scholarly journals Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning

2021 ◽  
Vol 13 (7) ◽  
pp. 1341
Author(s):  
Simon Appeltans ◽  
Jan G. Pieters ◽  
Abdul M. Mouazen
Keyword(s):  
Machine Learning ◽  
Field Conditions ◽  
Environmental Cost ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Complex Data ◽  
Rust Disease ◽  
Proximal Sensing ◽  
Full Field ◽  
Extensive Evaluation

Rust disease is an important problem for leek cultivation worldwide. It reduces market value and in extreme cases destroys the entire harvest. Farmers have to resort to periodical full-field fungicide applications to prevent the spread of disease, once every 1 to 5 weeks, depending on the cultivar and weather conditions. This implies an economic cost for the farmer and an environmental cost for society. Hyperspectral sensors have been extensively used to address this issue in research, but their application in the field has been limited to a relatively low number of crops, excluding leek, due to the high investment costs and complex data gathering and analysis associated with these sensors. To fill this gap, a methodology was developed for detecting leek rust disease using hyperspectral proximal sensing data combined with supervised machine learning. First, a hyperspectral library was constructed containing 43,416 spectra with a waveband range of 400–1000 nm, measured under field conditions. Then, an extensive evaluation of 11 common classifiers was performed using the scikit-learn machine learning library in Python, combined with a variety of wavelength selection techniques and preprocessing strategies. The best performing model was a (linear) logistic regression model that was able to correctly classify rust disease with an accuracy of 98.14 %, using reflectance values at 556 and 661 nm, combined with the value of the first derivative at 511 nm. This model was used to classify unlabelled hyperspectral images, confirming that the model was able to accurately classify leek rust disease symptoms. It can be concluded that the results in this work are an important step towards the mapping of leek rust disease, and that future research is needed to overcome certain challenges before variable rate fungicide applications can be adopted against leek rust disease.

Nonprofit Role Classification Using Mission Descriptions and Supervised Machine Learning

2021 ◽  
pp. 089976402110573
Author(s):  
Megan LePere-Schloop
Keyword(s):  
Machine Learning ◽  
Geographic Variation ◽  
Mission Statements ◽  
Research Note ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Large Set ◽  
Large Sample ◽  
Qualitative Approaches

Scholars have used both quantitative and qualitative approaches to empirically study nonprofit roles. Mission statements and program descriptions often reflect such roles, however, until recently collecting and classifying a large sample has been labor-intensive. This research note uses data on United Ways that e-filed their 990 forms and supervised machine learning to illustrate an approach for classifying a large set of mission descriptions by roles. Temporal and geographic variation in roles detected in mission statements suggests that such an approach may be fruitful in future research.

Support Vector Machine

2009 ◽  
pp. 501-522 ◽  
Author(s):  
A. B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
System Analysis ◽  
Learning Algorithm ◽  
Mathematical Formulation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Support Vector ◽  
Analysis And Design ◽  
Simple Demonstration

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

scholarly journals Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

2021 ◽  
Vol 921 (2) ◽  
pp. 177
Author(s):  
Regina Sarmiento ◽  
Marc Huertas-Company ◽  
Johan H. Knapen ◽  
Sebastián F. Sánchez ◽  
Helena Domínguez Sánchez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Physical Properties ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Complex Data ◽  
Galaxy Surveys ◽  
Complex Data Sets ◽  
Low Mass ◽  
Field Unit ◽  
Integral Field

Abstract As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. We present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

scholarly journals Dam Siting: A Review

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2080
Author(s):  
Yang Wang ◽  
Yongzhong Tian ◽  
Yan Cao
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Site Selection ◽  
Flood Control ◽  
Research Literature ◽  
Future Research ◽  
Complex Data ◽  
Geological Conditions ◽  
Selection Factors ◽  
The Impact

Dams can effectively regulate the spatial and temporal distribution of water resources, where the rationality of dam siting determines whether the role of dams can be effectively performed. This paper reviews the research literature on dam siting in the past 20 years, discusses the methods used for dam siting, focuses on the factors influencing dam siting, and assesses the impact of different dam functions on siting factors. The results show the following: (1) Existing siting methods can be categorized into three types—namely, GIS/RS-based siting, MCDM- and MCDM-GIS-based siting, and machine learning-based siting. GIS/RS emphasizes the ability to capture and analyze data, MCDM has the advantage of weighing the importance of the relationship between multiple factors, and machine learning methods have a strong ability to learn and process complex data. (2) Site selection factors vary greatly, depending on the function of the dam. For dams with irrigation and water supply as the main purpose, the site selection is more focused on the evaluation of water quality. For dams with power generation as the main purpose, the hydrological factors characterizing the power generation potential are the most important. For dams with flood control as the main purpose, the topography and geological conditions are more important. (3) The integration of different siting methods and the siting of new functional dams in the existing research is not sufficient. Future research should focus on the integration of different methods and disciplines, in order to explore the siting of new types of dams.

scholarly journals Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation

2020 ◽  
Author(s):  
Ilke Öztekin ◽  
Mark A. Finlayson ◽  
Paulo A. Graziano ◽  
Anthony S. Dick
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Young Children ◽  
Diagnostic Category ◽  
Teacher Ratings ◽  
Future Research ◽  
Cognitive Measures ◽  
Hyperactivity Disorder ◽  
Incremental Value ◽  
Extensive Evaluation

