scholarly journals Potential distribution of clandestine graves in Guerrero using geospatial analysis and modelling

10.29007/48dt ◽  
2019 ◽  
Author(s):  
José Luis Silván Cárdenas ◽  
Ana Josseline Alegre Mondragon ◽  
Karime Gonzalez-Zuccolotto
Keyword(s):  
Machine Learning ◽  
Potential Distribution ◽  
Geospatial Analysis ◽  
Learning Approach ◽  
Time Cost ◽  
Machine Learning Approach ◽  
The World ◽  
Clandestine Graves

Searching clandestine graves is a huge task being conducted by many people around the world. In Mexico, this activity has steadily grown since the disappearance of the 43 students from Ayotzinapa, Gro. leading to the discovery of over a hundred of clandestine graves in the vicinity of Iguala, Gro. In order to facilitate extensive searches, a map of the potential distribution of clandestine graves would be valuable as it can reduce time, cost and effort paid by search brigades. This paper introduces the concept of clandestine space, shows its relation with known grave locations and uses it to map the potential distribution of clandestine graves in Guerrero by means of a machine learning approach.

scholarly journals Outlier detection in WSN by entropy based machine learning approach

2020 ◽  
Vol 20 (3) ◽  
pp. 1435
Author(s):  
Manmohan Singh Yadav ◽  
Shish Ahamad
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Random Subspace ◽  
Environmental Disasters ◽  
Machine Learning Approach ◽  
The World ◽  
Prediction Capability

<p>Environmental disasters like flooding, earthquake etc. causes catastrophic effects all over the world. WSN based techniques have become popular in susceptibility modelling of such disaster due to their greater strength and efficiency in the prediction of such threats. This paper demonstrates the machine learning-based approach to predict outlier in sensor data with bagging, boosting, random subspace, SVM and KNN based frameworks for outlier prediction using a WSN data. First of all database is pre processed with 14 sensor motes with presence of outlier due to intrusion. Subsequently segmented database is created from sensor pairs. Finally, the data entropy is calculated and used as a feature to determine the presence of outlier used different approach. Results show that the KNN model has the highest prediction capability for outlier assessment.</p>

scholarly journals Rain Prediction Using Rule-Based Machine Learning Approach

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Muchamad Taufiq Anwar ◽  
Saptono Nugrohadi ◽  
Vita Tantriyati ◽  
Vikky Aprelia Windarni
Keyword(s):  
Machine Learning ◽  
Natural Disasters ◽  
Human Life ◽  
Meteorological Data ◽  
Training Model ◽  
Weather Data ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
The World

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.

A Comprehensive Review on Online News Popularity Prediction using Machine Learning Approach

2019 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
Priyanka Rathord ◽  
Dr. Anurag Jain ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online News ◽  
Machine Learning Techniques ◽  
News Article ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The World ◽  
Popularity Prediction

With the help of Internet, the online news can be instantly spread around the world. Most of peoples now have the habit of reading and sharing news online, for instance, using social media like Twitter and Facebook. Typically, the news popularity can be indicated by the number of reads, likes or shares. For the online news stake holders such as content providers or advertisers, it’s very valuable if the popularity of the news articles can be accurately predicted prior to the publication. Thus, it is interesting and meaningful to use the machine learning techniques to predict the popularity of online news articles. Various works have been done in prediction of online news popularity. Popularity of news depends upon various features like sharing of online news on social media, comments of visitors for news, likes for news articles etc. It is necessary to know what makes one online news article more popular than another article. Unpopular articles need to get optimize for further popularity. In this paper, different methodologies are analyzed which predict the popularity of online news articles. These methodologies are compared, their parameters are considered and improvements are suggested. The proposed methodology describes online news popularity predicting system.

scholarly journals Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

2020 ◽  
Vol 2 (3) ◽  
pp. 172-177
Author(s):  
Shawni Dutta ◽  
◽  
Samir Kumar Bandyopadhyay ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Original Data ◽  
World Health ◽  
Learning Approach ◽  
Human Beings ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
The World

Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.

Short term Forecast of the COVID-19 Epidemic in top 15 Affected Countries in the World using ARIMA Model with Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Pavan Kumar ◽  
Ranjit Sah ◽  
Alfonso J. Rodriguez-Morales ◽  
Himangshu Kalita Jr ◽  
Akshaya Srikanth Bhagavathula ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Learning Approach ◽  
Average Model ◽  
Machine Learning Approach ◽  
The World ◽  
Moving Average Model ◽  
Auto Regressive

BACKGROUND The COVID-19 pendemic reached more than 200 countries, which was recognized during December-19 from CHINA and affected more than 28 lakh people on date April 26, 2020 (data source:Johns Hopkins Corona Virus Resource Center). OBJECTIVE We here predicted some trajectories of COVID-19 in the coming days (until July 2, 2020) using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA). METHODS Here we have used the Auto-Regressive Integrated Moving Average Model (ARIMA). Mathematical approaches are widely used to infer critical epidemiological transitions and parameters of COVID-19. Methods such as epidemic curve fitting, surveillance data during the early transmission R0, and other epidemic models are frequently applied to generate forecasts of COVID-19 pandemic across the world. RESULTS Our analysis predicted very frightening outcomes, which defines to worsen the conditions in Iran, entire Europe, especially Italy, Spain, and France. While South Korea, after the initial blast, has come to stability, the same goes for the COVID-19 origin country China with more positive recovery cases and confirm to remain stable. The United States of America (USA) is come as a surprise and going to become the epicenter for new cases during the mid-April 2020. CONCLUSIONS Based on our predictions, public health officials should tailor aggressive interventions to grasp the power exponential growth, and rapid infection control measures at hospital levels are urgently needed to curtail the COVID-19 pandemic. This study analyzed at global level and extracted data upon Machine Learning approach using Artificial intelligence techniques for top 10% or 20 countries.

