Search results for: socioeconomic features

Authors: Elias Nemer, Greg Vines

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Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

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Authors: Shyhrete Morina

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This paper deals with morphological features in Mjeda's works. To make such a distinction, these features will be compared to standard Albanian language, considering the linguistic structure in the morphological field, which represent an all-important segment of Albanian language. Therefore, the study will focus mainly on the description and construction of these paradigms, which will give a linguistic insight into the entire work of Mjeda as the author who wrote in the dialect of northwestern Geg. Therefore, we have tried to distinguish different parts of the author's language, as well as the distinctive features or even the similarities of these paradigms that arise in the literary work of Mjeda. By constructing the corpus of this phonetic and grammar segment from the whole of Mjeda's work, we have seen that in these fields has built a variety of grammar structures, which for the history of Albanian are of special importance, that in the full variant of the work, as far as we can investigate, we will point out in all the distinctive features. Therefore, our study aims to highlight the linguistic features, namely the author's deep knowledge toward the language, the authenticity of its use, and its mutual relationship with it.

Keywords: distinctive morpholgy, nouns, adjetives, pronouns, Albanian standard language

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Authors: Henrique Veras De Paiva Fonseca

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This paper investigates the long-term relationship between early life exposure to malaria and adult socioeconomic outcomes in Brazil. The identification strategy relies on exogenous variation in the risk of malaria outbreaks in different states and seasons of the year to identify early life exposure according to the timing and location of birth. Furthermore, Brazil has undergone a successful campaign of malaria eradication during the late 1950s, which allows for comparing outcomes of birth cohorts born just prior to and just after eradication to identify the extent of in utero exposure. Instrumental variables estimates find consistent negative treatment effects of in utero exposure to malaria on socioeconomic outcomes, such as educational attainment and health status. The effects are stronger for exposure during the first trimester of pregnancy than during other periods of gestation. Additionally, consistent with previous findings, men are more likely to exhibit larger long-term effects.

Keywords: malaria, exposure, eradication, instrumental variables, education, health

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Authors: Bedilu Tafesse

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Conservation of crop genetic resources presents a challenge of identifying specific determinants driving maintenance of diversity at farm and agroecosystems. The objectives of this study were to identify socioeconomic, market and agroecological determinants of farmers’ maintenance of wheat diversity at the household level and derive implications for policies in designing on-farm conservation programs. We assess wheat diversity at farm level using household survey data. A household decision making model is conceptualized using microeconomic theory to assess and identify factors influencing on-farm rice diversity. The model is then tested econometrically by using various factors affecting farmers’ variety choice and diversity decisions. The findings show that household-specific socioeconomic, agroecological and market factors are important in determining on-farm wheat diversity. The significant variables in explaining richness and evenness of wheat diversity include distance to the nearest market, subsistence ratio, modern variety sold, land types and adult labour working in agriculture. The statistical signs of the factors determining wheat diversity are consistent in explaining the richness, dominance and evenness among rice varieties. Finally, the study implies that the cost-effective means of promoting and sustaining on-farm conservation programmes is to target them in market isolated geographic locations of high crop diversity where farm households have more heterogeneity of agroecological conditions and more active family adult labour working on-farm.

Keywords: diversity indices, dominance, evenness, on-farm conservation, wheat diversity, richness

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Authors: Sanjeewa Seneviratne, Ian Campbell, Nina Scott, Ross Lawrenson