ABSTRACTGiven the negative trajectories of early behavior problems associated with Attention-Deficit/Hyperactivity Disorder (ADHD), early diagnosis of ADHD is considered critical to enable early intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, as well as behavioral and neural measures of executive function in predicting ADHD diagnostic category in a sample consisting of 162 young children (53.7% ADHD, ages 4 to 7, mean age 5.55, 67.9% male, 82.6% Hispanic/Latino). Among all the target measures assessed in the study, teacher ratings of executive function were identified as by far the most important measure in predicting ADHD diagnostic category. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that commonly used structural imaging measures of cortical thickness, as well as widely used cognitive measures of executive function, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. Future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.

scholarly journals OIL PALM PLANTATION DETECTION IN INDONESIA USING SENTINEL-2 AND LANDSAT-8 OPTICAL SATELLITE IMAGERY (CASE STUDY: ROKAN HULU REGENCY, RIAU PROVINCE)

2021 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Yunita Nurmasari ◽  
Arie Wahyu Wijayanto
Keyword(s):  
Machine Learning ◽  
Oil Palm ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Landsat 8 ◽  
Extensive Evaluation ◽  
Composite Indices ◽  
The Government ◽  
Supervised Machine Learning Classifiers

The objective of this work is to assess the capability of multispectral optical Landsat and Sentinel images to detect oil palm plantations in Rokan Hulu, Riau, one of the largest palm oil producers in Indonesia, by combining multispectral bands and composite indices. In addition to comparing two different sets of satellite images, we also ascertain which gives the best performance among the supervised machine learning classifiers CART Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. With the use of multispectral bands and derived composite indices, the best classifier achieved an overall accuracy of up to 92%. The findings and contributions of the study include: (1) insight into a set of feature combinations that provides the highest model accuracy, and (2) an extensive evaluation of machine learning-based classifiers on two different optical satellite imageries. Our study could further be beneficial for the government in providing more scalable plantation statistics.

scholarly journals Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

2021 ◽  
Author(s):  
Massimiliano Greco ◽  
Giovanni Angelotti ◽  
Pier Francesco Caruso ◽  
Alberto Zanella ◽  
Niccolò Stomeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Chronic Obstructive ◽  
Complex Data ◽  
Icu Admission ◽  
Lower Performance ◽  
Learning Techniques ◽  
Using Data

Abstract Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

scholarly journals Machine Learning for Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety after Myocardial Infarction: Insights from the U-CARE Heart Trial (Preprint)

2018 ◽  
Author(s):  
John Wallert ◽  
Emelie Gustafson ◽  
Claes Held ◽  
Guy Madison ◽  
Fredrika Norlund ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Verbal Ability ◽  
Predictive Modelling ◽  
Treatment Session ◽  
Supervised Machine Learning ◽  
Homework Assignment ◽  
Recursive Feature Elimination ◽  
Future Research ◽  
Early Predictors

BACKGROUND BACKGROUND: Low adherence to recommended treatments is a multifactorial problem in rehabilitation for patients with myocardial infarction (MI). In a nationwide trial of internet-delivered cognitive behavior therapy (iCBT) for the high-risk subgroup of patients with MI also reporting symptoms of anxiety, depression, or both (MI-ANXDEP), adherence was low. Since low adherence to psychotherapy leads to a waste of therapeutic resources and risky treatment abortion in MI-ANXDEP patients, identifying early predictors for adherence is potentially valuable for effective targeted care. OBJECTIVE Applied predictive modelling with supervised machine learning to investigate both established and novel predictors for iCBT adherence in MI-ANXDEP patients. METHODS Data were from 90 MI-ANXDEP patients recruited from 25 hospitals in Sweden and randomized to treatment in the iCBT trial U-CARE Heart. Time-point of prediction was at completion of the first homework assignment. Adherence was defined as having completed at least the first two homework assignments within the 14-week treatment period. A supervised machine learning procedure was applied to identify the most potent predictors for adherence available at the first treatment session from a range of demographic, clinical, psychometric, and linguistic predictors. The internal binary classifier was a random forest model within a 3x10-fold cross-validated recursive feature elimination (RFE) resampling, which selected the final predictor subset which best differentiated adherers versus non-adherers. RESULTS RESULTS: Patient mean age was 58.4 (9.4) years, 62% (56/90) were men, and 48% (43/90) were adherent. Out of the 34 potential predictors for adherence, RFE selected an optimal subset of 56% (19/34) (Accuracy 0.64, 95% CI 0.61-0.68, P < 0.01). The strongest predictors for adherence were in order of importance (1) self-assessed cardiac-related fear, (2) sex, and (3) the number of words the patient used to answer the first homework assignment. CONCLUSIONS CONCLUSIONS: Adherence to iCBT for MI-ANXDEP patients was best predicted by cardiac-related fear and sex, consistent with previous research, but also by novel linguistic predictors from written patient behavior which conceivably indicate verbal ability or therapeutic alliance. Future research should investigate potential causal mechanisms, seek to determine what underlying constructs the linguistic predictors tap into, and whether these findings replicate for other interventions, outside of Sweden, in larger samples, and for patients with other conditions whom are offered iCBT. CLINICALTRIAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01504191; https://clinicaltrials.gov/ct2/show/NCT01504191 (Archived at Webcite at http://www.webcitation.org/6xWWSEQ22)

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