A survey of deep learning approaches for WiFi-based indoor positioning

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

A Survey and Implementation of Machine Learning Algorithms for Customer Churn Prediction

Estimating customer traffic is an important task for businesses because it helps them identify customers who are most likely to leave and take preventative measures to retain them by improving customer satisfaction and further increasing their own revenue. In this article, we focus on developing a machine-learning model for predicting customer churn using historical customer data We performed engineering operations on the data, addressed the missing digits, coded the categorical variables, and preprocessed the data before evaluating it using a variety of performance indicators, including accuracy, precision, recall, f1 score, and ROC AUC_Score. Our feature significance analysis revealed that monthly fees, customer tenure, contract type, and payment method are the factors that have the most impact on forecasting customer churn. Finally, we conclude the best-performing model, the Soft Voting Classifier, implemented on the four best-performing classifiers with a good accuracy of 0.78 and a relatively better ROC AUC_Score of 0.82

A comparative study of tree-based models for churn prediction : a case study in the telecommunication sector

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMIn the recent years the topic of customer churn gains an increasing importance, which is the phenomena of the customers abandoning the company to another in the future. Customer churn plays an important role especially in the more saturated industries like telecommunication industry. Since the existing customers are very valuable and the acquisition cost of new customers is very high nowadays. The companies want to know which of their customers and when are they going to churn to another provider, so that measures can be taken to retain the customers who are at risk of churning. Such measures could be in the form of incentives to the churners, but the downside is the wrong classification of a churners will cost the company a lot, especially when incentives are given to some non-churner customers. The common challenge to predict customer churn will be how to pre-process the data and which algorithm to choose, especially when the dataset is heterogeneous which is very common for telecommunication companies’ datasets. The presented thesis aims at predicting customer churn for telecommunication sector using different decision tree algorithms and its ensemble models

Rectified softmax loss with all-sided cost sensitivity for age estimation

In Convolutional Neural Network (ConvNet) based age estimation algorithms, softmax loss is usually chosen as the loss function directly, and the problems of Cost Sensitivity (CS), such as class imbalance and misclassification cost difference between different classes, are not considered. Focus on these problems, this paper constructs a rectified softmax loss function with all-sided CS, and proposes a novel cost-sensitive ConvNet based age estimation algorithm. Firstly, a loss function is established for each age category to solve the imbalance of the number of training samples. Then, a cost matrix is defined to reflect the cost difference caused by misclassification between different classes, thus constructing a new cost-sensitive error function. Finally, the above methods are merged to construct a rectified softmax loss function for ConvNet model, and a corresponding Back Propagation (BP) training scheme is designed to enable ConvNet network to learn robust face representation for age estimation during the training phase. Simultaneously, the rectified softmax loss is theoretically proved that it satisfies the general conditions of the loss function used for classification. The effectiveness of the proposed method is verified by experiments on face image datasets of different races. © 2013 IEEE

The nexus between mobile phones diffusion, financial inclusion and economic growth: evidence on African countries.