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Introduction: Indigenous Māori women experience a 60% higher breast cancer mortality rate compared with European women in New Zealand. We explored the impact of difference in the rate of screen detected breast cancer between Māori and European women on more advanced disease at diagnosis and lower survival in Māori women. Methods: All primary in-situ and invasive breast cancers diagnosed in screening age women (as defined by the New Zealand National Breast Cancer Screening Programme) between 1999 and 2012 in the Waikato area were identified from the Waikato Breast Cancer Register and the national screening database. Association between screen versus non-screen detection and cancer stage at diagnosis and survival were compared by ethnicity and socioeconomic deprivation. Results: Māori women had 50% higher odds of being diagnosed with more advance staged cancer compared with NZ European women, a half of which was explained by the lower rate of screen detected cancer in Māori women. Significantly lower breast cancer survival rates were observed for Māori compared with NZ European and most deprived compared with most affluent socioeconomic groups for symptomatically detected breast cancer. No significant survival differences by ethnicity or socioeconomic deprivation were observed for screen detected breast cancer. Conclusions: Low rate of screen detected breast cancer appears to be a major contributor for more advanced stage disease at diagnosis and lower breast cancer survival in Māori compared with NZ European women. Increasing screening participation for Māori has the potential to substantially reduce breast cancer mortality inequity between Māori and NZ European women.

Keywords: breast cancer, screening, ethnicity, inequity

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-doppler features

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Authors: Tasawar Nawaz

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Promotion of socioeconomic justice through redistribution of wealth is one of the most salient features of Islamic economic system. Islamic financial institutions known as Islamic banks are used to implement this in practice under the guidelines of Islamic Shariah law. Islamic banking systems strive to promote and achieve financial inclusion among the society by offering interest-free banking and risk-sharing financing solutions. Shariah-compliant micro finance is one of the most popular financial instruments used by Islamic banks to enhance access to finance. Benevolent loan (or Qard-al-Hassanah) is one of the popular financial tools used by the Islamic banks to promote financial inclusion. This aspect of Islamic banking is empirically examined in this paper with specific reference to firm’s resources, largely defined here as intellectual capital. The paper finds that Islamic banks promote financial inclusion by exploiting available resources especially, the human intellectual capital.

Keywords: financial inclusion, intellectual capital, Qard-al-Hassanah, Islamic banking

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Authors: Birmohan Singh, V.K.Jain

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Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: Tayebeh Saghapour, Sara Moridpour, Russell George Thompson

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One of the common aims of transport policy makers is to switch people’s travel to active transport. For this purpose, a variety of transport goals and investments should be programmed to increase the propensity towards active transport mode choice. This paper aims to investigate whether built environment features in neighbourhoods could enhance the odds of active transportation. The present study introduces an index measuring public transport accessibility (PTAI), and a walkability index along with socioeconomic variables to investigate mode choice behaviour. Using travel behaviour data, an ordered logit regression model is applied to examine the impacts of explanatory variables on walking trips. The findings indicated that high rates of active travel are consistently associated with higher levels of walking and public transport accessibility.

Keywords: active transport, public transport accessibility, walkability, ordered logit model

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Authors: Wei Zhang, Yan He, Yan Wang, Yufeng Li, Chuanpeng Hao

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Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness.

Keywords: feature analysis, machine vision, PCA, surface roughness, SVM

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Authors: Muhammad Fezan Afzal, Asad Abbas, Imran Khan, Salma Imtiaz

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Software Product Line (SPL) paradigm is used for the development of the family of software products that share common and variable features. Feature model is a domain of SPL that consists of common and variable features with predefined relationships and constraints. Multiple SPLs consist of a number of similar common and variable features, such as mobile phones and Tabs. Reusability of common and variable features from the different domains of SPL is a complex task due to the external relationships and constraints of features in the feature model. To increase the reusability of feature model resources from domain engineering, it is required to manage the commonality of features at the level of SPL application development. In this research, we have proposed an approach that combines multiple SPLs into a single domain and converts them to a common feature model. Extracting the common features from different feature models is more effective, less cost and time to market for the application development. For extracting features from multiple SPLs, the proposed framework consists of three steps: 1) find the variation points, 2) find the constraints, and 3) combine the feature models into a single feature model on the basis of variation points and constraints. By using this approach, reusability can increase features from the multiple feature models. The impact of this research is to reduce the development of cost, time to market and increase products of SPL.

