Search results for: socioeconomic features

Authors: Alejandra Letelier, Anja Heilmann, Richard G. Watt, Stephen Jivraj, Georgios Tsakos

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Background: Socioeconomic position (SEP) influences adult health. People who experienced material disadvantages in childhood or adulthood tend to have higher adult disease levels than their peers from more advantaged backgrounds. Even so, life is a dynamic process and contains a series of transitions that could lead people through different socioeconomic paths. Research on social mobility takes this into account by adopting a trajectory approach, thereby providing a long-term view of the effect of SEP on health. Aim: The aim of this research examines the effects of intergenerational social mobility on adult general health, oral health and functioning in a population aged 50 and over in England. Methods: This study is based on the secondary analysis of data from the English Longitudinal Study of Ageing (ELSA). Using cross-sectional data, nine social trajectories were created based on parental and adult occupational socio-economic position. Regression models were used to estimate the associations between social trajectories and the following outcomes: adult self-rated health, self-rated oral health, oral health related quality of life, total tooth loss and grip strength; while controlling for socio-economic background and health related behaviours. Results: Associations with adult SEP were generally stronger than with childhood SEP, suggesting a stronger influence of proximal rather than distal SEP on health and oral health. Compared to the stable high group, being in the low SEP groups in childhood and adulthood was associated with poorer health and oral health for all examined outcome measures. For adult self-rated health and edentulousness, graded associations with social mobility trajectories were observed. Conclusion: Intergenerational social mobility was associated with self-rated health and total tooth loss. Compared to only those who remained in a low SEP group over time reported worse self-rated oral health and oral health related quality of life, and had lower grip strength measurements. Potential limitations in relation to data quality will be discussed.

Keywords: social determinants of oral health, social mobility, socioeconomic position and oral health, older adults oral health

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

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Authors: Natalia Limones, Javier Marzo, Marcus Wijnen, Aleix Serrat-Capdevila

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In this research we developed a structured approach for the detection of areas under the highest levels of drought risk that is suitable for data-scarce environments. The methodology is based on recent scientific outcomes and methods and can be easily adapted to different contexts in successive exercises. The research reviews the history of drought in the south of Angola and characterizes the experienced hazard in the episode from 2012, focusing on the meteorological and the hydrological drought types. Only global open data information coming from modeling or remote sensing was used for the description of the hydroclimatological variables since there is almost no ground data in this part of the country. Also, the study intends to portray the socioeconomic vulnerabilities and the exposure to the phenomenon in the region to fully understand the risk. As a result, a map of the areas under the highest risk in the south of the country is produced, which is one of the main outputs of this work. It was also possible to confirm that the set of indicators used revealed different drought vulnerability profiles in the South of Angola and, as a result, several varieties of priority areas prone to distinctive impacts were recognized. The results demonstrated that most of the region experienced a severe multi-year meteorological drought that triggered an unprecedent exhaustion of the surface water resources, and that the majority of their socioeconomic impacts started soon after the identified onset of these processes.

Keywords: drought risk, exposure, hazard, vulnerability

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

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Authors: Weiju Zhou, Alex Hopkins, Ruoling Chen

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Background: China has increased the gap in income between richer and poorer over the past 40 years, and the number of deaths from people with angina has been rising. It is unclear whether socioeconomic status (SES) is associated with increased mortality in older people with angina. Methods: Data from a cohort study of 2,380 participants aged ≥ 65 years, who were randomly recruited from 5-province urban communities were examined in China. The cohort members were interviewed to record socio-demographic and risk factors and document doctor-diagnosed angina at baseline and were followed them up in 3-10 years, including monitoring vital status. Multivariate Cox regression models were employed to examine all-cause mortality in relation to low SES. Results: The cohort follow-up identified 373 deaths occurred; 41 deaths in 208 angina patients. Compared to participants without angina (n=2,172), patients with angina had increased mortality (multivariate adjusted hazard ratio (HR) was 1.41, 95% CI 1.01-1.97). Within angina patients, the risk of mortality increased with low satisfactory income (2.51, 1.08-5.85) and having financial problem (4.00, 1.07-15.00), but significantly with levels of education and occupation. In non-angina participants, none of these four SES indicators were associated with mortality. There was a significant interaction effect between angina and low satisfactory income on mortality. Conclusions: In China, having low income and financial problem increase mortality in older people with angina. Strategies to improve economic circumstances in older people could help reduce inequality in angina survival.