Doctor of Philosophy in Finance. University of KwaZulu-Natal, Durban, 2018.The following thesis comprises three discrete empirical essays on the interplay among mobile phones diffusion, financial inclusion and economic growth in Africa. The first essay examines the condition of financial inclusion and its determinants in Africa. Using the World Development Indicators and the Principal Component Analysis to compute the financial inclusion index for 49 African countries over the period 2004 to 2016, the study finds low levels of financial inclusion in Africa compared to other regions. The region is also characterised by large financial inclusion gaps as shown by the minimum and maximum financial inclusion levels of 0 percent and 82 percent respectively. Since policymakers have over the past decade embraced both financial inclusion and economic growth as key policy initiatives, the second essay examines the interplay between financial inclusion and economic growth in terms of the transmission effect and nature of causality. To the best of the researcher’s knowledge, this is the first study to explore the transmission effect between financial inclusion and economic growth using a unique and robust Cointegrated Panel Structural Vector Autoregressive model. The study finds the existence of a cointegrating relationship between financial inclusion and economic growth. It also provides evidence that the relationship between financial inclusion and economic growth in Africa is growth-led supporting the demand following hypothesis. The increased internet-enabled phones adoption in Africa has also caused much optimism and speculation regarding its effects on financial inclusion. Policymakers, various studies and the media have all vaunted the potentials of mobile phones for financial inclusion. Therefore, this study examines the interplay between mobile phones and financial inclusion in Africa for the 2004-2016 period using pairwise Granger causality test and found that mobile phones Granger cause financial inclusion. The literature on financial inclusion has identified high-quality institutions and governance as the determinants of financial inclusion. Lack of deeper understanding of these issues results in ill-informed policy designs. Despite the cascading literature on issues impacting financial inclusion, the empirical literature on the impact of institutional quality and governance on financial inclusion are rare. Therefore, the third essay evaluates the impacts of institutional quality and governance on financial inclusion in Africa. Applying the two-step system generalised method of moments model, the study finds a positive relationship between institutional quality, governance and financial inclusion, indicating that good governance and economic freedom can lead to increases in financial inclusion. The study concluded that African countries have low levels of financial inclusion with a strong relationship between financial inclusion and other variables such as mobile phones diffusion, bank competition, financial stability, institutional quality and governance. The study recommended institutions to make the most out of the high concentration of the rural population to rollout high-volume transactions, rather than clustering in areas with the high-value transaction and to craft policies that remove restrictions to entrance in the banking sector thereby enhancing bank competition. Policymakers should also not just focus on enhancing financial inclusion, without corresponding improvements in institutional quality, governance, financial sector size, financial stability and financial sector development as they positively contribute to financial inclusion. The study also recommended the implementation of pro-growth policies and a review of existing banking sector policies to eradicate unnecessary barriers to financial inclusion

Is the lack of smartphone data skewing wealth indices in low-income settings?

BACKGROUND: Smartphones have rapidly become an important marker of wealth in low- and middle-income countries, but international household surveys do not regularly gather data on smartphone ownership and these data are rarely used to calculate wealth indices. METHODS: We developed a cross-sectional survey module delivered to 3028 households in rural northwest Burkina Faso to measure the effects of this absence. Wealth indices were calculated using both principal components analysis (PCA) and polychoric PCA for a base model using only ownership of any cell phone, and a full model using data on smartphone ownership, the number of cell phones, and the purchase of mobile data. Four outcomes (household expenditure, education level, and prevalence of frailty and diabetes) were used to evaluate changes in the composition of wealth index quintiles using ordinary least squares and logistic regressions and Wald tests. RESULTS: Households that own smartphones have higher monthly expenditures and own a greater quantity and quality of household assets. Expenditure and education levels are significantly higher at the fifth (richest) socioeconomic status (SES) quintile of full model wealth indices as compared to base models. Similarly, diabetes prevalence is significantly higher at the fifth SES quintile using PCA wealth index full models, but this is not observed for frailty prevalence, which is more prevalent among lower SES households. These effects are not present when using polychoric PCA, suggesting that this method provides additional robustness to missing asset data to measure underlying latent SES by proxy. CONCLUSIONS: The lack of smartphone data can skew PCA-based wealth index performance in a low-income context for the top of the socioeconomic spectrum. While some PCA variants may be robust to the omission of smartphone ownership, eliciting smartphone ownership data in household surveys is likely to substantially improve the validity and utility of wealth estimates

Quantifying the reliability of four global datasets for drought monitoring over a semiarid region