Keywords: software product line, feature model, variability management, multi-SPLs

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Authors: Santoshi Halder, Ranjini Ghosh

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Research on educational stratification suggests that inequality in education between different social strata continues and sometimes even widens in spite of educational growth. The backward classes are the most suppressed classes in society. In India, the Scheduled Castes are found as one of the backward classes. After independence there a lot of provisions were made for their uplift. Still they are facing a lot of problems in perusing education, getting jobs, choosing life style independently etc. The present study was conducted to explore the educational disparities in education with respect to caste. Sample consisted of 1020 students (540 scheduled caste and 540 general caste) from three different universities of West Bengal. Tools selected were General Information Schedule (GIS), socioeconomic status (SES), Achievement motivation scale. Findings indicated significant differences for the selected variables under the study with respect to caste. Findings have significant implication for the advocates, policy makers and educationists and sociologists for appropriate intervention.

Keywords: scheduled caste, educational barriers, achievement motivation, socioeconomic status

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Authors: Stefanie Siebenhütter

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Male and female speakers use language differently and with varying confidence levels. This paper contrasts gendered differences in language use with socioeconomic status and age factors. It specifically examines how Kui minority language use and competence are conditioned by the variable of gender and discusses potential reasons for this variation by examining gendered language awareness and sociolinguistic attitudes. Moreover, it discusses whether women in Kui society function as 'leaders of linguistic change', as represented in Labov’s sociolinguistic model. It discusses whether societal role expectations in collectivistic cultures influence the model of linguistic change. The findings reveal current Kui speaking preferences and give predictions on the prospective language use, which is a stable situation of multilingualism because the current Kui speakers will socialize and teach the prospective Kui speakers in the near future. It further confirms that Lao is losing importance in Kui speaker’s (female’s) daily life.

Keywords: gender, identity construction, language change, minority language, multilingualism, sociolinguistics, social Networks

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Authors: Shaira Karishma Sheriff, Zarinah Hamid

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The Arab Spring, which commenced during the end of 2010 and accelerated during 2011, was caused primarily due to poverty, unemployment and a general recession in the Middle East and North African (MENA) region. The core motivation of this revolution could be said to be the need for political, economic and social reforms that the region desires to experience. Though GDP growth has been significant in the region, the income distribution mechanism in MENA countries has been ineffective. This results in low levels of education, substandard health care facilities, unemployment, and poverty. This paper argues that MENA countries have great potential for experiencing socioeconomic development by being less dependent on oil exports and enhancing their services sector through better education which would eventually lead to job creation. Furthermore, the region can encourage better trade and political integration by forming transparent and accountable governments. The notion of Nation-State needs to be addressed and the countries in the region need to look for ways to develop effective supra-national institutions for better political and economic integration that goes beyond geographical borders.

Keywords: political reforms, social reforms, economic development, nation-state, economic integration

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Authors: Merey M. Sarsengeldin, Alexandr S. Kolokhmatov, Galiya Seidaliyeva, Alexandr Ozerov, Sanim T. Imatayeva

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This series of interdisciplinary studies is an attempt to investigate and develop a scalable model of fair socioeconomic relations on the base of blockchain using positive psychology techniques and Machine Learning algorithms for data analytics. In this particular study, we use hyperinteraction approach and intuition to investigate their influence on 'wisdom of crowds' via created mobile application which was created for the purpose of this research. Along with the public blockchain and private Decentralized Autonomous Organization (DAO) which were elaborated by us on the base of Ethereum blockchain, a model of fair financial relations of members of DAO was developed. We developed a smart contract, so-called, Fair Price Protocol and use it for implementation of model. The data obtained from mobile application was analyzed by ML algorithms. A model was tested on football matches.

