Search results for: supervised classification

Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

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Authors: Tamphasana Wairokpam

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Inflammatory myopathies (IMs) have long been recognised as a heterogenous family of myopathies with acute, subacute, and sometimes chronic presentation and are potentially treatable. Necrotizing autoimmune myopathies (NAM) are a relatively new subset of myopathies. Patients generally present with subacute onset of proximal myopathy and significantly elevated creatinine kinase (CK) levels. It is being increasingly recognised that there are limitations to the independent diagnostic utility of muscle biopsy. Immunohistochemistry tests may reveal important information in these cases. The traditional classification of IMs failed to recognise NAM as a separate entity and did not adequately emphasize the diversity of IMs. This review and case report on NAM aims to highlight the heterogeneity of this entity and focus on the distinct clinical presentation, biopsy findings, specific auto-antibodies implicated, and available treatment options with prognosis. This article is a meta-analysis of literatures on NAM and a case report illustrating the clinical course, investigation and biopsy findings, antibodies implicated, and management of a patient with NAM. The main databases used for the search were Pubmed, Google Scholar, and Cochrane Library. Altogether, 67 publications have been taken as references. Two biomarkers, anti-signal recognition protein (SRP) and anti- hydroxyl methylglutaryl-coenzyme A reductase (HMGCR) Abs, have been found to have an association with NAM in about 2/3rd of cases. Interestingly, anti-SRP associated NAM appears to be more aggressive in its clinical course when compared to its anti-HMGCR associated counterpart. Biopsy shows muscle fibre necrosis without inflammation. There are reports of statin-induced NAM where progression of myopathy has been seen even after discontinuation of statins, pointing towards an underlying immune mechanism. Diagnosisng NAM is essential as it requires more aggressive immunotherapy than other types of IMs. Most cases are refractory to corticosteroid monotherapy. Immunosuppressive therapy with other immunotherapeutic agents such as IVIg, rituximab, mycophenolate mofetil, azathioprine has been explored and found to have a role in the treatment of NAM. In conclusion,given the heterogeneity of NAM, it appears that NAM is not just a single entity but consists of many different forms, despite the similarities in presentation and its classification remains an evolving field. A thorough understanding of underlying mechanism and the clinical correlation with antibodies associated with NAM is essential for efficacious management and disease prognostication.

Keywords: inflammatory myopathies, necrotising autoimmune myopathies, anti-SRP antibody, anti-HMGCR antibody, statin induced myopathy

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Authors: Amjad Alsulami, Farah Albishri, Kholod Alzubidi, Lama Almehemadi, Salma Elhag

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Food allergy is a common and rising problem among children. Many students have their first allergic reaction at school, one of these is anaphylaxis, which can be fatal. This study discovered that several schools' processes lacked safety regulations and information on how to handle allergy issues and chronic diseases like diabetes where students were not supervised or monitored during the cafeteria purchasing process. There is no obvious prevention or effort in academic institutions when purchasing food containing allergens or negatively impacting the health status of students who suffer from chronic diseases. Students must always be stable to reflect positively on their educational development process. To address this issue, this paper uses a business reengineering process to propose the automation of the whole food-purchasing process, which will aid in detecting and avoiding allergic occurrences and preventing any side effects from eating foods that are conflicting with students' health. This may be achieved by designing a smart card with an embedded QR code that reveals which foods cause an allergic reaction in a student. A survey was distributed to determine and examine how the cafeteria will handle allergic children and whether any management or policy is applied in the school. Also, the survey findings indicate that the integration of QR technology into the food purchasing process would improve health condition detection. The suggested system would be beneficial to all parties, the family agreed, as they would ensure that their children didn't eat foods that were bad for their health. Moreover, by analyzing and simulating the as-is process and the suggested process the results demonstrate that there is an improvement in quality and time.

