Search results for: emotion mining

Authors: DEVASHISH KHULBE, MANU PATHAK

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The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.

Keywords: Web Mining, Geographic Information Systems, Business popularity, Spatial Data Analyses

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Authors: Revoti Prasad Bora, Nikita Katyal, Saurabh Yadav

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Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques.

Keywords: deep-learning, classification, pre-processing, computer vision, image processing, educational data mining

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Authors: Phumelele Kubheka, Pius Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model.

Keywords: big data, latent Dirichlet allocation, latent semantic indexing, telco, topic modeling, twitter

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Authors: Muhammad Farooq

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The present article explores the woman's consent as a paramount element in contracting a Muslim marriage. Also, whether permission of the wali (guardian) is a condition per se for a valid nikah (marriage deed) in the eye of law and Sharia. The researcher attempts to treat it through the related issues, inter alia; the marriage guardian, the women's legal capacity to give consent whether she is a virgin or nonvirgin and how that consent is to be given or may be understood. Does her laugh, tears or salience needs a legal interpretation as well as other female manifestations of emotion explained by the Muslim jurists? The silence of Muslim Family Law Ordinance 1961 (hereafter; MFLO 1961) in this regard and the likely reasons behind such silence is also inquired in brief. Germane to the theme, the various cases in which the true notion of woman's consent is interpreted by courts in Pakistan are also examined. In order to address the issue in hand, it is proposed to provide a brief overview of a few contemporary writers' opinions in which the real place of woman's consent in Muslim marriage is highlighted. Key to the idea of young Muslim woman's marriage, the doctrine of kafa'a (equality or suitability) between the man and woman is argued here to be grounded in the patriarchal and social norms. It is, therefore, concluded that such concept was the result of analogical reasoning and has less importance in the present time. As such it is not a valid factor in current scenarios to validate or invalidate marital bonds. A standard qualitative convention is used for this research. Among primary and secondary sources; for examples, Qur'an, Sunnah, Books, Scholarly articles, texts of law and case law is used to point out the researcher's view. In summation, the article is concluded with a bold statement that a young woman being a party to the contract, is absolutely entitled to 'full and free' consent for the Muslim marriage contract. It is the woman, an indispensable partaker and her consent (not the guardian' permission) that does validate or invalidate the said agreement in the eye of contemporary personal law and in Sharia.

Keywords: consent of woman, ejab (declaration), Nikah (marriage agreement), qabol (acceptance), sui juris (of age; independent), wali (guardian), wilayah (guardianship)

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Authors: Yu Hung Chiang, Hei Chia Wang

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Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently.

Keywords: text mining, sentiment analysis, product feature extraction, multi-lexicons

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Authors: Hyun-Woo Cho

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Continuous monitoring of industrial plants is one of necessary tasks when it comes to ensuring high-quality final products. In terms of monitoring and diagnosis, it is quite critical and important to detect some incipient abnormal events of manufacturing processes in order to improve safety and reliability of operations involved and to reduce related losses. In this work a new multivariate statistical online diagnostic method is presented using a case study. For building some reference models an empirical discriminant model is constructed based on various past operation runs. When a fault is detected on-line, an on-line diagnostic module is initiated. Finally, the status of the current operating conditions is compared with the reference model to make a diagnostic decision. The performance of the presented framework is evaluated using a dataset from complex industrial processes. It has been shown that the proposed diagnostic method outperforms other techniques especially in terms of incipient detection of any faults occurred.

Keywords: data mining, empirical model, on-line diagnostics, process fault, process monitoring

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Authors: Issa M. Shehabat, Huda F. Y. Nimri

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This paper aims to study the relationship between knowledge management in its processes, including knowledge creation, knowledge sharing, knowledge organization, and knowledge application, and business intelligence tools, including OLAP, data mining, and data warehouse, and their impact on the decision-making process in the banking sector in Jordan. A total of 200 questionnaires were distributed to the sample of the study. The study hypotheses were tested using the statistical package SPSS. Study findings suggest that decision-making processes were positively related to knowledge management processes. Additionally, the components of business intelligence had a positive impact on decision-making. The study recommended conducting studies similar to this study in other sectors such as the industrial, telecommunications, and service sectors to contribute to enhancing understanding of the role of the knowledge management processes and business intelligence tools.