Keywords: blockchain, Naïve Bayes algorithm, hyperinteraction, intuition, wisdom of crowd, decentralized autonomous organization

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Authors: Amira Masood, Zainah Alshouri, Noor Bantan, Samira Kutbi

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Artificial intelligence is continuously becoming more advanced and more widespread and is present in many of our day-to-day lives as a means of assistance in numerous different fields. A growing number of people, companies, and corporations are utilizing Canva and its AI tools as a method of quick and easy media production. Hence, in order to test the integrity of the rapid growth of AI, this paper will explore the usefulness of Canva's advanced design features as well as their accuracy by determining user satisfaction through a survey-based research approach and by investigating whether or not AI is successful enough that it eliminates the need for human alterations.

Keywords: artificial intelligence, canva, features, users, satisfaction

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Authors: Claudia Borges Colcerniani

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The paper is about a qualitative study whose main objective is to know how labor, as a Brazilian constitutionally established social right, can promote the social inclusion of female heads of one-parent families in a situation of poverty. The participants are six women, mothers, and workers living in Rocinha, a community located in the city of Rio de Janeiro, RJ, Brazil. According to the Brazilian Federal Constitution, social rights are based on the idea that socioeconomic inequalities should not limit or eliminate civil and political rights. In this perspective, labor can be a way to reach social justice, according to the theory of Nancy Fraser, the theoretical framework adopted in this research. Data were collected through socioeconomic questionnaires, and semi-structured interviews applied individually. The results analysis was made using the content analysis/categorical content analysis, according to Bardin's perspective. The results indicate that labor (as a social right) is considered, by the interviewed women, as an opportunity for social inclusion when there are the characteristics of the formality in accordance with the international labor regulations (Decent Work - International Labour Organization/United Nations).

Keywords: female labor, social justice, inclusion, women, decent work

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

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Authors: Kyung-Sook Bang

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Background: The health status of school-age children has a great influence on their growth and life-long health. The purposes of this study were to identify physical and mental health status of late school-age children in Seoul, South Korea and to investigate the related factors for their health. Methods: After gaining the approval from Institutional Review Board (IRB), a cross-sectional study was conducted with elementary students in grade 4 or 5. Questionnaires were distributed to eight elementary schools located different regions of Seoul in November, 2016, and 302 participants were finally included. From all participants, informed consents from the parents, and assents from children were received. Children's socioeconomic status, family functioning, peer relations, physical health symptoms, and depression were measured with self-reported questionnaires. Data were analyzed with descriptive statistics, t-test, Pearson’s correlations, and multiple regression. Results: Children's physical health symptoms and depression were not significantly different, and only their peer relations were significantly different according to their socioeconomic status (t=-3.93, p<.001). Depression showed significant positive correlation with physical health symptoms (r=.720, p<.001) and negative correlations with family functioning (r=-.428, p<.001) and peer relations (r=-.775, p<.001). The multiple regression model, which explained 73.5% of variance, showed peer relations (r2 =.604), physical health symptoms (r2 change=.125), and family functioning (r2 change=.005) as significant predictors for depression. Only the peer relations was significant predictor for their physical health symptoms and explained 50.6% of it. Conclusions: The peer relations was the most important factor in their physical and mental health at this age, and it can be affected by their socioeconomic status. Nursing interventions for promoting social relations and family functioning are required to improve children’s physical and mental health, especially for vulnerable population.

