupon
terminology. This work is part of an ontology-driven requirements engineering process, which builds
on top of the taxonomy. Evaluation shows that this taxonomy extraction approach outperforms common
hierarchical clustering techniques.
The document describes a comparative study of various machine learning and neural network models for detecting abusive language on Twitter. It finds that a bidirectional GRU network trained on word-level features, with a Latent Topic Clustering module, achieves the most accurate results with an F1 score of 0.805 for detecting abusive tweets. Additionally, it explores using context tweets as additional features and finds this improves some models' performance.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERkevig
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we
established a dynamic statistical model which learns and adapts processing and parsing rules. First, we
limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take
the most relevant cluster into account and focus only on those frequent patterns which lead to the desired
output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the
output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific,
however, flexible representation of a pattern for log file parsing to maintain high quality output. After
training our model on one system type and applying it to a different system with slightly different log file
patterns, we achieve an accuracy over 99.99%.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERijnlc
This document presents an adaptive log file parser that uses semantics and hidden Markov models. It first clusters log file lines based on semantics to limit unstructured text. It then builds a hidden Markov model to represent parsing patterns, with log entries as states and extracted values as emissions. When applied to a new system, it adapts the model's transition and emission probabilities to fit the new data. The approach achieves over 99.99% accuracy when trained on one system and applied to another with slightly different log patterns.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
The document summarizes text mining techniques in data mining. It discusses common text mining tasks like text categorization, clustering, and entity extraction. It also reviews several text mining algorithms and techniques, including information extraction, clustering, classification, and information visualization. Several literature papers applying these techniques to domains like movie reviews, research proposals, and e-commerce are also summarized. The document concludes that text mining can extract useful patterns from unstructured text through techniques like clustering, classification, and information extraction.
EXTENDING OUTPUT ATTENTIONS IN RECURRENT NEURAL NETWORKS FOR DIALOG GENERATIONijaia
In natural language processing, attention mechanism in neural networks are widely utilized. In this paper, the research team explore a new mechanism of extending output attention in recurrent neural networks for dialog systems. The new attention method was compared with the current method in generating dialog sentence using a real dataset. Our architecture exhibits several attractive properties such as better handle long sequences and, it could generate more reasonable replies in many cases.
An in-depth review on News Classification through NLPIRJET Journal
This document provides an in-depth literature review of news classification through natural language processing (NLP). It discusses several existing approaches to news classification, including models that use convolutional neural networks (CNNs), graph-based approaches, and attention mechanisms. The document also notes that current search engines often return too many irrelevant results, so classification could help layer search results. It concludes that while many techniques have been developed, inconsistencies remain in effectively classifying news, so further research on combining NLP, feature extraction, and fuzzy logic is needed.
NLP Techniques for Text Generation.docxKevinSims18
Natural Language Processing (NLP) techniques are a subset of artificial intelligence (AI) that deals with the interactions between computers and human language. Text generation is an important application of NLP that involves the automatic creation of human-like text. This blog post will explore some of the NLP techniques used for text generation.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
Similar to Prediction of Answer Keywords using Char-RNN (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Better Builder Magazine brings together premium product manufactures and leading builders to create better differentiated homes and buildings that use less energy, save water and reduce our impact on the environment. The magazine is published four times a year.
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
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Here's where you can reach us : ijcnc@airccse.org or ijcnc@aircconline.com
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Data Communication and Computer Networks Management System Project Report.pdfKamal Acharya
Networking is a telecommunications network that allows computers to exchange data. In
computer networks, networked computing devices pass data to each other along data
connections. Data is transferred in the form of packets. The connections between nodes are
established using either cable media or wireless media.
2. IJECE ISSN: 2088-8708 Ì 2165
Section 2. contains the Literature Review of the papers that are used as reference material for this
paper. Section 3.1. consists of the GRU based RNN model that is used in this paper. Section 3.2. includes the
proposed method, a model, and its evaluation. The results are recorded in section 4. Section 5. comprises the
future scope and conclusion.
2. RELATED WORK
2.1. Smart Reply: Automated Response Suggestion for Email
Replying to an Email is sometimes a tricky situation, especially with choosing the right words to
convey the right meaning which is somewhat a tedious task. In the past there have been many automated
response systems built within email clients but most of those are just static sentences that wouldn’t be flexible
to a given scenario. They would just be simple sentences. In this paper, The authors of the paper, devise a
system that would provide an automated response to a received email, based on the content of the email.
