Medicinal Plant Identification Using Machine Learning Techniques

2022 ◽  
pp. 120-130
Author(s):  
Udaya C. S. ◽  
Usharani M.
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Medicinal Plants ◽  
Data Cleaning ◽  
Active Role ◽  
Machine Learning Techniques ◽  
Plant Identification ◽  
The Public ◽  
Learning Techniques ◽  
Image Dataset

In this world there are thousands of plant species available, and plants have medicinal values. Medicinal plants play a very active role in healthcare traditions. Ayurveda is one of the oldest systems of medicinal science that is used even today. So proper identification of the medicinal plants has major benefits for not only manufacturing medicines but also for forest department peoples, life scientists, physicians, medication laboratories, government, and the public. The manual method is good for identifying plants easily, but is usually done by the skilled practitioners who have achieved expertise in this field. However, it is time consuming. There may be chances to misidentification, which leads to certain side effects and may lead to serious problems. This chapter focuses on creation of image dataset by using a mobile-based tool for image acquisition, which helps to capture the structured images, and reduces the effort of data cleaning. This chapter also suggests that by ANN, CNN, or PNN classifier, the classification can be done accurately.

scholarly journals Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

2019 ◽  
Vol 9 (1) ◽  
pp. 6705-6714
Keyword(s):  
Neural Network ◽  
Medicinal Plants ◽  
Leaf Shape ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Plant Identification ◽  
Learning Techniques ◽  
Whale Optimization ◽  
Medical Plant

Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses around the automatic identification proof of therapeutic plants that are regularly utilized in Ayurveda. The fundamental highlights required to distinguish a medicinal plant is its leaf shape, color and texture. In this paper, we propose efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques. In EAC-AMP, image corners detect first and top, bottom leaf edges are computed by the improved edge detection algorithm. After preprocessing, the segmentation can achieve using spider optimization neural network (SONN), which segments leaf regions from an image. The time and frequency domain features are computed by the symbolic accurate approximation (SAX); other features shape features, color features and tooth features are computed by the two-dimensional binary phase encoding (2DBPE). Finally, a whale optimization with deep neural network (DNN) classifier is used to characterize the type of plants. Accuracy in identification of any ayurvedic plant leaf is achieved by understanding and extracting the plant features. The main objective of the proposed EAC-AMP approach is to increase the accuracy of classifier. MATLAB experimental analysis showed better results such as accuracy, sensitivity and specificity.

Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public

2017 ◽  
Vol 29 (2) ◽  
pp. 190-209 ◽  
Author(s):  
Jennifer Helsby ◽  
Samuel Carton ◽  
Kenneth Joseph ◽  
Ayesha Mahmud ◽  
Youngsoo Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Police Department ◽  
Data Sources ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Police Legitimacy ◽  
The Public ◽  
Learning Techniques ◽  
Improve Accuracy

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

scholarly journals Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

10.2196/23957 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23957
Author(s):  
Chengda Zheng ◽  
Jia Xue ◽  
Yumin Sun ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Public Discourse ◽  
Latent Dirichlet Allocation ◽  
Prime Minister ◽  
Machine Learning Techniques ◽  
Future Health ◽  
The Public ◽  
Learning Techniques ◽  
The Government

Background During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective The aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening. Conclusions This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

scholarly journals Recognition of Soybean Diseases Using Machine Learning Techniques Based on Segmentation of Images Captured By UAVs

2020 ◽  
Author(s):  
Gercina Da Silva ◽  
Alessandro Ferreira ◽  
Denilson Guilherme ◽  
José Fernando Grigolli ◽  
Vanessa Weber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Computer Program ◽  
Random Forests ◽  
Machine Learning Techniques ◽  
Target Spot ◽  
Learning Techniques ◽  
Image Dataset ◽  
Segmentation Of Images ◽  
Soybean Diseases

