Diffuse lung disease classification based on texture features and weighted extreme learning machine

Raj, Shyla; Mahanand, B. S.; Vinod, D.

doi:10.1007/s11042-020-10469-5

Cited by 5 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a fault occurs in on-board equipment, maintenance personnel who are professionally trained and familiar with the working principles of on-board equipment observe the fault phenomena and data through their fault diagnosis efficiency, which directly depends on the technical level of the maintenance personnel and their understanding of the equipment. For inexperienced on-board equipment maintenance personnel, the problem of long diagnosis time and low diagnosis efficiency may occur in the fault diagnosis process [7][8]. In the event of a fault in the on-board equipment, the on-board maintenance personnel will use natural language to record the state and phenomenon of the on-board equipment when it fails [9][10].…”

Section: Auxiliary Maintenance Framework For In-vehicle Equipment Bas...mentioning

confidence: 99%

In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms

2020

View full text Add to dashboard Cite

With the rapid development of modern society, especially the popularity of the Internet and the rapid development of computer technology, people's lifestyles are undergoing fundamental changes. The rapid development of the Internet has led to an explosion of data and how people use this data has become one of the most popular research topics in modern society. In the past, the limitations of computer technology made it difficult to manage large amounts of data effectively, but the rapid development of computer technology has now made it possible to recognise that it is possible to manage and analyse such large amounts of data. The main objective of this paper is to develop a study of in-vehicle speech TC based on a variety of machine learning algorithms. In this paper, after acquiring the features of the text, a classification model is trained and this tagged data is learned by the model to obtain a classifier. Plain Bayesian classification, nearest-neighbour classification and decision trees are introduced and their advantages and disadvantages are analysed. The results of experiments on text from in-vehicle speech devices reveal that text classification (TC) using support vector machines has good results; natural language understanding can be achieved by combining a rule-based approach by first performing classification and information extraction operations on the text; and the feasibility of architectural modifications is demonstrated through functional verification.

show abstract

Section: Auxiliary Maintenance Framework For In-vehicle Equipment Bas...mentioning

confidence: 99%

In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms

2020

View full text Add to dashboard Cite

show abstract

Multi-scale lung tissue classification for interstitial lung diseases using learned Gabor filters

Dasari

Reddy

2023

Microsyst Technol

View full text Add to dashboard Cite

Cost-sensitive learning for imbalanced medical data: a review

Araf,

Idri,

Chairi

2024

Artif Intell Rev

View full text Add to dashboard Cite

Integrating Machine Learning (ML) in medicine has unlocked many opportunities to harness complex medical data, enhancing patient outcomes and advancing the field. However, the inherent imbalanced distribution of medical data poses a significant challenge, resulting in biased ML models that perform poorly on minority classes. Mitigating the impact of class imbalance has prompted researchers to explore various strategies, wherein Cost-Sensitive Learning (CSL) arises as a promising approach to improve the accuracy and reliability of ML models. This paper presents the first review of CSL for imbalanced medical data. A comprehensive exploration of the existing literature encompassed papers published from January 2010 to December 2022 and sourced from five major digital libraries. A total of 173 papers were selected, analysed, and classified based on key criteria, including publication years, channels and sources, research types, empirical types, medical sub-fields, medical tasks, CSL approaches, strengths and weaknesses of CSL, frequently used datasets and data types, evaluation metrics, and development tools. The results indicate a noteworthy publication rise, particularly since 2020, and a strong preference for CSL direct approaches. Data type analysis unveiled diverse modalities, with medical images prevailing. The underutilisation of cost-related metrics and the prevalence of Python as the primary programming tool are highlighted. The strengths and weaknesses analysis covered three aspects: CSL strategy, CSL approaches, and relevant works. This study serves as a valuable resource for researchers seeking to explore the current state of research, identify strengths and gaps in the existing literature and advance CSL’s application for imbalanced medical data.

show abstract

Diffuse lung disease classification based on texture features and weighted extreme learning machine

Cited by 5 publications

References 31 publications

In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms

In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms

Multi-scale lung tissue classification for interstitial lung diseases using learned Gabor filters

Cost-sensitive learning for imbalanced medical data: a review

Contact Info

Product

Resources

About