In this system, the authors create a sequence-to-sequence long short term memory network to predict
the sequences of text. The input is an incoming message, and the output is a series of responses that are
generated based on the provided text corpus. The LSTMs were first originally applied to Machine Translation
but has since seen success in other domains such as image captioning and speech recognition. (6)
2.2. Improving Context Aware Language Models
In the current age, many automated textual systems use the LSTM model to generate text responses.
The authors of this paper propose an alternative approach to the standard RNN used for text generation, which
is Recurrent Neural Network Language Model (RNNLM). What they propose is, to replace the domains which
are used to provide a sort of an inadequate context for the text generation, with context based variables. These
context variables are used to describe certain aspects of the language, such as topic, time, or other language.
These context variables are then dynamically combined to create to create a more coherent text. The combina-
tion of the context variables with context embedding creates the aforementioned RNNLM.
The data used to test the model is obtained from reddit, twitter and SCOTUS. On the reddit data,
the testing is done based on a given comment, and the objective for the model is to identify the subreddit
(discussion group of one topic), from which the sentence might have originated. There are eight specific
subreddits, and nine general subreddits, and the results show that the RNNLM model is comparatively better
on specific subreddits, compared to general subreddits, thus showing that context based language models were
much more effective. (7)
2.3. Contextual LSTM (CLSTM) models for Large scale NLP tasks
Text presented in any format is usually in the form of phrases, sentences, paragraphs, sections among
others. These formats are a way of abstracting sentences into a combined entity to present a cohesive meaning.
In this paper, the authors create a Contextual LSTM (CLSTM) model, and it is tested against a normal LSTM
model on Natural Language Processing tasks, such as word selection, next word prediction, and next sentence
prediction. The data used on this model was from English Wikipedia, and English Google News site.
The results on the English Google News data, showed improvements across the board when comparing
LSTM with CLSTM. In terms of next word prediction, LSTM using words as features had the perplexity of
37, and the CLSTM improvement was about 2 percent. In the case of next sentence selection, the improvement
was about 39 percent for the LSTM, when compared with CLSTM, which had an accuracy of 46 percent and,
finally next sentence topic prediction in LSTM, using current sentence topic as feature, the perplexity was about
5, and CLSTM improved on it by 9 percent. Thus one can see that the usage of context along with an LSTM
has an improving effect over a standard LSTM, for natural language processing tasks. (8)
2.4. Context-aware Natural Language Generation with Recurrent Neural Networks
Natural Language generation is a useful system in various applications, such as response generation
in messaging systems, text summarization and image captioning. Most of the natural language generation
works only with the provided amount of content, while ignoring the contextual information that is present.
However, in real life scenarios, these systems ignore the contextual component, and only focus on the current
data to generate text. The authors of the paper, propose two approaches for this. The first is a C2S (context
to sequences) model, which encodes a set of contexts into a continuous representation and then decode the
representation into a text sequence, through a recurrent neural network. However not all the words may depend
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3. 2166 Ì ISSN: 2088-8708
on the contexts, some of which may only depend on their preceding words. To resolve this, a gating mechanism
is introduced to control when the information from the contexts are accessed. The second model that they
propose, is called gC2S (Gated Contexts to Sequences).
The data used for this is customer reviews from sites such as Amazon and TripAdvisor (Travel website,
where travelers can review places). In Amazon the reviews were focused from the book, electronic, and movie
categories; and for TripAdvisor, the category was hotels. Upon testing, the authors found that, the gC2S
model significantly outperforms the C2S model as it adds skip-connections between the context representations
and the words in the sequences, allowing the information from the contexts to be able to directly affect the
generation of words. (9)
2.5. Generating Sentences from a Continuous Space
The standard Recurrent Neural Network Language Model (RNNLM) generates a single word at a time
and does not work from an explicit global sentence variational autoencoder generative model that incorporates
distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic
properties of sentences such as style, topic, and high-level syntactic features. The RNNLM is a probabilis-
tic model with no significant assumptions since it generates sentences word-by-word based on an evolving
distributed state representation. However, when the RNNLM model is broken down into a series of next-step
predictions, it does not expose interpretable representation of global features. To overcome such short comings,
the authors of the papers propose a concept, which they refer to as variational autoencoder, through which they
hope to capture the global features in a continuous latent variable.
