Formal concept analysis is the process of data analysis mechanism with emergent attractiveness across various fields such as data mining, robotics, medical, big data and so on. FCA is helpful to generate the new learning ontology based techniques. In medical field, some growing kids are facing the problem of representing their knowledge from their gathered prior data which is in the form of unordered and insufficient clustered data which is not supporting them to take the right decision on right time for solving the uncertainty based questionnaires. In the approach of decision theory, many mathematical replicas such as probability-allocation, crisp set, and fuzzy based set theory were designed to deals with knowledge representation based difficulties along with their characteristic. This paper is proposing new ideological blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-data, which the data is framed with data-units implicated composition which are formal statements of idea of human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore the relationship among the attributes and used the formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making. Secondly how the FLC derive the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate knowledge for uncertainty based questionnaires. Here the FCA is projected to expand the FCA based conception with help of the objective based item set notions considered as the target which is implicated with the expanded cardinalities along with its weights which is associated through the fuzzy based inference decision rules. This approach is more helpful for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer deficiency.
Implementation of Prototype Based Credal Classification approach For Enhanced...IRJET Journal
The document discusses a prototype-based credal classification approach for classifying patterns with incomplete data. It aims to provide accurate and efficient classification results for patterns with missing attribute values. The approach uses hierarchical clustering to identify class prototypes from the training data. It then applies an evidential reasoning technique called credal classification to determine the most likely class for each pattern after imputing missing values. Experimental results show the proposed approach performs better in terms of time and memory efficiency compared to other classification methods for incomplete patterns.
Indexing based Genetic Programming Approach to Record Deduplicationidescitation
In this paper, we present a genetic programming (GP) approach to record
deduplication with indexing techniques.Data de-duplication is a process in which data are
cleaned from duplicate records due to misspelling, field swap or any other mistake or data
inconsistency. This process requires that we identify objects that are included in more than
one list.The problem of detecting and eliminating duplicated data is one of the major
problems in the broad area of data cleaning and data quality in data warehouse. So, we
need to create such a algorithm that can detect and eliminate maximum duplications.GP
with indexing is one of the optimization technique that helps to find maximum duplicates in
the database. We used adeduplication function that is able to identify whether two or more
entries in a repository are replicas or not. As many industries and systems depend on the
accuracy and reliability of databases to carry out operations. Therefore, the quality of the
information stored in the databases, can have significant cost implications to a system that
relies on information to function and conduct business. Moreover, this is fact that clean and
replica-free repositories not only allow the retrieval of higher quality information but also
lead to more concise data and to potential savings in computational time and resources to
process this data.
Index
- 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.
The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...Editor IJCATR
Entrance of object orienting concept in database caused the relation database gradually to replace with object oriented
database in various fields. On the other hand for solving the problem of real world uncertain data, several methods were presented.
One of these methods for modeling database is an approach wich couples object-oriented database modeling with fuzzy logic. Many
queries that users to pose are expressed on the basis of linguistic variables. Because of classical databases are not able to support these
variables, leads to fuzzy approaches are considered. We investigate databases queries in this study both simple and complex ways. In
the complex way, we use conjunctive and disjunctive queries. In the following, we use the XML labels to express inqueries into fuzzy.
We can also communicate with other sections of software by entering into XML world as the most reliable opportunity. Also we want
to correct conjunctive and disjunctive queries related to fuzzy object oriented database using the concept of dependency measure and
weight, and weight be assigned to different phrases of a query based on user emphasis. The other aim of this research is mapping fuzzy
queries to fuzzy-XML. It is expected to be simple implement of query, and output of execution of queries be greatly closer to users'
needs and fulfill her expect. The results show that the proposed method explains the possible conjunctive and disjunctive queries the
database in the form of Fuzzy-XML.
An effective pre processing algorithm for information retrieval systemsijdms
The Internet is probably the most successful distributed computing system ever. However, our capabilities
for data querying and manipulation on the internet are primordial at best. The user expectations are
enhancing over the period of time along with increased amount of operational data past few decades. The
data-user expects more deep, exact, and detailed results. Result retrieval for the user query is always
relative o the pattern of data storage and index. In Information retrieval systems, tokenization is an
integrals part whose prime objective is to identifying the token and their count. In this paper, we have
proposed an effective tokenization approach which is based on training vector and result shows that
efficiency/ effectiveness of proposed algorithm. Tokenization on documents helps to satisfy user’s
information need more precisely and reduced search sharply, is believed to be a part of information
retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing
of documents and generates its respective tokens which is the basis of these tokens probabilistic
IR generate its scoring and gives reduced search space. The comparative analysis is based on the two
parameters; Number of Token generated, Pre-processing time.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
Implementation of Prototype Based Credal Classification approach For Enhanced...IRJET Journal
The document discusses a prototype-based credal classification approach for classifying patterns with incomplete data. It aims to provide accurate and efficient classification results for patterns with missing attribute values. The approach uses hierarchical clustering to identify class prototypes from the training data. It then applies an evidential reasoning technique called credal classification to determine the most likely class for each pattern after imputing missing values. Experimental results show the proposed approach performs better in terms of time and memory efficiency compared to other classification methods for incomplete patterns.
