scholarly journals A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

2019 ◽  
Vol 9 (2S2) ◽  
pp. 992-998
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Networking Sites ◽  
Online Social Network ◽  
User Profiling ◽  
Machine Learning Technique ◽  
The People ◽  
Learning Technique ◽  
The Government ◽  
Personal Interests

This paper aims to analyse the online social network for reconnaissance of people for finding their potentiality. The work considers one of the famous social networking sites, Twitter, where people express their thoughts and ideas, through which the people in the site knowingly or unknowingly reveal the information about themselves such as personal interests, likes and dislikes. The Machine Learning technique facilitates the work to mine the tweet data of a person to get his/her 360-degree profiling. This profiling is helpful to identify the personality type of a person, which is essential for the Government to identify the people involved in spreading the fake news in Twitter.

Anomaly Detection of Budgetary Allocations Using Machine-Learning-Based Techniques

2021 ◽  
Vol 107 ◽  
pp. 174-181
Author(s):  
Garba Aliyu ◽  
Ibrahim Enesi Umar ◽  
Irunokhai Eric Aghiomesi ◽  
Hassan Jimoh Onawola ◽  
Sandip Rakshit
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Unintended Consequences ◽  
Critical Examination ◽  
Machine Learning Technique ◽  
National Assembly ◽  
Substantial Modification ◽  
Short Period ◽  
Learning Technique ◽  
The Government

In Nigeria, a crucial responsibility of the executive arms of the government is to submit annual budgetary allocations to the national assembly for approval. Due to the diversity and complexity of the budget, the national assembly is mandated to carry out its constitutional duty of scrutinizing the budget to discover irregularity or anomaly, make recommendations, or substantial modification upon what it received. This is very challenging, particularly in Nigeria where there are many different ethnicities and regional, to ensure inclusiveness, the national assembly must carry out its constitutional duty diligently and carefully without fear or favor that often has unintended consequences. This might not be very easy to accomplish within a short period. Thus, this research aims at detecting an anomaly in the budget that will ease the legislative duty thereby facilitating the process of appropriation. The concept of Clustering for Machine learning technique was used for the detection of an anomaly, where the detected ones are noted and indicated for critical examination.

scholarly journals Public Trolling Detection on Twitter Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3097-3100
Author(s):  
Miss. Pooja Dilip Dhotre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
The People ◽  
The World ◽  
Added Benefit ◽  
Current Popularity

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.

scholarly journals An Algorithm for Deciding the Online Reputation of Hotels

2019 ◽  
Vol 8 (9) ◽  
pp. 2998-3003
Keyword(s):  
Machine Learning ◽  
Word Of Mouth ◽  
Online Reviews ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Detection Model ◽  
The People ◽  
Learning Technique ◽  
Online Reputation

After revolution in cell phone industry expansion and offering of promotional data packs by telecom companies like Reliance Jio, Airtel, Idea, Spice etc accessibility to the Internet has become very easy for the people. maximum people are now connected through social media viz. facebook, twitter, instagram etc. People are sharing their best and worst experiences for any brand. Various online review sites like Treebo, Yelp, Google Maps, and Tripadvisor OYO, Makemytrip, goibibo etc are used as an important source for the success of hotel businesses. Word of mouth has always been a powerful tool for marketing a business, Online reviews are today’s word of mouth marketing, and these can make or break your business; In this research paper it is proposed for analyzing online reviews about hotels our algorithm must able to detect and analyzing fake reviewers based on user, tweet, timestamp, IP, collision and manipulation concept as well as to develop optimal model (based on group theory) for detecting fake reviewers, Improvement in enhancing sentimental analysis and the review detection model which can be implemented on all positive or all negative reviews, also the algorithm must able to identify the best fit of four machine learning techniques: (supervised machine technique technique, text mining technique , support vector machine learning technique and Naïve bayes machine learning technique) for specify and verify the different parameters of classification of reviews. Algorithm must able to Quantify the results of above techniques and extract the parameters to analyze the Genuinity of reviews based on Location, Security, Price, Quality, Ambiance etc.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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