Keywords: child, depression, health, peer relation

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Mahya Naghipoor

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Purpose: Non-small cell lung cancer (NSCLC) is the most common lung cancer type. Epidermal growth factor receptor (EGFR) mutation is the main reason which causes NSCLC. Computed tomography (CT) is used for diagnosis and prognosis of lung cancers because of low price and little invasion. Semantic analyses of qualitative CT features are based on visual evaluation by radiologist. However, the naked eye ability may not assess all image features. On the other hand, radiomics provides the opportunity of quantitative analyses for CT images features. The aim of this review study was comparing accuracy of semantic and radiomics features in prognosis of EGFR mutation in NSCLC. Methods: For this purpose, the keywords including: non-small cell lung cancer, epidermal growth factor receptor mutation, semantic, radiomics, feature, receiver operating characteristics curve (ROC) and area under curve (AUC) were searched in PubMed and Google Scholar. Totally 29 papers were reviewed and the AUC of ROC analyses for semantic and radiomics features were compared. Results: The results showed that the reported AUC amounts for semantic features (ground glass opacity, shape, margins, lesion density and presence or absence of air bronchogram, emphysema and pleural effusion) were %41-%79. For radiomics features (kurtosis, skewness, entropy, texture, standard deviation (SD) and wavelet) the AUC values were found %50-%86. Conclusions: In conclusion, the accuracy of radiomics analysis is a little higher than semantic in prognosis of EGFR mutation in NSCLC.

Keywords: lung cancer, radiomics, computer tomography, mutation

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Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: Velly Anatasia

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In the last few decades extensive research has been conducted into information technology (IT) adoption, testing a series of factors considered to be essential for improved diffusion. Some studies analyze IT characteristics such as usefulness, ease of use and/or security, others focus on the emotions and experiences of users and a third group attempts to determine the importance of socioeconomic user characteristics such as gender, educational level and income. The situation is similar regarding e-commerce, where the majority of studies have taken for granted the importance of including these variables when studying e-commerce adoption, as these were believed to explain or forecast who buys or who will buy on the internet. Nowadays, the internet has become a marketplace suitable for all ages and incomes and both genders and thus the prejudices linked to the advisability of selling certain products should be revised. The objective of this study is to test whether the socioeconomic characteristics of experienced e-shoppers such as gender rally moderate the effect of their perceptions of online shopping behavior. Current development of the online environment and the experience acquired by individuals from previous e-purchases can attenuate or even nullify the effect of these characteristics. The individuals analyzed are experienced e-shoppers i.e. individuals who often make purchases on the internet. The Technology Acceptance Model (TAM) was broadened to include previous use of the internet and perceived self-efficacy. The perceptions and behavior of e-shoppers are based on their own experiences. The information obtained will be tested using questionnaires which were distributed and self-administered to respondent accustomed using internet. The causal model is estimated using structural equation modeling techniques (SEM), followed by tests of the moderating effect of socioeconomic variables on perceptions and online shopping behavior. The expected findings of this study indicated that gender moderate neither the influence of previous use of the internet nor the perceptions of e-commerce. In short, they do not condition the behavior of the experienced e-shopper.

Keywords: Internet shopping, age groups, gender, income, electronic commerce

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Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: Wencong Lu, Abdul Latif, Raza Ullah, Subhan Ullah

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Agricultural production is subject to risk and the attitudes of producers toward risk, in turn, may be affected by certain socioeconomic characteristics of producers. Although, it is important to assess the risk attitude of farmers and their perception towards different calamitous risk sources for better understanding of their risk management adoption decisions, to the best of our knowledge no studies have been carried out to analyze the risk attitude and risk perceptions in the context of tobacco production in Pakistan. Therefore the study in hand is conducted with an attempt to overcome the gap in existing literature by analyzing different catastrophic risk sources faced by tobacco growers, their attitude towards risk and the effect of socioeconomic and demographic characteristics, farmers’ participation in contract farming and off-farm diversification on their risk attitude and risk perception. Around 78% of Pakistan’s entire tobacco crop and nearly all of the country’s Flue-Cured Virginia (FCV) tobacco is produced in Khyber Pakhtunkhwa (KPK) province alone. The yield/hectare of tobacco produced in KPK province is 14% higher than the global average and 22 % higher than national average. Khyber Pakhtunkhwa province was selected as main study area as nearly all of the country’s Flue-Cured Virginia (FCV) tobacco is produced in Khyber Pakhtunkhwa (KPK) province alone. Six districts were purposely selected based on their contribution in overall production for the last five years which accounts for more than 94.84% of the tobacco production in KPK province. Specific objectives taken into considerations for this study are the risk attitude of the farmers for growing FCV tobacco crop, farmers’ risk perception for different risk sources related to tobacco production (as far as the incidence and severity of each risk source is concerned) and the effect of socioeconomic characteristics, contract farming participation and off-farm diversification (income) on the risk attitude and risk perception of FCV tobacco growers.