Indexing based Genetic Programming Approach to Record Deduplicationidescitation
In this paper, we present a genetic programming (GP) approach to record
deduplication with indexing techniques.Data de-duplication is a process in which data are
cleaned from duplicate records due to misspelling, field swap or any other mistake or data
inconsistency. This process requires that we identify objects that are included in more than
one list.The problem of detecting and eliminating duplicated data is one of the major
problems in the broad area of data cleaning and data quality in data warehouse. So, we
need to create such a algorithm that can detect and eliminate maximum duplications.GP
with indexing is one of the optimization technique that helps to find maximum duplicates in
the database. We used adeduplication function that is able to identify whether two or more
entries in a repository are replicas or not. As many industries and systems depend on the
accuracy and reliability of databases to carry out operations. Therefore, the quality of the
information stored in the databases, can have significant cost implications to a system that
relies on information to function and conduct business. Moreover, this is fact that clean and
replica-free repositories not only allow the retrieval of higher quality information but also
lead to more concise data and to potential savings in computational time and resources to
process this data.
Index
- 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.
The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...Editor IJCATR
Entrance of object orienting concept in database caused the relation database gradually to replace with object oriented
database in various fields. On the other hand for solving the problem of real world uncertain data, several methods were presented.
One of these methods for modeling database is an approach wich couples object-oriented database modeling with fuzzy logic. Many
queries that users to pose are expressed on the basis of linguistic variables. Because of classical databases are not able to support these
variables, leads to fuzzy approaches are considered. We investigate databases queries in this study both simple and complex ways. In
the complex way, we use conjunctive and disjunctive queries. In the following, we use the XML labels to express inqueries into fuzzy.
We can also communicate with other sections of software by entering into XML world as the most reliable opportunity. Also we want
to correct conjunctive and disjunctive queries related to fuzzy object oriented database using the concept of dependency measure and
weight, and weight be assigned to different phrases of a query based on user emphasis. The other aim of this research is mapping fuzzy
queries to fuzzy-XML. It is expected to be simple implement of query, and output of execution of queries be greatly closer to users'
needs and fulfill her expect. The results show that the proposed method explains the possible conjunctive and disjunctive queries the
database in the form of Fuzzy-XML.
An effective pre processing algorithm for information retrieval systemsijdms
The Internet is probably the most successful distributed computing system ever. However, our capabilities
for data querying and manipulation on the internet are primordial at best. The user expectations are
enhancing over the period of time along with increased amount of operational data past few decades. The
data-user expects more deep, exact, and detailed results. Result retrieval for the user query is always
relative o the pattern of data storage and index. In Information retrieval systems, tokenization is an
integrals part whose prime objective is to identifying the token and their count. In this paper, we have
proposed an effective tokenization approach which is based on training vector and result shows that
efficiency/ effectiveness of proposed algorithm. Tokenization on documents helps to satisfy user’s
information need more precisely and reduced search sharply, is believed to be a part of information
retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing
of documents and generates its respective tokens which is the basis of these tokens probabilistic
IR generate its scoring and gives reduced search space. The comparative analysis is based on the two
parameters; Number of Token generated, Pre-processing time.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
The document discusses a link mining methodology adapted from the CRISP-DM process to incorporate anomaly detection using mutual information. It applies this methodology in a case study of co-citation data. The methodology involves data description, preprocessing, transformation, exploration, modeling, and evaluation. Hierarchical clustering identified 5 clusters, with cluster 1 showing strong links and cluster 5 weak links. Mutual information validated the results, showing cluster 5 had the lowest mutual information, indicating independent variables. The case study demonstrated the approach can interpret anomalies semantically and be used with real-world data volumes and inconsistencies.
A SURVEY OF LINK MINING AND ANOMALIES DETECTIONIJDKP
This document discusses link mining and its application in detecting anomalies. It begins by defining link mining as focusing on discovering explicit links between objects, as opposed to data mining which aims to find patterns within datasets. The document then surveys different types of anomalies that can be detected through link mining, including contextual, point, collective, online, and distributed anomalies. It also discusses challenges in link mining like logical vs statistical dependencies and the skewed class distribution problem in link prediction. Applications of link mining mentioned include social networks, epidemiology, and bibliographic analysis. Overall, the document provides an overview of the emerging field of link mining and its relevance for detecting unusual or anomalous links within linked datasets.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
This document discusses methods for multimodal content-based medical image retrieval. It describes how medical images are used for diagnosis, research and education. Content-based image retrieval systems aim to search and retrieve similar images based on visual features extracted from images, such as color, texture and shape. The document outlines different image descriptors and similarity measures that can be used. It also discusses the need to fuse multiple modalities or features to improve retrieval accuracy, as single features do not fully capture image content. Feature level and decision level fusion techniques are significant for the multimodal fusion process.
A statistical data fusion technique in virtual data integration environmentIJDKP
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
Enhancement techniques for data warehouse staging areaIJDKP
This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
INTEGRATED ASSOCIATIVE CLASSIFICATION AND NEURAL NETWORK MODEL ENHANCED BY US...IJDKP
The document summarizes a proposed methodology that integrates associative classification and neural networks for improved classification accuracy. It begins by introducing association rule mining and associative classification. It then describes using chi-squared analysis and the Gini index for attribute selection and rule pruning to generate a reduced set of rules. These rules are used to train a backpropagation neural network classifier. The methodology is tested on datasets from a public repository, demonstrating improved accuracy over traditional associative classification alone. Future work to integrate optical neural networks is also proposed.
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
One of the most important problems in modern finance is finding efficient ways to summarize and visualize
the stock market data to give individuals or institutions useful information about the market behavior for
investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national
economy. So, at the present time many investors look to find criterion to compare stocks together and
selecting the best and also investors choose strategies that maximize the earning value of the investment
process. Therefore the enormous amount of valuable data generated by the stock market has attracted
researchers to explore this problem domain using different methodologies. Therefore research in data
mining has gained a high attraction due to the importance of its applications and the increasing generation
information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm
techniques are used to find association between different scripts of stock market, and also much of the
research and development has taken place regarding the reasons for fluctuating Indian stock exchange.
