Search results for: consumer data right

Authors: Valentina Marutyan

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Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

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Authors: Laila Ozola, Evita Straumite

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Celiac disease is a permanent enteropathy caused by the ingestion of gluten, a protein occurring in wheat, rye and barley. The only way of the effective daily treatment is a strict gluten-free diet. From the investigation of products available in the local market, it was found that Latvian producers do not offer gluten-free products. The aim of this research was to study and analyze changes of celiac patient’s attitude to gluten-free product quality and availability in the Latvian market and purchasing habits. The survey was designed using website www.visidati.lv, and a questionnaire was sent to people suffering from celiac disease. The first time the respondents were asked to fill in the questionnaire in 2011, but now repeatedly from the beginning of September 2013 till the end of January 2014. The questionnaire was performed with 75 celiac patients, respondents were from all Latvian regions and they answered 16 questions. One of the most important questions was aimed to find out consumers’ opinion about quality of gluten-free products, consumption patterns of gluten-free products, and, moreover, their interest in products made in Latvia. Respondents were asked to name gluten-free products they mainly buy and give specific purchase locations, evaluate the quality of products and necessity for products produced in Latvia. The results of questionnaire show that the consumers are satisfied with the quality of gluten-free flour, flour blends, sweets and pasta, but are not satisfied with the quality of bread and confectionery available in the Latvian markets.

Keywords: consumers, gluten-free products, quality, survey

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Y. Alrubyli

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Unmanned Arial Vehicles (UAVs), and more specifically, quadcopters need to be stable during their flights. Altitude stability is usually achieved by using a PID controller that is built into the flight controller software. Furthermore, the PID controller has gains that need to be tuned to reach optimal altitude stabilization during the quadcopter’s flight. For that, control system engineers need to tune those gains by using extensive modeling of the environment, which might change from one environment and condition to another. As quadcopters penetrate more sectors, from the military to the consumer sectors, they have been put into complex and challenging environments more than ever before. Hence, intelligent self-stabilizing quadcopters are needed to maneuver through those complex environments and situations. Here we show that by using online reinforcement learning with minimal background knowledge, the altitude stability of the quadcopter can be achieved using a model-free approach. We found that by using background knowledge instead of letting the online reinforcement learning algorithm wander for a while to tune the PID gains, altitude stabilization can be achieved faster. In addition, using this approach will accelerate development by avoiding extensive simulations before applying the PID gains to the real-world quadcopter. Our results demonstrate the possibility of using the trial and error approach of reinforcement learning combined with background knowledge to achieve faster quadcopter altitude stabilization in different environments and conditions.

Keywords: reinforcement learning, Q-leanring, online learning, PID tuning, unmanned aerial vehicle, quadcopter

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Authors: Philippa Sambevski

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Breast cancer is the most common cancer diagnosed in Australian women. The incidence of breast cancer for Aboriginal and Torres Strait Islander (ATSI) women is lower than for non-indigenous women. However, the mortality rate for ATSI women is higher. The participation rate of ATSI women in BreastScreen Australia is below the general population. In this thematic literature review, the author collates viable strategies to increase breast screening rates among culturally and linguistically diverse individuals and provide culturally competent care. Barriers to accessing BreastScreen for ATSI women include language or communication limits, isolation, and a lack of culturally sensitive information. Culturally competent strategies require healthcare workers with an appropriate cultural and social background, clear messages, and the embedding of cultural respect within healthcare organisations. Cultural safety is determined by partnering with local indigenous groups, recognising the consumer experience, and allowing people to raise their concerns. The corresponding academic poster identifies strategies for healthcare workers to provide culturally competent care in a BreastScreen setting.

