A Study on Pollution Prediction and Prevention using IoT and Machine Learning

2020 ◽  
Vol 1 (2) ◽  
pp. 1-6
Author(s):  
Shamik Kumar Roy ◽  
Sahitya Mondal
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Air Pollution ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Main Concern ◽  
Machine Learning Algorithm ◽  
Global Issue ◽  
Prediction And Prevention ◽  
The World

Climate change and Environmental Hazards has been burning issues all around the world. Air Pollution is a major contribution to the Environmental Pollution. Using Big Data and machine learning algorithm to formulate a solution to this burning global issue with an idea that applies techniques of IoT (Internet of Things) and Data Analytics to predict and prevent air pollution substantially. In this paper the main concern is to judge different works which are related to the air pollution and prevention mechanism which will definitely help the researchers for this domain.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Android-Based Skin Cancer Recognition System Using Convolutional Neural Network

2021 ◽  
pp. 59-85
Author(s):  
Sercan Demirci ◽  
Durmuş Özkan Şahin ◽  
Ibrahim Halil Toprak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Early Diagnosis ◽  
Learning Algorithm ◽  
Recognition System ◽  
Machine Learning Algorithm ◽  
The Sun ◽  
Human Beings ◽  
The World

Skin cancer, which is one of the most common types of cancer in the world, is a malignant growth seen on the skin due to various reasons. There was an increase in the number of the cases of skin cancer nearly 200% between 2004-2009. Since the ozone layer is depleting, harmful rays reflected from the sun cannot be filtered. In this case, the likelihood of skin cancer will increase over the years and pose more risks for human beings. Early diagnosis is very significant as in all types of cancers. In this study, a mobile application is developed in order to detect whether the skin spots photographed by using the machine learning technique for early diagnosis have a suspicion of skin cancer. Thus, an auxiliary decision support system is developed that can be used both by the clinicians and individuals. For cases that are predicted to have a risk higher than a certain rate by the machine learning algorithm, early diagnosis could be initiated for the patients by consulting a physician when the case is considered to have a higher risk by machine learning algorithm.

Plural marking patterns of nouns and their associates in the world’s languages

2020 ◽  
Vol 44 (1) ◽  
pp. 231-269
Author(s):  
Rong Chen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Component Structure ◽  
Universal Distribution ◽  
Two Component ◽  
The World ◽  
Plural Marking

Abstract Plural marking reaches most corners of languages. When a noun occurs with another linguistic element, which is called associate in this paper, plural marking on the two-component structure has four logically possible patterns: doubly unmarked, noun-marked, associate-marked and doubly marked. These four patterns do not distribute homogeneously in the world’s languages, because they are motivated by two competing motivations iconicity and economy. Some patterns are preferred over others, and this preference is consistently found in languages across the world. In other words, there exists a universal distribution of the four plural marking patterns. Furthermore, holding the view that plural marking on associates expresses plurality of nouns, I propose a hypothetical universal which uses the number of pluralized associates to predict plural marking on nouns. A data set collected from a sample of 100 languages is used to test the hypothetical universal, by employing the machine learning algorithm logistic regression.

scholarly journals Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

2018 ◽  
Author(s):  
Sergei Posysaev ◽  
Olga Miroshnichenko ◽  
Matti Alatalo ◽  
Duy Le ◽  
Talat S. Rahman
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Linear Regression ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Mixed Valence ◽  
Oxidation States ◽  
Machine Learning Algorithm ◽  
Binary Oxides ◽  
Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

scholarly journals Motion Detection and Prediction Using Machine Learning Algorithm

2020 ◽  
pp. 220-226
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
21St Century ◽  
Motion Detection ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Processing Data

Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared.

scholarly journals Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

2018 ◽  
Author(s):  
Sergei Posysaev ◽  
Olga Miroshnichenko ◽  
Matti Alatalo ◽  
Duy Le ◽  
Talat S. Rahman
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Linear Regression ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Mixed Valence ◽  
Oxidation States ◽  
Machine Learning Algorithm ◽  
Binary Oxides ◽  
Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

scholarly journals Discrimination of SARS-Cov 2 and arboviruses (DENV, ZIKV and CHIKV) clinical features using machine learning techniques: a fast and inexpensive clinical screening for countries simultaneously affected by both diseases

2021 ◽  
Author(s):  
João Daniel S. Castro
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Clinical Screening ◽  
Learning Techniques ◽  
The World ◽  
Area Under Roc Curve

AbstractSARS-Cov-2 (Covid-19) has spread rapidly throughout the world, and especially in tropical countries already affected by outbreaks of arboviruses, such as Dengue, Zika and Chikungunya, and may lead these locations to a collapse of health systems. Thus, the present work aims to develop a methodology using a machine learning algorithm (Support Vector Machine) for the prediction and discrimination of patients affected by Covid-19 and arboviruses (DENV, ZIKV and CHIKV). Clinical data from 204 patients with both Covid-19 and arboviruses obtained from 23 scientific articles and 1 dataset were used. The developed model was able to predict 93.1% of Covid-19 cases and 82.1% of arbovirus cases, with an accuracy of 89.1% and Area under Roc Curve of 95.6%, proving to be effective in prediction and possible screening of these patients, especially those affected by Covid-19, allowing early isolation.

scholarly journals Prediction of Heart Stroke using A Novel Framework – PySpark

2021 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Chitluri Sai Harish B ◽  
G gnana krishna vamsi ◽  
G jaya phani akhil ◽  
J n v hari sravan ◽  
V mounika chowdary
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Random Forest Algorithm ◽  
The World

Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.

scholarly journals Reconstruction of daily gridded snow water equivalent product for the Pan-Arctic region based on a ridge regression machine learning approach

2021 ◽  
Author(s):  
Donghang Shao ◽  
Hongyi Li ◽  
Jian Wang ◽  
Xiaohua Hao ◽  
Tao Che ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Regression Model ◽  
Spatial Resolution ◽  
Ridge Regression ◽  
Snow Water Equivalent ◽  
Learning Algorithm ◽  
Arctic Region ◽  
Machine Learning Algorithm ◽  
Snow Water

Abstract. Snow water equivalent is an important parameter of the surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing snow water equivalent products. In the Pan-Arctic region, the existing snow water equivalent products are limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of snow water equivalent data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing snow water equivalent (SWE) products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare snow water equivalent products on a global scale. We evaluated the accuracy of the RRM SWE product using Global Historical Climatology Network (GHCN) data and Russian snow survey data. The MAE, RMSE, R, and R2; between the RRM SWE products and observed snow water equivalents are 0.24, 30.29 mm, 0.87, and 0.76, respectively. The accuracy of the RRM SWE dataset is improved by 24 %, 25 %, 32 %, 7 %, and 10 % compared with the original AMSR-E/AMSR2 snow water equivalent dataset, ERA-Interim SWE dataset, Global Land Data Assimilation System (GLDAS) SWE dataset, GlobSnow SWE dataset, and ERA5-land SWE dataset, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely too much on an independent snow water equivalent product, it makes full use of the advantages of each snow water equivalent dataset, and it considers the altitude factor. The average MAE of RRM SWE product at different altitude intervals is 0.24 and the average RMSE is 23.55 mm, this method has good stability, it is extremely suitable for the production of snow datasets with large spatial scales, and it can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate snow water equivalent data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from the ‘A Big Earth Data Platform for Three Poles’ (http://dx.doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

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