A standard RNN predicts words based on conditioning of the previous word and an evolving hidden
state. Although effective, it does not learn about the vector representation of the full sentence. In order to
represent a continuous latent sentence, one should first find a method to map sentences and distributed repre-
sentations that can be trained in an unsupervised setting. Sequence autoencoders are somewhat successful, in
generating complete documents, an autoencoder consists of an encoder function and a probabilistic decoder
model, in the case of a sequence autoencoder, both encoder and decoder are RNNs and examples are token
sequences. The Variational Autoencoder (VAE) is a generative model that is based on a regularized version
of the standard autoencoder. This model imposes a prior distribution which enforces a regular geometry and
makes it possible to draw proper samples from the model using ancestral sampling.
The dataset used for this is a collection of text from K12 e-books, which contains approximately 80
million sentences. One of the test was to see the imputation of missing words and the authors observed that a
standard RNN can only predict from a previous set of provided tokens where as the RNN with VAE is much
more effective and can provide an intuitive based completion which fit the context much better and provided a
more cohesive meaning to a sentence. (10)
2.6. A Neural Attention Model for Abstractive Sentence Summarization
Summarization is one of the most important components in the current world, it is especially required
by the news outlets and discussion forums, among others to summarize contents that are otherwise too long
to read by users who would not have the time to do so. The objective is to create a condensed form of the
text corpus that is able to capture the whole meaning of the corpus. Most common form of summarization is
extraction style, where certain parts of the text is extracted out and then stitched together. The other form is
abstractive summarization where the approach is to do the summarization from the bottom up. The authors of
this paper propose a new concept of summarization called as attention based summarization. It incorporates
linguistic structure when compared with other approaches, and can scale easily to train on large amounts of
data. Since the system makes no assumptions about the vocabulary of the generated summary, it can be trained
directly on any document pairs, and thus can be used to summarize a given data into a headline.
The dataset used by the authors was a collection of 500 news articles from The New York Times
and Associated Press, paired with 4 different human generated reference summaries. The authors find that,
the proposed model is able to perform comparatively much better than the older approaches and in a more
abstractive summaries. (11)
2.7. Neural Turing Machines
The neural network is an excellent concept. It is able to mimic the human brain quite well. But one of
the missing components is the memory part of it, the neural network is not able to remember the activity it has
completed. It trains for a given task, and executes the task but once completed, it forgets the method, and if the
IJECE, Vol. 9, No. 3, June 2019 : 2164 – 2176
4. IJECE ISSN: 2088-8708 Ì 2167
said task is to be performed again, it would have to be trained yet again and then the task could be performed.
The authors of this paper propose a concept in which the neural network (in this case, an LSTM) is paired with
a memory bank. This neural network is able to interact with both standard input and output vectors, and also
with a memory bank.
The authors train the model in performing two simple tasks; reading and writing, and then store the
concept of the task into its memory banks. Then they perform a few tests, first being the copy test. The model
is provided with random values of 8-bit binary values of random lengths, and then it is to output a value, that is
a copy of the initial sequence. The authors find that after training both the LSTM model and an NTM model,
the NTM model is able to perform subsequent tasks of copying in much lesser time, and it need not be trained
again. Another test that the authors performed was the sorting. The model was provided with random binary
values, along with priority which is between -1 and 1. They found that the NTM was able to understand the
concept of sorting and was able to perform the task much more cohesively. It was able to approach this concept
as a human mind would, compared to an LSTM model. (12)
After examining various papers which are aforementioned, it was found that the system that the au-
thors were looking for does not exist, and thus, through this paper the authors wants to propose such a system.
3. PROPOSED WORK
3.1. RNN Model
In this section, the authors describe the model that is being used in this paper. An example of this
RNN Model is shown in Figure 1. The first thing to note is the input which is a vector
X = (x1, . . . , xT ) (1)
This along with certain weights is passed as input, to a stack of recurrently connected and hidden
layers, represented by A, to calculate the first hidden vector sequence of
hn
= (hn
1 , . . . , hn
T ) (2)
and the output vector sequence is
y = (y1, . . . , yT ) (3)
This output vector y is parameterized, looped and redirected back into the network as a set of inputs.
This looping feature is what provides the neural network, its key recurrence nature. To observe the actual nature
of the RNN one can see that the ‘unrolled’ RNN is just chained normal neural network, which processes the
data as required and passes the information to the next successor.
Now, this can be considered as a single layer network. To make it a ‘deep’ RNN, one can stack them
one on top of the other. Now, one of the main problems that occurs as the information traverses the layers is
that, the components in the subsequent layers are learning faster compared to the previous layers. This problem
is referred to as the ‘Vanishing Gradient Problem’ (13). Here, gradients are the values that refer to speed at
which networks are able to pick up and learn the information. This is an important factor in the case of RNNs
since the data is propagated back into the network and if the values keep reducing as they propagate through
the layers, then eventually it would tend to zero, and that would make the model useless. Thus to overcome
this, the output is directly repropagated to input layers, and not to the middle layers.