Keywords: breast screen, closing the gap, Australia, cultural safety, Aboriginal and Torres Strait Islander

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Hae-Won Seo, Yong-Gu Lee

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Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: connected-car, data modeling, route planning, navigation system

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Georgii Emelianov, Mikhail Polikarpov, Fabian Hübner, Jochen Deuse, Jochen Schiemann

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Recycling refrigerating appliances plays a major role in protecting the Earth's atmosphere from ozone depletion and emissions of greenhouse gases. The performance of refrigerator recycling plants in terms of material retention is the subject of strict environmental certifications and is reviewed periodically through specialized audits. The continuous collection of Refrigerator data required for the input-output analysis is still mostly manual, error-prone, and not digitalized. In this paper, we propose an automated data collection system for recycling plants in order to deduce expected material contents in individual end-of-life refrigerating appliances. The system utilizes laser scanner measurements and optical data to extract attributes of individual refrigerators by applying transfer learning with pre-trained vision models and optical character recognition. Based on Recognized features, the system automatically provides material categories and target values of contained material masses, especially foaming and cooling agents. The presented data collection system paves the way for continuous performance monitoring and efficient control of refrigerator recycling plants.

Keywords: automation, data collection, performance monitoring, recycling, refrigerators

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Authors: Soojin Kim, Jiwon Yang, Sungzoon Cho

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As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies.

Keywords: clustering, distribution, sales pattern, seasonal product

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Authors: Chris Houliez

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This study addresses the complex dynamics of the modern retail environment, focusing on how the ubiquitous nature of mobile communication technologies has reshaped the shopper experience and tested the limits of the conventional "servicescape" concept commonly used to describe retail experiences. The objective is to redefine the conceptualization of retail space by introducing an approach to space that aligns with a retail context where physical and digital interactions are increasingly intertwined. To offer a more shopper-centric understanding of the retail experience, this study draws from phenomenology, particularly Henri Lefebvre’s work on the production of space. The presented protocol differs from traditional methodologies by not making assumptions about what constitutes a retail space. Instead, it adopts a perspective based on Lefebvre’s seminal work, which posits that space is not a three-dimensional container commonly referred to as “servicescape” but is actively produced through shoppers’ spatial practices. This approach allows for an in-depth exploration of the retail experience by capturing the everyday spatial practices of shoppers without preconceived notions of what constitutes a retail space. The designed protocol was tested with eight participants during 209 hours of day-long field trips, immersing the researcher into the shopper's lived experience by combining multiple data collection methods, including participant observation, videography, photography, and both pre-fieldwork and post-fieldwork interviews. By giving equal importance to both locations and connections, this study unpacked various spatial practices that contribute to the production of retail space. These findings highlight the relative inadequacy of some traditional retail space conceptualizations, which often fail to capture the fluid nature of contemporary shopping experiences. The study's emphasis on the customization process, through which shoppers optimize their retail experience by producing a “fully lived retail space,” offers a more comprehensive understanding of consumer shopping behavior in the digital age. In conclusion, this research presents a significant shift in the conceptualization of retail space. By employing a phenomenological approach rooted in Lefebvre’s theory, the study provides a more efficient framework to understand the retail experience in the age of mobile communication technologies. Although this research is limited by its small sample size and the demographic profile of participants, it offers valuable insights into the spatial practices of modern shoppers and their implications for retail researchers and retailers alike.

Keywords: shopper behavior, mobile telecommunication technologies, qualitative research, servicescape, servicespace

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Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

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Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

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Authors: Ratna Sharma, Kaveri Dutta

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Assam (India), the gate way to the Northeast India is mainly known for its exquisite silks, the art and craft. The state has a rich collection of traditional jewellery which is unique and exclusive to the state. These jewelleries hold a special place in the heart of the Assamese women. Similarly handloom industry of Assam is basically silk oriented. Among the wild silk, Eri silk fabric has remained as “the poor man’s silk” but it is closely attached to the assamese society, dress for it's warm quality. In view of the changing market trends, fashion and consumer demands, Silk is emerging as a fashion fabric both in India and abroad. In case of Eri silk fabric it has limited use in clothing and accessories. Hence the restructured and redesigned traditional jewellery motifs of Assam (India) over Eri silk products will have greater potential in reviving the decline of art, generate revenue, self employment towards craftsmen and also recognition of the art. The information incorporated in the paper is primary and the data have been collected by purposive sampling method. This work of art was expressed on Eri silk fabric in the form of traditional hand embroidery as it is closely connected with the era of the individual in history of mankind and reflects the personal expression of an entity. For this study selected traditional motifs of Assamese ornaments was used. Some of the popular traditional Assamese jewellery include earrings with exquisite Lokaparo, Keru, Thuriya, Jangphai, etc. An array of necklaces including Golpata, Satsori, Jon biri, Bena, Gejera, Dhol biri, Doog doogi, Biri Moni, Mukuta Moni, Poalmoni, Silikha Moni and Magardana and diversified rings including Senpata, Horinsakua, Jethinejia, bakharpata and others. Selected two motifs each from necklace, earring and finger ring designs. Selected motifs were further developed into 3 categories- the border, the main motif and all over butta followed by placement of developed patterns on products. Products developed were stoles, scarf’s, purses, brooch pins, skirts for women and ties, handkerchief, jackets for men. The developed products were surveyed by selected respondents. From the present study it can be observed that the embellished traditional jewellery motifs resulted in fresh and colourful pattern on developed Eri silk products. Moreover the motifs which were gradually fading among the community itself showed a very good recognition towards art. The embroidered Eri silk fabric also created a huge change in a positive way among craftsman.

