Search results for: grey clustering

Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

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Authors: Sachin Nagargoje

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Complete labeled data is often difficult to obtain in a practical scenario. Even if one manages to obtain the data, the quality of the data is always in question. In shopping vertical, offers are the input data, which is given by advertiser with or without a good quality of information. In this paper, an author investigated the possibility of using a very simple Semi-supervised learning approach to increase the recall of unhealthy offers (has badly written Offer Title or partial product details) in shopping vertical domain. The author found that the semisupervised learning method had improved the recall in the Smart Phone category by 30% on A=B testing on 10% traffic and increased the YoY (Year over Year) number of impressions per month by 33% at production. This also made a significant increase in Revenue, but that cannot be publicly disclosed.

Keywords: semi-supervised learning, clustering, recall, coverage

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Authors: Bilal Zubair

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In a constantly challenging internal security environment, Pakistan is making ways to improvise and respond to the new variations in the pervasive phenomenon of terrorism. The state’s endeavors towards securitizing terrorism as an existential threat are both extensive and intensive which have systematically incorporated both military and non-military means. Since 2007, the military has been conducting intermittent operations and by 2014 has successfully neutralized the terrorist ability to target vital security installations and security personal. The terrorists have responded by targeting communities which are soft targets and extremely vulnerable to organized assaults. Within this context, the study aims to explain the emerging trends of terrorism in Pakistan, which multi-layered and complex developments are having far-reaching implications for state and society. With a view to explore the underlining reasons, present trends and ensuing ramifications of the emerging trends in terrorism, this study would examine the following: First, the historical processes and development of Terrorism in Pakistan; secondly the processes of securitization which include political consensus, legal frameworks and military operations against the terrorist groups; thirdly , the socio-cultural dimensions and geopolitical influences on the transforming nature of sectarian terrorism. The study will also highlight the grey areas and weak links in the ongoing securitization process. Finally, the study will thoroughly explore the societal insecurity which is manifested in internal displacements, identity crisis and weakening the socio-political fabric of the state.

Keywords: counter-terrorism, terrorism, sectarianism, securitizing

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Authors: Morteza Giti

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As online shopping is becoming increasingly popular, computing customer lifetime value for better knowing the customers is also gaining more importance. Two distinct factors that can affect the value of a customer in the context of online shopping is the number of returned orders and payment method. Returned orders are those which have been shipped but not collected by the customer and are returned to the store. Payment method refers to the way that customers choose to pay for the price of the order which are usually two: Pre-pay and Cash-on-delivery. In this paper, a novel model called RFMSP is presented to calculated the customer lifetime value, taking these two parameters into account. The RFMSP model is based on the common RFM model while adding two extra parameter. The S represents the order status and the P indicates the payment method. As a case study for this model, the purchase history of customers in an online shop is used to compute the customer lifetime value over a period of twenty months.

Keywords: RFMSP model, AHP, customer lifetime value, k-means clustering, e-commerce

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Oumaima Ben Abderrahim, Mohamed Houcine Elhedhili, Leila Saidane

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In this paper, we propose a trust management system based on clustering architecture for the social internet of things called TMBCO-SIOT. The proposed model integrates numerous factors such as direct and indirect trust; transaction factor; precaution factor; and social modeling of trust. The novelty of our approach can be summed up in two aspects. The first aspect concerns the architecture based on the community of interest (CoT) where each community is headed by an administrator (admin). However, the second aspect is the trust management system that tries to prevent On-Off attacks and mitigates dishonest recommendations using the k-means algorithm and guarantor things. The effectiveness of the proposed system is proved by simulation against malicious nodes.

Keywords: IoT, trust management system, attacks, trust, dishonest recommendations, K-means algorithm

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Authors: Yuan-lin Liu, Ye Li, Tian Xia

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There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library.

