Search results for: socioeconomic features

Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Ismail Baniadam, Nafisa Tadayyon, Javid Fereidoni

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This study aimed to investigate medical students’ attitudes toward some teachers’ talking features regarding their gender in the Iranian context. To do so, 60 male and 60 female medical students of Urmia University of Medical Sciences (UMSU) participated in the research. A researcher made Likert-type questionnaire which was initially piloted and was used to gather the data. Comparing the four different factors regarding the features of teacher talk, it was revealed that visual and extra-linguistic information factor, Lexical and syntactic familiarity, Speed of speech, and the use of Persian language had the highest to the lowest mean score, respectively. It was also indicated that female students rather than male students were significantly more in favor of speed of speech and lexical and syntactic familiarity.

Keywords: attitude, gender, medical student, teacher talk

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Authors: Olivier Chaigneau, Damianos Oikonomidis, Marie-Cecile Delmas

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GAMBAS (Galileo Advanced features for the Maritime domain: Breakthrough Applications for Safety and security) is a project funded by the European Space Program Agency (EUSPA) aiming at identifying the search-and-rescue and ship security alert system needs for maritime users (including operators and fishing stakeholders) and developing operational concepts to answer these needs. The general objective of the GAMBAS project is to support the deployment of Galileo exclusive features in the maritime domain in order to improve safety and security at sea, detection of illegal activities and associated surveillance means, resilience to natural and human-induced emergency situations, and develop, integrate, demonstrate, standardize and disseminate these new associated capabilities. The project aims to demonstrate: improvement of the SAR (Search And Rescue) and SSAS (Ship Security Alert System) detection and response to maritime distress through the integration of new features into the beacon for SSAS in terms of cost optimization, user-friendly aspects, integration of Galileo and OS NMA (Open Service Navigation Message Authentication) reception for improved authenticated localization performance and reliability, and at sea triggering capabilities, optimization of the responsiveness of RCCs (Rescue Co-ordination Centre) towards the distress situations affecting vessels, the adaptation of the MCCs (Mission Control Center) and MEOLUT (Medium Earth Orbit Local User Terminal) to the data distribution of SSAS alerts.

Keywords: Galileo new advanced features, maritime, safety, security

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Authors: Qazi Mahdia Ghyas

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Electronic Government Procurement (e-GP) has implemented in Bangladesh to ensure the good Governance. e-GP has transformed Bangladesh's procurement process electronically. But, to our best knowledge, there is no study to understand the key features of e-GP in Bangladesh. So, this study tries to identify the features of performance improvement after implementing an e-GP system that will help for further improvements. Data was collected from the PROMIS Overall Report (Central Procurement Technical Unit website) for the financial year from Q1 _July- Sep 2015-16 to Q4 _Apr- Jun 2021-22. This study did component factor analysis on KPIs and found nineteen KPIs that are statistically significant and represent time savings, efficiency, accountability, anti-corruption and compliance key features in procurement activities of e-GP. Based on the analysis, some practical measures have been recommended for better improvement of e-GP. This study has some limitations. Because of having multicollinearity issues, all the 42 KPIs (except 19) did not show a good fit for component factor analysis.

Keywords: public procurement, electronic government procurement, KPI, performance evaluation

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Authors: Malarvizhi Selvaraj, Paul Fergus, Andy Shaw

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Uterine contractions produced during labour have the potential to damage the foetus by diminishing the maternal blood flow to the placenta. In order to observe this phenomenon labour and delivery are routinely monitored using cardiotocography monitors. An obstetrician usually makes the diagnosis of foetus hypoxia by interpreting cardiotocography recordings. However, cardiotocography capture and interpretation is time-consuming and subjective, often lead to misclassification that causes damage to the foetus and unnecessary caesarean section. Both of these have a high impact on the foetus and the cost to the national healthcare services. Automatic detection of foetal heart rate may be an objective solution to help to reduce unnecessary medical interventions, as reported in several studies. This paper aim is to provide a system for better identification and interpretation of abnormalities of the fetal heart rate using RStudio. An open dataset of 552 Intrapartum recordings has been filtered with 0.034 Hz filters in an attempt to remove noise while keeping as much of the discriminative data as possible. Features were chosen following an extensive literature review, which concluded with FIGO features such as acceleration, deceleration, mean, variance and standard derivation. The five features were extracted from 552 recordings. Using these features, recordings will be classified either normal or abnormal. If the recording is abnormal, it has got more chances of hypoxia.