Keywords: Art and craft of Assam, eri silk, hand embroidery, traditional Assamese jewellery motifs

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Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: A. S. V Polgampala, F. Huang

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This is an overview of what is entailed in an evaluation and issues to be aware of when class observation is being done. This study examined the effects of evaluating teaching practice of a 7-day ‘block teaching’ session in a pre -service science teacher training program at a reputed National College of Education in Sri Lanka. Effects were assessed in three areas: evaluation of the training process, evaluation of the training impact, and evaluation of the training procedure. Data for this study were collected by class observation of 18 teachers during 9th February to 16th of 2017. Prospective teachers of science teaching, the participants of the study were evaluated based on newly introduced format by the NIE. The data collected was analyzed qualitatively using the Miles and Huberman procedure for analyzing qualitative data: data reduction, data display and conclusion drawing/verification. It was observed that the trainees showed their confidence in teaching those competencies and skills. Teacher educators’ dissatisfaction has been a great impact on evaluation process.

Keywords: evaluation, perceptions & perspectives, pre-service, science teachering

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Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Abhijith Asok

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This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

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Authors: Avdasheva Svetlanaa, Kryuchkova Polinab

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Comparative social efficiency of private and public enforcement of law is debated. This question is not of academic interest only, it is also important for the development of the legal system and regulations. Generally, involvement of ‘common citizens’ in public law enforcement is considered to be beneficial, while involvement of interest groups representatives is not. Institutional economics as well as law and economics consider the difference between public and private enforcement to be rather mechanical. Actions of bureaucrats in government agencies are assumed to be driven by the incentives linked to social welfare (or other indicator of public interest) and their own benefits. In contrast, actions of participants in private enforcement are driven by their private benefits. However administrative law enforcement may be designed in such a way that it would become driven mainly by individual incentives of alleged victims. We refer to this system as reactive public enforcement. Citizens may prefer using reactive public enforcement even if private enforcement is available. However replacement of public enforcement by reactive version of public enforcement negatively affects deterrence and reduces social welfare. We illustrate the problem of private vs pure public and private vs reactive public enforcement models with the examples of three legislation subsystems in Russia – labor law, consumer protection law and competition law. While development of private enforcement instead of public (especially in reactive public model) is desirable, replacement of both public and private enforcement by reactive model is definitely not.

Keywords: public enforcement, private complaints, legal errors, competition protection, labor law, competition law, russia

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Authors: Begzod Nishanov, Farhod Karimov

