Search results for: stock market prediction

Authors: Koe Han Beng, Khoo Boo Cheong

Abstract:

The rising interest of stepped hull design has been led by the demand of more efficient high-speed boat. At the same time, the need of accurate prediction method for stepped planing hull is getting more important. By understanding the flow at high Froude number is the key in designing a practical step hull, the study surrounding stepped hull has been done mainly in the towing tank which is time-consuming and costly for initial design phase. Here the feasibility of predicting hydrodynamics of high-speed planing hull both with and without step using computational fluid dynamics (CFD) with the volume of fluid (VOF) methodology is studied in this work. First the flow around the prismatic body is analyzed, the force generated and its center of pressure are compared with available experimental and empirical data from the literature. The wake behind the transom on the keel line as well as the quarter beam buttock line are then compared with the available data, this is important since the afterbody flow of stepped hull is subjected from the wake of the forebody. Finally the calm water performance prediction of a conventional planing hull and its stepped version is then analyzed. Overset mesh methodology is employed in solving the dynamic equilibrium of the hull. The resistance, trim, and heave are then compared with the experimental data. The resistance is found to be predicted well and the dynamic equilibrium solved by the numerical method is deemed to be acceptable. This means that computational fluid dynamics will be very useful in further study on the complex flow around stepped hull and its potential usage in the design phase.

Keywords: planing hulls, stepped hulls, wake shape, numerical simulation, hydrodynamics

Procedia PDF Downloads 279

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 77

Authors: Yasir Khan, Qiu Bin, Antonio-Mihi Ramirez

Abstract:

This study determines the inflow of international students to China in recent years and the corresponding internationalization strategies in the higher education sector. China has placed attracting international students on in its plan along with the growing of global impact. Acknowledging the stable economy, growth rate, trade, lower renminbi rate, high wages, employment opportunities, high level income per capita, relative low taxes and political system consolidate to attract more international students. A large number of international students making a vast contribution to the higher education sector of China. Understanding the significance of education mission as well as of financial ‘bottom line’ the Chinese government gave great importance to invite more international students from worldwide. The large number of international students in the China has been particularly notable from Asian countries specifically neighboring countries, Pakistan, Thailand, India, Vietnam, South Korea, Magnolia, Malaysia, and Russia. This study summarizes internationalization of higher education in China and also provides directions for future research in this regard.

Keywords: international student mobility, 2020 Govt Planning, emerging market, internationalization of higher education

Procedia PDF Downloads 249

Authors: Akkouche Karim, Nekmouche Aghiles, Bouzid Leyla

Abstract:

This study focuses on the development of a multifunctional Expert System (ES) called post-seismic damage inspection tool (PSDIT), a powerful tool which allows the evaluation, the processing and the archiving of the collected data stock after earthquakes. PSDIT can be operated by two user types; an ordinary user (engineer, expert or architect) for the damage visual inspection and an administrative user for updating the knowledge and / or for adding or removing the ordinary user. The knowledge acquisition is driven by a hierarchical knowledge model, the Information from investigation reports and those acquired through feedback from expert / engineer questionnaires are part.

Keywords: buildings, earthquake, seismic damage, damage assessment, expert system

Procedia PDF Downloads 84

Authors: Rancy Chepchirchir Kosgey, John Olukuru

Abstract:

This study examines the impact of introduction of futures trading on the spot price volatility in the commodity market. The paper considers the United States of America, South Africa and Ethiopian economies. Three commodities i.e. coffee, maize and wheat from New York Merchantile Exchange, South African Futures Exchange and Ethiopian Commodity Exchange are analyzed. ARCH LM test is used to check for heteroskedasticity and GARCH and EGARCH are used to check for the behavior of volatility between the pre- and post-futures periods. For all the three economies, the results indicate presence of the ARCH effect in the log returns. For conditional and unconditional variances; spot price volatility for coffee has decreased after futures trading in all the economies and the EGARCH has also shown reduction in persistence of volatility in the post-futures period in the three economies; while that of maize has reduced for the Ethiopian economy while there has been an increase in both the US and South African economies. For wheat, the conditional variance has been found to rise in the post-futures period in all the three economies.