Keywords: cardiotocography, foetus, intrapartum, hypoxia

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Michael Daly

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Background/Aims: The interplay of childhood self-control and early life social background in predicting adult health is currently unclear. We drew on rich data from two large nationally representative cohort studies to test whether individual differences in childhood self-control may: (i) buffer the health impact of social disadvantage, (ii) act as a mediating pathway underlying the emergence of health disparities, or (iii) compensate for the health consequences of socioeconomic disadvantage across the lifespan. Methods: We examined data from over 25,000 participants from the British Cohort Study (BCS) and the National Child Development Study (NCDS). Child self-control was teacher-rated at age 10 in the BCS and ages 7/11 in the NCDS. The Early life social disadvantage was indexed using measures of parental education, occupational prestige, and housing characteristics (i.e. housing tenure, home crowding). A range of health outcomes was examined: the presence of chronic conditions, whether illnesses were limiting, physiological dysregulation (gauged by clinical indicators), mortality, and perceptions of pain, psychological distress, and general health. Results: Childhood self-control and social disadvantage predicted each measure of adult health, with similar strength on average. An examination of mediating factors showed that adult smoking, obesity, and socioeconomic status explained the majority of these linkages. There was no systematic evidence that self-control moderated the health consequences of early social disadvantage and limited evidence that self-control acted as a key pathway from disadvantage to later health. Conclusions: Childhood self-control predicts adult health and may compensate for early life social disadvantage by shaping adult health behaviour and social status.

Keywords: personality and health, social disadvantage, health psychology, life-course development

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Authors: Nibal Al-Batsh, Issam A. Al-Khatib, Subha Ghannam

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Yatta is a town in the Governorate of Hebron, located 9 km south of Hebron City in the West Bank. The town houses over 100,000 people, 49% of which are females; a population that doubles every 15 years. Yatta has been connected to a water network since 1974 serving nearly 85% of the households. The water network is old and inadequate to meet the needs of the population. The water supply made available to the area is also very limited, estimated to be around 20 l/c/d. Residents are thus forced to rely on water vendors which supply water with a lower quality compared to municipal water while being 400% more expensive. As a cheaper and more reliable alternative, rainwater harvesting is a common practice in the area, with the majority of the households owning at least one cistern. Rainwater harvesting is of great socioeconomic importance in areas where water sources are scarce or polluted. In this research, the quality of harvested rainwater used for drinking and domestic purposes in the Yatta area was assessed throughout a year. A total of 100 samples, were collected from (cisterns) with an average capacity of 69 m3, which are adjacent to cement-roof catchment areas with an average area of 145 m2. Samples were analyzed for a number of parameters including: pH, alkalinity, hardness, turbidity, Total Dissolved Solids (TDS), NO3, NH4, chloride and salinity. Biological and microbiological contents such as Total Coliforms (TCC) and Fecal Coliforms (FC) bacteria were also tested. Results showed that most of the rainwater samples were within WHO and EPA guidelines set for chemical parameters. The research also addressed the impact of different socioeconomic attributes on rainwater harvesting through questionnaire that was pre-tested before the actual statically sample is collected.

Keywords: rainwater, harvesting, water quality, socio-economic aspects

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: data mining, Korean linguistic feature, literary fiction, relationship extraction

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Authors: Amardass Dhami, Clare Samuelson