Now as this process is happening, the other problem is that, as it learns new information, the system
fails to keep track of the previously learnt information. To overcome this caveat, an LSTM style of RNN is
used. LSTM (14). The LSTM variant of RNN essentially eliminates the main drawback of the standard RNN,
which is its ability to remember, and this variant works on a large class of problems.
Prediction of Answer Keywords using Char-RNN (Pratheek)
5. 2168 Ì ISSN: 2088-8708
Figure 1. Recurrent Neural Network
Figure 2. RNN expanded
3.2. Proposed Work
Many algorithms exist to predict keywords, but most of such systems exist for data which is quite large.
The authors want to design a system that has a low-overhead, in terms of model building or the amount of data
required to predict. The model the authors have chosen is an GRU variant of the Recurrent Neural Network. The
authors split the overall process as three subprocesses and then expand each of those subprocesses’ individually
as shown in Figure 3
Data Collecting and Preprocessing
Building the model
Evaluating the model
Figure 3. Overall process
3.3. Sub Process 1: Data Collecting and Pre-processing
For this, the data is of homemade or self-made dataset. It is a collection of questions, stored in a text
file, interleaved with a new line. An example of the dataset is shown in Figure 4.
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6. IJECE ISSN: 2088-8708 Ì 2169
Figure 4. Example of the homemade dataset
First, the question is searched on a normal internet search engine, and then the results obtained are
processed. This is done by first converting all of the content to lowercase, and then the common stop words
are removed. For this task, one uses a certain application called SpaCy(15), and NLTK(16), (17) which is used
for natural language processing, to clear out the said words and the punctuation marks as well (While, these
punctuation marks are useful for a normal human reader,they only add noise to the model). Then, depending
on the type of the question, the user can also filter out numeric values which may be present (Having numeric
values is useful, only if the answer keywords that are being predicted have them, else they add noise to our
model). Once this pre-processing is repeated for all the fetched data, it is stored into a file, and this becomes
our input data to the model. The process for which is shown below, Figure 5.
Query provided by the user
Perform an internet search on the query
Extract all the links from the response
Perform a request on each of those
links, and store the responses
Extract the news headlines
from each of the responses
Figure 5. Data pre-processing flow diagram
Prediction of Answer Keywords using Char-RNN (Pratheek)
7. 2170 Ì ISSN: 2088-8708
3.4. Sub Process 2: Building the model
Here, the input data is split into chunks of certain size. These chunks form the input vectors for the
model. Each chunk forms a single input vector and each value from the vector is provided as an input for the
first layer of the RNN. So, logically, once these values are “encoded”, the user can start to train the model on
these values. After they are processed by the first layer, the output is captured. This becomes the first state.
This state is retropropagated as input, back into the model. As the model goes over a new encoded vector each
time, it remembers what it has trained on as well as the newer information that it has learnt.
Suppose, the first input is considered to have an error and the user isn’t allowed to modify it, then the
magnitude of the error multiplies as the model builds and it causes problems. But, with an RNN one can tweak
the data that is propagated from the subsequent layers, thus being able to diminish the error rate in the system.
This is because, one can control the amount of information that is being retropropagated back into the system.
So, if the first input contains error, then one can take the output after the first iteration, tweak that output and
then provide that as the input for the second iteration. So, using the dataset once can train the whole model,
and after this one can start evaluating the model.
3.5. Sub Process 3: Evaluating the model
There are two possible ways to evaluate the model. The first method is the manual method, where in
the authors manually verify the predictions, by noting the answer keywords and then, manually searching for
the query and verifying the results. This method is a bit tedious. The other method is where one can have a
program that automates this. This would make it much simpler. In the case of automation, the process would
be similar to the manual process. The authors provide a set of questions at a time, five questions, for example.
Now, they perform the operation for each of the question, and capture the output. Then, they tokenize the
captured output, and compare this with a set answer keywords, that the authors themselves have provided.
If the provided answer keywords is said to be found in the tokenized words, then the model is said to have
predicted the answer correctly, else the model has predicted inaccurately.
4. RESULTS
As mentioned in the section 3.5., for the automated process, one has obtained a set of questions from
the user, and provided this as the testing dataset for the model. After each complete run of the model, the output
was captured and compared with the answers that was provided by the users themselves.