Keywords: business intelligence, knowledge management, decision making, Jordan, banking sector

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Authors: Yulin Zhang, Kaixi Dong, Guozhen Zhao

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Background: Different levels of life satisfaction are associated with some individual differences. Understanding the mechanism between them will help to enhance an individual’s well-being. On the one hand, traditional personality such as extraversion has been considered as the most stable and effective factor in predicting life satisfaction to the author’s best knowledge. On the other, individual emotional difference, such as alexithymia (difficulties identifying and describing one’s own feelings), is also closely related to life satisfaction. With the development of positive psychology, positive personalities such as virtues attract wide attention. And according to the broaden-and-build theory, social connectivity may mediate between emotion and life satisfaction. Therefore, the current study aims to explore the mediating role of social connectivity in the effect of positive personality and alexithymia on life satisfaction. Method: This study was conducted with 318 healthy Chinese college students whose age range from 18 to 30. Positive personality (including interpersonal, vitality, and cautiousness) was measured by the Chinese version of Values in Action Inventory of Strengths (VIA-IS). Alexithymia was measured by the Toronto Alexithymia Scale (TAS), and life satisfaction was measured by Satisfaction With Life Scale (SWLS). And social connectivity was measured by six items which have been used in previous studies. Each scale showed high reliability and validity. The mediating model was examined in Mplus 7.2 within a structural equation modeling (SEM) framework. Findings: The model fitted well and results revealed that both positive personality (95% confidence interval of indirect effect was [0.023, 0.097]) and alexithymia (95% confidence interval of indirect effect was [-0.270, -0.089]) predicted life satisfaction level significantly through social connectivity. Also, only positive personality significantly and directly predicted life satisfaction compared to alexithymia (95% confidence interval of direct effect was [0.109, 0.260]). Conclusion: Alexithymia predicts life satisfaction only through social connectivity, which emphasizes the importance of social bonding in enhancing the well-being of Chinese college students with alexithymia. And the positive personality can predict life satisfaction directly or through social connectivity, which provides implications for enhancing the well-being of Chinese college students by cultivating their virtue and positive psychological quality.

Keywords: alexithymia, life satisfaction, positive personality, social connectivity

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Authors: Shan Jiang, Xinyu Tang

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Game-based learning is an emerging pedagogy to make the learning experience more effective, enjoyable, and fun. However, most games used in classroom settings have been overly simplistic. This paper presents a case study on a Python-based online game designed to improve the effectiveness in both teaching and research in higher education. The proposed game system not only creates a fun and enjoyable experience for students to learn various topics in programming but also improves the effectiveness of teaching in several aspects, including material presentation, helping students to recognize the importance of the subjects, and linking theoretical concepts to practice. The proposed game system also serves as an information cyber-infrastructure that automatically collects and stores data from players. The data could be useful in research areas including human-computer interaction, decision making, opinion mining, and artificial intelligence. They further provide other possibilities beyond these areas due to the customizable nature of the game.

Keywords: game-based learning, programming, research-teaching integration, Hearthstone

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Authors: Lauren Benyo Linford, Jared Warren, Jeremy Bekker, Gus Salazar