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Background: Sickle cell disease (SCD) is a complex, multisystem condition that significantly impacts patients' quality of life, characterized by acute illness episodes, progressive organ damage, and reduced life expectancy. In the UK, over 13,000 individuals are affected, with South Yorkshire having the fifth highest prevalence, including approximately 800 patients. Retinal complications in SCD can manifest as either proliferative or non-proliferative disease, with proliferative changes being more prevalent. These retinal issues can cause significant morbidity, including visual loss and increased care requirements, underscoring the need for regular monitoring. An integrated approach was applied to ensure timely interventions, ultimately enhancing patient outcomes and reduce ‘did not attend’ rates. Aim: To assess the factors which may influence attendance to Haematology and Ophthalmology Clinics with attention towards levels of deprivation towards non-attendance. Method : A retrospective study on 84 eligible patients, from the regional tertiary Centre for Sickle Cell Care (Sheffield Teaching Hospital) from 2017 to 2023. The study focused on the incidence of sickle cell eye disease, specifically examining the outcomes of patients who attended the combined haematology and ophthalmology clinics. Patients who did not attend either clinic were excluded from the analysis to ensure a clear understanding of the combined clinic's impact. This data was then compared with the United Kingdom’s Index of Multiple Deprivation (IMD) datasets to assess if inequalities of care affected this population. Results: The study concluded that the effectiveness of combining haematology and ophthalmology clinics was reduced following the intervention. The DNA rates increased to 40% for the haematology clinic. Additionally, a significant proportion of the cohort was classified as residing in areas of deprivation, suggesting a possible link between socioeconomic factors and non-attendance rates Conclusion: These findings underscore the challenges of integrating care for SCD patients, particularly in relation to socioeconomic barriers. Despite the intent to streamline care and improve patient outcomes, the increase in DNA rates points to the need for further investigation into the underlying causes of non-attendance. Addressing these issues, especially in deprived areas, could enhance the effectiveness of combined clinics and ensure that patients receive the necessary monitoring and interventions for their eye health and overall well-being. Future strategies may need to focus on improving accessibility, outreach, and support for patients to mitigate the impact of socioeconomic factors on healthcare attendance.

Keywords: south yorkshire, sickle cell anemia, deprivation, factors, haematology

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Authors: Mumtaz Ali Baloch

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This article aims at to review the policies and gaps including the socioeconomic and institutional factors that affected the enrollment of disabled children and caused drop-outs. It provides insights to scrutinize the gaps in policies, socioeconomic, and institutional factors with the specific concern in enrollment and drop out of disabled children in Pakistan, and Balochistan in particular. The findings of this study revealed that the old-age centralized policies and a number of socio-economic and institutional factors seemed to have significantly affected the enrollment and quality education in the case of physically disabled children. There were only a few schools functional in entire Balochistan. For example, an entire province (Balochistan) there are only two schools for disabled children, established in Quetta city. In the other 31 districts, an estimated population of 300,000 people of each district there were no schools for the disabled children. The findings of this study revealed that there is a great distinction between the policy and practice in the case of physically disabled children in Quetta, Balochistan. Consequently, such children seemed to have been out of schools. Dropout after the class eighth grade is almost 100%, as there are no high schools available for physically/disabled children, in Balochistan. The concerned organizations and authorities need to develop and ratify specific policies, provide required) facilities to the schools including sufficient budget, streamline the academic planning, and an effective monitoring and evaluation system. Only awareness and motivation could not help in improving the enrollment rate and decreasing the dropout in the case of physically disabled children. There is an urgent need to provide the required facilities to the schools. Almost all students needed assistive equipment, effective physical therapy as well as regular medical facilities. Such measures can improve the enrolment and rehabilitation of children.

Keywords: education policy, practices, physically disabled children, challenges, Balochistan, Pakistan

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Authors: Kadir Akkuş, Burcu A. Hamat, Kaan Ciloglu

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Additive Manufacturing (AM) is the respectable improvement of this century in the field of manufacturing and regarded as a breakthrough that represents the third industrial revolution by the leading authorities such as Wohlers Associates Inc., The Economist, and MIT Technology Review. Thanks to the stacking and unifying methodology of AM, design of lighter but stiffer parts with really more complex shapes and geometrical features, which were not possible by traditional subtractive manufacturing methods, became achievable. Through analysis of the AM process must be performed and mechanical properties of manufactured test parts must be studied to provide input for design. Furthermore, process capabilities, constraints, limitations and challenges regarding AM must be examined so that the design must be compatible with the process to be able to take all the advantages of the AM. In this paper, capabilities and limitations of AM will be investigated through a test part including unique features and manufactured from Ti-6Al-4V by employing Electron Beam Melting (EBM) technology by comparing to the test parts introduced in literature.