scholarly journals Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach

2020 ◽  
Author(s):  
Mengyuan Li ◽  
Zhilan Zhang ◽  
Shanmei Jiang ◽  
Qian Liu ◽  
Canping Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Control Measures ◽  
Mitigation Measures ◽  
Learning Approach ◽  
Foreseeable Future ◽  
Stringent Control ◽  
Machine Learning Approach ◽  
The World ◽  
Death Cases

AbstractBackgroundAlthough COVID-19 has been well controlled in China, it is rapidly spreading outside the country and may have catastrophic results globally without implementation of necessary mitigation measures. Because the COVID-19 outbreak has made comprehensive and profound impacts on the world, an accurate prediction of its epidemic trend is significant. Although many studies have predicted the COVID-19 epidemic trend, most have used early-stage data and focused on Chinese cases.MethodsWe first built models to predict daily numbers of cumulative confirmed cases (CCCs), new cases (NCs), and death cases (DCs) of COVID-19 in China based on data from January 20, 2020, to March 1, 2020. Based on these models, we built models to predict the epidemic trend across the world (outside China). We also built models to predict the epidemic trend in Italy, Spain, Germany, France, UK, and USA where COVID-19 is rapidly spreading.ResultsThe COVID-19 outbreak will have peaked on February 22, 2020, in China and will peak on May 22, 2020, across the world. It will be basically under control in early April 2020 in China and late August 2020 across the world. The total number of COVID-19 cases will reach around 89,000 in China and 6,126,000 across the world during the epidemic. Around 4,000 and 290,000 people will die of COVID-19 in China and across the world, respectively. The COVID-19 outbreak will have peaked recently in Italy and will peak in Spain, Germany, France, UK, and USA within two weeks.ConclusionThe COVID-19 outbreak is controllable in the foreseeable future if comprehensive and stringent control measures are taken.

scholarly journals Securing IoT Environment using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 870-873
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Learning Approaches ◽  
Digital Devices ◽  
Learning Techniques ◽  
Security Challenges ◽  
Machine Learning Approach ◽  
The World ◽  
Iot Devices

One of the most dynamic and invigorate advancement in information technology is advent of Internet of Things (IoT). IoT is territory of interrelated computational and digital devices with intelligence to transfer data. Along with swift expansion of IoT devices through the world security of things is not at expected height. As a consequence of ubiquitous nature of IoT environment most of the user do not have expertise or willingness to secure devices by themselves. Machine learning approach could be very effective to address security challenges in IoT environment. In recent related papers, the researcher have used machine learning techniques, approaches or methods for securing things in IoT environment. This paper attempts to review the related research on machine learning approaches to secure IoT devices

scholarly journals Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis

2021 ◽  
Author(s):  
José Solenir L. Figuerêdo ◽  
Renata F. Araújo-Calumby ◽  
Rodrigo T. Calumby
Keyword(s):  
Machine Learning ◽  
Relative Risk ◽  
Oxygen Saturation ◽  
Large Scale ◽  
Risk Estimation ◽  
Descriptive Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
The World

This work proposes a machine learning approach to predict the prognosis of patients with COVID-19. To assist in this task, a descriptive analysis and relative risk estimation were performed. In addition, the importance of variables in the perspective of machine learning algorithms was computed and discussed. The experiments were performed with large-scale nation-wide dataset from Brazil. The results reveal that the model developed was able to predict the patient's prognosis with an AUC = 0.8382. The results also point out that the chance of death is greater among patients over 60 years old, with comorbidities, and symptoms such as dyspnea and Oxygen saturation (< 95%), confirming results observed in other regions of the world.

scholarly journals The Effect of Chemical Parameters on Water Quality Index in Machine Learning Studies: A Meta-Analysis

2021 ◽  
Vol 2084 (1) ◽  
pp. 012007
Author(s):  
Nur Hanisah Abdul Malek ◽  
Wan Fairos Wan Yaacob ◽  
Syerina Azlin Md Nasir ◽  
Norshahida Shaadan
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Meta Analysis ◽  
Learning Approach ◽  
Heterogeneity Index ◽  
Machine Learning Approach ◽  
The World ◽  
Chi Squared

Abstract According to the World Health Organization (WHO), approximately 2 billion people worldwide use drinking water sources that are contaminated with faeces. This is a serious issue since contaminated water may lead to certain waterborne diseases such as cholera, hepatitis A, dysentery, jaundice, and typhoid fever. Therefore, many researchers around the world are interested in studying the water quality. One of the most commonly used approaches is by using machine learning. Machine learning approach has grabbed the interest of many researchers since the last several years due to its power to compute complicated mathematical computations on big data analysis. Therefore, this study explored the correlation between different water quality parameters and Water Quality Index (WQI) in water quality studies that used machine learning by using a meta-analysis approach. This study used estimated variance, heterogeneity index, Chi-squared heterogeneity test and the random effects model. Based on the selected articles, pH, dissolved oxygen (DO) and biochemical oxygen demand (BOD) are the parameters commonly used in water quality studies which use a machine learning approach. This study found that pH is the best chemical factor which greatly affects the Water Quality Index since it has the highest mean correlation and lowest estimated variance due to sampling error. The result showed that the correlation between pH and WQI are heterogeneous across studies based on the Chi-squared of heterogeneity, Q and heterogeneity index, I2 value. The 95% confidence interval of effect summary supports the findings that the correlation of pH is different among the studies. This study also found that there is no evidence of publication bias using Egger and Begg’s test. Therefore, in order to ensure good water quality supply, the local authorities and government agencies should give more attention to this parameter since pH of water plays an important role in determining the water quality status.

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