Keywords: derivatives, futures exchange, agricultural commodities, spot price volatility

Procedia PDF Downloads 423

Authors: Muhammad Luthfi, Sutikno Sutikno, Purhadi Purhadi

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Weather forecast has necessarily been improved to provide the communities an accurate and objective prediction as well. To overcome such issue, the numerical-based weather forecast was extensively developed to reduce the subjectivity of forecast. Yet the Numerical Weather Predictions (NWPs) outputs are unfortunately issued without taking dynamical weather behavior and local terrain features into account. Thus, NWPs outputs are not able to accurately forecast the weather quantities, particularly for medium and long range forecast. The aim of this research is to aid and extend the development of ensemble forecast for Meteorology, Climatology, and Geophysics Agency of Indonesia. Ensemble method is an approach combining various deterministic forecast to produce more reliable one. However, such forecast is biased and uncalibrated due to its underdispersive or overdispersive nature. As one of the parametric methods, Bayesian Model Averaging (BMA) generates the calibrated ensemble forecast and constructs predictive PDF for specified period. Such method is able to utilize ensemble of any size but does not take spatial correlation into account. Whereas space dependencies involve the site of interest and nearby site, influenced by dynamic weather behavior. Meanwhile, Geostatistical Output Perturbation (GOP) reckons the spatial correlation to generate future weather quantities, though merely built by a single deterministic forecast, and is able to generate an ensemble of any size as well. This research conducts both BMA and GOP to generate the calibrated ensemble forecast for the daily temperature at few meteorological sites nearby Indonesia international airport.

Keywords: Bayesian Model Averaging, ensemble forecast, geostatistical output perturbation, numerical weather prediction, temperature

Procedia PDF Downloads 276

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 66

Authors: Manuela Villani, Anila Xhahysa, Christopher Brooks, Marco Pagani

Abstract:

The geological structure of Western Balkans is strongly affected by the collision between Adria microplate and the southwestern Euroasia margin, resulting in a considerably active seismic region. The Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project (BSHAP) (2007-2011, 2012-2015) by NATO supported the preparation of new seismic hazard maps of the Western Balkan, but when inspecting the seismic hazard models produced later by these countries on a national scale, significant differences in design PGA values are observed in the border, for instance, North Albania-Montenegro, South Albania- Greece, etc. Considering the fact that the catalogues were unified and seismic sources were defined within BSHAP framework, obviously, the differences arise from the Ground Motion Prediction Equations selection, which are generally the component with highest impact on the seismic hazard assessment. At the time of the project, a modest database was present, namely 672 three-component records, whereas nowadays, this strong motion database has increased considerably up to 20,939 records with Mw ranging in the interval 3.7-7 and epicentral distance distribution from 0.47km to 490km. Statistical analysis of the strong motion database showed the lack of recordings in the moderate-to-large magnitude and short distance ranges; therefore, there is need to re-evaluate the Ground Motion Prediction Equation in light of the recently updated database and the new generations of GMMs. In some cases, it was observed that some events were more extensively documented in one database than the other, like the 1979 Montenegro earthquake, with a considerably larger number of records in the BSHAP Analogue SM database when compared to ESM23. Therefore, the strong motion flat-file provided from the Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project was merged with the ESM23 database for the polygon studied in this project. After performing the preliminary residual analysis, the candidate GMPE-s were identified. This process was done using the GMPE performance metrics available within the SMT in the OpenQuake Platform. The Likelihood Model and Euclidean Distance Based Ranking (EDR) were used. Finally, for this study, a GMPE logic tree was selected and following the selection of candidate GMPEs, model weights were assigned using the average sample log-likelihood approach of Scherbaum.