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In order to accurately identify areas needing improvement and track growth, the availability of valid and reliable measures of different facets of well-being is vital. Because no specific measures currently exist for many facets of well-being, the purpose of this study was to construct and validate measures of the following constructs: Purpose, Values, Mindfulness, Savoring, Gratitude, Optimism, Supportive Relationships, Interconnectedness, Compassion, Community, Contribution, Engaged Living, Personal Growth, Flow Experiences, Self-Compassion, Exercise, Meditation, and an overall measure of subjective well-being—the Survey on Flourishing. In order to assess their psychometric properties, each measure was examined for internal consistency estimates, and items with poor item-test correlations were dropped. Additionally, the convergent validity of the Survey on Flourishing (SURF) was assessed. Total score correlations of SURF and other commonly used measures of well-being such as the Positive and Negative Affect Schedule (PANAS), The Satisfaction with Life Scale (SWLS), the PERMA Profiler (measure of Positive Emotion, Engagement, Relationships, Meaning, and Achievement) were examined to establish convergent validity. The Kessler Psychological distress scale (K6) was also included to determine the divergent validity of the SURF measure. Three week test-retest reliability was also assessed for the SURF measure. Additionally, normative data from general population samples was collected for both the Self-Compassion and Survey on Flourishing (SURF) measures. The purpose of this study is to introduce each of these measures, divulge the psychometric findings of this study, as well as explore additional psychometric properties of the SURF measure in particular. This study will highlight how these measures can be used in future research exploring these positive psychology constructs. Additionally, this study will discuss the utility of these measures to guide individuals in their use of the online self-directed, self-administered My Best Self 101 positive psychology resources developed by the researchers. The goal of My Best Self 101 is to disseminate real, research-based measures and tools to individuals who are seeking to increase their well-being.

Keywords: measurement, psychometrics, test validation, well-Being

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Authors: Partha Saha, Uttam Kumar Banerjee

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Maternal and Child Healthcare (MCH) has always been regarded as one of the important issues globally. Reduction of maternal and child mortality rates and increase of healthcare service coverage were declared as one of the targets in Millennium Development Goals till 2015 and thereafter as an important component of the Sustainable Development Goals. Over the last decade, worldwide MCH indicators have improved but could not match the expected levels. Progress of both maternal and child mortality rates have been monitored by several researchers. Each of the studies has stated that only less than 26% of low-income and middle income countries (LMICs) were on track to achieve targets as prescribed by MDG4. Average worldwide annual rate of reduction of under-five mortality rate and maternal mortality rate were 2.2% and 1.9% as on 2011 respectively whereas rates should be minimum 4.4% and 5.5% annually to achieve targets. In spite of having proven healthcare interventions for both mothers and children, those could not be scaled up to the required volume due to fragmented health systems, especially in the developing and under-developed countries. In this research, a knowledge discovery based interactive Decision Support System (DSS) has been developed on web platform which would assist healthcare policy makers to develop evidence-based policies. To achieve desirable results in MCH, efficient resource planning is very much required. In maximum LMICs, resources are big constraint. Knowledge, generated through this system, would help healthcare managers to develop strategic resource planning for combatting with issues like huge inequity and less coverage in MCH. This system would help healthcare managers to accomplish following four tasks. Those are a) comprehending region wise conditions of variables related with MCH, b) identifying relationships within variables, c) segmenting regions based on variables status, and d) finding out segment wise key influential variables which have major impact on healthcare indicators. Whole system development process has been divided into three phases. Those were i) identifying contemporary issues related with MCH services and policy making; ii) development of the system; and iii) verification and validation of the system. More than 90 variables under three categories, such as a) educational, social, and economic parameters; b) MCH interventions; and c) health system building blocks have been included into this web-based DSS and five separate modules have been developed under the system. First module has been designed for analysing current healthcare scenario. Second module would help healthcare managers to understand correlations among variables. Third module would reveal frequently-occurring incidents along with different MCH interventions. Fourth module would segment regions based on previously mentioned three categories and in fifth module, segment-wise key influential interventions will be identified. India has been considered as case study area in this research. Data of 601 districts of India has been used for inspecting effectiveness of those developed modules. This system has been developed by importing different statistical and data mining techniques on Web platform. Policy makers would be able to generate different scenarios from the system before drawing any inference, aided by its interactive capability.

Keywords: maternal and child heathcare, decision support systems, data mining techniques, low and middle income countries

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Authors: Hacer Kobya Bulut, Kıymet Yeşilçiçek Çalık, Birsel Canan Demirbağ, Hacer Erdöl, Songül Aktaş

