Search results for: market data
26000 Analysis of Peoples' Adherence to Safety Measures that Curb Ebola Virus Diseases in Nigeria (A Case Study of State of Osun)
Authors: Shittu Bisi Agnes
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Ebola virus Diseases outbreak in Nigeria caused a lot of concerns considering the mode of transmission and no known cure discovered. Therefore a lot of safety measures were taken which eventually led to the eradication of the virus in Nigeria. This therefore attempted to determine the various safety measures, how socio-economic characteristic of the people affected adherence to safety measures. And provide reasonable recommendations for total eradication of the virus, future outbreak and general environmental safety Data were collected with the aid of well structured questionnaires and administered 180 randomly selected of the state and oral interview was also utilize. Data collected were analysed using both descriptive tools and inferential statistics vis-a-vis regression analysis. Finding showed that 70.5% was strongly adhere to almost all the measures, 15.2% was fairly advent, 3% was poorly observing the selected measures while 1.3% was in different. 65% of the respondents was strongly aware of the advent of ebola virus diseases, 20% was fairly in awareness, 8.5% was poorly in awareness while 6.55% was in aware of any disease outbreak. Safety measures put forwards were; hand washing, use of hand sanitize-rs, no shaking of hands non-consumption of wildlife games(Bush Meat) and general health and environmental safety measures. It was recommended that policy instrument to increase peoples income will accelerate eradication of diseases as this will enable households to pay for monetary safety measures, health and environmental education, in form of talk shop, workshop, lectures could be organised at the political ward levels, schools, market women, religious bodies functional unions and mass media.Keywords: ebola diseases, pay, safety, outbreak
Procedia PDF Downloads 59725999 Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Authors: Walid Cherif
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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification
Procedia PDF Downloads 46425998 Seismic Data Scaling: Uncertainties, Potential and Applications in Workstation Interpretation
Authors: Ankur Mundhra, Shubhadeep Chakraborty, Y. R. Singh, Vishal Das
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Seismic data scaling affects the dynamic range of a data and with present day lower costs of storage and higher reliability of Hard Disk data, scaling is not suggested. However, in dealing with data of different vintages, which perhaps were processed in 16 bits or even 8 bits and are need to be processed with 32 bit available data, scaling is performed. Also, scaling amplifies low amplitude events in deeper region which disappear due to high amplitude shallow events that saturate amplitude scale. We have focused on significance of scaling data to aid interpretation. This study elucidates a proper seismic loading procedure in workstations without using default preset parameters as available in most software suites. Differences and distribution of amplitude values at different depth for seismic data are probed in this exercise. Proper loading parameters are identified and associated steps are explained that needs to be taken care of while loading data. Finally, the exercise interprets the un-certainties which might arise when correlating scaled and unscaled versions of seismic data with synthetics. As, seismic well tie correlates the seismic reflection events with well markers, for our study it is used to identify regions which are enhanced and/or affected by scaling parameter(s).Keywords: clipping, compression, resolution, seismic scaling
Procedia PDF Downloads 46925997 Markov Switching of Conditional Variance
Authors: Josip Arneric, Blanka Skrabic Peric
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Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets.Keywords: emerging markets, Markov switching, GARCH model, transition probabilities
Procedia PDF Downloads 45525996 Exploring Utility and Intrinsic Value among UAE Arabic Teachers in Integrating M-Learning
Authors: Dina Tareq Ismail, Alexandria A. Proff
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The United Arab Emirates (UAE) is a nation seeking to advance in all fields, particularly education. One area of focus for UAE 2021 agenda is to restructure UAE schools and universities by equipping them with highly developed technology. The agenda also advises educational institutions to prepare students with applicable and transferrable Information and Communication Technology (ICT) skills. Despite the emphasis on ICT and computer literacy skills, there exists limited empirical data on the use of M-Learning in the literature. This qualitative study explores the motivation of higher primary Arabic teachers in private schools toward implementing and integrating M-Learning apps in their classrooms. This research employs a phenomenological approach through the use of semistructured interviews with nine purposefully selected Arabic teachers. The data were analyzed using a content analysis via multiple stages of coding: open, axial, and thematic. Findings reveal three primary themes: (1) Arabic teachers with high levels of procedural knowledge in ICT are more motivated to implement M-Learning; (2) Arabic teachers' perceptions of self-efficacy influence their motivation toward implementation of M-Learning; (3) Arabic teachers implement M-Learning when they possess high utility and/or intrinsic value in these applications. These findings indicate a strong need for further training, equipping, and creating buy-in among Arabic teachers to enhance their ICT skills in implementing M-Learning. Further, given the limited availability of M-Learning apps designed for use in the Arabic language on the market, it is imperative that developers consider designing M-Learning tools that Arabic teachers, and Arabic-speaking students, can use and access more readily. This study contributes to closing the knowledge gap on teacher-motivation for implementing M-Learning in their classrooms in the UAE.Keywords: ICT skills, m-learning, self-efficacy, teacher-motivation