Keywords: residual analysis, GMPE, western balkan, strong motion, openquake

Procedia PDF Downloads 76

Authors: Jerome Joshi

Abstract:

The paper presents the modelling of financial markets using the Stochastic Pi Calculus model. The Stochastic Pi Calculus model is mainly used for biological applications; however, the feature of this model promotes its use in financial markets, more prominently in high frequency trading. The trading system can be broadly classified into exchange, market makers or intermediary traders and fundamental traders. The exchange is where the action of the trade is executed, and the two types of traders act as market participants in the exchange. High frequency trading, with its complex networks and numerous market participants (intermediary and fundamental traders) poses a difficulty while modelling. It involves the participants to seek the advantage of complex trading algorithms and high execution speeds to carry out large volumes of trades. To earn profits from each trade, the trader must be at the top of the order book quite frequently by executing or processing multiple trades simultaneously. This would require highly automated systems as well as the right sentiment to outperform other traders. However, always being at the top of the book is also not best for the trader, since it was the reason for the outbreak of the ‘Hot – Potato Effect,’ which in turn demands for a better and more efficient model. The characteristics of the model should be such that it should be flexible and have diverse applications. Therefore, a model which has its application in a similar field characterized by such difficulty should be chosen. It should also be flexible in its simulation so that it can be further extended and adapted for future research as well as be equipped with certain tools so that it can be perfectly used in the field of finance. In this case, the Stochastic Pi Calculus model seems to be an ideal fit for financial applications, owing to its expertise in the field of biology. It is an extension of the original Pi Calculus model and acts as a solution and an alternative to the previously flawed algorithm, provided the application of this model is further extended. This model would focus on solving the problem which led to the ‘Flash Crash’ which is the ‘Hot –Potato Effect.’ The model consists of small sub-systems, which can be integrated to form a large system. It is designed in way such that the behavior of ‘noise traders’ is considered as a random process or noise in the system. While modelling, to get a better understanding of the problem, a broader picture is taken into consideration with the trader, the system, and the market participants. The paper goes on to explain trading in exchanges, types of traders, high frequency trading, ‘Flash Crash,’ ‘Hot-Potato Effect,’ evaluation of orders and time delay in further detail. For the future, there is a need to focus on the calibration of the module so that they would interact perfectly with other modules. This model, with its application extended, would provide a basis for researchers for further research in the field of finance and computing.

Keywords: concurrent computing, high frequency trading, financial markets, stochastic pi calculus

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Authors: Brizita Djordjevic, Bojana Vidovic, Milica Zrnic, Uros Cakar, Ivan Stankovic, Davor Korcok, Sladjana Sobajic

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Histamine is a biogenic amine, which is formed by enzymatic decarboxylation from the amino acid histidine. It can be found in foods such as fish and fish products, meat and fermented meat products, cheese, wine and beer. The presence of histamine in these foods can indicate microbiological spoilage or poor manufacturing processes. The consumption of food containing large amounts of histamine can have toxicological consequences. In 62 food products (31 canned fish products, 19 wines and 12 cheeses) from the market of Serbia the content of histamine was determined using enzyme-linked immunosorbent assay (ELISA) test kit according to the manufacturer's instructions (Immunolab GmbH, Kassel, Germany). The detection limits of this assay were 20 µg/kg for fish and cheese and 4 µg/L for wine. The concentration of histamine varied between 0.16-207 mg/kg in canned fish products, 0.03-1.47 mg/kg in cheeses and 0.01- 0.18 mg/L in wines. In all analyzed canned fish products the results obtained for the histamine were below the limits set by European and national legislation, so they can be considered acceptable and safe for the health consumers. The levels of histamine in analyzed cheeses and wines were very low and did not pose safety concerns.

Keywords: cheese, enzyme-linked immunosorbent assay, histamine, fish products, wine

Procedia PDF Downloads 442

Authors: Ebrahim Sabermaash Eshghi, Donna Sandsmark

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In changing modern economic conditions of the world, one the most important issues facing managers of firms, is increasing the sales and profitability through sales of newly developed products. This is while purpose of decreasing unnecessary costs is one of the most essential programs of smart managers for more implementation with new conditions in current business. In modern life, condition of misgiving is dominant in all of the industries. Accordingly, in this research, influence of different aspects of presenting products to the market is investigated. This study is done through a Quantitative-Qualitative (Interviews and Questionnaire) approach. In sum, 103 of informed managers and experts of Pars-Khazar Company have been examined through census. Validity of measurement tools was approved through judgments of experts. Reliability of tools was gained through Cronbach's alpha coefficient in size of 0.930 and in sum, validity and reliability of tools were approved generally. Results of regression test revealed that the influence of all aspects of product introduction supported the performance of product, positively and significantly. In addition that influence of two new factors raised from the interview, namely Human Resource Management and Management of product’s pre-test on performance of products was approved.