Keywords: angina, mortality, older people, socio-economic status

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Dyego Ramos Henrique, Viviane Pimentel, Katia Souto, Ana Valéria Mendonça, Andrea Gallassi

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Socioeconomic inequalities in Brazil have accentuated the aggravations of poverty among the most vulnerable populations, among which are the quilombola communities. The objective was to reflect on a situation of food and nutritional security in two Brazilian quilombola communities. The data were collected by means of reports of experience through the production of talk wheels in two quilombola communities (Itamatatiua and Mesquita), located in the cities of Alcântara and Cidade Ocidental. Access to health services and health promotion actions were still incipient in the quilombola communities visited. The perceptions of the participants of the quilombolas revealed that there are still repressed demands that have rendered the fulfillment of the principles of equity, universality and integrality, both for access to health and for access and availability of food. They recognize in governmental instances a socioeconomic-cultural valorization and nutritional qualities intrinsic to the foods produced by them. Although they have been used as communities of quilombolas live and their level of access to services and programs, dealing with quilombola communities does not mean dealing with 'isolated groups or a strictly homogeneous population.' It demands a great need of attention in relation to the access and availability of food, besides overcoming barriers that made it an unfeasible valuation of social, economic and cultural precepts, intrinsic to the thought about food and nutritional security in Brazilian quilombos.

Keywords: access to services, food and nutrition security, health promotion, quilombo population

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Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Priyanka R. Sagar

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Social economic status (SES) is a variable that denotes the social standing of a person in society, and quality of life is a measure of health, happiness, and comfort of an individual. During early 2020, the world was stuck by the blow of the COVID-19 pandemic resulting in minimal or no economic activities to takes place. The present research paper is an attempt to analyze the socioeconomic status and quality of life of construction workers dwelling in the sub-urban areas of Hoskote located in the Bengaluru rural district pre and post-COVID-19. It also tries to analyze the difference in these variables pre and post-COVID-19. The study uses a retrospective design and data collected through a questionnaire survey from the respondents of Hoskote. A total of 100 samples were collected, out of which 73% were men and 27% were women. The mean age group of the participants is 41.04 ± 6.97 years. The overall analysis of the study shows that there is a significant difference in the socioeconomic status of construction workers pre and post-COVID-19. The study shows SES of the workers pre-pandemic is higher than post-pandemic. The other variable is quality of life which consists of physical health, psychological health, social relationships, and environmental domains. The study depicts that the psychological domain alone has been impacted by the pandemic; workers had better mental health pre-COVID-19. The other domains, i.e., physical health, social relationship, and environment, remain unaffected.

Keywords: socio-economic status, quality of life, construction workers, COVID-19

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Authors: Samina Nazli, Nadia Qamar, Quratulain, Akasha, Saman Jamal

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Objective: The objective is to determine the frequency of iron deficiency anemia among children having febrile seizures. A descriptive Cross-Sectional Study was done in the Pediatric Unit of Allama Iqbal Memorial Teaching Hospital Sialkot from September 2020 to February 2021. Material & Methods: A total of 70 children were studied aged six months to 10 years, with either gender presenting with febrile seizures. All data of the patients was documented, including demographic data like age, gender, residential area, educational status, socioeconomic status and clinical findings at the time of presentation like fever, fits and duration of symptoms etc. Blood hemoglobin and ferritin levels were tested for each patient to evaluate iron deficiency anemia. Results: There were 65.7% male and 34.3% female cases in this study. The age range of the patients was 6 months to 10 years, with a mean age of 4.36 ± 2.71 years. Most of the children (60%) were below three years of age. Most children belonged to low and middle socioeconomic status with a frequency of 42.8% and 45.7%, respectively. Iron deficiency anemia was found in 38.6% of cases. The majority of the mothers were illiterate (65%). There were 44.3% cases from rural areas and 55.7% from urban areas. Conclusion: Iron deficiency anemia is a common problem among children with febrile seizures, younger than 03 years and belonging to rural areas. Illiterate mothers are an important risk factor for iron deficiency anemia in their children.

Keywords: febrile seizure, iron deficiency anemia, illetrate mother, low scioeconomic status, febrile siezure

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Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

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Authors: Han Xue, Zhang Lanyue

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In order to seek more effective feature extraction technology, feature extraction method based on MFCC combined with vector hydrophone is exposed in the paper. The sound pressure signal and particle velocity signal of two kinds of ships are extracted by using MFCC and its evolution form, and the extracted features are fused by using fisher-ratio and correlated distance criterion. The features are then identified by BP neural network. The results showed that MFCC, First-Order Differential MFCC and Second-Order Differential MFCC features can be used as effective features for recognition of underwater targets, and the fusion feature can improve the recognition rate. Moreover, the results also showed that the recognition rate of the particle velocity signal is higher than that of the sound pressure signal, and it reflects the superiority of vector signal processing.