Keywords: credit, mobile internet, e-hailing taxi, management mode, weighted cluster

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Authors: L. Belkhiri, L. Mouni, A. Tiri, T.S. Narany

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In the present study, the self-organizing map (SOM) clustering technique was applied to identify homogeneous clusters of hydrochemical parameters in El Milia plain, Algeria, to assess the quality of groundwater for potable and agricultural purposes. The visualization of SOM-analysis indicated that 35 groundwater samples collected in the study area were classified into three clusters, which showed progressive increase in electrical conductivity from cluster one to cluster three. Samples belonging to cluster one are mostly located in the recharge zone showing hard fresh water type, however, water type gradually changed to hard-brackish type in the discharge zone, including clusters two and three. Ionic ratio studies indicated the role of carbonate rock dissolution in increases on groundwater hardness, especially in cluster one. However, evaporation and evapotranspiration are the main processes increasing salinity in cluster two and three.

Keywords: groundwater quality, self-organizing maps, drinking water, irrigation water

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Authors: Abhilasha Dadhich, Manisha Mathur, Sanjay Kumar, Hemant Dadhich

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The camel (Camelus dromedarius) is an important animal component of the fragile desert eco-system of India. Apart from others, impaired milk and meat production decrease in performance and even death are some of the major consequences of parasitic disease like coccidiosis in camel. Coccidiosis which is an acute invasion and destruction of intestinal mucosa by protozoa of the genera Eimeria or isospora spp. Post-Mortem examinations of 5 carcasses of dromedary of different age groups aged from 2 to 5 years were conducted. The history indicated that the camels were suffering from diarrhoea, dysentery, pyrexia, inappetence, weight loss, and emaciation. Post mortem examinations showed macroscopic and microscopic alterations in the small intestine, particularly in jejunum and ileum regions. The mucosae were congested, and haemorrhagic on which there were numerous whitish-grey nodular foci were observed. The affected intestinal tissue specimens were preserved in 10% formal saline and processed mechanically for paraffin embedding by acetone and benzene technique. The sections were stained with haematoxylin and eosin method of staining for histopathological examinations. Histologically, typical lesions such as congestion and haemorrhages were present. The intestinal villi were oedematous; mucosa degenerated and desquamated, along with infiltration of eosinophils and macrophages. Crypts of lieberkuhn were obliterated due to presence of schizonts in lamina propria. Older camels served as the source of spread of coccidial infection and were also predisposed to secondary infections.

Keywords: camel, coccidiosis, Eimeria, histopathology

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Authors: H. El Shayab, G. M. Kamh, N. G. Abdel Ghafour, M. L. Abdel Latif

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Nowadays, marbleized limestone becomes one of the most important sources of the mineral wealth in Egypt as they have beautiful colors (white, grey, rose, yellow and creamy, etc.) make it very suitable for decoration purposes. Non-styolitic marbleized limestone which not contains styolitic surfaces. The current study aims to study different factors controlling durability of non-styolitic marbleized limestone against salt crystallization weathering. The achievement aim of the research was required nine representative samples were collected from the studied areas. Three samples from each of the studied areas. The studied samples was characterized by various instrumental methods before salt weathering, to determine its mineralogical composition, chemical composition and pore physical properties respectively. The obtained results revealed that both of Duwi and Delga studied samples nearly have the same average ∆M% 1.63 and 1.51 respectively and consequently A.I. stage of deformation. On the other hand, average ∆M% of Wata studied samples is 0.29 i.e. lower than two other studied areas. Wata studied samples are more durable against salt crystallization test than Duwi and Delga. The difference in salt crystallization durability may be resulted from one of the following factors: Microscopic textural effect as both of micrite and skeletal percent are in directly proportional to durability of stones to salt weathering. Dolomite mineral present as a secondary are in indirectly proportional to durability of stones to salt weathering. Increase in MgO% also associated with decrease the durability of studied samples against salt crystallization test. Finally, all factors affecting positively against salt crystallization test presents in Wadi Wata studied samples rather than others two areas.

Keywords: marbleized limestone, salt weathering, Wata, salt weathering

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Bakhtiar Ul Haq, R. Ahmed, A. Shaari, Mazmira Binti Mohamed, Nisar Ali

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The capability of exploiting the electronic charge and spin properties simultaneously in a single material has made diluted magnetic semiconductors (DMS) remarkable in the field of spintronics. We report the designing of DMS based on zinc-blend ZnO doped with Cr impurity. The full potential linearized augmented plane wave plus local orbital FP-L(APW+lo) method in density functional theory (DFT) has been adapted to carry out these investigations. For treatment of exchange and correlation energy, generalized gradient approximations have been used. Introducing Cr atoms in the matrix of ZnO has induced strong magnetic moment with ferromagnetic ordering at stable ground state. Cr:ZnO was found to favor the short range magnetic interaction that reflect the tendency of Cr clustering. The electronic structure of ZnO is strongly influenced in the presence of Cr impurity atoms where impurity bands appear in the band gap.