Keywords: introducing products, performance, home appliances, price, advertisement, production

Procedia PDF Downloads 208

Authors: Supranee Wattanasin

Abstract:

The objectives of this research are to find and collect the knowledge in order to transmit the food wisdom of Salaya community. The research is qualitative tool to gather the data. Phase 1: Collect and analyze related literature review on food wisdom including documents about Salaya community to have a clear picture on Salaya community context. Phase 2: Conduct an action research, stage a people forum to exchange knowledge in food wisdom of Salaya community. Learning stage on cooking, types, and benefits of the food wisdom of Salaya community were also set up, as well as a people forum to find ways to transmit and add value to the food wisdom of Salaya community. The result shows that Salaya old market community was once a marketplace located by Mahasawat canal. The old market had become sluggish due to growing development of land transportation. This had affected the ways of food consumption. Residents in the community chose 3 menus that represent the community’s unique food: chicken green curry, desserts in syrup and Khanom Sai-Sai (steamed flour with coconut filling). The researcher had the local residents train the team on how to make these meals. It was found that people in the community transmit the wisdom to the next generation by teaching and telling from parents to children. ‘Learning through the back door’ is one of the learning methods that the community used and still does.

Keywords: transmission, food wisdom, Salaya, cooking

Procedia PDF Downloads 395

Authors: Pradyumna Mohanty

Abstract:

The paper aims to explore whether accounting information of Indian companies in the Steel sector are value relevant or not. Ohlson’s model which usually takes into consideration book value per share (BV) and earnings per share (EARN) has been used and the same has been expanded to include two more variables such as cash flow from operations (CFO) and return on equity (ROE). The data were collected from CMIE-Prowess data base in respect of BSE-listed steel companies and the time frame spans from 2010 to 2014. OLS regression has been used to test the value relevance of these accounting numbers. Results indicate that both CFO and BV are having significant influence on the stock price in two out of five years of study. But, BV is emerging as the most significant and highly value relevant of all the four variables during the entire period of study.

Keywords: value relevance, accounting information, book value per share, earnings per share

Procedia PDF Downloads 156

Authors: Tim Giesen, Raphael Adamietz, Pablo Mayer, Philipp Stiefel, Patrick Alle, Dirk Schlenker

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The competitiveness and success of new electrical energy storages such as battery cells are significantly dependent on a short time-to-market. Producers who decide to supply new battery cells to the market need to be easily adaptable in manufacturing with respect to the early customers’ needs in terms of cell size, materials, delivery time and quantity. In the initial state, the required output rates do not yet allow the producers to have a fully automated manufacturing line nor to supply handmade battery cells. Yet there was no solution for manufacturing battery cells in low to medium volumes in a reproducible way. Thus, in terms of cell format and output quantity, a concept for the flexible assembly of battery cells was developed by the Fraunhofer-Institute for Manufacturing Engineering and Automation. Based on clustered processes, the modular system platform can be modified, enlarged or retrofitted in a short time frame according to the ordered product. The paper shows the analysis of the production steps from a conventional battery cell assembly line. Process solutions were found by using I/O-analysis, functional structures, and morphological boxes. The identified elementary functions were subsequently clustered by functional coherences for automation solutions and thus the single process cluster was generated. The result presented in this paper enables to manufacture different cell products on the same production system using seven process clusters. The paper shows the solution for a batch-wise flexible battery cell production using advanced process control. Further, the performed tests and benefits by using the process clusters as cyber-physical systems for an integrated production and value chain are discussed. The solution lowers the hurdles for SMEs to launch innovative cell products on the global market.