Keywords: vector information, MFCC, differential MFCC, fusion feature, BP neural network

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, molecular descriptors, machine learning, random forest

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Authors: German Marino Rivera

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Linear plantations are a common practice in several countries as living fences (LF) delimiting agroecosystems. They are composed of multipurpose perennial woods that provide assets, protection, and supply services. However, not much is known in some traditional communities like the Andean region, including the species composition and the social and ecological benefits of the species used. In the High Andean Colombian region, LF seems to be very typical and diverse. This study aimed to analyze the traditional knowledge about LF systems, including the species composition and their uses in rural communities of Alto Casanare, Colombia. Field measurements, interviews, guided tours, and species sampling were carried out in order to describe traditional practices and the species used in the LF systems. The use values were estimated through the Coefficient of Importance of the Species (CIS). A total of 26 farms engage in LF practices, covering an area of 9283.3 m. In these systems, 30 species were identified, belonging to 23 families. Alnus acuminata was the specie with the highest CIS. The species presented multipurpose uses for both economic and ecological purposes. The transmission of knowledge (TEK) about the used species is very heterogeneous among the farmers. Many species used were not documented, with reciprocal gaps between the literature and traditional species uses. Exchanging this information would increase the species' versatility, the socioeconomic aspects of these communities, increases the agrobiodiversity and ecological services provided by LF. The description of the TEK on LF provides a better understanding of the relationship of these communities with the natural resources, pointing out creative approaches to achieve local environment conservation in these agroecosystems and promoting socioeconomic development.

Keywords: ethnobotany, living fences, traditional communities, agroecology

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Authors: Elif N. Coban

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With the political instability that has prevailed in Lebanon since October 2019, followed by a global pandemic and a deepening concurrent economic crisis after the Beirut Port explosion on August 4, 2020, Syrian refugees in Lebanon have struggled to survive what the World Bank has described as one of the worst economic crises in decades. This study aims to assess the vulnerability of Syrian refugee women. It will present a comparative analysis of refugee and Lebanese households using data from Lebanon’s Labour Force and Household Conditions Survey (LFHLCS) and from VASyr surveys, which are comprehensive annual surveys conducted jointly by the United Nations High Commissioner for Refugees (UNHCR), the United Nations Children’s Fund (UNICEF), and the United Nations World Food Programme (WFP). The study adopts an intersectionality-based method, which deals with gender and marginalized communities from many different perspectives, to put forward a gender-oriented approach. Examining the distribution of socioeconomic status among Syrian and Lebanese households might help to understand the disproportionate burdens borne by women. In this context, multidimensional poverty (MP) helps depict fragile communities’ socioeconomic status and allows a fuller grasp the multiple aspects of deprivation. Finally, this understanding may pave the way to more inclusive policy for decision-makers and practitioners working on refugee issues.

Keywords: multidimensional poverty, gender studies, intersectionality, Syrian refugees, Lebanon

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Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: Kawalpreet Kalra, Alpana Goel

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The behaviour of the rotational bands should be smooth but due to large amount of inertia and decreased pairing it is not so. Many experiments have been done in the last few decades, and a large amount of data is available for comprehensive study in this region. Peculiar features like signature dependence, signature inversion, and signature reversal are observed in many two quasiparticle rotational bands of doubly odd and doubly even nuclei. At high rotational frequencies, signature and parity are the only two good quantum numbers available to label a state. Signature quantum number is denoted by α. Even-angular momentum states of a rotational band have α =0, and the odd-angular momentum states have α =1. It has been observed that the odd-spin members lie lower in energy up to a certain spin Ic; the normal signature dependence is restored afterwards. This anomalous feature is termed as signature inversion. The systematic of signature inversion in high-j orbitals for doubly odd rare earth nuclei have been done. Many unusual features like signature dependence, signature inversion and signature reversal are observed in rotational bands of even-even/odd-odd nuclei. Attempts have been made to understand these phenomena using several models. These features have been analyzed within the framework of the Two Quasiparticle Plus Rotor Model (TQPRM).