But, now days there are two important factors such as gold prices and US Dollar Prices are more
dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and
BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors
and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar
prices and transactions of customers. Hence researcher has considered these problems as a topic for
research.
Prediction of Default Customer in Banking Sector using Artificial Neural Networkrahulmonikasharma
The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm.
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
On the benefit of logic-based machine learning to learn pairwise comparisonsjournalBEEI
This document describes a study that used a logic-based machine learning approach called APARELL to learn user preferences from pairwise comparisons in a recommender system. APARELL learns description logic rules from examples of users' pairwise item preferences and background knowledge about item properties. It was implemented in a used car recommender system. A user study evaluated the pairwise preference elicitation method compared to a standard list interface. Results showed the pairwise interface was significantly better in recommending items that matched users' preferences.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A C...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing one single patient’s information gathered from a large volume of data over a long period of time. The main objective of this paper is to represent our ontology-based information retrieval approach for clinical Information System. We have performed a Case Study in the real life hospital settings. The results obtained illustrate the feasibility of the proposed approach which significantly improved the information retrieval process on a large volume of data over a long period of time from August 2011 until January 2012.
DALL-E 2 - OpenAI imagery automation first developed by Vishal Coodye in 2021...MITAILibrary
The document provides a review of machine learning interpretability methods. It begins with an introduction to explainable artificial intelligence and a discussion of key concepts like interpretability and explainability. It then presents a taxonomy of interpretability methods that are divided into four main categories: methods for explaining black-box models, creating white-box models, promoting fairness, and analyzing model sensitivity. Specific machine learning interpretability techniques are summarized within each category.
Selecting Experts Using Data Quality Conceptsijdms
Personal networks are not always diverse or large enough to reach those with the right information. This
problem increases when assembling a group of experts from around the world, something which is a
challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel
of experts, specifically how to select a group of experts from a huge group of people. We propose an
approach which uses data quality dimensions to improve expert selection quality and provide quality
metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data
quality methods to support the expert search process.
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
The document discusses a link mining methodology adapted from the CRISP-DM process to incorporate anomaly detection using mutual information. It applies this methodology in a case study of co-citation data. The methodology involves data description, preprocessing, transformation, exploration, modeling, and evaluation. Hierarchical clustering identified 5 clusters, with cluster 1 showing strong links and cluster 5 weak links. Mutual information validated the results, showing cluster 5 had the lowest mutual information, indicating independent variables. The case study demonstrated the approach can interpret anomalies semantically and be used with real-world data volumes and inconsistencies.
A SURVEY OF LINK MINING AND ANOMALIES DETECTIONIJDKP
This document discusses link mining and its application in detecting anomalies. It begins by defining link mining as focusing on discovering explicit links between objects, as opposed to data mining which aims to find patterns within datasets. The document then surveys different types of anomalies that can be detected through link mining, including contextual, point, collective, online, and distributed anomalies. It also discusses challenges in link mining like logical vs statistical dependencies and the skewed class distribution problem in link prediction. Applications of link mining mentioned include social networks, epidemiology, and bibliographic analysis. Overall, the document provides an overview of the emerging field of link mining and its relevance for detecting unusual or anomalous links within linked datasets.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
This document discusses methods for multimodal content-based medical image retrieval. It describes how medical images are used for diagnosis, research and education. Content-based image retrieval systems aim to search and retrieve similar images based on visual features extracted from images, such as color, texture and shape. The document outlines different image descriptors and similarity measures that can be used. It also discusses the need to fuse multiple modalities or features to improve retrieval accuracy, as single features do not fully capture image content. Feature level and decision level fusion techniques are significant for the multimodal fusion process.
A statistical data fusion technique in virtual data integration environmentIJDKP
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
Enhancement techniques for data warehouse staging areaIJDKP
This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
INTEGRATED ASSOCIATIVE CLASSIFICATION AND NEURAL NETWORK MODEL ENHANCED BY US...IJDKP
The document summarizes a proposed methodology that integrates associative classification and neural networks for improved classification accuracy. It begins by introducing association rule mining and associative classification. It then describes using chi-squared analysis and the Gini index for attribute selection and rule pruning to generate a reduced set of rules. These rules are used to train a backpropagation neural network classifier. The methodology is tested on datasets from a public repository, demonstrating improved accuracy over traditional associative classification alone. Future work to integrate optical neural networks is also proposed.
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
One of the most important problems in modern finance is finding efficient ways to summarize and visualize
the stock market data to give individuals or institutions useful information about the market behavior for
investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national
economy. So, at the present time many investors look to find criterion to compare stocks together and
selecting the best and also investors choose strategies that maximize the earning value of the investment
process. Therefore the enormous amount of valuable data generated by the stock market has attracted
researchers to explore this problem domain using different methodologies. Therefore research in data
mining has gained a high attraction due to the importance of its applications and the increasing generation
information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm
techniques are used to find association between different scripts of stock market, and also much of the
research and development has taken place regarding the reasons for fluctuating Indian stock exchange.
But, now days there are two important factors such as gold prices and US Dollar Prices are more
dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and
BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors
and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar
prices and transactions of customers. Hence researcher has considered these problems as a topic for
research.
Prediction of Default Customer in Banking Sector using Artificial Neural Networkrahulmonikasharma
The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm.
