• Analyses & Alternatives
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• v.2 no.2
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• pp.93-110
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• 2018

Due to its speciality, Johannesburg has drawn wide attention from the international academia, which the existing urban theory cannot easily explain. This paper focuses on the spatial pattern of Johannesburg, and finds that it is highly fragmented: first North-South Division due to apartheid and gold mining, secondly, the continuous sprawl due to population densification, and thirdly formation of city-region due to the fill-in spatial policy. The fragmented spatial pattern undermines the social integration and weakens the potential economic sustainability, which make the balanced multi-core spatial pattern inevitable under the influence of the international planning thought. The Johannesburg's spatial transformation comes not only from social integration, but also from the development vison of world-class status of an African city. In reality, since the collapse of apartheid, the municipal government has released series of strategic planning in different executive phase. These different plannings unanimously focus on the world class status although since 2008, the social inclusiveness is more emphasized. Thus, Johannesburg implements some spatial policies, as promoting the corridor of freedom, identifying the key nodes in the city then developing with high density, implementing the in-filling policy, managing the urban growth boundary, effectively preserving the natural system. However, the industrial structure in Johannesburg is already quite high-ended which cannot produce many jobs and also require much investment in advanced infrastructure. Thus, the strategic goals of world-class status of an African city and social inclusiveness which really need widely shared public services to some extent are in tension. After evaluation, we can see that spatial transformation is quite limited. Obviously spatial transformation largely depends on the social-economy. The population is still in flow. In this sense Johannesburg must energetically support the employment-based industry, effectively control the spatial sprawl and carry out institutional innovation which further incentive investment, gradually build an integrated regional governance. In general, how Johannesburg combines globalization and its own condition is still worth thinking in both theory and practice.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.102-108
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• 2023

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.151-154
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• 2021

Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study was analyzed for 21 papers in the March 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 305 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.326-327
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• 2021

Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study were analyzed for 29 papers in a specific journal. In the final analysis results, the most frequently mentioned keyword was "Research", which ranked first 743 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• The Journal of Bigdata
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• v.7 no.2
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• pp.103-111
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• 2022

There are many institutions, organizations, and sites related to responding to infectious diseases, but as the pandemic situation such as COVID-19 continues for years, there are many changes in the initial and current aspects, and accordingly, policies and response systems are evolving. As a result, regional gaps arise, and various problems are scattered due to trust, distrust, and implementation of policies. Therefore, in the process of analyzing social data including information transmission, Twitter data, one of the major social media platforms containing inaccurate information from unknown sources, was developed to prevent facts in advance. Based on social data, which is unstructured data, an algorithm that can automatically detect infectious disease threats is developed to create an objective basis for responding to the infectious disease crisis to solidify international competitiveness in related fields.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Steel and Composite Structures
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• v.48 no.1
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• pp.19-25
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• 2023

Due to high application of concrete structures in construction industry, however, the quality improvement is essential. One of the new ways for this purpose is adding the nanoparticles to the concrete. In this work, vibration analysis of concrete beams reinforced by graphene oxide (GO) nanoparticles based on mathematical model has been investigated. For the accuracy of the presented model, the experimental study is done for comparing the compressive strength. Since the nanoparticles can not be solved in water without any specific process, at the first, GO nanoparticles should be dispersed in water by using shaker, magnetic striker, ultrasonic devices and finally mechanical mixer. For modelling of the strucuture, sinusoidal shear deformation beam theory (SSDBT) is utilized. Mori-Tanak model model is utilized for obtaining the effective properties of the beam including agglomeration influences. Utilizing the energy method and Hamilton's principal, the motion equations are calculated. The frequency of the concrete beam is obtanied by analytical method. Three samples with 0.02% GO nanoparticles are built and its compressive strength is compared which shows a good accuracy with maximum 1.29% difference with mathematical model and other papers. The aim of this work from the theoretical study is investigating the effects of nanoparticles volume percentage and agglomeration, length and thickness of the beam on the frequency of the structure. The results show that the with enhancing the GO nanoparticles, the frequency is increased. For example, with enhancing the volume percent of GO nanoparticles from zero to 0.08%, the compressive strength is increased 48.91%. and 46.83%, respectively for two cases of with and without agglomeration.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Safety and Health at Work
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• v.15 no.1
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• pp.24-32
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• 2024

Background: Learning from incidents for accident prevention is a two-stage process, involving the investigation of past accidents to identify the causal factors, followed by the identification and implementation of remedial measures to address the identified causal factors. The focus of past research has been on the identification of causal factors, with limited focus on the identification and implementation of remedial measures. This research begins to contribute to this gap. The motivation for the research is twofold. First, previous analyses show the recurring nature of accidents within the Ghanaian mining industry, and the causal factors also remain the same. This raises questions on the nature and effectiveness of remedial measures identified to address the causes of past accidents. Secondly, without identifying and implementing remedial measures, the full benefits of accident investigations will not be achieved. Hence, this study aims to assess the nature of remedial measures proposed to address investigation causal factors. Method: The study adopted SMARTER from business studies with the addition of HMW (H - Hierarchical, M - Mapping, and W - Weighting of causal factors) to analyse the recommendations from 500 individual investigation reports across seven different mines in Ghana. Results: The individual and the work environment (79%) were mostly the focused during the search for causes, with limited focus on organisational factors (21%). Forty eight percentage of the recommendations were administrative, focussing on fixing the problem in the immediate affected area or department of the victim(s). Most recommendations (70.4%) were support activities that only enhance the effectiveness of control but do not prevent/mitigate the failure directly. Across all the mines, there was no focus on evaluating the performance of remedial measures after their implementation. Conclusion: Identifying sharp-end causes leads to proposing weak recommendations which fail to address latent organisational conditions. The study proposed a guide for effective planning and implementation of remedial actions.