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Introduction: As an expression of an emotion which always exists in life, crying is regarded as one of the problematic behaviors of patients by nurses. Towards such patients, nurses may exhibit emotional and behavioral reactions such as feeling helpless, anger, indifferent, defense, and opposition. However crying either meets a need, reduces the tension to cope with problems or helps patient to gain strength. Therefore, nurses must accept that crying is a normal mechanism that reduces emotional tension and should approach a crying patient accordingly. Objective: This study was carried out to evaluate the communicational behaviors of the nurses towards ‘crying patient’. Methods: This descriptive study was conducted with the nurses working at a university hospital in a city in the Eastern Black Sea in June-September 2015. The entire universe was tried to be reached without sampling. 90% of the population was reached and the study was completed with 309 nurses who volunteered to participate in the study. Data were collected through a questionnaire which was prepared reviewing the literature by researchers. Data were evaluated in SPSS analysis program using percentages, numbers and chi-square test with the 95% confidence interval and p <0.05significance level. Findings: The findings showed that the average age of nurses was 31.52 ± 7.96, work experience was 10:09 ± 7.69 and only 22.7% had training about ‘approach to crying patient’ during their education. 97.1% of the nurses often faced with crying patients in their professional lives, 62.8% stated that they faced crying women patients. When they see crying patients, 84.8% of the nurses ‘do not want the patient to cry’, 80.9% wonder ‘why they are crying’, % 79.6 ‘feel uneasiness’,% 79.3 ‘feel sorry’ and 41.4% ‘ feel helpless’. The question ‘Why do you think the patient is crying?’ was answered by 93.5% nurses as ‘they are suffering’, by 86.1% ‘they are helpless’, 80.9% ‘they are sad’, 79.6% ‘they need help’, 54.4% ‘because they feel inadequate,’ and 44.7% ‘they fail to control their crying behavior. ‘How do you approach to your patient when she/he is crying?’ question was answered by 82.5% of nurses as ‘I would console’, 77.3% as ‘I would ask the reason’, 63.1% as ‘I would try to stop her from crying’ all of which are actually inappropriate nursing approaches. However, 92.2% of the nurses stated that ‘I do not judge the crying patient’, ‘87.1% said ‘I allocate time to crying patients’ and 85.8% said ‘ I ask patient whether they want to cry alone’. The study showed that educational background and work experience of the nurses affected the appropriate approach to crying patients (P <0.05). Conclusion: As a result of the study, it was found out that nurses do not want patients to cry, so they exhibit inappropriate approach such as consoling the patients and they have difficulty in approaching crying patients.

Keywords: approach to patient, communication, crying patient, nurse, Turkey

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Authors: Vesile Evrim, Aliyu Awwal

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

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Yemisrach Mihertu Amsale

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The purpose of this study was to compare burnout and coping strategies between HIV counselors in face to face and online counseling settings in Addis Ababa. The study was mixed approach design that was quantitative and qualitative. For the quantitative data the participants involved in this study included 64 face to face and 47 online HIV counselors in both counseling settings. In addition, 23 participants were involved to offer qualitative data from both counseling settings. For the purpose of gathering the quantitative data, the instruments, namely, demographic questionnaire, Maslach Burnout Inventory and the COPE questionnaire, were used to gather quantitative data. Qualitative data was also gathered in the FGD Guide and Interview Guide. Thus, this study revealed that HIV counselors in online counseling settings scored high on emotional exhaustion, depersonalization and low in personal accomplishment dimensions of burnout as compared to HIV counselors in face to face setting and the difference was statistically significant in emotional exhaustion and personal accomplishment, but there was no a significant difference on depersonalization dimension of burnout between the two groups. In addition, the present study revealed a statistically significant difference on problem focused coping strategy between the two groups and yet for on the emotion focused coping strategy the difference was not statistically significant. Statistically negative correlation was observed between some demographic variables such as age with emotional exhaustion and depersonalization dimensions of burnout; years of experiences and personal accomplishment dimension of burnout. A statistically positive correlation was also observed between average number of clients served per day and emotional exhaustion. Sex was having a statistically positive correlation with coping strategy. Lastly, a significant positive correlation was also observed in the emotional exhaustion dimension of the burnout and the emotional focused coping strategy. Generally, this study has shown that HIV counselors suffer from moderate to high level of burnout. Based on the findings, conclusions were made and recommendations were forwarded.