Keywords: automation, battery production, carrier, advanced process control, cyber-physical system

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Authors: Muhammad Awais Kiani, Maryam Kiani

Abstract:

This abstract explores the confluence of these two domains and highlights the key factors driving success in sales marketing for electronics. The abstract begins by digging into the ever-evolving landscape of consumer electronics, emphasizing how technological advancements and the growth of smart devices have revolutionized the way people interact with electronics. This paradigm shift has created tremendous opportunities for sales and marketing professionals to engage with consumers on various platforms and channels. Next, the abstract discusses the pivotal role of effective sales marketing strategies in the electronics industry. It highlights the importance of understanding consumer behavior, market trends, and competitive landscapes and how this knowledge enables businesses to tailor their marketing efforts to specific target audiences. Furthermore, the abstract explores the significance of leveraging digital marketing techniques, such as social media advertising, search engine optimization, and influencer partnerships, to establish brand identity and drive sales in the electronics market. It emphasizes the power of storytelling and creating captivating content to engage with tech-savvy consumers. Additionally, the abstract emphasizes the role of customer relationship management (CRM) systems and data analytics in optimizing sales marketing efforts. It highlights the importance of leveraging customer insights and analyzing data to personalize marketing campaigns, enhance customer experience, and ultimately drive sales growth. Lastly, the abstract concludes by underlining the importance of adapting to the ever-changing landscape of the electronics industry. It encourages businesses to embrace innovation, stay informed about emerging technologies, and continuously evolve their sales marketing strategies to meet the evolving needs and expectations of consumers. Overall, this abstract sheds light on the captivating realm of sales marketing in the electronics industry, emphasizing the need for creativity, adaptability, and a deep understanding of consumers to succeed in this rapidly evolving market.

Keywords: marketing industry, electronics, sales impact, e-commerce

Procedia PDF Downloads 69

Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

Procedia PDF Downloads 132

Authors: Naser Zouri

Abstract:

Purpose: Measures of formulate and implementation strategy shows amount of product rate-market based strategic management category such as courtesy, competence, and compliance to reach the high loyalty of financial ecosystem. Despite, it solves the market place error intention to fair trade organization. Finding: Finding shows the ability of executives’ level of management to motivate and better decision-making to solve the treatments in business organization. However, it made ideal level of each interposition policy for a hypothetical household. Methodology/design. Style of questionnaire about the data collection was selected to survey of both pilot test and real research. Also, divide of questionnaire and using of Free Scale Semiconductor`s between the finance employee was famous of this instrument. Respondent`s nominated basic on non-probability sampling such as convenience sampling to answer the questionnaire. The way of realization costs to performed the questionnaire divide among the respondent`s approximately was suitable as a spend the expenditure to reach the answer but very difficult to collect data from hospital. However, items of research survey was formed of implement strategy, environment, supply chain, employee from impact of implementation strategy on reach to better financial consequences and also formulate strategy, comprehensiveness strategic design, organization performance from impression on formulate strategy and financial consequences. Practical Implication: Dynamic capability approach of formulate and implement strategy focuses on the firm-specific processes through which firms integrate, build, or reconfigure resources valuable for making a theoretical contribution. Originality/ value of research: Going beyond the current discussion, we show that case studies have the potential to extend and refine theory. We present new light on how dynamic capabilities can benefit from case study research by discovering the qualifications that shape the development of capabilities and determining the boundary conditions of the dynamic capabilities approach. Limitation of the study :Present study also relies on survey of methodology for data collection and the response perhaps connection by financial employee was difficult to responds the question because of limitation work place.

Keywords: financial ecosystem, loyalty, Malaysian market error, dynamic capability approach, rate-market, optimization intelligence strategy, courtesy, competence, compliance

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Authors: C. Slim

Abstract:

Traditional techniques for analyzing time series are based on the notion of stationarity of phenomena under study, but in reality most economic and financial series do not verify this hypothesis, which implies the implementation of specific tools for the detection of such behavior. In this paper, we study nonstationary non-seasonal time series tests in a non-exhaustive manner. We formalize the problem of nonstationary processes with numerical simulations and take stock of their statistical characteristics. The theoretical aspects of some of the most common unit root tests will be discussed. We detail the specification of the tests, showing the advantages and disadvantages of each. The empirical study focuses on the application of these tests to the exchange rate (USD/TND) and the Consumer Price Index (CPI) in Tunisia, in order to compare the Power of these tests with the characteristics of the series.