Keywords: risk attitude, risk perception, contract farming, off-farm diversification, probit model

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Authors: U. Ravi Babu, Mohd Mastan

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The present paper proposes a novel approach for handwritten digit recognition system. The present paper extract digit image features based on distance measure and derives an algorithm to classify the digit images. The distance measure can be performing on the thinned image. Thinning is the one of the preprocessing technique in image processing. The present paper mainly concentrated on an extraction of features from digit image for effective recognition of the numeral. To find the effectiveness of the proposed method tested on MNIST database, CENPARMI, CEDAR, and newly collected data. The proposed method is implemented on more than one lakh digit images and it gets good comparative recognition results. The percentage of the recognition is achieved about 97.32%.

Keywords: handwritten digit recognition, distance measure, MNIST database, image features

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Authors: Germans Natuhwera, Peter Ellis, Acuda Wilson, Anne Merriman, Martha Rabwoni

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Objective: The aim of the study was to diagnose the socio-economic burden and impact of a diagnosis of cervical cancer (CC) in rural women in the context of low-resourced country Uganda, using a phenomenological enquiry. Methods: This was a multi-site phenomenological inquiry, conducted at three hospice settings; Mobile Hospice Mbarara in southwestern, Little Hospice Hoima in Western, and Hospice Africa Uganda Kampala in central Uganda. A purposive sample of women with a histologically confirmed diagnosis of CC was recruited. Data was collected using open-ended audio-recorded interviews conducted in the native languages of participants. Interviews were transcribed verbatim in English, and Braun and Clarke’s (2019) framework of thematic analysis was used. Results: 13 women with a mean age of 49.2 and age range 29-71 participated in the study. All participants were of low socioeconomic status. The majority (84.6%) had advanced disease at diagnosis. A fuller reading of transcripts produced four major themes clustered under; (1) socioeconomic characteristics of women, (2) impact of CC on women’s relationships, (3) disrupted and impaired activities of daily living (ADLs), and (4) economic disruptions. Conclusions: A diagnosis of CC introduces significant socio-economic disruptions in a woman’s and her family’s life. CC causes disability, impairs the woman and her family’s productivity hence exacerbating levels of poverty in the home. High and expensive out-of-pocket expenditure on treatment, investigations, and transport costs further compound the socio-economic burden. Decentralizing cancer care services to regional centers, scaling up screening services, subsidizing costs of cancer care services, or making cervical cancer care treatment free of charge, strengthening monitoring mechanisms in public facilities to curb the vice of healthcare workers soliciting bribes from patients, increased mass awareness campaigns about cancer, training more healthcare professionals in cancer investigation and management, and palliative care, and introducing an introductory course on gynecologic cancers into all health training institutions are recommended.

Keywords: activities of daily living, cervical cancer, out-of-pocket, expenditure, phenomenology, socioeconomic

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Authors: Li Xiaoge, Wang Chaodong

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We introduce the first Chinese Coreference Resolution Model based on BERT (CCRM-BERT) and show that it significantly outperforms all previous work. The key idea is to consider the features of the mention, such as part of speech, width of spans, distance between spans, etc. And the influence of each features on the model is analyzed. The model computes mention embeddings that combine BERT with features. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the Chinese OntoNotes benchmark.

Keywords: BERT, coreference resolution, deep learning, nature language processing

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Ramayanam Suresh, A. Nagaraja Rao, B. Eswara Reddy

Abstract:

Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.

Keywords: texture features, region of interest, multi-ROI segmentation, benchmarked images

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