Keywords: QR code, smart card, food allergies, business process reengineering, health condition detection

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Authors: Wenqi Liu, Reginald Bailey

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This study proposes a comprehensive and effective approach to business-to-business (B2B) sales forecasting by integrating advanced machine learning models with a rule-based decision-making framework. The methodology addresses the critical challenge of optimizing sales pipeline performance and improving conversion rates through predictive analytics and actionable insights. The first component involves developing a classification model to predict the likelihood of conversion, aiming to outperform traditional methods such as logistic regression in terms of accuracy, precision, recall, and F1 score. Feature importance analysis highlights key predictive factors, such as client revenue size and sales velocity, providing valuable insights into conversion dynamics. The second component focuses on forecasting sales value using a regression model, designed to achieve superior performance compared to linear regression by minimizing mean absolute error (MAE), mean squared error (MSE), and maximizing R-squared metrics. The regression analysis identifies primary drivers of sales value, further informing data-driven strategies. To bridge the gap between predictive modeling and actionable outcomes, a rule-based decision framework is introduced. This model categorizes leads into high, medium, and low priorities based on thresholds for conversion probability and predicted sales value. By combining classification and regression outputs, this framework enables sales teams to allocate resources effectively, focus on high-value opportunities, and streamline lead management processes. The integrated approach significantly enhances lead prioritization, increases conversion rates, and drives revenue generation, offering a robust solution to the declining pipeline conversion rates faced by many B2B organizations. Our findings demonstrate the practical benefits of blending machine learning with decision-making frameworks, providing a scalable, data-driven solution for strategic sales optimization. This study underscores the potential of predictive analytics to transform B2B sales operations, enabling more informed decision-making and improved organizational outcomes in competitive markets.

Keywords: machine learning, XGBoost, regression, decision making framework, system engineering

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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Authors: Yating Mu

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Young children acquire native language with amazing rapidity. After noticing this interesting phenomenon, lots of linguistics, as well as psychologists, devote themselves to exploring the best explanations. Thus researches on first language acquisition emerged. Early lexical development is an important branch of children’s FLA (first language acquisition). Verb, the most significant class of lexicon, the most grammatically complex syntactic category or word type, is not only the core of exploring syntactic structures of language but also plays a key role in analyzing semantic features. Obviously, early verb development must have great impacts on children’s early lexical acquisition. Most scholars conclude that verbs, in general, are very difficult to learn because the problem in verb learning might be more about mapping a specific verb onto an action or event than about learning the underlying relational concepts that the verb or relational term encodes. However, the previous researches on early verb development mainly focus on the argument about whether there is a noun-bias or verb-bias in children’s early productive vocabulary. There are few researches on general characteristics of children’s early verbs concerning both semantic and syntactic aspects, not mentioning a general survey on Chinese-speaking children’s verb acquisition. Therefore, the author attempts to examine the general conditions and characteristics of Chinese-speaking children’s early productive verbs, based on data from a longitudinal study on three Chinese-speaking children. In order to present an overall picture of Chinese verb development, both semantic and syntactic aspects will be focused in the present study. As for semantic analysis, a classification method is adopted first. Verb category is a sophisticated class in Mandarin, so it is quite necessary to divide it into small sub-types, thus making the research much easier. By making a reasonable classification of eight verb classes on basis of semantic features, the research aims at finding out whether there exist any universal rules in Chinese-speaking children’s verb development. With regard to the syntactic aspect of verb category, a debate between nativist account and usage-based approach has lasted for quite a long time. By analyzing the longitudinal Mandarin data, the author attempts to find out whether the usage-based theory can fully explain characteristics in Chinese verb development. To sum up, this thesis attempts to apply the descriptive research method to investigate the acquisition and the usage of Chinese-speaking children’s early verbs, on purpose of providing a new perspective in investigating semantic and syntactic features of early verb acquisition.

Keywords: Chinese-speaking children, early verb acquisition, verb classes, verb grammatical structures

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: I. Fahi, T. Remmal, F. El Kamel, B. Ayoub

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The treatment of the geotechnical database of the region of Casablanca was difficult to achieve due to the heterogeneity of the nomenclature of the lithological formations composing its soil. It appears necessary to harmonize the nomenclature of the facies and to produce cartographic documents useful for construction projects and studies before any investment program. To achieve this, more than 600 surveys made by the Public Laboratory for Testing and Studies (LPEE) in the agglomeration of Casablanca, were studied. Moreover, some local observations were made in different places of the metropolis. Each survey was the subject of a sheet containing lithological succession, macro and microscopic description of petrographic facies with photographic illustration, as well as measurements of geomechanical tests. In addition, an X-ray diffraction analysis was made in order to characterize the surficial formations of the region.