Keywords: ZnO, density functional theory, diluted agnetic semiconductors, ferromagnetic materials, FP-L(APW+lo)

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: magnetic resonance image (MRI), c-means model, image segmentation, information entropy

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: Huda Al-Shuaily, E. M. Lacap

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Higher Education is one of the most essential fundamentals for the advancement and progress of a country. It demands to be as accessible as possible and as comprehensive as it can be reached. In this paper, we succeeded to expand the accessibility and delivery of higher education using an Open Educational Resources (OER), a freely accessible, openly licensed documents, and media for teaching and learning. This study creates a comparative design of student’s academic performance on the course Introduction to Database and student engagement to the virtual learning environment (VLE). The study was done in two successive semesters - one without using the OER and the other is using OER. In the study, we established that there is a significant increase in student’s engagement in VLE in the latter semester compared to the former. By using the latter semester’s data, we manage to show that the student’s engagement has a positive impact on students’ academic performance. Moreso, after clustering their academic performance, the impact is seen higher for students who are low performing. The results show that these engagements can be used to potentially predict the learning styles of the student with a high degree of precision.

Keywords: EDM, learning analytics, moodle, OER, student-engagement

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Authors: Damayanthie Eluwawalage

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Historically, the color-coded attire demarcated differences, for example, differences in social position and differences in gender, etc. Distinctive colors are worn by different classes in medieval England. By the twentieth-century Western society, certain colors were firmly associated with the specific gender; as pink for girls, and blue for boys. The color-coded gender phenomenon was a novelty at the turn of the twentieth-century and became widely practiced after World War II. Prior to that era, there were no distinctions or differences in the dress of younger children, in relation to their gender. In the nineteenth century, pink suits were highly acceptable for gentlemen’s attire. Frenchmen in the eighteenth-century wore colors with an infinite range of hues like pink, plum, white, cream, blue, yellow, puce and sea green. Nineteenth-century European male austerity, primarily caused by the usage of sombre colors such as black, white and grey, has been described as an element for dignity, control and morality. In the nineteenth century, there were many color-associated distinctions, as certain colors were reserved for the unmarried, the single or the aged. Two luminous colors in one dress was ‘vulgar’ and yellow was generally regarded as unladylike. Yellow was the color utilised for most correctional attire. Orange was prohibited for the unmarried. Fashionable dressing in the nineteenth century was more gender-differentiated than in previous centuries. Masculine austerity, emphasized a shift in class relations. As a result of that shift, male attire became more uniform, homogeneous and integrated (amongst the classes), than its traditional hierarchal approach.

Keywords: textiles, fashion, gender symbolism, color

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Authors: Lena Payati, Maria Kazou, Effie Tsakalidou