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Globalization and the internet have completely changed the way in which businesses operate as well as has equipped customers with endless potential. Today, consumers’ product choice is not only affected by branding, price and quality of the product, but also by the country-of-origin information. Precisely, ‘Made In’ label is considered as one of the driving factors which directly impact on consumers’ preferences. Generally, it is obvious that products manufactured in less developed countries are considered to be of lower quality and riskier compared to the products made in developed countries. In this regard, it is worth to note that this phenomenon is mainly applicable to western developed countries. However, there is a lack of empirical research on underlying the influence of country-of-origin phenomenon in emerging economies such as Uzbekistan. Today, Uzbek market is being dominated by growing number of foreign made products. Uzbek manufacturers are facing intense competition not only from local producers but also from the availability of foreign goods suppliers. Consequently, consumers are given wider choice of products than ever before. In this regard, it is important to define the importance of country-of-origin information in order to understand Uzbek consumers’ preference. The methodology of the research is formulated based on the methodology of previous papers. A total 527 online questionnaires were completed. Data analysis was conducted using factor analysis and analysis of variance test (ANOVA). Findings of the research support the view that Uzbek consumers attach great importance to the country-of-origin information of products. Precisely, it can be stated that Uzbek people perceive product quality by its ‘Made in...’ label, especially when buying high involvement goods such as car or refrigerator. Another findings of the paper show that products manufactured in developed countries including Germany, Japan and USA are found to be of high quality, while products manufactured in less developed countries are considered to be of lower quality. Marketers can use this information for segmentation purposes. For example, products manufactured in less developed countries can be targeted for low-to-middle income families while goods manufactured in developed countries can be targeted for higher income families. In conclusion, it can be stated that perceived product quality of products that are made in Uzbekistan has slightly increased since 18 years. It implies that nowadays products under ‘Made in Uzbekistan’ label is continually becoming available to many consumers in foreign markets, especially among Commonwealth of Independent States (CIS) countries. Therefore, conducting further research to explore the phenomenon of country-of-origin information and perceived product quality in emerging markets is of paramount importance.

Keywords: country-of-origin, consumer behavior, product evaluation, perceived quality

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Authors: Kar Lin Nyam, Phey Yee Lim

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The aim of this study was to determine the antioxidant potential, nutritional composition, and functional properties of kenaf leaves powder. Besides, the effect of kenaf leaves powder in bread qualities, properties, and consumer acceptability were evaluated. Different formulations of bread fortified with 0%, 4% and 8% kenaf leaves powder, respectively were produced. Physical properties of bread, such as loaf volume, dough expansion, crumb colour, and bread texture, were determined. Nine points hedonic scale was utilized in sensory evaluation to determine the best formulation (the highest overall acceptability). Proximate composition, calcium content, and antioxidant properties were also determined for the best formulation. 4% leaves powder bread was the most preferred by the panelists followed by control bread, and the least preferred was being 8% leaves powder bread. 4% leaves powder bread had significantly higher value of DPPH radical scavenging capacity (8.05 mg TE/100g), total phenolic content (12.88 mg GAE/100g) and total flavonoid content (13.26 mg QE/100g) compared to control bread (1.38 mg TE/100g, 8.17 mg GAE/100g, and 8.77 mg QE/100g respectively). Besides, 4% leaves powder bread also showed higher in calcium content and total dietary fiber compared to control bread. Kenaf leaves powder is suitable to be used as a source of natural antioxidant for fortification and nutrient improver in bread.

Keywords: dietary fibre, calcium, total phenolic content, total flavonoid content

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Authors: Abdullah Mohammad, Abdullah Alshemali, Esmaeil Alsaleh

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The chemical composition of desalinated water is modified to make it more acceptable to the end-user. Sometimes, this modification is approached by mixing with brackish water that is known to contain a variety of minerals. Expectedly, besides minerals, brackish water indigenous bacterial communities access the final mixture hence reaching the end consumer. The current project examined the safety of using brackish water as an ingredient in potable water. Pseudomonas aeruginosa strains were detected in potable and brackish water samples collected from storage facilities in residential areas as well as from main water distribution and storage tanks. The application of molecular and biochemical fingerprinting methods, including phylogeny, RFLP (restriction fragment length polymorphism), MLST (multilocus sequence typing) and substrate specificity testing, suggested that the potable water P. aeruginosa strains were most probably originated from brackish water. Additionally, all the sixty-four isolates showed multi-drug resistance (MDR) phenotype and harboured the three genes responsible for biofilm formation. These virulence factors represent serious health hazards compelling the scientific community to revise the WHO (World Health Organization) and USEP (US Environmental Protection Agency) A potable water quality guidelines, particularly those related to the types of bacterial genera that evade the current water quality guidelines.

