scholarly journals Air Quality Prediction based on Supervised Machine Learning Methods

2019 ◽  
Vol 8 (9S4) ◽  
pp. 206-212
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Quality Prediction ◽  
Validation Data ◽  
Air Quality Prediction ◽  
Learning Techniques

Generally, Air pollution alludes to the issue of toxins into the air that are harmful to human well being and the entire planet. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Subsequently, air quality assessment and prediction has turned into a significant research zone. The aim is to investigate machine learning based techniques for air quality prediction. The air quality dataset is preprocessed with respect to univariate analysis, bi-variate and multi-variate analysis, missing value treatments, data validation, data cleaning/preparing. Then, air quality is predicted using supervised machine learning techniques like Logistic Regression, Random Forest, K-Nearest Neighbors, Decision Tree and Support Vector Machines. The performance of various machine learning algorithms is compared with respect to Precision, Recall and F1 Score. It is found that Decision Tree algorithm works well for predicting air quality. This application can help the meteorological Department in predicting air quality. In future, this work can be optimized by applying Artificial Intelligence techniques.

scholarly journals Prediction and Analysis of Pollutant using Supervised Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 50-54
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Decision Tree ◽  
Prediction Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Air Quality Prediction

Air is the most essential natural resource for the survival of humans, animals, and plants on the planet. Air is polluted due to the burning of fuels, exhaust gases from factories and industries, and mining operations. Now, air pollution becomes the most dangerous pollution that humanity ever faced. This causes many health effects on humans like respiratory, lung, and skin diseases, which also causes effects on plants, and animals to survive. Hence, air quality prediction and evaluation as becoming an important research area. In this paper, a machine learning-based prediction model is constructed for air quality forecasting. This model will help us to find the major pollutant present in the location along with the causes and sources of that particular pollutant. Air Quality Index value for India is used to predict air quality. The data is collected from various places throughout India so that the collected data is preprocessed to recover from null values, missing values, and duplicate values. The dataset is trained and tested with various machine learning algorithms like Logistic Regression, Naïve Bayes Classification, Random Forest, Support Vector Machine, K Nearest Neighbor, and Decision Tree algorithm in order to find the performance measurement of the above-mentioned algorithms. From this, the prediction model is constructed using the Decision Tree algorithm to predict the air quality, because it provides the best and highest accuracy of 100%. The machine learning-based air quality prediction model helps India meteorological department in predicting the future of air quality, and its status and depends on that they can take action.

scholarly journals Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Syed Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Farm Machinery ◽  
Learning Techniques

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Machine Learning Frameworks in Cancer Detection

2021 ◽  
Vol 297 ◽  
pp. 01073
Author(s):  
Sabyasachi Pramanik ◽  
K. Martin Sagayam ◽  
Om Prakash Jena
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Cancer Development ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Fact Finding ◽  
Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

scholarly journals Framework for Providing Security in Private Cloud using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7641-7645
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithms ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cyber Attack ◽  
Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

scholarly journals A Classification Approach for Predicting COVID-19 Patient Survival Outcome with Machine Learning Techniques

2020 ◽  
Author(s):  
Abdulhameed Ado Osi ◽  
Hussaini Garba Dikko ◽  
Mannir Abdu ◽  
Auwalu Ibrahim ◽  
Lawan Adamu Isma'il ◽  
...  
Keyword(s):  
Machine Learning ◽  
Survival Outcome ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
P Value ◽  
Kappa Index ◽  
Quality Health Care ◽  
Linear Discriminant ◽  
Learning Techniques

COVID-19 is an infectious disease discovered after the outbreak began in Wuhan, China, in December 2019. COVID-19 is still becoming an increasing global threat to public health. The virus has been escalated to many countries across the globe. This paper analyzed and compared the performance of three different supervised machine learning techniques; Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) on COVID-19 dataset. The best level of accuracy between these three algorithms was determined by comparison of some metrics for assessing predictive performance such as accuracy, sensitivity, specificity, F-score, Kappa index, and ROC. From the analysis results, RF was found to be the best algorithm with 100% prediction accuracy in comparison with LDA and SVM with 95.2% and 90.9% respectively. Our analysis shows that out of these three classification models RF predicts COVID-19 patient's survival outcome with the highest accuracy. Chi-square test reveals that all the seven features except sex were significantly correlated with the COVID-19 patient's outcome (P-value < 0.005). Therefore, RF was recommended for COVID-19 patient outcome prediction that will help in early identification of possible sensitive cases for quick provision of quality health care, support and supervision.

scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

scholarly journals An Innovative Method for Predicting and Classifying Inadequate Accuracy in Heart Disease by Using Decision Tree with K-Nearest Neighbors Algorithm

2021 ◽  
Vol 36 (1) ◽  
pp. 609-615
Author(s):  
Mandhapati Rajesh ◽  
Dr.K. Malathi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Learning Techniques

Aim: Predicting the Heartdiseases using medical parameters of cardiac patients to get a good accuracy rate using machine learning methods like innovative Decision Tree (DT) algorithm. Materials and Methods: Supervised Machine learning Techniques with innovative Decision Tree (N = 20) and K Nearest Neighbour (KNN) (N = 20) are performed with five different datasets at each time to record five samples. Results: The Decision Tree is used to predict heart disease with the help of various medical conditions, the accuracy is achieved for DT is 98% and KNN is 72.2%. The two algorithms Decision Tree and KNN are statistically insignificant (=.737) with the independent sample T-Test value (p<0.005) with a confidence level of 95%. Conclusion: Prediction and classification of heart disease significantly seem to be better in DT than KNN.

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