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
On the benefit of logic-based machine learning to learn pairwise comparisonsjournalBEEI
This document describes a study that used a logic-based machine learning approach called APARELL to learn user preferences from pairwise comparisons in a recommender system. APARELL learns description logic rules from examples of users' pairwise item preferences and background knowledge about item properties. It was implemented in a used car recommender system. A user study evaluated the pairwise preference elicitation method compared to a standard list interface. Results showed the pairwise interface was significantly better in recommending items that matched users' preferences.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A C...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing one single patient’s information gathered from a large volume of data over a long period of time. The main objective of this paper is to represent our ontology-based information retrieval approach for clinical Information System. We have performed a Case Study in the real life hospital settings. The results obtained illustrate the feasibility of the proposed approach which significantly improved the information retrieval process on a large volume of data over a long period of time from August 2011 until January 2012.
DALL-E 2 - OpenAI imagery automation first developed by Vishal Coodye in 2021...MITAILibrary
The document provides a review of machine learning interpretability methods. It begins with an introduction to explainable artificial intelligence and a discussion of key concepts like interpretability and explainability. It then presents a taxonomy of interpretability methods that are divided into four main categories: methods for explaining black-box models, creating white-box models, promoting fairness, and analyzing model sensitivity. Specific machine learning interpretability techniques are summarized within each category.
Selecting Experts Using Data Quality Conceptsijdms
Personal networks are not always diverse or large enough to reach those with the right information. This
problem increases when assembling a group of experts from around the world, something which is a
challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel
of experts, specifically how to select a group of experts from a huge group of people. We propose an
approach which uses data quality dimensions to improve expert selection quality and provide quality
metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data
quality methods to support the expert search process.
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...hamidnazary2002
This document discusses integrating enterprise and data mining ontologies to extract actionable knowledge. It notes that existing data mining techniques provide large volumes of knowledge but much of it is not useful for making business decisions. The objectives are to 1) design an artifact to formally apply business understanding in data mining and 2) semi-automate the business understanding phase to help users. The expected outcomes are an enterprise ontology and relations between enterprise and data mining ontologies to bridge the gap between business needs and data mining results.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGYcscpconf
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
Neuroinformatics_Databses_Ontologies_Federated Database.pptxJagannath University
This will introduce and describe NIF(Neuroscience information framework), Federated databases, data federation vs data warehouse, ontology, ontology vs database, steps in creating ontology.
Neuroinformatics Databases Ontologies Federated Database.pptxJagannath University
Neuroscience Information Framework(NIF), Federated Database, Data Federation vs Data warehouse, ontology, steps in creating ontology, ontology vs database
Great model a model for the automatic generation of semantic relations betwee...ijcsity
The
large
a
v
ailable
am
ou
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t
of
non
-
structured
texts
that
b
e
-
long
to
differe
n
t
domains
su
c
h
as
healthcare
(e.g.
medical
records),
justice
(e.g.
l
a
ws,
declarations),
insurance
(e.g.
declarations),
etc. increases
the
effort
required
for
the
analysis
of
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in
a
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pro
-
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Differe
n
t
pr
o
jects
and t
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h
av
e
pro
p
osed
strategies
to
reduce
this
complexi
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b
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summarizing
or
annotating
the
texts.
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ularl
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summary
strategies
h
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pr
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ery
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compact
view
of
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text.
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ow
e
v
er,
the
a
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ailable
strategies
to
generate
these
summaries
do
not
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ery
w
ell
within
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that
require
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consideration
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of
the
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(e.g.
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in
a
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p
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and
the
profile
of
the
p
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requires
the
summary
(e.g
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ecialization).
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this
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prese
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”GRe
A
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m
o
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for
automatic
summary
generation
that
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-
lies
on
natural
language
pr
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cessing
and
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mining
te
c
hniques
to
extract
the
most
rele
v
a
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t
information
from
narrati
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texts
and
disc
o
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new
in
-
formation
from
the
detection
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related
information. GRe
A
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M
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del
w
as impleme
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ted
on
sof
tw
are
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where
it
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sh
o
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to
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to displ
a
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preview
of
the
information
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b
ou
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medical
health
records
and
disc
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v
er
new
facts
and
h
y
p
otheses
within
the
information.
Se
v
eral
tests
w
ere
executed
su
c
h
as
F
unctional
-
i
t
y
,
Usabili
t
y
and
P
erformance
regarding
to
the
impleme
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ted
sof
t
w
are.
In
addition,
precision
and
recall
measures
w
ere
applied
on
the
results
ob
-
tained
through
the
impleme
n
ted
t
o
ol,
as
w
ell
as
on
the
loss
of
information
obtained
b
y
pr
o
viding
a
text
more
shorter than
the
original
Knowledge Science for AI-based biomedical and clinical applicationsCatia Pesquita
The great barrier to AI adoption in healthcare and biomedical research is lack of trust.
Assessing trustworthiness requires data, domain and user context, which can be supported by ontologies, knowledge graphs and FAIR data.
This document discusses semantic networks and their applications in artificial intelligence. It begins with describing how semantic networks originated in psychology for analyzing relationships between concepts, and were later adopted by AI to analyze textual data. Key applications of semantic networks discussed include idea generation, visual text analysis, and conceptual design. The document also provides historical background on semantic networks and how they are used for knowledge representation.
