• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.665-673
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• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.845-848
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• 2005

Technology transfer (TT) has been defined as the shared responsibility between the source and the destination for ensuring that technology is accepted and at least understood by someone with the knowledge and resources to apply and/or use the technology. The adoption of TT in construction industries is necessary for economic growth to occur in developing countries such as Thailand. This process should provide numerous benefits for the host sector in areas such as increased productivity, enhancement of product quality, cost savings, improvements in market share and entry to new markets. However, there are many factors, which may impact on the TT process and its subsequent outcomes for Thai construction firms and individuals, including, the transfer environment, learning environment, transferor characteristics and transferee characteristics. The performance and interaction of these enablers will influence the degree of value added to the local construction sectors in areas such as economic advancement, knowledge advancement and project performance. This paper presents a conceptual framework for international TT that accommodates the numerous factors believed to impact on the processes effectiveness. Through a Pilot Study, where 27 industry professionals from Thailand were interviewed, the significant factors which impact on the TT process have been identified along with the strength of interrelationship between individual and groups of factors. Future research seeks to target a greater sample of respondents with the view to validate the conceptual model and apply it on a number of large Thai projects where international TT was incorporated into the project agreement.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.686-693
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• 2022

Construction is a collaborative endeavor. The complexity in delivering construction projects successfully is impacted by the effective collaboration needs of a multitude of stakeholders throughout the project life-cycle. Technologies such as Building Information Modelling and relational project delivery approaches such as Alliancing and Integrated Project Delivery have developed to address this conundrum. However, with the onset of the pandemic, the digital economy has surged world-wide and advances in technology such as in the areas of machine learning (ML) and Artificial Intelligence (AI) have grown deep roots across specializations and domains to the point of matching its capabilities to the human mind. Several recent studies have both explored the role of AI in the construction process and highlighted its benefits. In contrast, literature in the organization studies field has highlighted the fear that tasks currently done by humans will be done by AI in future. Motivated by these insights and with the understanding that construction is a labour intensive sector where knowledge is both fragmented and predominantly tacit in nature, this paper explores the integration of AI in construction processes across project phases from planning, scheduling, execution and maintenance operations using literary evidence and experiential insights. The findings show that AI can complement human skills rather than provide a substitute for them. This preliminary study is expected to be a stepping stone for further research and implementation in practice.

• Asia Marketing Journal
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• 제14권4호
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• pp.1-22
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• 2013

A growing gap between market needs and the capabilities of the enterprise prompts repositioning (Corstjens and Dolye 1989). This article examines the strategic repositioning of 'Korando Sports' undertaken by SYMC throughout the period from Jan. 2012 to Jun. 2012, to boost sales volume and market share by entering market of active-lifestyle consumers currently occupied by SUVs. SYMC's experience indicates that it is essential to close the gap between the market needs and the ability of the enterprise to make a shift to new consumer segment with a new positioning. The successful repositioning framework(Ryan et al. 2007) were employed in this paper. This framework is comprised of six elements: core strategic values, strategic flexibility/learning capabilities, customer awareness and sensitivity, external orientation, management commitment, and belief in the product and brand. The evaluation based on the successful framework also confirms that 'Korando Sports' case meets all the requirements of the successful strategic repositioning. This paper provides some of the managerial implications with aim of assisting executives in identifying strategic repositioning opportunities. Primarily, the 'Korando Sports' case affirms the repositioning as a viable strategy and indicates that repositioning is a feasible means for strategic change. Second, this case shows the influence of a target consumer and SYMC's repositioning to follow consumer preference for a particular attribute. Moreover, we can understand how a product formerly considered weak in attributes can enjoy benefits in other segments with the same attributes.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.589-603
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• 2023

Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.9-22
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• 2023

본 논문에서는 인공지능의 무한한 발전 가능성을 유지하면서 공정성과 윤리적 책임을 유지하는 AI 규제에 대한 균형 잡힌 방안을 제시합니다. AI 시스템이 일상생활에 점점 더 통합됨에 따라, 특정 인구 집단에 대한 편견과 불이익을 방지하기 위한 규제 개발이 필수적입니다. 본 논문에서는 책임 있는 개발과 적용을 보장하기 위해 AI 애플리케이션의 규제 프레임워크와 사례 분석 연구를 진행합니다. 본 논문을 통하여 AI 규제에 대한 지속적인 논의를 이끌어내며, 혁신과 공정성 사이의 균형을 맞추는 정책을 수립을 제안합니다.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.45-53
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• 2023

The educational benefits and potential of XR as a new medium are well recognized. However, there are still limitations in understanding the specific effects of XR compared to the more widely utilized representation of images on computer monitors. This study therefore aims to demonstrate the differences in effectiveness between the two technologies and to draw implications from a cognitive comparison of three-dimensional objects represented on a flat surface and virtually. The study was conducted a quantitative research method with an experiment involving two independent groups, and the results were tested using regression analysis. The results showed that for low-level, two-dimensional objects, the computer monitor method may be more effective, but above a certain level of complexity, the effectiveness of learning through the monitor tends to decrease rapidly. On the other hand, the group that used extended reality technology showed relatively high comprehension compared to the monitor group even as the complexity increased, and in particular, unlike the monitor group's rapidly decreasing comprehension level, the extended reality technology group showed a trend of decreasing comprehension with the level of complexity, suggesting the potential for compatibility and predictability in the use of technology.

• 한국과학교육학회지
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• 제28권7호
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• pp.724-729
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• 2008

Well-integrated science instruction with art often motivates students to more engage in science learning and to freely express their thoughts and feelings on what they have learned in science classes. This study, therefore, attempted to explore Korean children's imaginary science drawings. Ninety elementary students ($3^{rd}-6^{th}$ graders) in Seoul, South Korea, participated in this study. The guiding research questions were 1) what overall characteristics of students' imaginary science drawings are and how these characteristics represent children's image of science, and 2) what educational value of children's imaginary science drawing activity as a case of science-art integration is. Data sources included a set of children's drawings and individual interviews with selected students. From the drawings, it was found that most of the subjects that children drew tended to be limited to the space. In addition, the children tended to assimilate science into technology that makes our life more convenient. We also found imaginary science drawing can be a good science-art integrated instruction method. Imaginary science drawing has educational benefits; one is a tool to investigate children's thoughts and knowledge of science while the other is method that motivate children to learn science effectively.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.59-66
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• 2024

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.