An effective digit recognition model using enhanced convolutional neural network based chaotic grey wolf optimization

Preethi, P.; Asokan, R.; Thillaiarasu, N.; Thangavel, Saravanan

doi:10.3233/jifs-211242

Cited by 37 publications

(11 citation statements)

References 10 publications

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“…In the linked graph, compute heuristic dataset for all vertices and their edges. Many important aspects are recognised while applying the ACO is an extraction association rules, such as collecting heuristic data for all vertices of the diagram, determining the weight for each edge of vertices, and initializing the cardinality variables [32]. Initializing the number of ants and the no.…”

Section: Gaco Description Based On Graph Ontologymentioning

confidence: 99%

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

2022

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HUIM (High Utility Itemset Mining) is one of the most analyzed data mining activities. Product suggestion, e-learning, bioinformatics, text mining, market basket analysis, and web click stream analysis are few of the areas where it can be used. Cost savings, greater competitive advantage, and increased revenue are the advantages gained by pattern analysis. However, because HUIM approaches do not examine the correlation of retrieved patterns, they may uncover false patterns. As a result, a number of technique for mining related HUIs have been presented. These algorithms still have issues with computational cost, both in conditions of period and memory usage. As a result, a method for mining weighted temporal patterns is proposed. The suggested method begins by preprocessing time series-based data into fuzzy itemsets. These are fed into the Improved Adaptive Fuzzy C Means (IAFCM) technique, which is a hybrid of the FCM clustering method and the Graph based Ant Colony Optimization (GACO) technique. The proposed IAFCM technique accomplishes two goals: IAFCM clustering and data reduction in FCM clusters, and ii) optimal itemet placement in clusters using GACO. Using GACO, the suggested technique produces high-quality clusters. On these clusters, weighted sequential pattern mining is used to find the most effective sequential patterns, which take into account knowledge of patterns with low frequency and high weight in a repository that is updated over time. The results of this method show that when compared to other traditional methodologies, the IAFCM with GACO improves execution time. Furthermore, it improves the data representation process by increasing accuracy while using less memory.

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Section: Gaco Description Based On Graph Ontologymentioning

confidence: 99%

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

2022

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“…Pulse indicates logical '0' if the transmitter is quiet or '1' otherwise. Energyefficient repaired code-words with continuous weights have been suggested to improve dependability [22].…”

Section: Description Of Module 1 (Second Phase)mentioning

confidence: 99%

Intra and Inter Body Area Network Communication System for Out- of-hospital Nursing of Pcos Patients by Coupling Medical Big Data Mining With Cloud

Saravanabhavan¹,

Preethi²,

Anguraju³

et al. 2022

Preprint

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Hospital admissions, readmissions, and other cost of healthcare are significantly impacted by the growing number of patients with chronic conditions and the accumulation of healthcare resources. The hormonal imbalance PCOS produced in women in their reproductive years also contributes to a number of chronic ailments, including irregular periods, excessive weight gain, acne, and the growth of facial hair etc. A delayed or missing menstrual cycle brought on by the hormonal imbalance results in infertility. The current approaches and therapies are insufficient for earlier stage diagnosis, especially from their home-centric environment. Till date, no technology has been able to independently identify the presence of PCOS in the ovaries which eventually affects fallopian tube and alert the patient, consulting doctor, or nurse so that the next course of action can be started as soon as possible. Hence, to solve the aforementioned issues, the proposed research processes the information gathered from PCOS patients using a cloud computing platform integrated with medical big data mining and machine learning (ML) algorithms. In this study, a conceptual design is proposed from the perspective of communications engineering. In order to detect Fallopian Tube (FT) activity, the architecture combines an intra-body-based nano-sensor with a body-area network. This network receives data from the intra-body networking and transmits it across the air to the relevant personnel (doctor, nurse, patients). The relationship between feasible information rates, and other key metrics has been investigated through preliminary simulations utilising a particle oriented stochastic simulator. Data from sensor are utilised by the Apache Kafka acts as ingestion tool, then given into cloud service computing architecture wherein Advanced Apriori (AA) algorithm is applied over the data to detect characteristics featuring with strong correlations between them before undergoing CatBoost Decision Tree model for optimised prediction of PCOS. The comparison analysis demonstrates notable results in terms of scalability and computation times with an ideal accuracy range.

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“…The adoption of a Stacked Regression model to forecast insurance premiums for individuals may benefit users. Dutta et al [9] calculated the healthcare costs using prediction analysis. Actuaries in the insurance industry use a number of numerical approaches to anticipate yearly medical claims expense.…”

Section: Literature Reviewmentioning

confidence: 99%

Random forest regression with hyper parameter tuning for medical insurance premium prediction

Prakash¹,

Bushra

Subramanian³

et al. 2022

ijhs

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The proposed effort has the purpose of predicting an individuals insurance expenses also identifying people having medical insurance plans and clinical data, irrespective of their health concerns. A patient will require many types of health insurance. Regardless of the type of insurance coverage a person has, it is feasible to estimate their health insurance expenditures depends on the degree of critical care they get. The random forest Regression is one of the regressors used in this investigation. When the accuracies were compared, hyper parameter tuning was the most effective of all the approaches, with a 98 percent accuracy. Finally, the prediction fit will calculate the insurance expense of the user and calculate the insurance costs.

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An effective digit recognition model using enhanced convolutional neural network based chaotic grey wolf optimization

Cited by 37 publications

References 10 publications

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

Intra and Inter Body Area Network Communication System for Out- of-hospital Nursing of Pcos Patients by Coupling Medical Big Data Mining With Cloud

Random forest regression with hyper parameter tuning for medical insurance premium prediction

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