Keywords: counseling, burnout management, psychological, behavioral sciences

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Samira Karimi-Mansoub, Rahem Abri

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Currently, users expect high quality and personalized information from search results. To satisfy user’s needs, personalized approaches to web search have been proposed. These approaches can provide the most appropriate answer for user’s needs by using user context and incorporating information about query provided by combining search technologies. To carry out personalized web search, there is a need to make different techniques on whole of user search process. There are the number of possible deployment of personalized approaches such as personalized web search, personalized recommendation, personalized summarization and filtering systems and etc. but the common feature of all approaches in various domains is that user modeling is utilized to provide personalized information from the Web. So the most important work in personalized approaches is user model mining. User modeling applications and technologies can be used in various domains depending on how the user collected information may be extracted. In addition to, the used techniques to create user model is also different in each of these applications. Since in the previous studies, there was not a complete survey in this field, our purpose is to present a survey on applications and techniques of user modeling from the viewpoint of improvement in search results by considering the existing literature and researches.

Keywords: filtering systems, personalized web search, user modeling, user search behavior

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Authors: Vichai Tienthavorn

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Background: Recently, the patients of non-communicable diseases (NCDs) are increasing in Thailand; especially hypertension and diabetes. Hypertension and Diabetes patients were found to be of 3.7 million in 2008. The varieties of human behaviors have been extensively changed in health. Hence, Thai Government has a policy to reduce NCDs. Generally, primary care plays an important role in treatment using medical process. However, NCDs patients have not been decreased. Objectives: This study not only reduce the patient and mortality rate but also increase the quality of life, could apply in different areas and propose to be the national policy, effectively for a long term operation. Methods: Here we report that primary health care (PHC), which is a primary process to screening, rapidly seek the person's risk. The screening tool of the study was Vichai's 7 color balls model, the medical education tool to transfer knowledge from student health team to community through health volunteers, creating community engagement in terms of social participation. It was found that people in community were realized in their health and they can evaluate the level of risk using this model. Results: Projects implementation (2015) in Nong Lom Health Center in Phayao (target group 15-65 years, 2529); screening hypertension coveraged 99.01%, risk group (light green) was decreased to normal group (white) from 1806 to 1893, significant severe patient (red) was decreased to moderate (orange) from 10 to 5. Health Program in behaving change with best practice of 3Es (Eating, Exercise, Emotion) and 3Rs (Reducing tobacco, alcohol, obesity) were applied in risk group; and encourage strictly medication, investigation in severe patient (red). Conclusion: This is the first demonstration of knowledge transfer to community engagement by student, which is the sustainable education in PHC.

Keywords: non-communicable disease, surveillance control and prevention systems, community engagement, primary health care

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Authors: Ramadan M. El-Kahawy, M. A. El-Shafeiy, Mohamed Abd El-Wahab, S. A. Helal, Nabil Aboul-Ela

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Due to the economic importance of Safaga Bay, Quseir harbor and Ras Gharib harbor , a multidisciplinary approach was adopted to invistigate 27 surfecial sediment samples from the three sites and 9 samples for each in order to use the benthic foraminifera as bio-indicators for characterization of the environmental variations. Grain size analyses indicate that the bottom facies in the inner part of quseir is muddy while the inner part of Ras Gharib and Safaga is silty sand and those close to the entrance of Safaga bay and Ras Gharib is sandy facies while quseir still also muddy facies. geochemical data show high concentration of heavy-metals mainly in Ras Gharib due to oil leakage from the hydrocarbon oil field and Safaga bay due to the phosphate mining while quseir is medium concentration due to anthropocentric effect.micropaelontological analyses indicate the boundaries of the highest concentration of heavy metals and those of low concentration as well.the dominant benthic foraminifera in these three sites are Ammonia beccarii, Amphistigina and sorites. the study highlights the worsening of environmental conditions and also show that the areas in need of a priority recovery.

