Early detection and classification of internal leakage in boom actuator of mobile hydraulic machines using SVM

2021 ◽  
Vol 106 ◽  
pp. 104492
Author(s):  
Joseph T. Jose ◽  
J. Das ◽  
Santosh Kr. Mishra ◽  
Gyan Wrat
Keyword(s):  
Early Detection ◽  
Hydraulic Machines

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

scholarly journals EVALUATION AND CLASSIFICATION OF VAGINAL STENOSIS AFTER BRACHYTHERAPY

2016 ◽  
Vol 25 (2) ◽  
Author(s):  
Luciana Martins da Rosa ◽  
Karina Silveira de Almeida Hammerschmidt ◽  
Vera Radünz ◽  
Patrícia Ilha ◽  
Andrelise Viana Rosa Tomasi ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Data Collection ◽  
Early Detection ◽  
Scientific Production ◽  
Narrative Review ◽  
Time Limits ◽  
Vaginal Stenosis ◽  
Evaluative Instrument

ABSTRACT This narrative review identified, in the scientific production, the methods used for evaluating and classifying vaginal stenosis in women who have undergone brachytherapy. Data collection was undertaken in July 2013 in the publications of SciELO, MEDLINE and PubMed, without time limits, and in studies cited by two scientific reviews which addressed the issue investigated here. The search protocol included the description of the method for evaluating and classifying vaginal stenosis. Comparative analysis between the findings showed there to be diversity among the methods used by different researchers. In the light of this finding, this study proposes elements for making an evaluative instrument to be applied by nurses. The standardization of the technique will help in the early detection of vaginal stenosis and in the care for women subsequent to vaginal brachytherapy.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Role of EEG as Biomarker in the Early Detection and Classification of Dementia

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Noor Kamal Al-Qazzaz ◽  
Sawal Hamid Bin MD. Ali ◽  
Siti Anom Ahmad ◽  
Kalaivani Chellappan ◽  
Md. Shabiul Islam ◽  
...  
Keyword(s):  
Signal Processing ◽  
Early Detection ◽  
Patient Treatment ◽  
Treatment Programs ◽  
Treatment Benefit ◽  
Medical Practitioners ◽  
Early Stages ◽  
Clinical Support

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

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