scholarly journals A Machine Learning Framework for Olive Farms Profit Prediction

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3461
Author(s):  
Panagiotis Christias ◽  
Mariana Mocanu
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Management Practices ◽  
Learning Algorithms ◽  
Real Data ◽  
Olive Tree ◽  
Climatic Conditions ◽  
Machine Learning Algorithms ◽  
Related Factors ◽  
Learning Framework

Agricultural systems are constantly stressed due to higher demands for products. Consequently, water resources consumed on irrigation are increased. In combination with the climatic change, those are major obstacles to maintaining sustainable development, especially in a semi-arid land. This paper presents an end-to-end Machine Learning framework for predicting the potential profit from olive farms. The objective is to estimate the optimal economic gain while preserving water resources on irrigation by considering various related factors such as climatic conditions, crop management practices, soil characteristics, and crop yield. The case study focuses on olive tree farms located on the Hellenic Island of Crete. Real data from the farms and the weather in the area will be used. The target is to build a framework that will preprocess input data, compare the results among a group of Machine Learning algorithms and propose the best-predicted value of economic profit. Various aspects during this process will be thoroughly examined such as the bias-variance tradeoff and the problem of overfitting, data transforms, feature engineering and selection, ensemble methods as well as pursuing optimal resampling towards better model accuracy. Results indicated that through data preprocessing and resampling, Machine Learning algorithms performance is enhanced. Ultimately, prediction accuracy and reliability are greatly improved compared to algorithms’ performances without the framework’s operation.

scholarly journals A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Absalom E. Ezugwu ◽  
Ibrahim Abaker Targio Hashem ◽  
Olaide N. Oyelade ◽  
Mubarak Almutari ◽  
Mohammed A. Al-Garadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Framework ◽  
National Healthcare ◽  
Machine Learning Approach ◽  
The Internet Of Things ◽  
Analyze Data

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

scholarly journals FIRE BEHAVIOR PREDICTION USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 38 (3) ◽  
pp. 343
Author(s):  
Vinícius Barros RODRIGUES ◽  
Fillpe Tamiozzo Pereira TORRES
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Fire Behavior ◽  
Eucalyptus Grandis ◽  
Climatic Conditions ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Fire Intensity ◽  
Ecosystem Structure ◽  
Modeling Analysis

Wildfires can affect ecosystem structure and threaten human lives. Understanding fire behavior and predicting fire activities is a crucial issue to mitigate fire impacts. Machine Learning is currently an important tool for the modeling, analysis, and visualization of environmental data and wildfire events. In this study, we assessed the performance of two machine learning algorithms for modeling and predicting fire intensity, the height of flames, and fire rate of spreading in Eucalyptus urophylla (Myrtaceae, Myrtales) and Eucalyptus grandis (Myrtaceae, Myrtales) plantations spatially located in Viçosa - MG, Brazil. The Random Forest showed to be the best algorithm for fire modeling, with climatic conditions, and moisture of the combustible material being the variables that significantly affect the prediction of fire behavior.

scholarly journals Dairy management practices associated with multi-drug resistant fecal commensals and Salmonella in cull cows: a machine learning approach

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11732
Author(s):  
Pranav S. Pandit ◽  
Deniece R. Williams ◽  
Paul Rossitto ◽  
John M. Adaska ◽  
Richard Pereira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Learning Algorithms ◽  
Multidrug Resistant ◽  
Machine Learning Algorithms ◽  
Commensal Bacteria ◽  
Resistant Bacteria ◽  
Multidrug Resistant Bacteria ◽  
Fecal Shedding ◽  
Tree Classifier

Background Understanding the effects of herd management practices on the prevalence of multidrug-resistant pathogenic Salmonella and commensals Enterococcus spp. and Escherichia coli in dairy cattle is key in reducing antibacterial resistant infections in humans originating from food animals. Our objective was to explore the herd and cow level features associated with the multi-drug resistant, and resistance phenotypes shared between Salmonella, E. coli and Enterococcus spp. using machine learning algorithms. Methods Randomly collected fecal samples from cull dairy cows from six dairy farms in central California were tested for multi-drug resistance phenotypes of Salmonella, E. coli and Enterococcus spp. Using data on herd management practices collected from a questionnaire, we built three machine learning algorithms (decision tree classifier, random forest, and gradient boosting decision trees) to predict the cows shedding multidrug-resistant Salmonella and commensal bacteria. Results The decision tree classifier identified rolling herd average milk production as an important feature for predicting fecal shedding of multi-drug resistance in Salmonella or commensal bacteria. The number of culled animals, monthly culling frequency and percentage, herd size, and proportion of Holstein cows in the herd were found to be influential herd characteristics predicting fecal shedding of multidrug-resistant phenotypes based on random forest models for Salmonella and commensal bacteria. Gradient boosting models showed that higher culling frequency and monthly culling percentages were associated with fecal shedding of multidrug resistant Salmonella or commensal bacteria. In contrast, an overall increase in the number of culled animals on a culling day showed a negative trend with classifying a cow as shedding multidrug-resistant bacteria. Increasing rolling herd average milk production and spring season were positively associated with fecal shedding of multidrug- resistant Salmonella. Only six individual cows were detected sharing tetracycline resistance phenotypes between Salmonella and either of the commensal bacteria. Discussion Percent culled and culling rate reflect the increase in culling over time adjusting for herd size and were associated with shedding multidrug resistant bacteria. In contrast, number culled was negatively associated with shedding multidrug resistant bacteria which may reflect producer decisions to prioritize the culling of otherwise healthy but low-producing cows based on milk or beef prices (with respect to dairy beef), amongst other factors. Using a data-driven suite of machine learning algorithms we identified generalizable and distant associations between antimicrobial resistance in Salmonella and fecal commensal bacteria, that can help develop a producer-friendly and data-informed risk assessment tool to reduce shedding of multidrug-resistant bacteria in cull dairy cows.

scholarly journals Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 143
Author(s):  
Nianjiao Peng ◽  
Xinlei Zhou ◽  
Ben Niu ◽  
Yuanyue Feng
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Publicly Funded ◽  
Health Organizations ◽  
The Sustainable Development ◽  
Performance Predictions ◽  
Public Health Organizations ◽  
Health Related

The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people that addresses health-related needs. However, the fact remains that medical fundraising from crowdfunding sources is relatively low and only a few studies have been conducted regarding this issue. Therefore, the performance predictions and multi-model comparisons of medical crowdfunding have important guiding significance to improve the fundraising rate and promote the sustainable development of medical crowdfunding. Based on the data of 11,771 medical crowdfunding campaigns from a leading donation-based platform called Weibo Philanthropy, machine-learning algorithms were applied. The results demonstrate the potential of ensemble-based machine-learning algorithms in the prediction of medical crowdfunding project fundraising amounts and leave some insights that can be taken into consideration by new researchers and help to produce new management practices.

scholarly journals BOOSTING SEGMENTATION ACCURACY OF THE DEEP LEARNING MODELS BASED ON THE SYNTHETIC DATA GENERATION

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 33-40
Author(s):  
V. V. Danilov ◽  
O. M. Gerget ◽  
D. Y. Kolpashchikov ◽  
N. V. Laptev ◽  
R. A. Manakov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coordinate System ◽  
Learning Algorithms ◽  
Synthetic Data ◽  
Real Data ◽  
Machine Learning Algorithms ◽  
Dice Similarity Coefficient ◽  
Data Generation ◽  
Heart Chamber ◽  
Echocardiographic Images

Abstract. In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterogeneity.

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