Achieving Semantic Integration of Medical Knowledge for Clinical Decision Sup...AmrAlaaEldin12
Abstract. Enhancing the outputs of the Clinical Decision Support systems (CDS) is a permanent concern for many research communities, which have to deal with an abundance of entities, data, structures, methods, application, tools, and so on. In the few past decades, there were theorized and standardized tech- nologies that could help researchers to obtain better results. The paper presents a method to enrich the inputs of the CDS through a semantic integration of sev- eral medical knowledge sources, by using the Topic Maps standard, in order to obtain more refined medical recommendations. Future research directions and challenges are summarized and conclusions are issued.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Top Cited Articles in Data Mining - International Journal of Data Mining & Kn...IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Research on ontology based information retrieval techniquesKausar Mukadam
The document summarizes and compares three novel ontology-based information retrieval techniques. It discusses a technique for retrieving information in the domain of Traditional Chinese Medicine that uses an ontology to represent concepts and measures concept similarity to sort search results. It also describes a framework for semantic indexing and querying that uses an ontology and entity-attribute-value model to improve scalability, usability, and retrieval performance for transport systems. Additionally, it outlines a semantic extension retrieval model that uses ontology annotation and semantic extension of queries to address limitations of keyword-based search. The techniques are evaluated based on precision and recall measures to analyze their effectiveness compared to traditional methods.
The document discusses data and information fusion (DIF) techniques. It introduces several DIF models including the Intelligence Cycle, Boyd Control Loop, Waterfall Model, Dasarathy's Model, and the Joint Directors of Laboratories (JDL) Data Fusion Model. The JDL model is described as having five levels (Level 0 to Level 4) aimed at facilitating coordination between data fusion communities. Applications of DIF techniques discussed include strategic defense, computer security, crisis management, and biomedical fields.
An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
Similar to Blended intelligence of FCA with FLC for knowledge representation from clustered data in medical analysis (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%.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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by deriving the prescribed perception of concepts of human’s brain thinking nature for generating the
significant intelligible analysis statements and FCA is also holding another advantage of conceptual
clustering technique which is used to offer the intended similes for the perceptions of abstracts along with the
data-unit productivity implication [1]. Formal Concept Analysis (FCA), pioneered in the 1980’s by Wille [2],
is a method for extracting formal concepts–natural clusters of objects and attributes–from binary object-
attribute relational data. FCA has great appeal in the context of knowledge discovery [3], information
retrieval [4] and social networking analysis applications [5] because arranging data as a concept lattice yields
a powerful and intuitive representation of the dataset [4], [6]. The FCA based formal context is derives the set
of objects of objectives and attributes of objects through the binary relationship mechanism, but in real-time
scenario it is supplementary significant to judge the straight relationships between objects of individual
objectives of concern data-sets [7]. The definitional arguments stay unsettled, mainly because the
psychologists and anthropologists are acknowledged through the various languages with help of logical
symbol based structure which is formed by the individuals to signal meanings suggest various
oncologists [9].
In various fields of applications has faced difficulties while processing the indistinguishable
formless knowledge which is not suitable to robust into the classical logics of binary innovation mechanism.
The binary based implicated representations such as the combinational answers of yes or no, true or false and
1 or 0 is difficult to generate the significant answers for the some complex questions such as whether a place
is large or whether two states are close to each other [10]. The various range of size of degrees and proximity
computations are used to represent the vague knowledge. Fuzzy based logical implications are best fit for
deriving the scale of truth degree values to represent the vague knowledge. From these advancements, firstly
it has formalized for propositional based implicated logics [11] and it has computationally implicated with
various other logics along with their associated logical formalisms sequences such as the FCA and
description logics (DL) [12], [13]. While implementing the fuzzy logic sequences to a past designed
formalism some complications are noticed such as whether the real data-unit intermission values will be used
for the set of degrees of truth values or used for the set of a supplementary degrees of truth values of complex
lattices, retrieval of conjunction based semantics, In existing proposed methodology to decide which part of
functionalities are continued for furthered execution without any modifications and which logical are missing
whether these laggings logics will successfully solvable by the fuzzy logic based matching part. There type
of decisional based thinking’s are generated by using the fuzzy logic implicated FCA (FLiFCA) and fuzzy
logic implicated DL (FLiDL). Researchers has proposed various approaches by using the FCA implicated DL
sequences. Some approaches are executed with help of the computational based concept sequences [14],
some approaches [15] ontology based computational progression which is used for developing and retrieving
the embedded based learning data units by taking the support of graph implicated data sets [16], [17]. For
executing the conjunctions in excess of concept based data, the set of attributes (SoA) are used for
representing the objects in FCA based approached developments and the concept based descriptions are used
for representing the individuals in DL based methods. The same class of semantics are flexible, sharable for
executing the SoA in FCA and concept names based conjunctives in DL based methods [16].
FCA is helpful to generate the new learning ontology based techniques. The major problems are
identified in medical field, some growing kids are facing the problem of representing their knowledge from
their gathered prior data which is in the form of unordered and insufficient clustered data which is not
supporting them to take the right decision on right time for solving the uncertainty based questionnaires. the
FCA is projected to expand the FCA based conception with help of the objective based item set notions
considered as the target which is implicated with the expanded cardinalities along with its weights which is
associated through the fuzzy based inference decision rules. This approach is more helpful for medical
experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge
explorer deficiency.
2. PAPER OBJECTIVES
This paper is proposing new ideological blended approach of FCA with FLC and described with
major objectives: The FCA analyzes the data based on relationships between the set of objects of prior-
attributes and the set of attributes based prior-data, which the data is framed with data-units implicated
composition which are formal statements of idea of human thinking with conversion of significant intelligible
explanation. How the FLC derive the fuzzification, rule-construction and defuzzification methods implicated
for representing the accurate knowledge for uncertainty based questionnaires. This approach is more helpful
for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing
knowledge explorer deficiency.
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637
3. FCA BASED IMPLICATIONS
The FCA implications will derive two major objective based classifications such as formal
implicated contexts (FICtx) and formal concepts (FICpt) as shown in Figure 1.