Keywords: Casablanca, guidebook, petrography, soil

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Evelyn Asante Yeboah, Kwabena O. Asubonteng, Justice Camillus Mensah, Christine Furst

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Smallholder-dominated mosaic landscapes in rural Africa are relevant for food production, biodiversity conservation, and climate regulation. Land-use transitions threaten the multifunctionality of such landscapes, especially the production capacity of arable lands resulting in food security challenges. Using land-cover maps derived from maximum likelihood classification of Landsat satellite images for the years 2002, 2015, and 2020, post-classification change detection, landscape metrics, and key informant interviews, the study assessed the implications of rubber plantation expansion and oil business development on the food production capacity of Ahanta West District, Ghana. The analysis reveals that settlement and rubber areas expanded by 5.82% and 10.33% of the landscape area, respectively, between 2002 and 2020. This increase translates into over twice their initial sizes (144% in settlement change and 101% in rubber change). Rubber plantation spread dominates the north and southwestern areas, whereas settlement is widespread in the eastern parts of the landscape. Rubber and settlement expanded at the expense of cropland, palm, and shrublands. Land-use transitions between cropland, palm, and shrubland were targeting each other, but the net loss in shrubland was higher (-17.27%). Isolation, subdivision, connectedness, and patch adjacency indices showed patch consolidation in the landscape configuration from 2002 to 2015 and patch fragmentation from 2015 to 2020. The study also found patches with consistent increasing connectivity in settlement areas indicating the influence of oil discovery developments and fragmentation tendencies in rubber, shrubland, cropland, and palm, indicating springing up of smaller rubber farms, the disappearance of shrubland, and splitting up of cropland and palm areas respectively. The results revealed a trend in land-use transitions in favor of smallholder rubber plantation expansion and oil discovery developments, which suggest serious implications on food production systems and poses a risk for food security and landscape multifunctional characteristics. To ensure sustainability in land uses, this paper recommends the enforcement of legislative instruments governing spatial planning and land use in Ghana as embedded in the 2016 land-use and spatial planning act.

Keywords: food production systems, food security, Ghana’s west coast, land-use transitions, multifunctional rural landscapes

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Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

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This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. For this, it gropes for methods for reducing conceptual ambiguity. Further, it arranges the concept of emotional labor; and examines and reviews comparatively the currents of the existing studies and looks for the characteristics and correlations of their classification criteria. That is, this study intends to arrange systematically and examine theories on emotional labor suggested hitherto, and suggest a future direction of research on emotional labor on the basis thereof. In addition, it attempts to look for positive aspects of the results of emotional labor.

Keywords: emotion labor, dimensions of emotional labor, surface acting, deep acting

Procedia PDF Downloads 357

Authors: Sergey A. Burikov, Tatiana A. Dolenko, Kirill A. Gushchin, Sergey A. Dolenko

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The paper presents the results of clusterization by Kohonen self-organizing maps (SOM) applied for analysis of array of Raman spectra of multi-component solutions of inorganic salts, for determination of types of salts present in the solution. It is demonstrated that use of SOM is a promising method for solution of clusterization and classification problems in spectroscopy of multi-component objects, as attributing a pattern to some cluster may be used for recognition of component composition of the object.

Keywords: Kohonen self-organizing maps, clusterization, multi-component solutions, Raman spectroscopy

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Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Selcen Özyurt Ulutaş

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March as a musical form in Ottoman Music has started after Sultan II. Mahmud. Owing to the modernization process on Ottoman Empire, marches had accepted and embraced by the sultanate in a short period of time. The reasons behind sultans favor against marches that is actually a European Music form is closely related to attribute meanings to marches. After Sultan II. Mahmud, marches became a symbol of westernization and became a symbol of sultanate. After that period besides sultans also princes started to compose marches. The presentation includes the demonstration of the marches classification in achieves to be able to give information on the composers of those marches. Through that process, this study aims to show attributed meanings to those marches and what those marches represent.