Soybean is an important product for the Brazilian economy, however it has factors that can limit its productive income, like the diseases that are generally difficult to control. Thus, this article aims to use a computer program to recognize diseases in images obtained by a UAV in a soybean plantation. The program is based on computer vision and machine learning, using the SLIC algorithm to segment the images into superpixels. To achieve the objective, after the segmentation of the images, an image dataset was created with the following classes: mildew, target spot, Asian rust, soil, straw and healthy leaves, totaling 22,140 images. Diagrammatic scales were used to assess disease severity. The disease recognition computer program explored four supervised learning techniques: SVM, J48, Random Forest and KNN. The techniques that obtained the best performance were SVM and Random Forests, taking into account the results obtained with all the evaluation metrics used. It was found that the program is efficient to differentiate the classes of diseases treated in this article.

scholarly journals Automating Historical Source Transcription

2021 ◽  
Vol 10 ◽  
pp. 59-63
Author(s):  
Gunnar Thorvaldsen
Keyword(s):  
Machine Learning ◽  
20Th Century ◽  
Machine Learning Techniques ◽  
Population Register ◽  
Historical Source ◽  
Population Groups ◽  
The Public ◽  
Learning Techniques ◽  
Central Population ◽  
Text Version

Transcribing the 1950 Norwegian census with 3.3 million person records and linking it to the Central Population Register (CPR) provides longitudinal information about significant population groups during the understudied period of the mid-20th century. Since this source is closed to the public, we receive no help from genealogists and rather use machine learning techniques to semi-automate the transcription. First the scanned manuscripts are split into individual cells and multiple names are divided. After the birthdates were transcribed manually in India, a lookup routine searches for families with matching sets of birthdates in the 1960 census and the CPR. After manual checks with GUI routines, the names are copied to the text version of the 1950 census, also storing the links to the CPR. Other fields like occupations or gender contain numeric or letter codes and are transcribed wholesale with routines interpreting the layout of the graphical images. Work employing these methods has also started on the 1930 census, which is the last of the Norwegian censuses to be transcribed.

scholarly journals Applied Machine Learning Techniques on Selection and Positioning of Human Resources in the Public Sector

2021 ◽  
Vol 09 (02) ◽  
pp. 536-556
Author(s):  
Panagiota Pampouktsi ◽  
Spyridon Avdimiotis ◽  
Manolis Μaragoudakis ◽  
Markos Avlonitis
Keyword(s):  
Machine Learning ◽  
Public Sector ◽  
Human Resources ◽  
Machine Learning Techniques ◽  
The Public ◽  
Applied Machine Learning ◽  
Learning Techniques

scholarly journals Analysis and Prediction of Recorded COVID-19 Infections in the Constitutional Departments of Peru using Specialized Machine Learning Techniques

2021 ◽  
Vol 11 (11) ◽  
pp. 39-47
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Third Wave ◽  
Data Set ◽  
Infected People ◽  
The Public ◽  
Learning Techniques ◽  
Learning Software ◽  
The Stability

The COVID-19 pandemic in Peru caused thousands of losses where it can be seen that until the year 2021 there are more than 200,000 deaths among men and women throughout the country. This figure is alarming and could have been avoided in time if the necessary care had been taken and the norms imposed by the Peruvian government had been followed. In the last months of the year 2020, we began to see a decrease in deaths and infected by COVID-19, which caused the public to calm down, which led to some citizens not following the biosecurity protocols, consequently causing a second wave of infected people. Therefore, it is necessary to be able to prevent a third wave since in 2021 a reduction was again visualized, which meant a reduction of deaths and infected by COVID-19, so one option to be alert to a possible third wave is to use machine learning techniques with a data set with the ministry of health to predict in which parts of the country there is the possibility of new contagions and identify which gender will be more prone to be infected in this way be aware of which parts of the country should be prioritized and thus contribute to the stability and harmony of the country. Keywords— Covid-19; Machine Learning; Software prototype; Prediction models

scholarly journals Physical activity prediction using fitness data: Challenges and issues

2021 ◽  
Vol 10 (1) ◽  
pp. 419-426
Author(s):  
Nur Zarna Elya Zakariya ◽  
Marshima Mohd Rosli
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Active Role ◽  
Physical Activities ◽  
Machine Learning Techniques ◽  
Behavior Model ◽  
Learning Techniques ◽  
Healthcare Technologies ◽  
Personal Context ◽  
Behavior Change Technique

In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in Behavior Change Technique (BCT) and we selected two suitable models which are Fogg Behavior Model (FBM) and Trans-theoretical Behavior Model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health.

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