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 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 Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Utkarsh Saxena ◽  
Soumen Moulik ◽  
Soumya Ranjan Nayak ◽  
Thomas Hanne ◽  
Diptendu Sinha Roy
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Support Vector ◽  
Human Beings ◽  
Medical Terminology ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Health Parameters

We attempt to predict the accidental fall of human beings due to sudden abnormal changes in their health parameters such as blood pressure, heart rate, and sugar level. In medical terminology, this problem is known as Syncope. The primary motivation is to prevent such falls by predicting abnormal changes in these health parameters that might trigger a sudden fall. We apply various machine learning algorithms such as logistic regression, a decision tree classifier, a random forest classifier, K-Nearest Neighbours (KNN), a support vector machine, and a naive Bayes classifier on a relevant dataset and verify our results with the cross-validation method. We observe that the KNN algorithm provides the best accuracy in predicting such a fall. However, the accuracy results of some other algorithms are also very close. Thus, we move one step further and propose an ensemble model, Majority Voting, which aggregates the prediction results of multiple machine learning algorithms and finally indicates the probability of a fall that corresponds to a particular human being. The proposed ensemble algorithm yields 87.42% accuracy, which is greater than the accuracy provided by the KNN algorithm.

scholarly journals Detailed Analysis of Intrusion Detection using Machine Learning Algorithms

2020 ◽  
Vol 9 (1) ◽  
pp. 1894-1899 ◽  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Internet Users ◽  
Tree Classifier ◽  
Challenging Tasks

The number of internet users has increased exponentially over the years and so have increased intrusive activities significantly. To detect an intrusion attack in a system connected over a network is one of the most challenging tasks in today’s world. A significant number of techniques have been developed which are based on machine learning approaches to detect these intrusion attacks. Even though these techniques are good, they are not good enough to detect all kinds of attacks. In this paper, the analysis of different machine learning algorithm will be performed on the NSL-KDD dataset with pre-processing steps like One-hot encoding, feature selection and random sampling to use in different machine learning models to find the best performing model to detect these attacks. The attacks are from the datasets are classified into four types of attacks: Probe, DoS, U2R, R2L while the non- attack is the Normal. The dataset is in two parts: KDD-Train and KDD-Test. The dataset is trained and tested to find accuracy and understand the performance of different machine learning algorithms and compare them. The Machine Learning algorithms used are Naive Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, KNeighbours Classifier, Logistic Regression, SVM Classifier, Voting Classifier. These techniques are compared according to their capability to detect the attacks. This comparison will help to find the algorithm which would work the best to detect different kinds of intrusion attacks.

scholarly journals A Study of Machine Learning Algorithms for DDoS Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 174-178
Author(s):  
Sheikh Shehzad Ahmed
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Random Forest Classifier ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Ddos Attacks ◽  
Decision Tree Classifier ◽  
Tree Classifier

The Internet is used practically everywhere in today's digital environment. With the increased use of the Internet comes an increase in the number of threats. DDoS attacks are one of the most popular types of cyber-attacks nowadays. With the fast advancement of technology, the harm caused by DDoS attacks has grown increasingly severe. Because DDoS attacks may readily modify the ports/protocols utilized or how they function, the basic features of these attacks must be examined. Machine learning approaches have also been used extensively in intrusion detection research. Still, it is unclear what features are applicable and which approach would be better suited for detection. With this in mind, the research presents a machine learning-based DDoS attack detection approach. To train the attack detection model, we employ four Machine Learning algorithms: Decision Tree classifier (ID3), k-Nearest Neighbors (k-NN), Logistic Regression, and Random Forest classifier. The results of our experiments show that the Random Forest classifier is more accurate in recognizing attacks.

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 Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 30-40
Author(s):  
Pankaj Bhowmik ◽  
◽  
Pulak Chandra Bhowmik ◽  
U. A. Md. Ehsan Ali ◽  
Md. Sohrawordi
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Health Risks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Model Assessment ◽  
Decision Tree Classifier ◽  
Chi Square ◽  
Fetal Health ◽  
Tree Classifier

A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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