Examining the Evolution of E-Government Development of Nations Through Machine Learning Techniques

Member States ◽

Theoretical Perspectives ◽

Learning Techniques ◽

Cluster Profiles ◽

Over Time ◽

Government Development

Several initiatives have tried to measure the efforts nations have made towards developing e-government. The UN E-Government Development Index (EGDI) is the only global report that ranks and classifies the UN Member States into four categories based on a weighted average of normalized scores on online service, telecom infrastructure, and human capital. The authors argue that the EGDI fails in showing the efforts of nations over time and in informing nations and policymakers as to what and from whom to draw policy lessons. Using the UN EGDI data from 2008 to 2020, they profile the UN Member States and show the relevance of machine learning techniques in addressing these issues. They examine the resulting cluster profiles in terms of theoretical perspectives in the literature and derive policy insights from the different groupings of nations and their evolution over time. Finally, they discuss the policy implications of the proposed methodology and the insights obtained.

Profiles and Evolution of E-Government Readiness in Africa

International Journal of Information Systems and Social Change ◽

10.4018/ijissc.2020010104 ◽

2020 ◽

Vol 11 (1) ◽

pp. 43-65

Author(s):

Niguissie Mengesha ◽

Anteneh Ayanso ◽

Dawit Demissie

Keyword(s):

Public Administration ◽

Composite Index ◽

Policy Implications ◽

Machine Learning Technique ◽

Measurement Framework ◽

Theoretical Perspectives ◽

Telecommunication Infrastructure ◽

Learning Technique ◽

Cluster Profiles ◽

E-government has been one of the top government strategies in recent years. Several studies and projects have attempted to understand the scope of e-government and the measurement framework that can be deployed to track the readiness as well as progress of nations overtime. Among these initiatives is the United Nations Public Administration Network (UN PAN) that assesses the e-government readiness of nations according to a quantitative composite index based on telecommunication infrastructure, human capital, and online services. Using the UN PAN index data from 2008 to 2016, the article profiles African nations using unsupervised machine learning technique. It also examines the resulting cluster profiles in terms of theoretical perspectives in the literature and derive policy insights from the different groupings of nations and their evolution over time. Finally, the article discusses the policy implications of the proposed methodology and the insights obtained.

A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

International Journal of Computational Intelligence and Applications ◽

10.1142/s146902681950010x ◽

2019 ◽

Vol 18 (02) ◽

pp. 1950010 ◽

Cited By ~ 2

Author(s):

Niddal Imam ◽

Biju Issac ◽

Seibu Mary Jacob

Keyword(s):

Machine Learning ◽

Supervised Learning ◽

Research Community ◽

Spam Detection ◽

Learning Approach ◽

New Approach ◽

Detection Systems ◽

Learning Techniques ◽

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

Popular music lyrics and musicians’ gender over time: A computational approach

Psychology of Music ◽

10.1177/0305735619871602 ◽

2019 ◽

pp. 030573561987160 ◽

Cited By ~ 1

Author(s):

Manuel Anglada-Tort ◽

Amanda E Krause ◽

Adrian C North

Keyword(s):

Machine Learning ◽

Popular Music ◽

Mixed Effect ◽

Gender Distribution ◽

Learning Techniques ◽

Inflection Points ◽

The Uk ◽

Music Lyrics ◽

The present study investigated how the gender distribution of the United Kingdom’s most popular artists has changed over time and the extent to which these changes might relate to popular music lyrics. Using data mining and machine learning techniques, we analyzed all songs that reached the UK weekly top 5 sales charts from 1960 to 2015 (4,222 songs). DICTION software facilitated a computerized analysis of the lyrics, measuring a total of 36 lyrical variables per song. Results showed a significant inequality in gender representation on the charts. However, the presence of female musicians increased significantly over the time span. The most critical inflection points leading to changes in the prevalence of female musicians were in 1968, 1976, and 1984. Linear mixed-effect models showed that the total number of words and the use of self-reference in popular music lyrics changed significantly as a function of musicians’ gender distribution over time, and particularly around the three critical inflection points identified. Irrespective of gender, there was a significant trend toward increasing repetition in the lyrics over time. Results are discussed in terms of the potential advantages of using machine learning techniques to study naturalistic singles sales charts data.

