Detection and prediction of pluvial flood using machine learning techniques

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
K.A. Oladapo ◽  
F.Y. Ayankoya ◽  
F.A. Adekunle ◽  
S.A. Idowu
Keyword(s):  
Machine Learning ◽  
Flood Risk ◽  
Scientific Method ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Machine Learning Techniques ◽  
Emergency Situations ◽  
Learning Techniques ◽  
Proposed Model ◽  
Flood Detection

The periodical occurrence of emergency situations represents an important issue for mankind. Over the years, the world at large has experienced multiple misadventures both natural and man-made. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. Handling flood risk with the intention of safety and comfort of the citizens as well as saving their environment is one of the major responsibilities of the leadership in each country especially in flood prone areas. Machine learning predictive analytic applications can improve the risk management. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification to appraise the performance of the machine learning algorithm on pluvial flood conditioning variables.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

scholarly journals An Advance Resource Reservation Approach in a Cloud Database Environment

2017 ◽  
Author(s):  
Vinicius Da S. Segalin ◽  
Carina F. Dorneles ◽  
Mario A. R. Dantas
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Resource Reservation ◽  
Machine Learning Techniques ◽  
Advance Reservation ◽  
Running Time ◽  
Knowing That ◽  
Learning Techniques ◽  
Proposed Model ◽  
Cloud Database

AA well-known challenge with long running time queries in database environments is how much time a query will take to execute. This prediction is relevant for several reasons. For instance, by knowing that a query will take longer to execute than desired, one resource reservation mechanism can be performed, which means reserving more resources in order to execute this query in a shorter time in a future request. In this research work, it is presented a proposal in which the use of an advance reservation mechanism in a cloud database environment, considering machine learning techniques, provides resource recommendation. The proposed model is presented, in addition to some experiments that evaluate benefits and the efficiency of this enhanced proposal.

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

2020 ◽  
pp. 1314-1330 ◽  
Author(s):  
Mohamed Elhadi Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Texture Feature ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Curve Shape ◽  
Distance Curve ◽  
Shape Signature ◽  
Learning Techniques

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.

In-game behaviour analysis of football players using machine learning techniques based on player statistics

2020 ◽  
pp. 174795412095976
Author(s):  
Abraham García-Aliaga ◽  
Moisés Marquina ◽  
Javier Coterón ◽  
Asier Rodríguez-González ◽  
Sergio Luengo-Sánchez
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Football Players ◽  
Machine Learning Algorithm ◽  
Reduction Techniques ◽  
Learning Techniques ◽  
Dimensionality Reduction Techniques ◽  
Player Positions

The purpose of this research was to determine the on-field playing positions of a group of football players based on their technical-tactical behaviour using machine learning algorithms. Each player was characterized according to a set of 52 non-spatiotemporal descriptors including offensive, defensive and build-up variables that were computed from OPTA’s on-ball event records of the matches for 18 national leagues between the 2012 and 2019 seasons. To test whether positions could be identified from the statistical performance of the players, the dimensionality reduction techniques were used. To better understand the differences between the player positions, the most discriminatory variables for each group were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. From the combination of both techniques, we obtained useful conclusions to enhance the performance of players and to identify positions on the field. The study demonstrates the suitability and potential of artificial intelligence to characterize players' positions according to their technical-tactical behaviour, providing valuable information to the professionals of this sport.

scholarly journals Machine learning algorithms: a background artifact

2017 ◽  
Vol 7 (1.1) ◽  
pp. 143 ◽  
Author(s):  
J. Deepika ◽  
T. Senthil ◽  
C. Rajan ◽  
A. Surendar
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Knowledge Discovery ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Intelligence ◽  
Machine Learning Algorithm ◽  
Human History ◽  
Learning Techniques

With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.  

scholarly journals Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques

2021 ◽  
Vol 11 (5) ◽  
pp. 343
Author(s):  
Fabiana Tezza ◽  
Giulia Lorenzoni ◽  
Danila Azzolina ◽  
Sofia Barbar ◽  
Lucia Anna Carmela Leone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hospital Mortality ◽  
Learning Algorithm ◽  
Vital Signs ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Techniques

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the “Ospedali Riuniti Padova Sud” COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78–0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.

scholarly journals An Ensemble-Based Malware Detection Model Using Minimum Feature Set

MENDEL ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Zelinka ◽  
Eslam Amer
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model ◽  
Learning Techniques ◽  
Proposed Model ◽  
Positive Rate ◽  
Minimum Number

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

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