scholarly journals Machine learning approach for multidimensional poverty estimation

2021 ◽  
Vol 33 (2) ◽  
pp. 205-225
Author(s):  
Mario Esteban Ochoa Guaraca ◽  
Ricardo Castro ◽  
Alexander Arias Pallaroso ◽  
Antonia Machado ◽  
Dolores Sucozhañay
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Geographical Area ◽  
Multidimensional Poverty ◽  
Data Set ◽  
Multidimensional Poverty Index ◽  
The Social ◽  
Machine Learning Approach ◽  
Statistical Studies ◽  
Use Of The Internet

In the social sciences, a theoretical analysis has predominated in its research. The scarcity of data and its difficulty in collecting and storing it, has been the main limitation for the social sciences to adopt quantitative approaches. However, the large amount of information generated in recent years, mainly through the use of the Internet, has allowed the social sciences to include more and more quantitative analysis. This study proposes the use of technologies such as Machine Learning (ML) are the answers to solving this data scarcity. The objective is to estimate the multidimensional poverty index at the personal level in a particular territory of Ecuador by using Machine Learning (ML) regression models based on a limited amount of data for training. Ten ML models are compared, such as linear, regularized, and assembled models and Random Forest performs outstandingly against the other models. An error of 7.5% was obtained in the cross-validation and 7.48% with the test data set. The estimates are compared with statistical approximations of the MPI in a geographical area and it is obtained that the average MPI estimated by the model compared to the average reported by the statistical studies differs by 1%.

scholarly journals Reviewing Sentiment Analysis at the Shallow End

2020 ◽  
Vol 8 (4) ◽  
pp. 47-62
Author(s):  
Francisca Oladipo ◽  
Ogunsanya, F. B ◽  
Musa, A. E. ◽  
Ogbuju, E. E ◽  
Ariwa, E.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
Training Data ◽  
Data Set ◽  
The Social ◽  
Machine Learning Approach ◽  
Media Space ◽  
Social Media Platforms

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.  

scholarly journals Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

2021 ◽  
Vol 7 ◽  
pp. 237802312110244
Author(s):  
Katrin Auspurg ◽  
Josef Brüderl
Keyword(s):  
Social Sciences ◽  
Causal Reasoning ◽  
Research Question ◽  
Social Science Research ◽  
Science Research ◽  
Skin Tone ◽  
Data Set ◽  
The Social ◽  
The Many ◽  
Definition Of

In 2018, Silberzahn, Uhlmann, Nosek, and colleagues published an article in which 29 teams analyzed the same research question with the same data: Are soccer referees more likely to give red cards to players with dark skin tone than light skin tone? The results obtained by the teams differed extensively. Many concluded from this widely noted exercise that the social sciences are not rigorous enough to provide definitive answers. In this article, we investigate why results diverged so much. We argue that the main reason was an unclear research question: Teams differed in their interpretation of the research question and therefore used diverse research designs and model specifications. We show by reanalyzing the data that with a clear research question, a precise definition of the parameter of interest, and theory-guided causal reasoning, results vary only within a narrow range. The broad conclusion of our reanalysis is that social science research needs to be more precise in its “estimands” to become credible.

Towards a Universal Social Impact Metric for Engineered Products That Alleviate Poverty

2017 ◽  
Author(s):  
Phillip D. Stevenson ◽  
Christopher A. Mattson ◽  
Kenneth M. Bryden ◽  
Nordica A. MacCarty
Keyword(s):  
Quality Of Life ◽  
Developing Countries ◽  
Social Impact ◽  
Multidimensional Poverty ◽  
Social Impacts ◽  
Five Dimensions ◽  
Multidimensional Poverty Index ◽  
The Social ◽  
The Impact

More than ever before, engineers are creating products for developing countries. One of the purposes of these products is to improve the consumer’s quality of life. Currently, there is no established method of measuring the social impact of these types of products. As a result, engineers have used their own metrics to assess their product’s impact, if at all. Some of the common metrics used include products sold and revenue, which measure the financial success of a product without recognizing the social successes or failures it might have. In this paper we introduce a potential metric, the Product Impact Metric (PIM), which quantifies the impact a product has on impoverished individuals — especially those living in developing countries. It measures social impact broadly in five dimensions: health, education, standard of living, employment quality, and security. The PIM is inspired by the Multidimensional Poverty Index (MPI) created by the United Nations Development Programme. The MPI measures how the depth of poverty within a nation changes year after year, and the PIM measures how an individual’s quality of life changes after being affected by an engineered product. The Product Impact Metric can be used to predict social impacts (using personas that represent real individuals) or measure social impacts (using specific data from products introduced into the market).