Keywords: Ottoman marches, music, Europe, European musicians

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Authors: Johannes Mertl

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Our surrounding air contains various particles. Besides typical representatives of inorganic dust, such as soot and ash, also particles originating from animals, microorganisms or plants are floating through the air, so-called bioaerosols. The group of bioaerosols consists of a broad spectrum of particles of different size, including fungi, bacteria, viruses, spores, or tree, flower and grass pollen that are of high relevance for allergy sufferers. In dependence of the environmental climate and the actual season, these allergenic particles can be found in enormous numbers in the air and are inhaled by humans via the respiration tract, with a potential for inflammatory diseases of the airways, such as asthma or allergic rhinitis. As a consequence air filter systems of ventilation and air conditioning devices are required to meet very high standards to prevent, or at least lower the number of allergens and airborne germs entering the indoor air. Still, filter systems are merely classified for their separation rates using well-defined mineral test dust, while no appropriate sufficiently standardized test methods for bioaerosols exist. However, determined separation rates for mineral test particles of a certain size cannot simply be transferred to bioaerosols, as separation efficiency of particularly fine and respirable particles (< 10 microns) is dependent not only on their shape and particle diameter, but also defined by their density and physicochemical properties. For this reason, the OFI developed a test method, which directly enables a testing of filters and filter media for their separation rates on bioaerosols, as well as a classification of filters. Besides allergens from an intact or fractured tree or grass pollen, allergenic proteins bound to particulates, as well as allergenic fungal spores (e.g. Cladosporium cladosporioides), or bacteria can be used to classify filters regarding their separation rates. Allergens passing through the filter can then be detected by highly sensitive immunological assays (ELISA) or in the case of fungal spores by microbiological methods, which allow for the detection of even one single spore passing the filter. The test procedure, which is carried out in laboratory scale, was furthermore validated regarding its sufficiency to cover real life situations by upscaling using air conditioning devices showing great conformity in terms of separation rates. Additionally, a clinical study with allergy sufferers was performed to verify analytical results. Several different air conditioning filters from the car industry have been tested, showing significant differences in their separation rates.

Keywords: airborne germs, allergens, classification of filters, fine dust

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Authors: John M. Ikome, Martha E. Ikome, Therese Van Wyk

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Productivity optimization of a company can significantly increase the company’s output and productivity which can be in the form of corrective actions of ineffective activities, process simplification, and reduction of variations, responsiveness, and reduction of set-up-time which are all under the classification of waste within the manufacturing environment. Deriving a means to eliminate a number of these issues has a key importance for manufacturing organization. This paper focused on a number of industrial engineering techniques which include a cause and effect diagram, to identify and optimize the method or systems being used. Based on our results, it shows that there are a number of variations within the production processes that can significantly disrupt the expected output.

Keywords: optimization, fishbone, diagram, productivity

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: Byeongju Choi, Sanggil Lee, Kyung-Eun Lee

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Introduction: In the republic of Korea, the number of workers in the beauty industry has been increasing. Because the prevalence of hepatitis B carriers in Korea is higher than in other countries, the risk of blood-borne infection including viral hepatitis B and C, among the workers by using the sharp and contaminated instruments during procedure can be expected among beauty salon workers. However, the health care policies for the workers to prevent the blood-borne infection are not established due to the lack of evidences. Moreover, the workers in hair and nail salon were mostly employed at small businesses, where national mandatory systems or policies for workers’ health management are not applied. In this study, the risk of the viral hepatitis B and C from the job experiencing the hair and nail procedures in the mortality was assessed. Method: We conducted a retrospective review of the job histories and causes of death in the female deaths from 2006-2016. 132,744 of female deaths who had one more job experiences during their lifetime were included in this study. Job histories were assessed using the employment insurance database in Korea Employment Information Service (KEIS) and the causes of death were in death statistics produced by Statistics Korea. Case group (n= 666) who died from viral hepatitis was classified the death having record involved in ‘B15-B19’ as a cause of deaths based on Korean Standard Classification of Diseases(KCD) with the deaths from other causes, control group (n=132,078). The group of the workers in the beauty service industry were defined as the employees who had ever worked in the industry coded as ‘9611’ based on Korea Standard Industry Classification (KSIC) and others were others. Other than job histories, birth year, marital status, education level were investigated from the death statistics. Multiple logistic regression analysis were used to assess the risk of deaths from viral hepatitis in the case and control group. Result: The number of the deaths having ever job experiences at the hair and nail salon was 255. After adjusting confounders of age, marital status and education, the odds ratio(OR) for deaths from viral hepatitis was quite high in the group having experiences with working in the beauty service industry with 3.14(95% confidence interval(CI) 1.00-9.87). Other associated factors with increasing the risk of deaths from viral hepatitis were low education level(OR=1.34, 95% CI 1.04-1.73), married women (OR=1.42, 95% CI 1.02-1.97). Conclusion: The risk of deaths from viral hepatitis were high in the workers in the beauty service industry but not statistically significant, which might attributed from the small number of workers in beauty service industry. It was likely that the number of workers in beauty service industry could be underestimated due to their temporary job position. Further studies evaluating the status and the incidence of viral infection among the workers with consideration of the vertical transmission would be required.

