Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak

Abstract Public health-related misinformation spread rapidly in online networks, particularly, in social media during any disease outbreak. Misinformation of coronavirus disease 2019 (COVID-19) drug protocol or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities are utilizing several surveillance tools to detect, and slow down the rapid misinformation spread online, still millions of misinformation are found online. However, there is no currently available tool for receiving real-time misinformation notification during online health or COVID-19 related inquiries. Our proposed novel combinational approach, where we have integrated machine learning techniques with novel search engine misinformation notifier extension (SEMiNExt), helps to understand which news or information is from unreliable sources in real-time. The extension filters the search results and shows notification beforehand; it is a new and unexplored approach to prevent the spread of misinformation. To validate the user query, SEMiNExt transfers the data to a machine learning algorithm or classifier which predicts the authenticity of the search inquiry and sends a binary decision as either true or false. The results show that the supervised learning algorithm works best when 80% of the data set have been used for training purpose. Also, 10-fold cross-validation demonstrate a maximum accuracy and F1-score of 84.3% and 84.1% respectively for the Decision Tree classifier while the K-nearest-neighbor (KNN) algorithm shows the least performance. The SEMiNExt approach has introduced the possibility to improve online health communication system by showing misinformation notifications in real-time which enables safer web-based searching while inquiring on health-related issues.

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Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Healthcare ◽

10.3390/healthcare9020156 ◽

2021 ◽

Vol 9 (2) ◽

pp. 156

Author(s):

Abdullah Bin Shams ◽

Ehsanul Hoque Apu ◽

Ashiqur Rahman ◽

Md. Mohsin Sarker Raihan ◽

Nazeeba Siddika ◽

...

Keyword(s):

Public Health ◽

Machine Learning ◽

Real Time ◽

Search Engine ◽

Language Processing ◽

Web Search ◽

Training Data ◽

Small Data ◽

Web Search Engine ◽

User Query

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

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.

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Application of Machine Learning for Lithology-on-Bit Prediction using Drilling Data in Real-Time

10.2118/206622-ms ◽

2021 ◽

Author(s):

Temirlan Zhekenov ◽

Artem Nechaev ◽

Kamilla Chettykbayeva ◽

Alexey Zinovyev ◽

German Sardarov ◽

...

Keyword(s):

Machine Learning ◽

Data Quality ◽

Real Time ◽

Hybrid Modeling ◽

Data Set ◽

Drilling Parameters ◽

Drilling Data ◽

Stable Solutions ◽

Mud Logging

SUMMARY Researchers base their analysis on basic drilling parameters obtained during mud logging and demonstrate impressive results. However, due to limitations imposed by data quality often present during drilling, those solutions often tend to lose their stability and high levels of predictivity. In this work, the concept of hybrid modeling was introduced which allows to integrate the analytical correlations with algorithms of machine learning for obtaining stable solutions consistent from one data set to another.

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Real-time gaze estimation via pupil center tracking

Paladyn Journal of Behavioral Robotics ◽

10.1515/pjbr-2018-0002 ◽

2018 ◽

Vol 9 (1) ◽

pp. 6-18 ◽

Cited By ~ 2

Author(s):

Dario Cazzato ◽

Fabio Dominio ◽

Roberto Manduchi ◽

Silvia M. Castro

Keyword(s):

Real Time ◽

Learning Algorithm ◽

Natural Environments ◽

Gaze Estimation ◽

Head Pose ◽

Data Set ◽

Gaze Tracking ◽

Illumination Changes ◽

Wide Range ◽

Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

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Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5808.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 2758-2762

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Storage ◽

Clinical Data ◽

Disease Risk ◽

Learning Algorithm ◽

Information Storage ◽

Support Vector ◽

Machine Learning Algorithm ◽

Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

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Low-cost real-time non-intrusive appliance identification and controlling through machine learning algorithm

2018 International Symposium on Consumer Technologies (ISCT) ◽

10.1109/isce.2018.8408911 ◽

2018 ◽

Cited By ~ 3

Author(s):

Sheharyar Khan ◽

Ahmad Farhan Latif ◽

Sarmad Sohaib

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Algorithm ◽

Low Cost ◽

Machine Learning Algorithm

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DETERMINING LEARNING SUCCESS of kNN ALGORITHM on ZOO DATASET

Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences ◽

10.38065/euroasiaorg.762 ◽

2021 ◽

Vol 8 (18) ◽

pp. 78-82

Author(s):

Ahmet ÇELİK

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Learning Method ◽

Data Set ◽

Intelligent Machines ◽

Weight Parameter ◽

Living Things ◽

Learning Success ◽

Similarities And Differences ◽

Two Parameters

People learn by examining, observing and researching their environment. They actually gains experience from what they have learned. By using the experience they have gained, they can adapt to the new situation they encounter and make decisions. People always make decisions by comparing their previous knowledge while describing objects and classifying them. Similarities and differences to previously learned objects are very effective in decision making. It has been shown in the studies that the experiential learning method can also be used on machines. Intelligent machines and devices that use machine learning methods in their structure are widely used in many areas. Machine learning can be performed using different algorithms. These algorithms use the attributes of the objects in the data set when making decisions. Similarities and differences in the attributes of objects are obtained by comparing them with previous experiences. As a result of the comparison, a decision is made and predictions are made about the classes of the objects. In this study, kNN machine learning algorithm, which is a supervised learning method, was used on the Zoo dataset. In this data set, there are attributes of common living things. By using these attributes, the classes of living things in the data set are determined. The “k” neighbor value and weight parameter selected in the kNN algorithm affect the learning success. In this study, the effect of two parameters used in the kNN algorithm on learning success is shown. According to the results obtained, the "k=1" neighbor value and the "Distance Weight" parameter were selected and the highest success result was obtained.

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Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm

Waste Management ◽

10.4018/978-1-7998-1210-4.ch029 ◽

2020 ◽

pp. 609-623

Author(s):

Arun Kumar Beerala ◽

Gobinath R. ◽

Shyamala G. ◽

Siribommala Manvitha

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Training Data ◽

Machine Learning Techniques ◽

Statistical Tool ◽

Data Set ◽

Water Quality Prediction ◽

Living Things ◽

Sampling Locations ◽

Different Seasons

Water is the most valuable natural resource for all living things and the ecosystem. The quality of groundwater is changed due to change in ecosystem, industrialisation, and urbanisation, etc. In the study, 60 samples were taken and analysed for various physio-chemical parameters. The sampling locations were located using global positioning system (GPS) and were taken for two consecutive years for two different seasons, monsoon (Nov-Dec) and post-monsoon (Jan-Mar). In 2016-2017 and 2017-2018 pH, EC, and TDS were obtained in the field. Hardness and Chloride are determined using titration method. Nitrate and Sulphate were determined using Spectrophotometer. Machine learning techniques were used to train the data set and to predict the unknown values. The dominant elements of groundwater are as follows: Ca2, Mg2 for cation and Cl-, SO42, NO3− for anions. The regression value for the training data set was found to be 0.90596, and for the entire network, it was found to be 0.81729. The best performance was observed as 0.0022605 at epoch 223.

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