Procedia PDF Downloads 10625995 Alignment between Governance Structures and Food Safety Standards on the Shrimp Supply Chain in Indonesia
Authors: Maharani Yulisti, Amin Mugera, James Fogarty
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Food safety standards have received significant attention in the fisheries global market due to health issues, free trade agreements, and increasing aquaculture production. Vertical coordination throughout the supply chain of fish producing and exporting countries is needed to meet food safety demands imposed by importing countries. However, the complexities of the supply chain governance structures and difficulties in standard implementation can generate safety uncertainty and high transaction costs. Using a Transaction Cost Economics framework, this paper examines the alignment between food safety standards and the governance structures in the shrimp supply chain in Indonesia. We find the supply chain is organized closer to the hierarchy-like governance structure where private standard (organic standard) are implemented and more towards a market-like governance structure where public standard (IndoGAP certification) are more prevalent. To verify the statements, two cases are examined from Sidoarjo district as a centre of shrimp production in Indonesia. The results show that public baseline FSS (Food Safety Standards) need additional mechanism to achieve a coordinated chain-wide response because uncertainty, asset specificity, and performance measurement problems are high in this chain. Organic standard as private chain-wide FSS is more efficient because it has been achieved by hierarchical-like type of governance structure.Keywords: governance structure, shrimp value chain, food safety standards, transaction costs economics
Procedia PDF Downloads 37925994 Association of Social Data as a Tool to Support Government Decision Making
Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias
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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.Keywords: social data, government decision making, association of social data, data mining
Procedia PDF Downloads 36925993 A Particle Filter-Based Data Assimilation Method for Discrete Event Simulation
Authors: Zhi Zhu, Boquan Zhang, Tian Jing, Jingjing Li, Tao Wang
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Data assimilation is a model and data hybrid-driven method that dynamically fuses new observation data with a numerical model to iteratively approach the real system state. It is widely used in state prediction and parameter inference of continuous systems. Because of the discrete event system’s non-linearity and non-Gaussianity, traditional Kalman Filter based on linear and Gaussian assumptions cannot perform data assimilation for such systems, so particle filter has gradually become a technical approach for discrete event simulation data assimilation. Hence, we proposed a particle filter-based discrete event simulation data assimilation method and took the unmanned aerial vehicle (UAV) maintenance service system as a proof of concept to conduct simulation experiments. The experimental results showed that the filtered state data is closer to the real state of the system, which verifies the effectiveness of the proposed method. This research can provide a reference framework for the data assimilation process of other complex nonlinear systems, such as discrete-time and agent simulation.Keywords: discrete event simulation, data assimilation, particle filter, model and data-driven
Procedia PDF Downloads 1325992 Nexus among Foreign Private Investment, CO2 Emissions, Energy Consumption and Sustainable Economic Growth
Authors: Aysha Zamir
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This study examines to what extent foreign private investment (FPI) affects the clean industrial environment and sustainable economic growth through developed countries investment in China. Moreover, this study investiage an association among FPI, CO2 emission, energy consumption, and sustainable economic growth. This study uses random effects and generalized least squares (GLS) and panel VAR estimators for data analysis. The results indicate that the Chinese economy has a vastly positive influenced regarding the location and choice of emerging and developed countries’ investment in the domestic market. Furthermore, emerging and developed economies investment increases the contribution among domestic firms, environment sustainability toward the national economy. The further results show that foreign private investment and gross domestic investment have a positive impact on sustainable economic growth.Keywords: clean industrial environment, energy consumption, CO2 emmission, foreign private investment, developed and emerging economies
Procedia PDF Downloads 12925991 Frequent-Pattern Tree Algorithm Application to S&P and Equity Indexes
Authors: E. Younsi, H. Andriamboavonjy, A. David, S. Dokou, B. Lemrabet
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Software and time optimization are very important factors in financial markets, which are competitive fields, and emergence of new computer tools further stresses the challenge. In this context, any improvement of technical indicators which generate a buy or sell signal is a major issue. Thus, many tools have been created to make them more effective. This worry about efficiency has been leading in present paper to seek best (and most innovative) way giving largest improvement in these indicators. The approach consists in attaching a signature to frequent market configurations by application of frequent patterns extraction method which is here most appropriate to optimize investment strategies. The goal of proposed trading algorithm is to find most accurate signatures using back testing procedure applied to technical indicators for improving their performance. The problem is then to determine the signatures which, combined with an indicator, outperform this indicator alone. To do this, the FP-Tree algorithm has been preferred, as it appears to be the most efficient algorithm to perform this task.Keywords: quantitative analysis, back-testing, computational models, apriori algorithm, pattern recognition, data mining, FP-tree