Keywords: stationarity, unit root tests, economic time series, ADF tests

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Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Wen-Yu Chen, Chia-Yuan Hsu, Sasinee Vongsrikul

Abstract:

The present study is to explore the perception of Taiwanese females towards medical tourism in Thailand for the development of applicable marketing strategy, integrating travel motivation and cosmetic surgery trend to attract potential medical tourists from Taiwan. Since previous studies relevant to this research issue are limited, qualitative study is firstly employed by using one focus group interview and in-depth interviews with Taiwanese females. Moreover, the present research collected questionnaires from 290 Taiwanese females to provide greater understanding of research results. The top three factors that affect Taiwanese females’ decision for not going to Thailand for medical tourism are “physicians and nurses cannot speak Chinese”, “low quality of the cosmetic surgery product that I want to do”, and “the county does not have laws to protect medical tourists’ right”. The finding of the empirical part would suggest the area in medical tourism industry which Thailand should promote and emphasizes in order to increase its presence as a hub for cosmetic surgery and attract Taiwanese female market. Therefore, the study contributes to the potential development of marketing strategy for medical tourism, specifically in the area of cosmetic surgery in Thailand while targeting Taiwan market.

Keywords: Thailand, Taiwanese female tourists, medical tourism, cosmetic surgery

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Authors: Cristian Pauna

Abstract:

Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Raptis Sotirios

Abstract:

Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

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Authors: H. Hruschka

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This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.

Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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Authors: Babatunde Stephen Oladeji, Gloria Asuquo Edet

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The proximate, functional, pasting, and sensory properties of semolina from blends of cassava, maize, and rice were investigated. Cassava, maize, and rice were milled and sieved to pass through a 1000 µm sieve, then blended in the following ratios to produce five samples; FS₁ (40:30:30), FS₂ (20:50:30), FS₃ (25:25:50), FS₄ (34:33:33) and FS₅ (60:20:20) for cassava, maize, and rice, respectively. A market sample of wheat semolina labeled as FSc served as the control. The proximate composition, functional properties, pasting profile, and sensory characteristics of the blends were determined using standard analytical methods. The protein content of the samples ranged from 5.66% to 6.15%, with sample FS₂ having the highest value and being significantly different (p ≤ 0.05). The bulk density of the formulated samples ranged from 0.60 and 0.62 g/ml. The control (FSc) had a higher bulk density of 0.71 g/ml. The water absorption capacity of both the formulated and control samples ranged from 0.67% to 2.02%, with FS₃ having the highest value and FSc having the lowest value (0.67%). The peak viscosity of the samples ranged from 60.83-169.42 RVU, and the final viscosity of semolina samples ranged from 131.17 to 235.42 RVU. FS₅ had the highest overall acceptability score (7.46), but there was no significant difference (p ≤ 0.05) from other samples except for FS₂ (6.54) and FS₃ (6.29). This study establishes that high-quality and consumer-acceptable semolina that is comparable to the market sample could be produced from blends of cassava, maize, and rice.

Keywords: semolina, gluten, celiac disease, wheat allergies

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Authors: Bita Mashayekhi, Hamid Kalhornia

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One of the most important roles of financial reporting is improving the firms’ investment decisions; however, there is not much supporting evidence for this claim in emerging markets like Iran. In this study, the effect of financial reporting transparency in investment efficiency of Iranian firms has been investigated. In order to do this, 336 listed companies on Tehran Stock Exchange (TSE) has been selected for time period 2012 to 2015 as research sample. For testing our main hypothesis, we classified sample firms into two groups based on their deviation from expected investment: under-investment and over-investment cases. The results indicate that there is positive significant relationship between financial transparency and investment efficiency. In the other words, transparency can mitigate both underinvestment and overinvestment situations.

