Cardiac Image-Based Heart Disease Diagnosis Using Bio-Inspired Optimized Technique for Feature Selection to Enhance Classification Accuracy

Pradhan, Manaswini

doi:10.4018/978-1-6684-4671-3.ch009

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature selection has been demonstrated to increase the performance of classification across multiple applications. In recent studies, it has been used for a high-dimensional cancer microarray dataset, in the field of Arabic text classification, and in predicting heart diseases using bio-inspired optimization techniques [29,30].…”

Section: Data Selectionmentioning

confidence: 99%

Data Analytics for Optimizing and Predicting Employee Performance

Tanasescu,

Vines,

Bologa

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The need to increase employee performance and productivity has become vital in most companies nowadays, considering the number of changes that processes and people have faced during recent years in many organizations. This becomes even more important as it can sustain the growth of the company, as well as the competitiveness. This work will present multiple methods and comparisons between them for the process of building a machine learning algorithm to predict performance scores for employees in one organization; these methods include pre-processing the data, selecting the best variables, building the best algorithms for the available data, and tuning their hyperparameters. The current research aims to conclude on a collection of practices that will determine the best predictions for the given variables, so that human opinion can become less influential in employee appraisal, increasing objectivity and overall productivity.

show abstract

Section: Data Selectionmentioning

confidence: 99%

Data Analytics for Optimizing and Predicting Employee Performance

Tanasescu,

Vines,

Bologa

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Also, hybrid methods can be utilized for optimal feature selection. In Reference Pradhan (2023), for feature extraction, GLCM techniques are used, and to select optimal features, the chimp optimization algorithm (ChOA) is chosen.…”

Section: Plant Disease Identification: Challenges and Solutionsmentioning

confidence: 99%

An analysis to investigate plant disease identification based on machine learning techniques

Duhan,

Gulia,

Gill

et al. 2024

Expert Systems

View full text Add to dashboard Cite

In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

show abstract

Cardiac Image-Based Heart Disease Diagnosis Using Bio-Inspired Optimized Technique for Feature Selection to Enhance Classification Accuracy

Cited by 2 publications

References 12 publications

Data Analytics for Optimizing and Predicting Employee Performance

Data Analytics for Optimizing and Predicting Employee Performance

An analysis to investigate plant disease identification based on machine learning techniques

Contact Info

Product

Resources

About