Figure 1. Execution and computational sequences of FCA based formal implicated contexts (FICtx) and
formal concepts (FICpt)
4. FUZZY LOGIC IMPLICATIONS
The classical based set (CbS) theory and fuzzy based set (FbS) theory are the major basic
computational components for designing and executing the embedded based systems. The CbS theory is
representing the sets with an objects either it is member or not and the FbS theory is deriving the classes of
degree for representing the membership functions, the membership function based object degrees are
represented by real numbers such as 0 treated as no value in membership and 1 treated as full membership or
some times between 0 and 1 [8]. FLC based reasoning (FLCbR) along with the graded concepts is holding
the various advantages along with the various practical applications based uses [18]. FLCbR is extensively
using in robotics, home need appliances, pattern recognition, image processing and many more. The FLCbR
is best suitable in soft computing based hybrid systems such as fuzzy based neural system, fuzzy implicated
genetic algorithms, neural-genetic systems, virtual and artificial intelligence and their application sequences
[19]. The FLC system implicated microcontroller exists two division which is connected with the object code
based correlated sectors. The first division deriving the feasibility to perform the functionalities of FLC based
algorithm such as fuzzification, inference rule based assessment and defuzzification along with the FLC
inference rule based mechanism [10]. Another division is help full to prepared the FLC based inputs and
output come response along with application depended specific inference rules to carry towards to execute
the FLC rule based mechanism. In FLC system, the Granulation is represented as in form of relations of input
or output variables and in the form of information compression of variables.
5. METHODOLOGY IMPLICATIONS
In medical field some growing kids are facing the problem of representing their knowledge from
their gathered prior data which is in the form of unordered and insufficient clustered data which is not
supporting them to take the right decision on right time for solving the uncertainty based questionnaires. The
medical assumptions based process deviate in the degree parameters which involves attempting to treaty with
different cause difficulties aspect of hypothesis such as importance of relative-objectives, varied-postulation
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and the measuring the weak and strong relationships between questionnaires along with expected results
which is gathered from their clients memory cells. In this process the FCA data analysis and FLC concept
based lattices are more useful to retrieve the related knowledge from their client with this represented
knowledge doctors will get the conclusion about their mental level of knowledge retrieval and is help full to
prescribe the concern medicine to improve their reasoning skills. The FCA is used to analyze the data and
FLC concept based lattices based inference rules used to perform the decision making methods with the help
of the concern FLC reasoning approach for knowing the knowledge representation level or range of the
children or clients and for deriving some class of conceptualization for the medical field by analyzing the
medical test case based texts related to the generating the knowledge based on the some questionnaires. The
table1 is representing the derived formal context based objects along with the concern attributes with help of
related text-fields. Here the assumptions are build by concerning the relations which is represented in Table
1, they are holding the additional and-or insufficient in their intelligence that we have very closer in day to
day life activities and all non-occurring relations in the text are considered as partial negative opinions. Here
the classes are generated with help of group of objects and generating the concept based chain of relation by
analyzing their concern shared linguistic-contexts of objects. Here the meaningful classes are formed along
with their hierarchies by using the cluster techniques based assumptions used for generating the group terms
for knowledge retrieval process based on their age group. Table 1 is representing the concept lattice of the
FICtx values illustrate in Figure 2 which is representing the relationships between the various nodes of
objects along with their concern attributes and also it altered into a biased-order in a simple way of the
removable mode of bottom object attribute by establishing an ontological concept for every node of FICpt
along with their names by representing the concern intent and it is initiated with the sub-concept for every
object based attribute in the dependent of the FICpt in concern questionnaires generated by the expert.
The natural language parser is helpful for deriving the essential object based dependencies from the
words of concern attributes and every answer based statements are generated with help of FLC concept based
lattices intent (FLCCLI) which is representing the language based depiction, FLC concept based lattices
extent (FLCCLE) which is representing the SoO of concern objectives used for referring the depiction, FLC
concept based lattices context (FLCCLC) in the concern words and these dependencies achieved through the
in a simple way of execution helpful to retrieve the range of concern knowledge from the clients and to make
possible by the human sympathetic of mechanically derived lattices and concept hierarchies with concern
objects along with their attributes. This assumption of entirety of information is helpful to find all the
possible occurrences by comparing the attributes of objects of individual with some group of attributes of
objects by conserving the combination of SoO and SoA for generating the FCA implicated fuzzy sets which
is needed to retrieve the knowledge based order and choices of the questionnaires as mentioned in Table 2
which is representing the logical attributes: fuzzy attributes with concern FCA implicated FLC lattice based
computations such as Gödel based t-norm, intuition, hedge and various range of fuzzy sets as shown in
Figure 3 and in section 5.2. In the experimental results generated the context with 8 number of objects, 9
number of attributes, 17 number of nodes and 27 number of arcs of lattice based relationship. Table 1 shows