Keywords: corporate governance, disclosure, investment decisions, investment efficiency, transparency

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Authors: Hicham Benamirouche, Oum Elkheir Moussi

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The purpose of the study is to examine the dynamics of Algeria’s natural gas exports through the Autoregressive Distributed Lag (ARDL) bounds testing approach with break points. The analysis was carried out for the period from 1967 to 2015. Based on imperfect substitution specification, the ARDL approach reveals a long-run equilibrium relationship between Algeria’s Natural gas exports and their determinant factors (Algeria’s gas reserves, Domestic gas consumption, Europe’s GDP per capita, relative prices, the European gas production and the market share of competitors). All the long-run elasticities estimated are statistically significant with a large impact of domestic factors, which constitute the supply constraints. In short term, the elasticities are statistically significant, and almost comparable to those of the long term. Furthermore, the speed of adjustment towards long-run equilibrium is less than one year because of the little flexibility of the long term export contracts. Two break points have been estimated when we employ the domestic gas consumption as a break variable; 1984 and 2010, which reflect the arbitration policy between the domestic gas market and gas exports.

Keywords: natural gas exports, elasticity, ARDL bounds testing, break points, Algeria

Procedia PDF Downloads 194

Authors: Mornay Roberts-Lombard

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Consumers’ perceptions of value are crucial to ensuring their future commitment and behavioral intentions. As a result, service providers, such as Islamic banks, must provide their customers with products and services that are regarded as valuable, stimulating, collaborative, and competent. Therefore, the value provided to customers must meet or surpass their expectations, which can drive customers’ commitment (affective and calculative) and eventually favorably impact their future behavioral intentions. Consequently, Islamic banks in South Africa, as a growing African market, need to obtain a better understanding of the variables that impact Islamic banking customers’ value perceptions and how these impact their future behavioral intentions. Furthermore, it is necessary to investigate how customers’ perceived value perceptions impact their affective and calculative commitment and how the latter impact their future behavioral intentions. The purpose of this study is to bridge these gaps in knowledge, as the competitiveness of the Islamic banking industry in South Africa requires a deeper understanding of the aforementioned relationships. The study was exploratory and quantitative in nature, and data was collected from 250 Islamic banking customers using self-administered questionnaires. These banking customers resided in the Gauteng province of South Africa. Exploratory factor analysis, Pearson’s coefficient analysis, and multiple regression analysis were applied to measure the proposed hypotheses developed for the study. This research will aid Islamic banks in the country in potentially strengthening customers’ future commitment (affective and calculative) and positively impact their future behavioral intentions. The findings of the study established that service quality has a significant and positive impact on perceived value. Moreover, it was determined that perceived value has a favorable and considerable impact on affective and calculative commitment, while calculative commitment has a beneficial impact on behavioral intention. The research informs Islamic banks of the importance of service engagement in driving customer perceived value, which stimulates the future affective and calculative commitment of Islamic bank customers in an emerging market context. Finally, the study proposes guidelines for Islamic banks to develop an enhanced understanding of the factors that impact the perceived value-commitment-behavioral intention link in a competitive Islamic banking market in South Africa.

Keywords: perceived value, affective commitment, calculative commitment, behavioural intention

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Authors: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

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Authors: Mert Kıranç, Ekrem Duman, Murat Özbilen

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In order to achieve new product development (NPD) activities on time and within budgetary constraints, the NPD managers need a well-designed methodology. This study intends to create an integrated project management methodology for the ones who focus on new product development projects. In the scope of the study, four different management systems are combined. These systems are called as 'Schedule-oriented Stage-Gate Method, Risk Management, Change Management and Earned Value Management'. New product development term is quite common in many different industries such as defense industry, construction, health care/dental, higher education, fast moving consumer goods, white goods, electronic devices, marketing and advertising and software development. All product manufacturers run against each other’s for introducing a new product to the market. In order to achieve to produce a more competitive product in the market, an optimum project management methodology is chosen, and this methodology is adapted to company culture. The right methodology helps the company to present perfect product to the customers at the right time. The benefits of proposed methodology are discussed as an application by a company. As a result, how the integrated methodology improves the efficiency and how it achieves the success of the project are unfolded.

Keywords: project, project management, management methodology, new product development, risk management, change management, earned value, stage-gate

Procedia PDF Downloads 311