scholarly journals Tourism Carbon Kuznets-Curve Hypothesis

2020 ◽  
pp. 004728752091527
Author(s):  
Emmanouil F. Papavasileiou ◽  
Panagiotis Tzouvanas
Keyword(s):  
Social Sciences ◽  
Kuznets Curve ◽  
Data Set ◽  
Tourism Research ◽  
Performance Orientation ◽  
The Social ◽  
Reporting Process ◽  
Carbon Performance ◽  
Robust Result

Since the introduction of the carbon Kuznets-curve hypothesis in the mid-1990s, the inverted U–shaped relationship between economic development and carbon emissions has remained a subject of debate in the social sciences. We engage tourism research in this debate, in a fourfold manner. First, we offer a systematic literature review concerning the role of tourism in the carbon Kuznets-curve hypothesis using a protocol-based reporting process. Second, we present the level of consensus with the carbon Kuznets-curve hypothesis and the conceptual gaps in the identified literature (n = 22). Third, we introduce an emerging concept, offering a novel tourism corporate/performance orientation to the carbon Kuznets-curve hypothesis. Fourth, we provide evidence of empirical validity using different econometric techniques from an international tourism corporation (n = 86) data set (2005–2018). The inverted U–shaped relationship between measures of economic and carbon performance among tourism corporations is a robust result under many different specifications.

A hierarchical approach to mood classification in blogs

2011 ◽  
Vol 18 (1) ◽  
pp. 61-81 ◽  
Author(s):  
FAZEL KESHTKAR ◽  
DIANA INKPEN
Keyword(s):  
Machine Learning ◽  
Error Analysis ◽  
Learning Approach ◽  
Hierarchical Approach ◽  
Data Set ◽  
Novel Approach ◽  
Machine Learning Approach ◽  
Sentiment Orientation ◽  
Mood Classification

AbstractIn this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

scholarly journals Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

2021 ◽  
Vol 11 (24) ◽  
pp. 11710
Author(s):  
Matteo Miani ◽  
Matteo Dunnhofer ◽  
Fabio Rondinella ◽  
Evangelos Manthos ◽  
Jan Valentin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Optimization ◽  
Automatic Identification ◽  
Learning Approach ◽  
Ann Model ◽  
Data Set ◽  
Bituminous Mixtures ◽  
Novel Approach ◽  
The Neural Network ◽  
Machine Learning Approach

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

scholarly journals Simpler Machine Learning Using Spreadsheets: Neural Network Predict

2020 ◽  
Vol 4 (1) ◽  
pp. 124
Author(s):  
Leong Thin-Yin ◽  
Leong Yonghui Jonathan
Keyword(s):  
Social Sciences ◽  
Neural Network ◽  
Machine Learning ◽  
Business Analytics ◽  
The Core ◽  
Core Content ◽  
The Social ◽  
Business Application ◽  
Recursive Computation ◽  
Sales Pitch

Machine Learning as a phenomenon has gone viral, with many technologists and software vendors promoting it. However, offered tools remain highly technical and not accessible to those without rigorous training in Computer Science or Business Analytics. It would be more useful if end-users can understand it beyond the sales pitch or blind application, and perhaps, even work from scratch to build simple models without much additional training. With better assimilation and acceptance of this AI methodology as an acquired skill and not just head knowledge, many more may want to invest the intensive effort to learn the required tough mathematics and cryptic programming. Or, after simple trial explorations, be willing to put aside substantial budgets to employ skilled professionals for full-scale business application. With simplicity and accessibility in mind, this paper renders Neural Network, a key machine learning methodology, on the ubiquitous and easily comprehensible spreadsheet without macros or add-ins, employing only elementary operations and if so desired, optionally leveraging on its built-in Solver. We will show that backpropagation can be achieved using the elegant though obscure recursive computation feature, with no need for Solver. We will demonstrate the application of neural network on a familiar problem: early and prior prediction of students’ graduation GPA. The paper can be used to form the core content for introducing machine learning to non-technical audiences, particularly those majoring in Business and the Social Sciences.

scholarly journals Machine Learning Model for GSM BSC Control Plane Units

2019 ◽  
Vol 8 (6S) ◽  
pp. 219-223
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Parameters ◽  
Large Set ◽  
Data Set ◽  
Wide Acceptance ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Accuracy Of Prediction

At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model

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