Keywords: benthic foraminifera, Ras Gharib, Safaga, Quseir, Red Sea, Egypt

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Authors: Yi-Ting Wang, Wen-Shu Lai

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The subject of this paper is the photography in the 1983 film Paris, Texas, directed by Wim Wenders. Wenders is well known as a film director as well as a photographer. We have found that photography is shown as a photographic element in many of his films. Some of these photographs serve as details within the films, while others play important roles that are relevant to the story. This paper aims to consider photographs in film as a specific type of text, which is the output of both still photography and the film itself. In the film Paris, Texas, three sets of important photographs appear whose symbolic meanings are as dialectical as their text types. The relationship between the existence of these photos and the storyline is both dependent and isolated. The film’s images fly by and progress into other images, while the photos in the film serve a unique narrative function by stopping the continuously flowing images thus provide the viewer a space for imagination and contemplation. They are more than just artistic forms; they also contained multiple meanings. The photographs in Paris, Texas play the role of both presence and absence according to their shifting meanings. There are references to their presence: photographs exist between film time and narrative time, so in terms of the interaction between the characters in the film, photographs are a common symbol of the beginning and end of the characters’ journeys. In terms of the audience, the film’s photographs are a link in the viewing frame structure, through which the creative motivation of the film director can be explored. Photographs also point to the absence of certain objects: the scenes in the photos represent an imaginary map of emotion. The town of Paris, Texas is therefore isolated from the physical presence of the photograph, and is far more abstract than the reality in the film. This paper embraces the ambiguous nature of photography and demonstrates its presence and absence in film with regard to the meaning of text. However, it is worth reflecting that the temporary nature of the interpretation of the film’s photographs is far greater than any other type of photographic text: the characteristics of the text cause the interpretation results to change along with the variations in the interpretation process, which makes their meaning a dynamic process. The photographs’ presence or absence in the context of Paris, Texas also demonstrates the presence and absence of the creator, time, the truth, and the imagination. The film becomes more complete as a result of the revelation of the photographs, while the intertextual connection between these two forms simultaneously provides multiple possibilities for the interpretation of the photographs in the film.

Keywords: film, Paris, Texas, photography, Wim Wenders

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Inkeri Jääskeläinen

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The purpose of this study is to shed light on what is it like to study in apprenticeship training using Finnish as second language. This study examines the stories and experiences of apprenticeship students learning and studying Finnish as part of their vocational studies. Also, this pilot study examines the effects of learning to pronounce Finnish through body motions and gestures. Many foreign students choose apprenticeships and start vocational training too early, while their language skills in Finnish are still very weak. Both duties at work and school assignments require reasonably good general language skills (B1.1) and, especially at work, language skills are also a safety issue. At work students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressing environment. Learning and understanding new things is very challenging under these circumstances and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other and so are their ways to learn. Thereafter, one of the most important features of apprenticeship training and second language learning is good understanding of adult learners and their needs. Kinaesthetic methods are an effective way to support adult students’ cognitive skills and make learning more relaxing and fun. Empirical findings show that language learning can indeed be supported physical ways, by body motions and gestures. The method used here, named TFFL (Touch and Feel Foreign Languages), was designed to support adult language learning, to correct or prevent language fossilization and to help the student to manage emotions. Finnish is considered as a difficult language to learn, mostly because it is so different from nearly all other languages. Many learners complain that they are lost or confused and there is a need to find a way to simultaneously learn the language and to handle negative emotion which come from Finnish language and the learning process itself. Due to the nature of Finnish language good pronunciation skills are needed just to understand the way the language work. Movements (body movements etc.) are a natural part of many cultures but not Finnish – In Finland students have traditionally been expected to stay still and that is not a natural way for many foreign students. However, kinaesthetic TFFL method proved out to be a useful way to help some L2 students to feel phonemes, rhythm and intonation, to improve their Finnish and, thereby, also to successfully complete their vocational studies.

Keywords: Finnish, fossilization, interference, kinaesthetic method

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Authors: Kazushige Wada, Mayuko Ueda