Keywords: additive manufacturing, DfAM, EBM, test artifact, Ti-6Al-4V

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Guoliang Lu, Changhou Lu, Xueyong Li

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In action recognition, previous studies have demonstrated the effectiveness of using multiple features to improve the recognition performance. We focus on two practical issues: i) most studies use a direct way of concatenating/accumulating multi features to evaluate the similarity between two actions. This way could be too strong since each kind of feature can include different dimensions, quantities, etc; ii) in many studies, the employed classification methods lack of a flexible and effective mechanism to add new feature(s) into classification. In this paper, we explore an unified scheme based on recently-proposed multi-dimensional dynamic time warping (MD-DTW). Experiments demonstrated the scheme's effectiveness of combining multi-feature and the flexibility of adding new feature(s) to increase the recognition performance. In addition, the explored scheme also provides us an open architecture for using new advanced classification methods in the future to enhance action recognition.

Keywords: action recognition, multi features, dynamic time warping, feature combination

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Authors: Aemro Tazeze Terefe, Mengistu K. Aredo, Abule M. Workagegnehu, Wondimagegn M. Tesfaye

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Climate-induced shocks have been shown to reduce agricultural production and cause fluctuation in output in developing countries. When livelihoods depend on rain-fed agriculture, climate-induced shocks translate into consumption shocks. Despite the substantial improvements in household consumption, climate-induced shocks, and other factors adversely affect consumption dynamics at the household level in Ethiopia. Therefore, household consumption dynamics in the context of climate-induced shocks help to guide resilience capacity and establish appropriate interventions and programs. The research employed three-round panel data based on the Ethiopian Socioeconomic Survey with spatial rainfall data to define unique measures of rainfall variability. The linear dynamic panel model results show that the lagged value of consumption, market shocks, and rainfall variability positively affected consumption dynamics. In contrast, production shocks, temperature, and amount of rainfall had a negative relationship. Coping strategies mitigate adverse climate-induced shocks on consumption aftershocks that smooth consumption over time. Support to increase the resilience capacity of households can involve efforts to make existing livelihoods and forms of production or reductions in the vulnerability of households. Therefore, government interventions are mandatory for asset accumulation agendas that support household coping strategies and respond to shocks. In addition, the dynamic linkage between consumption and significant socioeconomic and institutional factors should be taken into account to minimize the effect of climate-induced shocks on consumption dynamics.

Keywords: climate shock, Ethiopia, fixed-effect model, food security

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Authors: Al Mutawa A. Amir, Liaqat Ali, Faisal Ali

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After the World War II, the world economy was disrupted and changed due to oil and its prices. The research in this paper presents the basic statistical features and economic characteristics of the Gulf economy. The main features of the Gulf economies and its heavy dependence on oil exports, its dualism between modern and traditional sectors and its rapidly increasing affluences are particularly emphasized.  In this context, the research in this paper discussed the problems of growth versus development and has attempted to draw the implications for the future economic development of this area.

Keywords: oil prices, GCC, economic growth, gulf oil

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Authors: Osayande Pascal Omondiagbe, Sherlock a Licorish

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Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models.

Keywords: feature selection, modeling and prediction, neural network, random forest, stack overflow

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Authors: Siran Offman, Hermes Pacule, Teofilo Nhamuhuco

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Marine invertebrates are very important for the livelihood of coastal communities, particularly in Pemba City. The study was conducted From June 2011 to March 2012. The aim of this study is to determine the socioeconomic impact of collecting marine invertebrates in communities and Chuiba Maringanha. Data were collected biweekly during the spring tide ebb in the intertidal zone, and through structured surveys, the confrontation of data was done through direct observation in the neighborhoods. In total 40 collectors was surveyed and it was found that activity of collecting marine invertebrates is practiced by women 57.2% and men 42.5%. Their ages ranged from 9 to 45 years, and the range was 25-32 dominant with 30.5% and collection practice 5-7 times per week they spend about 4-6 hours a day. The collection methods are direct harvesting by hand aided by knives, sharp irons, and transport use pots, buckets, basins, shawls. Were identified in total 8 marketable species namely: Octopus vulgaris 8.6 Kg, Cyprea Tigers 7 units, Cypraea annulus 48 kg, 40 kg holuturias, Cyprea bully, Atrina vexilium 10 kg, Modiulus philiphinarum and lambis lambis. The species with the greatest economic value are sea cucumber (3 Usd/ kg) and Octopus vulgaris ( 2.5 Usd/ kg) more commercialized. The socio-economic impacts on communities of collectors the average income of collectors varies from 0.5 to 5 Usd/ day and the money are intended to purchase food and agricultural instruments. The other socioeconomics impacts are illiteracy with 36% dropout, and 28% have never studied 87% of unemployed collectors, a high number of family members, weak economic power, poor housing made the basis of local materials and relies on community wells to access water, and most do not have electric power.