Procedia PDF Downloads 36125990 Intellectual Capital as Resource Based Business Strategy
Authors: Vidya Nimkar Tayade
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Introduction: Intellectual capital of an organization is a key factor to success. Many companies invest a huge amount in their Research and development activities. Any innovation is helpful not only to that particular company but also to many other companies, industry and mankind as a whole. Companies undertake innovative changes for increasing their capital profitability and indirectly increase in pay packages of their employees. The quality of human capital can also improve due to such positive changes. Employees become more skilled and experienced due to such innovations and inventions. For increasing intangible capital, the author has referred to a couple of books and referred case studies to come to a conclusion. Different charts and tables are also referred to by the author. Case studies are more important because they are proven and established techniques. They enable students to apply theoretical concepts in real-world situations. It gives solutions to an open-ended problem with multiple potential solutions. There are three different strategies for undertaking intellectual capital increase. They are: Research push strategy/ Technology pushed approach, Market pull strategy/ approach and Open innovation strategy/approach. Research push strategy, In this strategy, research is undertaken and innovation is achieved on its own. After invention inventor company protects such invention and finds buyers for such invention. In this way, the invention is pushed into the market. In this method, research and development are undertaken first and the outcome of this research is commercialized. Market pull strategy, In this strategy, commercial opportunities are identified first and our research is concentrated in that particular area. For solving a particular problem, research is undertaken. It becomes easier to commercialize this type of invention. Because what is the problem is identified first and in that direction, research and development activities are carried on. Open invention strategy, In this type of research, more than one company enters into an agreement of research. The benefits of the outcome of this research will be shared by both companies. Internal and external ideas and technologies are involved. These ideas are coordinated and then they are commercialized. Due to globalization, people from the outside company are also invited to undertake research and development activities. Remuneration of employees of both the companies can increase and the benefit of commercialization of such invention is also shared by both the companies. Conclusion: In modern days, not only can tangible assets be commercialized, but also intangible assets can also be commercialized. The benefits of such an invention can be shared by more than one company. Competition can become more meaningful. Pay packages of employees can improve. It Is a need for time to adopt such strategies to benefit employees, competitors, stakeholders.Keywords: innovation, protection, management, commercialization
Procedia PDF Downloads 16825989 PEINS: A Generic Compression Scheme Using Probabilistic Encoding and Irrational Number Storage
Authors: P. Jayashree, S. Rajkumar
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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding
Procedia PDF Downloads 29425988 Iot Device Cost Effective Storage Architecture and Real-Time Data Analysis/Data Privacy Framework
Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe
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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.Keywords: IoT, fog, cloud, data analysis, data privacy
Procedia PDF Downloads 9925987 Financial Reporting Quality and International Financial Reporting
Authors: Matthias Nnadi
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Using samples of 250 large listed firms by market capitalization in China and Hong Kong, we conducted empirical test to determine the impact of regulatory environment on reporting quality following IFRS convergence using three financial reporting measures; earning management, timely loss recognition and value relevance. Our results indicate that accounting data are more value relevant for Hong Kong listed firms than the Chinese A-share firms. The empirical results for timely loss recognition further reveal that there is a larger coefficient estimate on bad news earnings, which suggests that Chines A-share firms are more likely to report losses in a timely manner. The results support the evidence that substantial convergence of IFRS can improve financial reporting quality in a regulated environment such as China. This further supports the expectation that IFRS are relevant to China and has positive effect on its accounting practice and quality.Keywords: reporting, quality, earning, loss, relevance, financial, China, Hong Kong
Procedia PDF Downloads 46425986 Comparison of Selected Pier-Scour Equations for Wide Piers Using Field Data
Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif
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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.Keywords: field data, local scour, scour equation, wide piers
Procedia PDF Downloads 41325985 The Maximum Throughput Analysis of UAV Datalink 802.11b Protocol
Authors: Inkyu Kim, SangMan Moon
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This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations.Keywords: UAV datalink, UAV formation flight datalink, UAV WLAN datalink application, UAV IEEE 802.11b datalink application
Procedia PDF Downloads 39225984 Methods for Distinction of Cattle Using Supervised Learning
Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl
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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning
Procedia PDF Downloads 55025983 Development of a Bioprocess Technology for the Production of Vibrio midae, a Probiotic for Use in Abalone Aquaculture
Authors: Ghaneshree Moonsamy, Nodumo N. Zulu, Rajesh Lalloo, Suren Singh, Santosh O. Ramchuran
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The abalone industry of South Africa is under severe pressure due to illegal harvesting and poaching of this seafood delicacy. These abalones are harvested excessively; as a result, these animals do not have a chance to replace themselves in their habitats, ensuing in a drastic decrease in natural stocks of abalone. Abalone has an extremely slow growth rate and takes approximately four years to reach a size that is market acceptable; therefore, it was imperative to investigate methods to boost the overall growth rate and immunity of the animal. The University of Cape Town (UCT) began to research, which resulted in the isolation of two microorganisms, a yeast isolate Debaryomyces hansenii and a bacterial isolate Vibrio midae, from the gut of the abalone and characterised them for their probiotic abilities. This work resulted in an internationally competitive concept technology that was patented. The next stage of research was to develop a suitable bioprocess to enable commercial production. Numerous steps were taken to develop an efficient production process for V. midae, one of the isolates found by UCT. The initial stages of research involved the development of a stable and robust inoculum and the optimization of physiological growth parameters such as temperature and pH. A range of temperature and pH conditions were evaluated, and data obtained revealed an optimum growth temperature of 30ᵒC and a pH of 6.5. Once these critical growth parameters were established further media optimization studies were performed. Corn steep liquor (CSL) and high test molasses (HTM) were selected as suitable alternatives to more expensive, conventionally used growth medium additives. The optimization of CSL (6.4 g.l⁻¹) and HTM (24 g.l⁻¹) concentrations in the growth medium resulted in a 180% increase in cell concentration, a 5716-fold increase in cell productivity and a 97.2% decrease in the material cost of production in comparison to conventional growth conditions and parameters used at the onset of the study. In addition, a stable market-ready liquid probiotic product, encompassing the viable but not culturable (VBNC) state of Vibrio midae cells, was developed during the downstream processing aspect of the study. The demonstration of this technology at a full manufacturing scale has further enhanced the attractiveness and commercial feasibility of this production process.Keywords: probiotics, abalone aquaculture, bioprocess technology, manufacturing scale technology development
Procedia PDF Downloads 15225982 Router 1X3 - RTL Design and Verification
Authors: Nidhi Gopal
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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.Keywords: data packets, networking, router, routing
Procedia PDF Downloads 81325981 Should Local Governments Expect Benefits from Special Economic Zones: The Case of Poland
Authors: Radosław Pastusiak, Anna Kaźmierska, Magdalena Jasiniak
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The impact of Special Economic Zones (SEZs) has been analyzed for many years by researchers. There are lot of theoretical studies proving the SEZs importance for regional development, however, there is lack of empirical studies (and they are mainly focused on China market) that are based on available data. The theoretical studies indicate the various impacts of enterprises operating within SEZs on the economy. The article proves that, in case of Poland, locating SEZs in municipalities is an important part of increasing municipalities’ income. Therefore SEZs have a positive impact on regional development. Municipality income is understood as taxes paid by taxpayers who depend on SEZ companies’ performance. The analysis includes the Corporate Income Tax (CIT), Personal Income Tax (PIT) and real estate tax. The effects of SEZs on regional development were narrowed to a few variables that are most significant for the financial system. The analysis indicates the significant impact of SEZs on the amount of taxes influencing the municipality budget.Keywords: special economic zone, local finance, municipal finance, government
Procedia PDF Downloads 33225980 Achieving Supply Chain Competitiveness through Successful Buyer-Supplier Relationships
Authors: Kamran Rashid, Tashfeen M. Azhar, Asad-ur-Rahman Wahla
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Current research aims to understand the role of successful buyer-supplier relationship in achieving supply chain competitiveness in a developing country perspective. Five hypotheses are developed to test structural model. Survey data is collected from the manufacturing sector of Pakistan. Analysis is conducted using Partial Least Squares (PLS) Structural Equation Modeling (SEM) through Smart PLS version 2.0 M3. Results demonstrate positive impact of effective supplier selection, buyer-supplier engagement, and information sharing capability on success of buyer supplier relationship. This successful buyer supplier relationship drives the supply chain firm financial and market performance. Additional analyses with large sample sizes are required in other developing countries to cross validate the results. Current study provides empirical evidence of the role of successful buyer supplier relationship in achieving supply chain competitiveness.Keywords: supply chain management, successful buyer-supplier relationship, supply chain competitiveness, developing country
Procedia PDF Downloads 66025979 Investigating the Impact of Super Bowl Participation on Local Economy: A Perspective of Stock Market
Authors: Rui Du
