Machine Learning Algorithm Development for detection of Mango infected by Anthracnose Disease

2021 ◽  
Author(s):  
Suwit Wongsila ◽  
Parinya Chantrasri ◽  
Pradorn Sureephong
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Development ◽  
Anthracnose Disease

scholarly journals Curating Archival Anatomic Pathology Material For Machine Learning Algorithm Development

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S124-S125
Author(s):  
A Collins ◽  
A Norgan ◽  
J J Garcia
Keyword(s):  
Machine Learning ◽  
Cell Carcinoma ◽  
Learning Algorithm ◽  
Lessons Learned ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Anatomic Site ◽  
Algorithm Development ◽  
Anatomic Pathology

Abstract Introduction/Objective Advances in whole slide imaging have enabled the application of machine learning algorithms to anatomic pathology. In the current state, the development of accurate algorithms requires robust training data with correctly assigned diagnostic and classification labels. Increasingly, institutions have looked to their archival slides as a source of “ground truth” for algorithm development. However, the curation and use of archival data poses several challenges. Here, we share lessons learned from reviewing head and neck pathology consult cases spanning a 10- year period at Mayo Clinic Rochester. Methods Archived surgical pathology slides from 2,590 consult cases were reviewed. Clinical and demographic information was recorded for each case, including surgical date, surgical procedure, anatomic site, age, gender and diagnosis. Cases were excluded from the curated archive if there was insufficient volume or quality of tissue to render a specific diagnosis (141 cases, 5.6%). Slides with a range of tissue size and quality, from numerable laboratories were included in the curated archive. Selected cases were collated by anatomic site: ear, gnathic, larynx, nasopharynx, neck, oral cavity, oropharynx, salivary gland and sinonasal tract. Results Common diagnostic reconciliations (115 cases, 4.4%) fell within the following categories: (1) novel entities (59 cases, 2.3%), including biphenotypic sinonasal sarcoma and clear cell carcinoma; (2) novel classifications (21 cases, 0.8%), as seen in HPV-related oropharyngeal squamous cell carcinoma and polymorphous adenocarcinoma; and (3) novel grading schema (35 cases, 1.4%), as seen in keratinizing dysplasia and oropharyngeal malignancies. Conclusion Several nuances emerged in the process of reviewing slides, highlighting the need for continual amendment of any machine learning dataset over time. Curating anatomic pathology cases for machine learning algorithm development requires the recognition of emerging entities, with re-classification and re-grading as needed.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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