Keywords: socio-economic, impacts, collecting marine invertebrates, communities

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: J. S. Kim

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This paper presents a 0.13-µm Complementary Metal-Oxide Semiconductor (CMOS) vector modulator for beamforming system. The vector modulator features a 360° phase and gain range of -10 dB to 10 dB with a root mean square phase and amplitude error of only 2.2° and 0.45 dB, respectively. These features make it a suitable for wireless backhaul system in the 5 GHz industrial, scientific, and medical (ISM) bands. It draws a current of 20.4 mA from a 1.2 V supply. The total chip size is 1.87x1.34 mm².

Keywords: CMOS, vector modulator, beamforming, 802.11ac

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Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

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Authors: M. Benjamin Stacey

Abstract:

Energy efficiency has been increasingly recognized as a high value resource through state policies that require utility companies to implement efficiency programs. While policymakers have recognized the statewide economic, environmental, and health related value to residents who rely on this grid supplied resource, varying interests in energy efficiency between socioeconomic groups stands undifferentiated in most state legislation. Instead, the benefits are oftentimes assumed to be distributed equitably across these groups. Despite this fact, these policies are frequently sited by advocacy groups, regulatory bodies and utility companies for their ability to address the negative financial, health and other social impacts of energy poverty in low income communities. Yet, while most states like Michigan require programs that target low income consumers, oftentimes no requirements exist for the equitable investment and energy savings for low income consumers, nor does it stipulate minimal spending levels on low income programs. To further understand the impact of the absence of these factors in legislation, this study examines the distribution of program funds and energy efficiency savings to answer a fundamental energy justice concern; Are there disparities in the investment and benefits of energy efficiency programs between socioeconomic groups? This study compiles data covering the history of Michigan’s Energy Efficiency policy implementation from 2010-2016, analyzing the energy efficiency portfolios of Michigan’s two main energy providers. To make accurate comparisons between these two energy providers' investments and energy savings in low and non-low income programs, the socioeconomic variation for each utility coverage area was captured and accounted for using GIS and US Census data. Interestingly, this study found that both providers invested more equitably in natural gas efficiency programs, however, together these providers invested roughly three times less per household in low income electricity efficiency programs, which resulted in ten times less electricity savings per household. This study also compares variation in commission approved utility plans and actual spending and savings results, with varying patterns pointing to differing portfolio management strategies between companies. This study reveals that for the history of the implementation of Michigan’s Energy Efficiency Policy, that the 35% of Michigan’s population who qualify as low income have received substantially disproportionate funding and energy savings because of the policy. This study provides an overview of results from a social perspective, raises concerns about the impact on energy poverty and equity between consumer groups and is an applicable tool for law makers, regulatory agencies, utility portfolio managers, and advocacy groups concerned with addressing issues related to energy poverty.

Keywords: energy efficiency, energy justice, low income, state policy

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Authors: Hurgesa Hundera, Samuel Shibeshibikeko, Tarike Daba, Tesfaye Ganamo

Abstract:

The study aimed at examining the ongoing adaptation strategies used by smallholder farmers in response to climate variability in Boset district. It also assessed the socioeconomic factors that influence the choice of adaptation strategies of smallholder farmers to climate variability risk. For attaining the objectives of the study, both primary and secondary sources of data were employed. The primary data were obtained through a household questionnaire, key informant interviews, focus group discussions, and observations, while secondary data were acquired through desk review. Questionnaires were distributed and filled by 328 respondents, and they were identified through systematic random sampling technique. Descriptive statistics and binary logistic regression model were applied in this study as the main analytical methods. The findings of the study reveal that the sample households have utilized multiple adaptation strategies in response to climate variability, such as cropping early mature crops, planting drought resistant crops, growing mixed crops on the same farm lands, and others. The results of the binary logistic model revealed that education, sex, age, family size, off farm income, farm experience, access to climate information, access to farm input, and farm size were significant and key factors determining farmers’ choice of adaptation strategies to climate variability in the study area. To enable effective adaptation measures, Ministry of Agriculture and Natural Resource, with its regional bureaus and offices and concerned non–governmental organizations, should consider climate variability in their planning and budgeting in all levels of decision making.