One of the ways in which the system was tested, was by manually providing questions. Here the au-
thors consider two questions, the first being, “Who won the 2017 Nobel prize for economics?”
and the second question being, “Which company has launched the Indian Pale Ale (IPA),
its fifth beer in India”.
For the first question the obtained results are shown in Figure 6. After providing this as the dataset
for the model to train, the system is able to predict certain sequence of words which would match the results
that would be obtained from an internet search query. This is shown in the following figures. Answer keyword
patterns predicted by the system is shown in Figure 7 and the answers obtained from internet search is shown
in Figure 8.
The results of the second question was also verified in a similar manner. The results dataset is as
shown in the Figure 9. This is provided as input for the system to train on, and once trained the system begins
to generate answer keywords patterns, similar to the previous example and is shown in the Figure 10. The
observation to note here is that, when queried via the internet search engines, similar results are yielded, as
shown in the Figure11
The observed training loss for each of the questions is shown in the following figures. The graph of
the training loss for the first question is shown in Figure 12 and similarly for the second question in Figure 13.
One general observation to note in both the cases is that for each iteration, the amount of text being processed
increases, and the training loss decreases; indicating that the model is getting more effective in predicting
keywords.
IJECE, Vol. 9, No. 3, June 2019 : 2164 – 2176
8. IJECE ISSN: 2088-8708 Ì 2171
Figure 6. Example of the fetch results, for the provided query
Figure 7. Answer keyword patterns predicted by the system
Prediction of Answer Keywords using Char-RNN (Pratheek)
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Figure 8. Answers as obtained from an Internet search
Figure 9. Example of the fetch results for the second question
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Figure 10. Answer keywords as predicted by the system
Figure 11. Answers as obtained from an Internet search
Prediction of Answer Keywords using Char-RNN (Pratheek)
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0 20 40 60 80
Amount of Text processed by the system
0.5
1.0
1.5
2.0
2.5
RateofLoss
Rate of loss projected over the amount of text processed
Figure 12. The Rate of Loss over text processed for the first question
0 20 40 60 80
Amount of Text processed by the system
0.5
1.0
1.5
2.0
2.5
RateofLoss
Rate of loss projected over the amount of text processed
Figure 13. The Rate of Loss over text processed for the second question
Thus, in a similar manner, a total set of twenty-five questions was obtained from the user. And for
each run, a specific number was chosen (for the first run, one question was chosen, on the second run, five
questions were taken, and so on). Each run of the whole operation was performed for five times, per set of
questions. The accuracy score was calculated as the average number of times the right answer was predicted.
Table 1. Performance of the Char-RNN for a Range of Queries
Number of Questions Accuracy Score
1 96%
5 90%
10 80%
15 78%
20 74%
25 74%
Upon examination it can be noted that, as the number of number of questions increases, the accuracy
falls, but it eventually squares off at 74%. Upon multiple runs, it was found that the accuracy score averages
out to the same levels of accuracy, that is aforementioned.
The area where the accuracy of the model falls is, if the answers were to contain a numerical value
embedded in it, then the model would not able to predict those accurately. An example of these type of answer
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keywords would be “350m”, or “12ft”. If the model has to predict these type of answer keywords, they need
to occur enough number of times be an observable pattern for the model to pick up on, and the probability that
such type of keywords occurring, is quite less. But, if they are split (such as “350” and “m”), then the numbers
and the attached string parts individually have no meaning, and only add to the noise when training the model.
Hence, the accuracy falls in these types of questions.
The other observation is that, as the number of questions increase, the accuracy falls. This is because
there would be certain questions that would have numerical-embedded answers in them, and when cleaning or
filtering them out, one would lose the vital information, which would not help the model. Thus, leading to the
accuracy score dipping in value.
5. CONCLUSION AND FUTURE SCOPE
In terms of future scope, one of the main points where the accuracy of the model can be improved
is in terms of the data that is being provided to it, and the training being done on it. As mentioned, the fall
in the accuracy score occurs when the answer keyword contains an embedded numerical value in it. Thus, a
possible future improvement for this research would include allowing the model to train on data with embedded
numerical values, so that the accuracy score of the model could be improved.
On a concluding note, the authors would like to say that it was an exciting to delve into this exciting
research, and was an interesting concept to explore. While performing this research, the authors learnt about
various tools and concepts that are used in the research, and in the academic spheres. They were exposed to,
and learnt various concepts of Machine Learning and Natural Language Processing.
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