Keywords: beauty service, viral hepatitis, blood-borne infection, viral infection

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Authors: Elda Maraj, Shkelqim Kuka

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Coronary heart disease causes many deaths in the world. Unfortunately, this problem will continue to increase in the future. In this paper, a fuzzy logic model to predict coronary heart disease is presented. This model has been developed with seven input variables and one output variable that was implemented for 30 patients in Albania. Here fuzzy logic toolbox of MATLAB is used. Fuzzy model inputs are considered as cholesterol, blood pressure, physical activity, age, BMI, smoking, and diabetes, whereas the output is the disease classification. The fuzzy sets and membership functions are chosen in an appropriate manner. Centroid method is used for defuzzification. The database is taken from University Hospital Center "Mother Teresa" in Tirana, Albania.

Keywords: coronary heart disease, fuzzy logic toolbox, membership function, prediction model

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Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Carlos Alfredo Camargo Vila, Fredy Augusto Avellaneda Vargas, Debora Alcida Nabarlatz

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Due to the current need to implement environmentally friendly technologies, the possibility of using renewable raw materials to produce bioproducts such as biofuels, or in this case, to produce biolubricant bases, from residual oils (tallow), originating has been studied of the bovine industry. Therefore, it is hypothesized that through the study and control of the operating variables involved in the reverse transesterification method, a biolubricant base with high performance is obtained on a laboratory scale using animal fats from the bovine industry as raw materials, as an alternative for material recovery and environmental benefit. To implement this process, esterification of the crude tallow oil must be carried out in the first instance, which allows the acidity index to be decreased ( > 1 mg KOH/g oil), this by means of an acid catalysis with sulfuric acid and methanol, molar ratio 7.5:1 methanol: tallow, 1.75% w/w catalyst at 60°C for 150 minutes. Once the conditioning has been completed, the biodiesel is continued to be obtained from the improved sebum, for which an experimental design for the transesterification method is implemented, thus evaluating the effects of the variables involved in the process such as the methanol molar ratio: improved sebum and catalyst percentage (KOH) over methyl ester content (% FAME). Finding that the highest percentage of FAME (92.5%) is given with a 7.5:1 methanol: improved tallow ratio and 0.75% catalyst at 60°C for 120 minutes. And although the% FAME of the biodiesel produced does not make it suitable for commercialization, it does ( > 90%) for its use as a raw material in obtaining biolubricant bases. Finally, once the biodiesel is obtained, an experimental design is carried out to obtain biolubricant bases using the reverse transesterification method, which allows the study of the effects of the biodiesel: TMP (Trimethylolpropane) molar ratio and the percentage of catalyst on viscosity and yield as response variables. As a result, a biolubricant base is obtained that meets the requirements of ISO VG (Classification for industrial lubricants according to ASTM D 2422) 32 (viscosity and viscosity index) for commercial lubricant bases, using a 4:1 biodiesel molar ratio: TMP and 0.51% catalyst at 120°C, at a pressure of 50 mbar for 180 minutes. It is necessary to highlight that the product obtained consists of two phases, a liquid and a solid one, being the first object of study, and leaving the classification and possible application of the second one incognito. Therefore, it is recommended to carry out studies of the greater depth that allows characterizing both phases, as well as improving the method of obtaining by optimizing the variables involved in the process and thus achieving superior results.

Keywords: biolubricant base, bovine tallow, renewable resources, reverse transesterification

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Authors: Zhan Haiyang, Gu Miao

Abstract:

This article mainly introduces the control system architecture of space environment simulator, simultaneously also briefly introduce the automation control technology of industrial process and the measurement technology of vacuum and cold black environment. According to the volume of chamber, the space environment simulator is divided into three types of small, medium and large. According to the classification and application of space environment simulator, the control system is divided into the control system of small, medium, large space environment simulator and the centralized control system of multiple space environment simulators.