Keywords: rotational bands, signature dependence, signature quantum number, two quasiparticle

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Lindy Valdez, Daryl Parker

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Introduction: There has been much research on academic achievement in elementary schools. Most of the research has been on school leadership. While research has focused on the role of leadership on school improvement, little research has examined what variables the top elementary schools have in common. To undertake school improvement, it is important to understand what factors the best schools share. The purpose of this study was to examine data of the “Best Elementary Schools in California,” based on academic achievement as rated by three prominent websites and determine if these schools had any common factors which were different than the statewide averages. The variables examined included access to subject matter specialists (physical education, art, and music), librarians, after school programs, class size, socioeconomic status, and diversity. The participants consisted of the top public elementary schools in California based on the websites i)https://www.niche.com/k12/search/best-schools/, ii)https://www.finder.com/best-schools-california,and iii)https://www.schooldigger.com/go/CA/schoolrank.aspx. The data for subject matter specialists (physical education, art, and music), librarians, after school programs, class size, socioeconomic status, and diversity were collected from these top schools and compared to California statewide averages. Results indicate that top public elementary schools in California have a high number of subject matter specialists that teach physical education, art, and music. These positions are on the decline in the average public elementary school in California, but the top schools have abundant access to these specialists. The physical education specialist has the highest statistically significant difference between the nationwide average and the top schools—librarians, and after school programs are also most commonly high in top public elementary schools in California. The high presence of these programs may be aiding academic achievement in less visible ways. Class size is small, socio-economic status is high, and diversity is low among top public elementary schools in California when compared to the statewide average public elementary schools in California. The single largest area of discrepancy was between physical education specialists in a top school and their state and nationwide averages. The socioeconomic status of schools and parents may be an underlining factor affecting several other variables. This affluence could explain how these schools were able to have access to subject matter specialists, after-school activities, and, therefore, more opportunities for physical activity and greater learning opportunities affecting academic achievement.

Keywords: academic achievement, elementary education, factors, schools

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Goran Sporiš, Dinko Vuleta, Stefan Lovro

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The purpose of the present study was to determine the associations between health-related quality of life (HRQOL) and physical activity (PA) in different domains. In this cross-sectional study, participants were 169 Croatian police officers (mean age 35.14±8.95 yrs, mean height 180.93±7.53 cm, mean weight 88.39±14.05 kg, mean body-mass index 26.90±3.39 kg/m2). The dependent variables were two general domains extracted from the HRQOL questionnaire: (1) physical component scale (PCS) and (2) mental component scale (MCS). The independent variables were job-related, transport, domestic and leisure-time PA, along with other factors: age, body-mass index, smoking status, psychological distress, socioeconomic status and time spent in sedentary behaviour. The associations between dependent and independent variables were analyzed by using multiple regression analysis. Significance was set up at p < 0.05. PCS was positively associated with leisure-time PA (β 0.28, p < 0.001) and socioeconomic status (SES) (β 0.16, p=0.005), but inversely associated with job-related PA (β -0.15, p=0.012), domestic-time PA (β -0.14, p=0.014), age (β -0.12, p=0.050), psychological distress (β -0.43, p<0.001) and sedentary behaviour (β -0.15, p=0.009). MCS was positively associated with leisure-time PA (β 0.19, p=0.013) and SES (β 0.20, p=0.002), while inversely associated with age (β -0.23, p=0.001), psychological distress (β -0.27, p<0.001) and sedentary behaviour (β -0.22, p=0.001). Our results added new information about the associations between domain-specific PA and both physical and mental component scale in police officers. Future studies should deal with the same associations in other stressful occupations.

Keywords: health, fitness, police force, relations

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: So-Young Lee, Myoung-Won Oh, Soon-Cheol Eom, Yeon-Won Suh

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Buildings produce more than one thirds of the total energy consumption and CO₂ emissions. Korean government agency pronounced and initiated Zero Energy Buildings policy for construction as of 2025. The net zero energy design features include passive (daylight, layout, materials, insulation, finishes, etc.) and active (renewable energy sources) elements. The Zero Energy House recently built in Nowon-gu, Korea is provided for 121 households as a public rental housing complex. However most of public rental housing did not include sustainable features which can reduce housing maintaining cost significantly including energy cost. It is necessary to implement net zero design features to the obsolete public rental housing during the remodeling procedure since it can reduce housing cost in long term. The purpose of this study is to investigate sustainable design elements implemented in Net Zero Energy House in Korea and passive and active housing design features in order to apply the sustainable features to the case public rental apartment for remodeling. Housing complex cases in this study are Nowan zero Energy house, Gangnam Bogemjari House, and public rental housings built in more than 20 years in Seoul areas. As results, energy consumption in public rental housing built in 5-years can be improved by exterior surfaces. Energy optimizing in case housing built in more than 20 years can be enhanced by renovated materials, insulation, replacement of windows, exterior finishes, lightings, gardening, water, renewable energy installation, Green IT except for sunlight and layout of buildings. Further life costing analysis is needed for energy optimizing for case housing alternatives.