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In the present study, we reanalyzed lapse errors in the last phase of a job, by re-counting near lapse errors and increasing the number of participants. We also examined the results of this study from the perspective of prospective memory (PM), which concerns future actions. This study was designed to investigate whether perceiving the importance of PM tasks caused lapse errors in the last phase of a job and to determine if such errors could be explained from the perspective of PM processing. Participants (N = 34) conducted a computerized clicking task, in which they clicked on 10 figures that they had learned in advance in 8 blocks of 10 trials. Participants were requested to click the check box in the start display of a block and to click the checking off box in the finishing display. This task was a PM task. As a measure of PM performance, we counted the number of omission errors caused by forgetting to check off in the finishing display, which was defined as a lapse error. The perceived importance was manipulated by different instructions. Half the participants in the highly important task condition were instructed that checking off was very important, because equipment would be overloaded if it were not done. The other half in the not important task condition was instructed only about the location and procedure for checking off. Furthermore, we controlled workload and the emotion of surprise to confirm the effect of demand capacity and attention. To manipulate emotions during the clicking task, we suddenly presented a photo of a traffic accident and the sound of a skidding car followed by an explosion. Workload was manipulated by requesting participants to press the 0 key in response to a beep. Results indicated too few forgetting induced lapse errors to be analyzed. However, there was a weak main effect of the perceived importance of the check task, in which the mouse moved to the “END” button before moving to the check box in the finishing display. Especially, the highly important task group showed more such near lapse errors, than the not important task group. Neither surprise, nor workload affected the occurrence of near lapse errors. These results imply that high perceived importance of PM tasks impair task performance. On the basis of the multiprocess framework of PM theory, we have suggested that PM task performance in this experiment relied not on monitoring PM tasks, but on spontaneous retrieving.

Keywords: prospective memory, perceived importance, lapse errors, multi process framework of prospective memory.

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Authors: Inkeri Jaaskelainen

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The purpose of this study is to shed light on what it is like to study in apprenticeship training using Finnish as a second language. This study examines the stories and experiences of apprenticeship students learning and studying Finnish as part of their vocational studies. Also, this pilot study examines the effects of learning to pronounce Finnish through body motions and gestures. Many foreign students choose apprenticeships and start vocational training too early, while their language skills in Finnish are still very weak. Both duties at work and school assignments require reasonably good general language skills (B1.1), and, especially at work, language skills are also a safety issue. At work, students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressful environment. Learning and understanding new things is very challenging under these circumstances and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other and so are their ways to learn. Thereafter, one of the most important features of apprenticeship training and second language learning is a good understanding of adult learners and their needs. Kinaesthetic methods are an effective way to support adult students’ cognitive skills and make learning more relaxing and fun. Empirical findings show that language learning can indeed be supported in physical ways, by body motions and gestures. The method used here, named TFFL (Touch and Feel Foreign Languages), was designed to support adult language learning, to correct or prevent language fossilization, and to help the student to manage emotions. Finnish is considered as a difficult language to learn, mostly because it is so different from nearly all other languages. Many learners complain that they are lost or confused and there is a need to find a way to simultaneously learn the language and to handle negative emotion that comes from the Finnish language and the learning process itself. Due to the nature of the Finnish language, good pronunciation skills are needed just to understand the way the language work. Movements (body movements etc.) are a natural part of many cultures, but not Finnish. In Finland, students have traditionally been expected to stay still, and that is not a natural way for many foreign students. However, the kinaesthetic TFFL method proved out to be a useful way to help some L2 students to feel phonemes, rhythm, and intonation, to improve their Finnish, and, thereby, also to successfully complete their vocational studies.

Keywords: Finnish, fossilization, interference, kinaesthetic method

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Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

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Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: opinion mining, product feature extraction, sentiment analysis, SentiWordNet