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Table olives are one of the most popular fermented vegetables worldwide, which along with olive oil, have a crucial role in the world economy. They are highly appreciated by the consumers for their characteristic taste and pleasant aromas, while several health and nutritional benefits have been reported as well. Until recently, microbial biogeography, i.e., the study of microbial diversity over time and space, has been mainly associated with wine. However, nowadays, the term 'terroir' has been extended to other crops and food products so as to link the geographical origin and environmental conditions to quality aspects of fermented foods. Taking the above into consideration, the present study focuses on the microbial fingerprinting of the most important olive varieties of Greece with the state-of-the-art amplicon-based metagenomics analysis. Towards this, in 2019, 61 samples from 38 different olive varieties were collected at the final stage of ripening from 13 well spread geographical regions in Greece. For the metagenomics analysis, total DNA was extracted from the olive samples, and the 16S rRNA gene and ITS DNA region were sequenced and analyzed using bioinformatics tools for the identification of bacterial and yeasts/fungal diversity, respectively. Furthermore, principal component analysis (PCA) was also performed for data clustering based on the average microbial composition of all samples from each region of origin. According to the composition, results obtained, when samples were analyzed separately, the majority of both bacteria (such as Pantoea, Enterobacter, Roserbergiella, and Pseudomonas) and yeasts/fungi (such as Aureobasidium, Debaromyces, Candida, and Cladosporium) genera identified were found in all 61 samples. Even though interesting differences were observed at the relative abundance level of the identified genera, the bacterial genus Pantoea and the yeast/fungi genus Aureobasidium were the dominant ones in 35 and 40 samples, respectively. Of note, olive samples collected from the same region had similar fingerprint (genera identified and relative abundance level) regardless of the variety, indicating a potential association between the relative abundance of certain taxa and the geographical region. When samples were grouped by region of origin, distinct bacterial profiles per region were observed, which was also evident from the PCA analysis. This was not the case for the yeast/fungi profiles since 10 out of the 13 regions were grouped together mainly due to the dominance of the genus Aureobasidium. A second cluster was formed for the islands Crete and Rhodes, both of which are located in the Southeast Aegean Sea. These two regions clustered together mainly due to the identification of the genus Toxicocladosporium in relatively high abundances. Finally, the Agrinio region was separated from the others as it showed a completely different microbial fingerprinting. However, due to the limited number of olive samples from some regions, a subsequent PCA analysis with more samples from these regions is expected to yield in a more clear clustering. The present study is part of a bigger project, the first of its kind in Greece, with the ultimate goal to analyze a larger set of olive samples of different varieties and from different regions in Greece in order to have a reliable olives’ microbial biogeography.

Keywords: amplicon-based metagenomics analysis, bacteria, microbial biogeography, olive microbiota, yeasts/fungi

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Authors: K. F. Khan, Samsuddin Khan

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The Paleoproterozoic phosphorite deposit of Sallopat block of Banswara district of Rajasthan belongs to kalinjara formation of lunavada group of Aravalli Super Group. The phosphorites are found to occur as massive, brecciated, laminated and stromatolitic associated with calcareous quartzite, interbedded dolomite and multi coloured chert. The phosphorites are showing alternate brown and grey coloured concentric rims which are composed of phosphate, calcite and quartz minerals. Petro-mineralogical studies of phosphorite samples using petrological microscope, XRD, FEG- SEM and EDX reveal that apatite-(CaF) and apatite-(CaOH) are phosphate minerals which are intermixed with minor amount of carbonate materials. Sporadic findings of the uniform tiny granules of partially anisotropic apatite-(CaF) along with dolomite, calcite, quartz, muscovite, zeolite and other gangue minerals have been observed with the replacement of phosphate material by quartz and carbonate. The presence of microbial filaments of organic matter and alternate concentric rims of stromatolitic structure may suggest that the deposition of the phosphate took place in shallow marine oxidizing environmental conditions leading to the formation of phosphorite layers as primary biogenic precipitates by bacterial or algal activities. Different forms and texture of phosphate minerals may be due to environmental vicissitudes at the time of deposition followed by some replacement processes and biogenic activities.

Keywords: apatite, petro-mineralogy, phosphorites, sallopat, stromatolites

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Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammad Hossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: facial image, segmentation, PCM, FCM, skin error, facial surgery

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Authors: Rugang Tian

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In this study, we generated whole-genome sequence (WGS) data from110 native cattle. Together with whole-genome sequences from world-wide cattle populations, we estimated the genetic diversity and population genetic structure of different cattle populations. Our findings revealed clustering of cattle groups in line with their geographic locations. We identified noticeable genetic diversity between indigenous cattle breeds and commercial populations. Among all studied cattle groups, lower genetic diversity measures were found in commercial populations, however, high genetic diversity were detected in some local cattle, particularly in Rashoki and Mongolian breeds. Our search for potential genomic regions under selection in native cattle revealed several candidate genes related with immune response and cold shock protein on multiple chromosomes such as TRPM8, NMUR1, PRKAA2, SMTNL2 and OXR1 that are involved in energy metabolism and metabolic homeostasis.