Use of Machine Learning in Stock Market Prediction

European Journal of Technology ◽

10.47672/ejt.634 ◽

2020 ◽

Vol 4 (1) ◽

pp. 60-73

Author(s):

Memoona Shaheen ◽

Mehreen Arshad

Keyword(s):

Machine Learning ◽

Policy Implications ◽

Support Vector ◽

Hybrid Techniques ◽

Current Trends ◽

Time Period ◽

Learning Techniques ◽

Vector Machines ◽

Prediction Problems

Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature. Methodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated. Finding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.

Anthrax on Twitter: Analysis of Public Discussion of Anthrax Over Twelve Months of Data Collection (Preprint)

10.2196/preprints.27976 ◽

2021 ◽

Author(s):

Michele Miller ◽

Will Romine ◽

Terry Oroszi

Keyword(s):

Machine Learning ◽

Data Collection ◽

Computational Framework ◽

Future Studies ◽

News Stories ◽

Learning Classifier ◽

Learning Techniques ◽

Twitter Analysis ◽

BACKGROUND A computational framework that utilizes machine learning methodologies was created to collect tweets discussing anthrax, further categorize them as relevant by month of data collection and detect anthrax related events. OBJECTIVE The objective of this study was to detect anthrax related events and to determine the relevancy of the tweets and topics of discussion over twelve months of data collection. METHODS Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was graphed to see if an event was detected (a three-fold spike in tweets). A machine learning classifier was created to categorize tweets as relevant. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how events influence that discussion. RESULTS Over the twelve months of data collection 204,008 tweets were collected. Logistic regression performed best for relevancy (precision=0.81, recall=0.81, and F1-score=0.80). Twenty-six topics were found relating to anthrax events, tweets that were highly re-tweeted, natural outbreaks, and news stories. CONCLUSIONS This study demonstrated that tweets relating to anthrax can be collected and analyzed over time to determine what people are discussing and detect key anthrax-related events. Future studies can focus on opinion tweets only, use the methodology to study other terrorism events, or use the methodology to monitor for threats.

Apparel Recommendation Engine Using Inverse Document Frequency and Weighted Average Word2vec

10.54216/jchci.010201 ◽

2021 ◽

pp. 46-56

Author(s):

Parvesh K ◽

◽

Tharun C ◽

...

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Business Models ◽

Rapid Development ◽

Weighted Average ◽

Machine Learning Algorithms ◽

Document Frequency ◽

Learning Techniques ◽

Marked Area

The rapid development of e-commerce shopping marketplaces necessitates the use of recommendation engines and quick, precise, and efficient algorithms in order for the company's business models to generate a massive amount of profit. A computer vision software programme enables a computer to learn a great deal from digital images or movies. Machine learning methods are used in computer vision, and several machine learning techniques have been developed specifically for this purpose. Information retrieval is the process of extracting useful information from a dataset, and computer vision is the most commonly used tool for this purpose nowadays. This project consists of a series of modules that run sequentially to retrieve information from a marked area on a receipt. A receipt image is used as an input for the model, and the model first uses various image processing algorithms to clean the data, after which the pre-processed data is applied to machine learning algorithms to produce better results, and the result is a string of numerical digits including the decimal point. The program's accuracy is primarily determined by the image quality or pixel density, and it is necessary to ensure that an input receipt is not damaged and content is not blurred.

Technology Management for Accelerated Recovery during COVID-19

SEISENSE Journal of Management ◽

10.33215/sjom.v3i5.445 ◽

2020 ◽

Vol 3 (5) ◽

pp. 33-53

Author(s):

Swapnil Morande ◽

Veena Tewari

Keyword(s):

Machine Learning ◽

Design Research ◽

Policy Implications ◽

Quantitative Methodology ◽

Time Data ◽

Treatment Protocols ◽

Clinical Trial Research ◽

Current State ◽

Learning Techniques

Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic. Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions. Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities. Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols. Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus.

Machine Learning Techniques for Credit Card Fraud Detection

Future Computing and Informatics Journal ◽

10.54623/fue.fcij.4.2.5 ◽

2020 ◽

Vol 4 (2) ◽

pp. 98-112

Author(s):

Hossam Eldin M. Abd Elhamid ◽

◽

Wael Khalif ◽

Mohamed Roushdy ◽

Abdel-Badeeh M. Salem ◽

...

Keyword(s):

Machine Learning ◽

Data Mining ◽

Credit Card ◽

Fraud Detection ◽

Data Sets ◽

Credit Card Fraud ◽

Data Mining Techniques ◽

Learning Techniques ◽

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher

Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Data Annotation ◽

Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.