a comparison of various optimization techniques based on certain criteria
Table 1. Logical attributes with crisp attributes and objects with concern relationships in FICtx
Road Water Air Wheels Wings
Passenger
Transportion
Goods
Transporation
Fuel
Usage
Animal
Usage
Cars X X X X X
Bikes X X X
Flights X X X X X X
Ships X X X X
Helicopter X X X
Auto X X X X X
Bicycle X X
Bullacord X X X
5.1. FLC concept based lattices
FLC concept based lattices are best for solving the complex problems in big-data, soft computing
and robotics based investigative analysis process. The FLC concept based lattices are majorly deriving the
three components such as: FLC concept based lattices intent (FLCCLI) which is representing the language
based depiction, FLC concept based lattices extent (FLCCLE) which isrepresenting the SoO of concern
objectives used for referring the depiction, FLC concept based lattices context (FLCCLC) which is
represented with the concern universe of entire probable objects and the universe of entire probable attributes
of objects along with the their related logical classification of the concern relation-type which the object
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possesses an attribute values then it indicates the relationships of SoA along with the SoA which is used to
execute the functionalities for further development of the system. In FLCCLC, with help of substance of
degree the decision making process can be occur such as whether an concern object is belongs to a concern
concept or not and whether it consists an attribute or not, some time the decision is treated as crisp if the
many attributes are holding the values in fuzzification [20]. In this approach the computational complexities
are performed by the numerical values. Here the numerical values are allocated to every attribute by
implicating with concern scaling factor. Finally the outcome responses are generated with help of table
formation by deriving the related associates of every allocated object based values within the mentioned
series to the numerical value by signifying the applicability of specified degree values. Here the FLCCLC are
represented with the graphs with help of lattices and concern nodes along with their various diverse inter-
connectivity ranges [23]. The crisp based data implementation way is differ in FLC based DL and FLC
implicated FCA. In generalized fuzzy logic sequences, sometimes the computational scenarios are not
flexible for executing the operations, but in FLC implicated FCA is deriving the future of fuzzy based SoA
[10]. The generalized DL based concept descriptions are similarly implementing the FLC based DL, here the
only semantics are fuzzy based but they are not pure fuzzy logic. The Gödel represented t-norm is used
accurately in the section of FLC based FCA where the semantics will partly covers by the fuzzy based DL
which is most probably consent to synergies as in the crisp case of scenarios [24]. In Gödel based t-norm
representations, sometimes it is holding the essential limit when the FLC based FCA implementation uses the
weak-conjunction (WC) for the semantics of SoA while DL utilized the strong-conjunction (SC) for its
concern semantics [25][26]. The SC and WC correspond solitary for the Gödel based t-norm. There is
another limit in DL point of view which is the Gödel based t-norm is the solitary t-norm for which the
standard DL based reasoning operations are acknowledged to be decisional scenario [21][22].
5.2. FCA implicated fuzzy logic set, t-norms and hedges
FLC is deriving the well-defined semantics for indicating the indistinct-data by using the scale of
truth degrees of range values [27][28]. This approach representing the truth degrees interval values such as
[0.0, 1.1] from the real-unit-interval [0, 1]. FLC is deriving the various computational operational sequences
to define the concern its semantics.
Table 2. Logical attributes along with the fuzzy attributes of input values
Road Water Air Wheels Wings
Passenger
Transportion
Goods
Transporation
Fuel
Usage
Animal
Usage
Cars 1 0.9 0.8 0.7 0.6
Bikes 1 0.9 0.8
Flights 1 0.9 0.8 0.7 0.6 0.5
Ships 1 0.9 0.8 0.7
Helicopter 1 0.9 0.8
Auto 1 0.9 0.8 0.7 0.6
Bicycle 1 0.9
Bullacord 1 0.8 0.7
5.2.1.Godel based t-norm
It represents the binary operation along with binary operator U: [0, 1] x [0, 1] [0, 1] which is
commutative, associative, monotone rule passing scenarios and finally it derives 1 as its outcome unit value.
Here each incessant t-norm presents to mount to a binary based operator V: [0, 1] x [0, 1] [0, 1] which is
derives the unique-operator fulfilling for all z ∈ [0, 1].
z ≤ x => y if and only if x U z ≤ y
5.2.2.FCA based intuition
The intuition is that the t-norm and the residuum can be used to interpret conjunction and
implication, respectively. Among the many continuous t-norms perhaps the simplest one, and the one we
shall be interested in, is the Gödel t-norm.
x U y = min{x, y}
Residuum: if x ≤ y
Then x => y = 1
Otherwise y
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5.2.3.FCA based hedge
It is a unary operator that is idempotent and satisfies 1* = 1, a* ≤ a, and (a => b) * ≤ a* => b* for all
a, b [0, 1]. It is used for truth-stressing, to increase the contrast between 1 and the smaller truth values. A
simple hedge is the globalization, defined as 1* = 1 and a* = 0 for a ≠ 0.
5.2.4.FCA based fuzzy set
Fuzzy sets are a central idea of Fuzzy Logics. Given a set M a fuzzy (sub-)set T of M is a function
T: M → [0; 1], that maps each element of M to its membership degree in T. The cardinality of a fuzzy set T
is defined as the cardinality of its support {x ∈ M | T(x) > 0}. Two fuzzy sets T1 and T2 can be compared
point wise by defining T1 ⊆ T2 iff T1(x) ≤ T2(x) for all x ∈ M. alternatively, one can associate a subset-hood
degree with T1 and T2 by defining;
S(T1, T2) = inf x∈ M T1(x) =>T2(x)
figure 2 shows the concept lattice of the FICtx values.
Figure 2. Experimented figure of logical attributes with crisp attributes and objects with concern relationships
in FICtx
Figure 3. Nodes of lattices mechanically derived from relationship indication of vehicle representational
knowledge based on Table 1 and Figure 2
5.3. Crisp FCA implicated with fuzzy logic
In the crisp setting has implicated with crisp FCA in addition to fuzzy FCA, as we shall need the
crisp version of the Duquenne-Guigues Base in the later sections.