Keywords: cattle, whole-genome, population structure, adaptation

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning

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Authors: Sirous Eydivandi

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Diacylglycerol-O-acyltransferase (DGAT1) gene encodes diacylglycerol transferase enzyme that plays an important role in glycerol lipid metabolism. DGAT1 is considered to be the key enzyme in controlling the synthesis of triglycerides in adipocytes. This enzyme catalyzes the final step of triglyceride synthesis (transform triacylglycerol (DAG) into triacylglycerol (TAG). A total of 20 DGAT1 gene sequences and corresponding amino acids belonging to 4 species include cattle, goats, sheep and yaks were analyzed, and the differentiation within and among the species was also studied. The length of the DGAT1 gene varies greatly, from 1527 to 1785 bp, due to deletion, insertion, and stop codon mutation resulting in elongation. Observed genetic diversity was higher among species than within species, and Goat had more polymorphisms than any other species. Novel amino acid variation sites were detected within several species which might be used to illustrate the functional variation. Differentiation of the DGAT1 gene was obvious among species, and the clustering result was consistent with the taxonomy in the National Center for Biotechnology Information.

Keywords: DGAT1gene, bioinformatic, ruminnants, biotechnology information

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Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Trapti Sharma, Devesh Kumar Srivastava

Abstract:

This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

Procedia PDF Downloads 369

Authors: C. Rana, S. K. Jain

Abstract:

The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

Procedia PDF Downloads 413

Authors: Efil Yusrianto, Noraini Marsi, Noraniah Kassim, Izzati Abdul Manaf, Hafizuddin Hakim Shariff

Abstract:

Today, municipal solid waste (MSW), noise pollution, and attack fire are three ongoing issues for inhabitants of urban including in Malaysia. To solve these issues, eco-friendly autoclaved aerated concrete (AAC) containing recycled ceramic and gypsum waste (CGW) as a partial replacement for sand with different ratios (0%, 5%, 10%, 15%, 20%, and 25% wt) has been prepared. The performance of samples, such as the physical, mechanical, sound absorption coefficient, and direct fire resistance, has been investigated. All samples showed normal color behavior, i.e., grey and free crack. The compressive strength was increased in the range of 6.10% to 29.88%. The maximum value of compressive strength was 2.13MPa for 15% wt of CGW. The positive effect of CGW on the compressive strength of AAC has also been confirmed by crystalline phase and microstructure analysis. The acoustic performances, such as sound absorption coefficients of samples at low frequencies (500Hz), are higher than the reference sample (RS). AAC-CGW samples are categorized as AAC material classes B and C. The fire resistance results showed the physical surface of the samples had a free crack and was not burned during the direct fire at 950ºC for 300s. The results showed that CGW succeeded in enhancing the performance of fresh AAC, such as compressive strength, crystalline phase, sound absorption coefficient, and fire resistance of samples.

Keywords: physical, mechanical, acoustic, direct fire resistance performance, autoclaved aerated concrete, recycled ceramic-gypsum waste

Procedia PDF Downloads 138

Authors: Matthieu-P. Schapranow

Abstract:

Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

Procedia PDF Downloads 493

Authors: Imam Taufik, Hilkatul Ilmi, Puryani, Mochammad Yuwono, Aty Widyawaruyanti

Abstract:

Artocarpus champeden Spreng stembark (Moraceae) in Indonesia well known as ‘cempedak’ had been traditionally used for malarial remedies. The difference of growth locations could cause the difference of metabolite profiling. As a consequence, there were difference antimalarial activities in spite of the same plants. The aim of this research was to obtain the profile of metabolites that contained in A. champeden stembark from different locations in Indonesia for authentication and quality control purpose of this extract. The profiling had been performed by HPTLC-Densitometry technique and antimalarial activity had been also determined by HRP2-ELISA technique. The correlation between metabolite fingerprinting and antimalarial activity had been analyzed by Principle Component Analysis, Hierarchical Clustering Analysis and Partial Least Square. As a result, there is correlation between the difference metabolite fingerprinting and antimalarial activity from several different growth locations.

Keywords: antimalarial, artocarpus champeden spreng, metabolite fingerprinting, multivariate analysis

Procedia PDF Downloads 311