Keywords: space environment simulator, control system, architecture, automation control technology

Procedia PDF Downloads 475

Authors: Klathea Sevilla, Marjorie Remolador, Bryan Baltazar, Imee Saladaga, Loureal Camille Inocencio, Ma. Rosario Concepcion Ang

Abstract:

The underutilization of biomass resources in the Philippines, combined with its growing population and the rise in fossil fuel prices confirms demand for alternative energy sources. The goal of this paper is to provide a comparison of MODIS-based and Landsat-based agricultural land cover maps when used in the estimation of rice hull’s available energy potential. Biomass resource assessment was done using mathematical models and remote sensing techniques employed in a GIS platform.

Keywords: biomass, geographic information system (GIS), remote sensing, renewable energy

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Authors: Auzair Mehmood, Mohammad Arif

Abstract:

The LCT (Li, Cs and Ta rich)-type pegmatites, genetically related to peraluminous S-type granites, are being mined for strategic metals (SMs) and rare earth elements (REEs) around the world. This study investigates the SMs and REEs potentials of pegmatites that are spatially associated with an S-type granitic suite of the Himalayan sequence, specifically Mansehra Granitic Complex (MGC), northwest Pakistan. Geochemical signatures of the pegmatites and some of their mineral extracts were analyzed using Inductive Coupled Plasma Mass Spectroscopy (ICP-MS) technique to explore and generate potential prospects (if any) for SMs and REEs. In general, the REE patterns of the studied whole-rock pegmatite samples show tetrad effect and possess low total REE abundances, strong positive Europium (Eu) anomalies, weak negative Cesium (Cs) anomalies and relative enrichment in heavy REE. Similar features have been observed on the REE patterns of the feldspar extracts. However, the REE patterns of the muscovite extracts reflect preferential enrichment and possess negative Eu anomalies. The trace element evaluation further suggests that the MGC pegmatites have undergone low levels of fractionation. Various trace elements concentrations (and their ratios) including Ta versus Cs, K/Rb (Potassium/Rubidium) versus Rb and Th/U (Thorium/Uranium) versus K/Cs, were used to analyze the economically viable mineral potential of the studied rocks. On most of the plots, concentrations fall below the dividing line and confer either barren or low-level mineralization potential of the studied rocks for both SMs and REEs. The results demonstrate paucity of the MGC pegmatites with respect to Ta-Nb (Tantalum-Niobium) mineralization, which is in sharp contrast to many Pan-African S-type granites around the world. The MGC pegmatites are classified as muscovite pegmatites based on their K/Rb versus Cs relationship. This classification is consistent with the occurrence of rare accessory minerals like garnet, biotite, tourmaline, and beryl. Furthermore, the classification corroborates with an earlier sorting of the MCG pegmatites into muscovite-bearing, biotite-bearing, and subordinate muscovite-biotite types. These types of pegmatites lack any significant SMs and REEs mineralization potentials. Field relations, such as close spatial association with parent granitic rocks and absence of internal zonation structure, also reflect the barren character and hence lack of any potential prospects of the MGC pegmatites.

Keywords: exploration, fractionation, Himalayas, pegmatites, rare earth elements

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Authors: Ghazi Bouaziz, Abderrahim Derouiche, Damien Brulin, Hélène Pigot, Eric Campo

Abstract:

Automatic ADL classification is a crucial part of ambient assisted living technologies. It allows to monitor the daily life of the elderly and to detect any changes in their behavior that could be related to health problem. But detection of ADLs is a challenge, especially because each person has his/her own rhythm for performing them. Therefore, we used a correlation matrix to extract custom rules that enable to detect ADLs, including eating activity. Data collected from 3 different individuals between 35 and 105 days allows the extraction of personalized eating patterns. The comparison of the results of the process of eating activity extracted from the correlation matrices with the declarative data collected during the survey shows an accuracy of 90%.

Keywords: elderly monitoring, ADL identification, matrix correlation, meal-taking activity

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

Abstract:

Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

Procedia PDF Downloads 156