Drought is one of the most relevant natural disasters, especially in arid regions such as Iran. One of the requirements to access reliable drought monitoring is long-term and continuous high-resolution precipitation data. Different climatic and global databases are being developed and made available in real time or near real time by different agencies and centers; however, for this purpose, these databases must be evaluated regionally and in different local climates. In this paper, a near real-time global climate model, a data assimilation system, and two gridded gauge-based datasets over Iran are evaluated. The ground truth data include 50 gauges from the period of 1980 to 2010. Drought analysis was carried out by means of the Standard Precipitation Index (SPI) at 2-, 3-, 6-, and 12-month timescales. Although the results show spatial variations, overall the two gauge-based datasets perform better than the models. In addition, the results are more reliable for the western portion of the Zagros Range and the eastern region of the country. The analysis of the onsets of the 6-month moderate drought with at least 3 months’ persistence indicates that all datasets have a better performance over the western portion of the Zagros Range, but display poor performance over the coast of the Caspian Sea. Base on the results of this study, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset is a preferred alternative for drought analysis in the region when gauge-based datasets are not available

A Machine Learning Approach to Obese-Inflammatory Phenotyping

Obesity is the accumulation of an abnormal, or excessive, amount of fat in the body, which can have negative effects on overall health. This excess accumulation of macronutrients in adipose tissue can cause the release of inflammatory mediators, leading to a proinflammatory state. Inflammation is a known risk factor for various health conditions, including cardiovascular diseases, metabolic syndrome, and diabetes. This study sought to examine the use of data mining methods, particularly clustering algorithms, to identify inflammatory biomarker phenotypes and their association with obesity in a local adolescent population. The algorithms evaluated in this study included: k-means, Ward\u27s hierarchical agglomerative method, fuzzy c-means, Gaussian mixture model, and principal component analysis (PCA). The algorithms were assessed using different validation indices, graphs, as well as clinical interpretation of the resulting clusters. The results showed that k-Means, k = 3, produced the most accurate clusters. Based on their characterization, the clusters were defined as: severe risk for metabolic dysfunction, moderate risk for metabolic dysfunction, and normal metabolic function. Adolescents with a higher BMI and waist circumference had higher odds of being classified in the severe metabolic risk cluster. Although PCA is a different type of clustering algorithm, it supported the resultant cluster by grouping their dominant inflammatory biomarkers characteristics into separate principal components. These findings suggested a strong relationship between CRP and Leptin inflammatory biomarkers and higher BMI and waist circumference in the local adolescent study population

The diffusion/adoption of innovation in the internal market

The main aim of the present study is to analyze the drivers of innovation adoption by (i) developing proper measures able to proxy for innovation adoption and internal market regulations, (ii) identifying the channels through which innovation adoption takes place and (iii) assessing the main determinants of this adoption process within the internal market. An original model is derived from the theoretical literature on innovation diffusion. Results show that the impact of the transmission channels on innovation adoption is especially important for cooperation, leaving trade and competition as apparently minor channels of innovation diffusion (and especially depending on the type of innovation adoption under examination). The overall result argues that more cooperation across firms and countries is going to be beneficial to the process of innovation adoption.The Diffusion/Adoption of Innovation in the Internal Market, Community Innovation Survey, Micro Data, Cooperation, Trade, Competition, Suriñach, Autant-Bernard, Manca, Massard, Moreno

A Hybrid Data Mining Method for Customer Churn Prediction

The expenses for attracting new customers are much higher compared to the ones needed to maintain old customers due to the increasing competition and business saturation. So customer retention is one of the leading factors in companies’ marketing. Customer retention requires a churn management, and an effective management requires an exact and effective model for churn prediction. A variety of techniques and methodologies have been used for churn prediction, such as logistic regression, neural networks, genetic algorithm, decision tree etc.. In this article, a hybrid method is presented that predicts customers churn more accurately, using data fusion and feature extraction techniques. After data preparation and feature selection, two algorithms, LOLIMOT and C5.0, were trained with different size of features and performed on test data. Then the outputs of the individual classifiers were combined with weighted voting. The results of applying this method on real data of a telecommunication company proved the effectiveness of the method