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This paper attempts to assess the impact of a major sporting event —the Super Bowl on the local economies. The identification strategy is to compare the winning and losing cities at the National Football League (NFL) conference finals under the assumption of similar pre-treatment trends. The stock market performances of companies headquartered in these cities are used to capture the sudden changes in local economic activities during a short time span. The exogenous variations in the football game outcome allow a straightforward difference-in-differences approach to identify the effect. This study finds that the post-event trends in winning and losing cities diverge despite the fact that both cities have economically and statistically similar pre-event trends. Empirical analysis provides suggestive evidence of a positive, significant local economic impact of conference final wins, possibly through city image enhancement. Further empirical evidence shows the presence of heterogeneous effects across industrial sectors, suggesting that city image enhancing the effect of the Super Bowl participation is empirically relevant for the changes in the composition of local industries. Also, this study also adopts a similar strategy to examine the local economic impact of Super Bowl successes, however, finds no statistically significant effect.Keywords: Super Bowl Participation, local economies, city image enhancement, difference-in-differences, industrial sectors
Procedia PDF Downloads 24025978 A Multi-Release Software Reliability Growth Models Incorporating Imperfect Debugging and Change-Point under the Simulated Testing Environment and Software Release Time
Authors: Sujit Kumar Pradhan, Anil Kumar, Vijay Kumar
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The testing process of the software during the software development time is a crucial step as it makes the software more efficient and dependable. To estimate software’s reliability through the mean value function, many software reliability growth models (SRGMs) were developed under the assumption that operating and testing environments are the same. Practically, it is not true because when the software works in a natural field environment, the reliability of the software differs. This article discussed an SRGM comprising change-point and imperfect debugging in a simulated testing environment. Later on, we extended it in a multi-release direction. Initially, the software was released to the market with few features. According to the market’s demand, the software company upgraded the current version by adding new features as time passed. Therefore, we have proposed a generalized multi-release SRGM where change-point and imperfect debugging concepts have been addressed in a simulated testing environment. The failure-increasing rate concept has been adopted to determine the change point for each software release. Based on nine goodness-of-fit criteria, the proposed model is validated on two real datasets. The results demonstrate that the proposed model fits the datasets better. We have also discussed the optimal release time of the software through a cost model by assuming that the testing and debugging costs are time-dependent.Keywords: software reliability growth models, non-homogeneous Poisson process, multi-release software, mean value function, change-point, environmental factors
Procedia PDF Downloads 7425977 Noise Reduction in Web Data: A Learning Approach Based on Dynamic User Interests
Authors: Julius Onyancha, Valentina Plekhanova
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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.Keywords: web log data, web user profile, user interest, noise web data learning, machine learning
Procedia PDF Downloads 26525976 Data Mining and Knowledge Management Application to Enhance Business Operations: An Exploratory Study
Authors: Zeba Mahmood
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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining
Procedia PDF Downloads 53825975 Career Guidance System Using Machine Learning
Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan
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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 8025974 Indexing and Incremental Approach Using Map Reduce Bipartite Graph (MRBG) for Mining Evolving Big Data
Authors: Adarsh Shroff
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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.Keywords: big data, map reduce, incremental processing, iterative computation
Procedia PDF Downloads 35025973 Effectiveness of Lean Manufacturing Technologies on Improving Business Performance: A Study of Indian Manufacturing Industries
Authors: Saumyaranjan Sahoo, Sudhir Yadav
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Indian manufacturing firms operating in rapidly changing and highly competitive market, over the last few decades, have embraced organization-wide transformation to achieve cultural and operational excellence. In recent years, numerous approaches have been proposed to improve business and manufacturing performance. Lean practices in particular, Total Productive Management (TPM) and Total Quality Management (TQM) have received considerable attention, as they being adopted and adapted for raising the performance standard of Indian manufacturing firms to world class levels. The complementary nature of TPM and TQM is being practiced in many companies to achieve synergy. Specifically, this research investigates whether joint TPM-TQM implementation contribute to higher business performance when compared to individual implementation. Data from 160 manufacturing firms were analyzed that demonstrate synergetic implementation of both TPM-TQM practices over a reasonable period of time, contributed in delivering better business performance as compared to individual implementation strategy.Keywords: total productive management, total quality management, Indian manufacturing firms, business performance
Procedia PDF Downloads 27125972 Career Guidance System Using Machine Learning
Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan
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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 7025971 Analyzing Large Scale Recurrent Event Data with a Divide-And-Conquer Approach
Authors: Jerry Q. Cheng
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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing
Procedia PDF Downloads 165