Keywords: affordable housing, remodeling, sustainable design, zero-energy house

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Authors: Mostafa Gabr, Rania Abdul Galil, Nihal Salim

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Various interventions are applied in cities with the aim to improve living and acoustic environmental conditions. Noise is one of the most influential and critical factors in the environment that has an effect on the QOL (quality of life) and urban environment. It ranks second among environmental pollution issues according to EEAA. Traffic noise is a major source of noise. Noise barriers are one of the physical techniques in landscape design used to reduce the impact of noise pollution in urban areas. Roadways noise pollution can be best controlled by a noise barrier. The aim of this paper is to consider all facets of sustainability when designing a comfortable acoustic environment in roadways, through different strategies related to planning and the design process. The study focuses on the relation between the design of noise barriers as a landscape noise mitigation installation and their materiality in so far as it influences the sustainability of the open space and the acceptability of users. According to previous studies, design of noise barrier mainly depends on cost as a decisive factor. This study asserts that environmental and socioeconomic costs associated are equally important. Hence, the paper presents a strategy for sustainable soundscape design. It builds a framework focusing on materiality considering the environmental and socioeconomic impact of noise barriers shaping urban open space around the road ways, and the different academic and market positions on noise barrier types and materials. Finally, it concludes with a matrix of the relation between the noise barrier design consideration and the three pillars of sustainability (social, economic and environmental).

Keywords: traffic noise level, acoustic sustainability, noise barrier, noise reduction, noise control, acoustical level

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Lovro Štefan

Abstract:

The main purpose of the present study was to investigate the associations between the overweight/obesity status and lifestyle factors. In this cross-sectional study, participants were 1950 urban secondary-school students (54.7% of female students) aged 17-18 years old. Dependent variable was body-mass index status derived from self-reported height and weight. The outcome was binarised, where participants with value <25 kg/m2 were collapsed into „normal“, while those ≥25 kg/m2 into „overweight/obesity“ category. Independent variables were gender, type of school, physical activity, sedentary behaviour, self-rated health, self-perceived socioeconomic status and psychological distress. The associations between the dependent and independent variables were analyzed by using multiple logistic regression analysis. In the univariate model, being overweight/obese was significantly associated with being a male student (OR 0.31; 95% CI 0.23 to 0.42), attending a vocational school (OR 1.87; 95% CI 1.42 to 2.48), not meeting the recommendations for moderate-to-vigorous physical activity (OR 0.44; 95% CI 0.22 to 0.88), more time spending in sedentary behaviour (OR 1.53; 95% CI 1.07 to 2.19), poor self-rated health (OR 0.35, 95% CI 0.20 to 0.56) and lower socioeconomic status (OR 0.63; 95% CI 0.48 to 0.84). In the multivariate model, the same associations occured between the dependent and independent variable. In both models, psychological distress was not associated with being overweight/obese. In conclusion, our findings suggest, that lifestyle factors are independently associated with body-mass index

Keywords: body mass index, secondary-school students, Croatia, physical activity, sedentary behaviour, logistic regression

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Authors: Ramy Hammady, Sylvester Arnab

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Significant attention has been directed to adopt game interventions practically to change certain behaviours in many disciplines such as health, education, psychology through many years. That’s due to the intrinsic motivation that games can cause and the substantial impact the games can leave on the player. Many review papers were induced to highlight and measure the effectiveness of the game’s interventions on changing behaviours; however, most of these studies neglected the game design process itself and the game features and elements that can stimuli changing behaviours. Therefore, this paper aims to identify the most game design mechanics and features that are the most influencing on changing behaviour during or after games interventions. This paper also sheds light on the theories of changing behaviours that clearly can led the game design process. This study gives directions to game designers to spot the most influential game features and mechanics for changing behaviour games in order to exploit it on the same manner.

Keywords: behaviour change, game design, serious gaming, gamification, review

Procedia PDF Downloads 181