Keywords: adaptation strategies, boset district, climate variability, smallholder farmers

Procedia PDF Downloads 88

Authors: Igor Perechuda, Wojciech Cieśliński

Abstract:

The paper identifies the features of Polish sports clubs in the particular organizational forms: profit and nonprofit. Identification and description of these features is carried out in terms of financial efficiency of the given organizational form. Under the terms of the efficiency the research allows you to specify the advantages of particular organizational sports club form and the following limitations. Paper considers features of sports clubs in range of Polish conditions as legal regulations. The sources of the functioning efficiency of sports clubs may lie in the organizational forms in which they operate. Each of the available forms can be considered either a for-profit or nonprofit enterprise. Depending on this classification there are different capabilities of increasing organizational and financial efficiency of a given sports club. Authors start with general classification and difference between for-profit and non-profit sport clubs. Next identifies specific financial and organizational conditions of both organizational form and then show examples of mixed activity forms and their efficiency effect.

Keywords: financial efficiency, for-profit, non-profit, sports club

Procedia PDF Downloads 551

Authors: Dieudonne Uwizeye, Reuben Muhayiteto

Abstract:

Teenage pregnancy is a challenging problem for sustainable development due to restrictions it poses to socioeconomic opportunities for young mothers, their children and families. Being unable to take appropriate economic and social responsibilities, teen mothers get trapped into poverty and become economic burden to their family and country. Besides, teenage pregnancy is also a health problem because children born to very young mothers are vulnerable with greater risk of illnesses and deaths, and teenage mothers are more likely to be exposed to greater risk of maternal mortality and to other health and psychological problems. In Kigali city, in Rwanda, teenage pregnancy rate is currently high and its increase in recent years is worrisome. However, only individual factors influencing the teenage pregnancy tend to be the basis of interventions. It is important to understand the important socioeconomic factors at the household level that are associated with teenage pregnancy to help government, parents, and other stakeholders to appropriately address the problem with sustainable measures. This study analyzed secondary data from the Fifth Rwanda Demographic and Health Survey (RDHS-V 2014-2015) conducted by the National Institute of Statistics of Rwanda (NISR). The aim was to examine household socio-economic factors that are associated with incidence of teenage pregnancies in Kigali city. In addition to descriptive analysis, Pearson’s Chi Square and Binary Logistic Regression were used in the analysis. Findings indicate that marital status and age of household head, number of members in a household, number of rooms used for sleeping, educational level of the household head and household's wealth are significantly associated with teenage pregnancy in Rwanda ( p< 0.05). It was found that teenagers living with parents, those having parents with higher education and those from richer families are less likely to become pregnant. Age of household head was pinpointed as factor to teenage pregnancy, with teenage-headed households being more vulnerable. The findings also revealed that household composition correlates with the probability of teenage pregnancy (p < 0.05) with teenagers from households with less number of members being more vulnerable. Regarding the size of the house, the study suggested that the more rooms available in households, the less incidences of teenage pregnancy are likely to be observed (p < 0.05). However, teenage pregnancy was not significantly associated with physical violence among parents (p = 0.65) and sex of household heads (p = 0.52), except in teen-headed households of which female are predominantly heads. The study concludes that teenage pregnancy remains a serious social, economic and health problem in Rwanda. The study informs government officials, parents and other stakeholders to take interventions and preventive measures through community sex education, policies and strategies to foster effective parental guidance, care and control of young girls through meeting their necessary social and financial needs within households.

Keywords: household socio-economic factors, Rwanda, Rwanda demographic and health survey, teenage pregnancy

Procedia PDF Downloads 179

Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

Abstract:

In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

Procedia PDF Downloads 267