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Authors: Tharwat El-Sakran

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The linguistic choices accident news reporters make when reporting killing cases within family circles aid in augmenting the wrath readers feel towards the perpetrators. Undoubtedly, when killers or murderers are labelled with particular words, prospective readers will associate them with the cultural connotations and emotions, whether positive or negative, attached to those words. One of these strategies is the use of kinship terms to anaphorically or cataphorically refer to the defendants. While some articles opt for using the killer’s name, others make use of other kinship labels such as “the mother,” “the father,” “the step-father, and “the step-mother.” The preference for proper nouns over kinship terms and vice versa can be indicative of some of the underlying implications that the article writer may be trying to make about either the status of the killer or the overall incident circumstances. This research examines how the use of referential kinship labels could point to hidden and shared connotations between writers and their prospective readers. This study examined seventy newspaper articles from English-medium publications based in the United Arab Emirates (UAE), the USA, and several other countries. Some of these articles make use of proper nouns referring to the individual directly by name, whereas others refer to individuals based on their kinship relation with the victim or by their occupational status. Furthermore, information was collected from two hundred fifty-one students at several UAE-based universities by asking them what certain kinship words meant to them. The survey questions allowed for real insight into some of the most prevalent interpretations attached to kinship labels and the possible implications for preferring kinship terms over occupational labels and persons’ proper names. Results indicate that newspaper writers employ kinship labels to inspire an emotion in their future readers’ reactions that may not be achieved through the use of the person’s proper name(s). Additionally, respondents to the survey believe that the use of kinship nouns like “mother,” “father,” “step-mother,” and “step-father” tends to inspire a stronger emotional response, as they are almost always associated with particular behavioral cultural codes and conventions. The study concludes with recommendations for teaching the grammar of English words to EFL and mass communication students and with suggestions for translation theorists and further research.

Keywords: kinship terms, accident reports, cultural connotations, translation of kinship terms

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Authors: Anita Kushwaha

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We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: Banu Hatice Gürcüm, Pınar Arslan, Mahmut Yalçın

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Fashion designers create lots of dresses, suits, shoes, and other clothing and accessories, which are purchased every year by consumers. Fashion trends, sketches of designs, accessories affect the apparel goods, but colors make the finishing touches to an outfit. In all fields of apparel men's, women's, and children's wear, including casual wear, suits, sportswear, formal wear, outerwear, maternity, and intimate apparel, color sells. Thus, specialization in color in apparel is a basic concern each season. The perception of color is the key to sales for every sector in textile business. Mechanism of color perception, cognition in brain and color emotion are unique subjects, which scientists have been investigating for many years. The parameters of color may not be corresponding to visual scales since human emotions induced by color are completely subjective. However, with a very few exception each manufacturer concern their top selling colors for each season through seasonal sales reports of apparel companies. This paper examines sensory and instrumental methods for quantifying color of fabrics and investigates the relationship between fabric color and sale numbers. 5 top selling colors for each season from 10 leading apparel companies in the same segment are taken. The compilation is based according to the sales of the companies for 5 to 10 years. The research’s main concern is the corelation with the magnitude of seasonal color selling figures and the CIE chromaticity coordinates. The colors are chosen from the globally accepted Pantone Textile Color System and the three-dimentional measurement system CIE L*a*b* (CIELAB) is used, L* representing the degree of lightness of color, a* the degree of color ranging from magenta to green, and b* the degree of color ranging from blue to yellow. The objective of this paper is to demonstrate the feasibility of relating color perceptance to a laboratory instrument yielding measurements in the CIELAB system. Our approach is to obtain a total of a hundred reference fabrics to be measured on a laboratory spectrophotometer calibrated to the CIELAB color system. Relationships between the CIE tristimulus (X, Y, Z) and CIELAB (L*, a*, b*) are examined and are reported herein.

Keywords: CIELAB, CIE tristimulus, color preference, fashion

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Roberto Cabezas H

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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

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Authors: Celia Marilia Martins, Sonia I. V. Guilundo, Iris M. Victorino, Antonio O. Quilambo

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In Mozambique rapid industrialization (mining, aluminium and cement activities) and urbanization processes has led to the incorporation of heavy metals on soil, thus degrading not only the quality of the environment, but also affecting plants, animals and human healthy. Several methods have been used to remediate contaminated soils, but most of them are costly and difficult to get optimum results. Currently, phytoremediation is an effective and affordable technological solution used to extract or remove inactive metals from contaminated soil. Phytoremediation is the use of plants to clean up a contamination from soils, sediments, and water. This technology is environmental friendly and potentially cost effective. The present investigation summarised the potential of edible vegetable to grow under the high level of heavy metals such as lead and zinc. The plants used in these studies include Tomatoes, lettuce and Soya beans. The studies have shown that edible plants can be grown under the high level of heavy metals on the soil. Further investigations are identifying mechanisms used by plants to ensure a safe and sustainable use for remediation of contaminated soils by heavy metals.

Keywords: contaminated soil, edible plants, heavy metals, phytoremediation

Procedia PDF Downloads 348