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In crisp FCA [11], data is typically represented in the form of cross tables such as the one in Table
1. More formally, a formal context is a triple K = (G,M,I) where G is a set, called the set of objects, M is a
set, called the set of attributes, and I ⊆ G×M is a binary relation, called the incidence relation. For sets A⊆ G
and B ⊆ M the derivation operators are defined as
A↑
={m ∈ M | ∀g ∈ A: (g, m) ∈ I},
B↓
={g ∈ G | ∀m ∈ B: (g,m) ∈ I}
The two derivation operators.↑
and ↓
form an antitone Galois-connection. An implication A → B,
where A, B ⊆ M, is said to hold in the context K if A↓
⊆B.↓
.
A set of attributes U ⊆ M respects A→B iffA does not ⊆ of U or B ⊆ U. A → B follows from a set
of implications L iff every set U that respects all implications from L also respects A → B.
In a fuzzy context K = (G, M, I) the incidence relation I is a fuzzy relation, i.e. a fuzzy subset of G x
M. The derivation operators are defined for fuzzy subsets A of G and fuzzy subsets B of M as follows:
A↑
(m) = inf(g∈G) (A(g)* => I(g, m))
B↓
(g) = inf(m∈M)(B(m) => I(g, m)
In fuzzy FCA the implications are allowed to be fuzzy. A fuzzy implication is a pair written as A →
B where A and B are fuzzy subsets of M. Let U be a fuzzy subset of M. The degree to which A → B holds in
U is defined as
||A → B||U = S(A,U)*) => S(B, U)
The degree to which A → B holds in IK is defined as
||A → B||IK = ming2G ||A → B||Ig,
Where Ig is the fuzzy set to which each m ∈ M belongs with degree I(g, m). Let L be a fuzzy set of
fuzzy implications.
A set U ⊆ M is called a model of L if ||A → B||U≥ I (A → B) holds for every fuzzy Implication of
A → B.
A → B follows from L to degree q if ||A → B||U ≥ q for all models U of L.
Figure 4 shows the visualized conceptual clusters related to vehicle representational knowledge compounds.
Figure 4. The resulting conceptual clustering of the text clusters along with visualized for the clusters related
to vehicle representational knowledge compounds and generated the context with 8 objects, 9 attributes, 17
nodes and 27 arcs
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Figure 5 and 6 shows the logical attributes by concerning the partial input fuzzy attribute values and the
complete fuzzy attribute values respectively
Figure 5. Logical attributes by concerning the partial input fuzzy attribute values
Figure 6. Logical attributes by concerning the complete fuzzy attribute values
Figure 7 shows the resulting of full concept lattice along with visualized clusters related to vehicle
representational knowledge compounds and Figure 8 shows the graphical representation of fuzzy logic
implicated interval relationship.
Figure 7. The resulting conceptual clustering of the text clusters along with visualized for the clusters related
to vehicle representational knowledge compounds
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Figure 8. Graphical representation of fuzzy logic implicated interval relationships
Figure 9 shows the extents and intents along with logical values and Figure 10 shows a particular lattices of
relationship of attributes of one object to another object.
All Concepts of AOC poset:
1-Extent: Bikes, Bicycle, Cars, Auto, Intent : Road, Wheels
2-Extent: Bullacord, Intent : Animal Usage, Passanger Transportation, Wheels
3-Extent: Bullacord, Flights, Cars, Auto, Ships, Helicopter, Intent : Passenger Transportation
4-Extent: Bikes, Bicycle, Bullacord, Flights, Cars, Auto, Intent : Wheels
6-Extent: Ships, Intent : Goods Transportation, Fuel Usage, Passenger Transportation, Water
7-Extent: Flights, Helicopter, Intents : Fuel Usage, Passenger Transportation, Air
10-Extent: Bikes, Cars, Auto, Intents : Road, Fuel Usage, Wheels
12-Extent: Flights, Cars, Auto, Ships, Intent : Goods Transportation, Fuel Usage, Passenger
Transportation
13-Extent: Bikes, Flights, Cars, Auto, Ships, Helicopter, Intent : Fuel Usage
15-Extent: Flights,Intent : Goods Transportation, Fuel Usage, Passenger Transportation, Wheels,
Wings, Air
16-Extent: Cars, Auto, Intent : Goods Transportation, Fuel Usage, Passenger Transportation, Wheels
Figure 9. Output of multi valued multi-contexts of extents and intents along with the logical scale
Figure 10.Particular lattices of relationship of attributes of one object to attributes of another object with the
conceptual clustering mechanism
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6. CONCLUSION
FCA analyzes data which describe relationship between a particular set of objects and a particular
set of attributes. The data are structured into units which are formal abstractions of concepts of human
thought, allowing meaningful comprehensible interpretation. This paper is recommending new ideological
blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the
data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-
data, which the data is framed with data-units implicated composition which are formal statements of idea of
human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore
the relationship among the attributes and used the formal concept analysis from these suitable rules to explore
better knowledge and most important factors affecting the decision making. Secondly how the FLC derive
the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate
knowledge for uncertainty based questionnaires. This approach is more helpful for medical experts for
knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer
deficiency.
ACKNOWLEDGEMENTS
I would like to thank to my supervisor S.S.V.N Sarma, my family, colleagues and friends.
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BIOGRAPHIES OF AUTHORS
Working at Kakatiya University. Research interest are Computer Networks, Genetic algorithms
and Formal Concept Analysis.
He has worked as Head, Chairman Board of Studies of Department of Informatics, Kakatiya
University for 39 years, Chairman Board of Studies in Computer Science, SKD University,
Ananthpur.Dean Faculty of Science, Kakatiya University.Presently working as Dean at
Vaagdevi engineering College, Bollikunta, warangal since 2011 to present day. Guided many
scholors from various universities and research areas are Computer Networking,, Cloud
Computing, Network Communications, information Security and Machine learning. He has
published many papers in national and international conferences and journals as author and co-
author.