Keywords: potable water, brackish water, pseudomonas aeroginosa, multidrug resistance

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Authors: Janet C. Read

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UClan set collaboration with Lingokids to assess the Lingokids learning app's impact on learning outcomes in classrooms in the UK for children with ages ranging from 3 to 5 years. Data gathered during the controlled study with 69 children includes attitudinal data, engagement, and learning scores. Data shows that children enjoyment while learning was higher among those children using the game-based app compared to those children using other traditional methods. It’s worth pointing out that engagement when using the learning app was significantly higher than other traditional methods among older children. According to existing literature, there is a direct correlation between engagement, motivation, and learning. Therefore, this study provides relevant data points to conclude that Lingokids learning app serves its purpose of encouraging learning through playful and interactive content. That being said, we believe that learning outcomes should be assessed with a wider range of methods in further studies. Likewise, it would be beneficial to assess the level of usability and playability of the app in order to evaluate the learning app from other angles.

Keywords: learning app, learning outcomes, rapid test activity, Smileyometer, early childhood education, innovative pedagogy

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Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

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The Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse the Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. In this paper, we do an exploratory data analysis using STATS19 data. For the past 30 years, the UK has had a good record in reducing fatalities. The UK ranked third based on the number of road deaths per million inhabitants. There were around 165,000 accidents reported in the Great Britain in 2009 and it has been decreasing every year until 2019 which is under 120,000. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe.

Keywords: road safety, data analysis, openstreetmap, feature expanding.

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

Abstract:

STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.

Keywords: rule induction, decision table, missing data, noise

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Authors: Mohd Zaimmudin Mohd Zain, Patsy Perry, Lee Quinn

Abstract:

This study explores how fashion consumers are influenced by fashion bloggers towards pre-purchase decision for online fashion products in a non-Western context. Malaysians rank among the world’s most avid online shoppers, with apparel the third most popular purchase category. However, extant research on fashion blogging focuses on the developed Western market context. Numerous international fashion retailers have entered the Malaysian market from luxury to fast fashion segments of the market; however Malaysian fashion consumers must balance religious and social norms for modesty with their dress style and adoption of fashion trends. Consumers increasingly mix and match Islamic and Western elements of dress to create new styles enabling them to follow Western fashion trends whilst paying respect to social and religious norms. Social media have revolutionised the way that consumers can search for and find information about fashion products. For online fashion brands with no physical presence, social media provide a means of discovery for consumers. By allowing the creation and exchange of user-generated content (UGC) online, they provide a public forum that gives individual consumers their own voices, as well as access to product information that facilitates their purchase decisions. Social media empower consumers and brands have important roles in facilitating conversations among consumers and themselves, to help consumers connect with them and one another. Fashion blogs have become an important fashion information sources. By sharing their personal style and inspiring their followers with what they wear on popular social media platforms such as Instagram, fashion bloggers have become fashion opinion leaders. By creating UGC to spread useful information to their followers, they influence the pre-purchase decision. Hence, successful Western fashion bloggers such as Chiara Ferragni may earn millions of US dollars every year, and some have created their own fashion ranges and beauty products, become judges in fashion reality shows, won awards, and collaborated with high street and luxury brands. As fashion blogging has become more established worldwide, increasing numbers of fashion bloggers have emerged from non-Western backgrounds to promote Islamic fashion styles, such as Hassanah El-Yacoubi and Dian Pelangi. This study adopts a qualitative approach using netnographic content analysis of consumer comments on two famous Malaysian fashion bloggers’ Instagram accounts during January-March 2016 and qualitative interviews with 16 Malaysian Generation Y fashion consumers during September-October 2016. Netnography adapts ethnographic techniques to the study of online communities or computer-mediated communications. Template analysis of the data involved coding comments according to the theoretical framework, which was developed from the literature review. Initial data analysis shows the strong influence of Malaysian fashion bloggers on their followers in terms of lifestyle and morals as well as fashion style. Followers were guided towards the mix and match trend of dress with Western and Islamic elements, for example, showing how vivid colours or accessories could be worked into an outfit whilst still respecting social and religious norms. The blogger’s Instagram account is a form of online community where followers can communicate and gain guidance and support from other followers, as well as from the blogger.

Keywords: fashion bloggers, Malaysia, qualitative, social media

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

Abstract:

Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Tesfaye Alamirew Dessie

Abstract:

Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters.

Keywords: MPC, PID, ARX, pasteurization

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Authors: Rana Rimawi, Ayman Baklizi

Abstract:

Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given.

Keywords: point estimation, type II generalized logistic distribution, progressive censoring, maximum likelihood estimation

Procedia PDF Downloads 198