Data Mining - Concepts and Techniques.

Social Network Analysis

Texture features for image classification and retrieval.

The new evolution of the production and industrial process called Industry 4.0, and its related technologies such as the Internet of Things, big data analytics, and cyber–physical systems, among others, still have an unknown potential impact on sustainability and the environment. In this paper, we conduct a literature-based analysis to discuss the sustainability impact and challenges of Industry 4.0 from four different scenarios: deployment, operation and technologies, integration and compliance with the sustainable development goals, and long-run scenarios. From these scenarios, our analysis resulted in positive or negative impacts related to the basic production inputs and outputs flows: raw material, energy and information consumption and product and waste disposal. As the main results, we identified both positive and negative expected impacts, with some predominance of positives that can be considered positive secondary effects derived from Industry 4.0 activities. However, only through integrating Industry 4.0 with the sustainable development goals in an eco-innovation platform, can it really ensure environmental performance. It is expected that this work can contribute to helping stakeholders, practitioners and governments to advance solutions to deal with the outcomes emerging through the massive adoption of those technologies, as well as supporting the expected positive impacts through policies and financial initiatives.

https://www.mdpi.com/2071-1050/10/10/3740/pdf

Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

https://www.mdpi.com/2071-1050/10/10/3765/pdf

Genetic algorithm-optimized long short-term memory network for stock market prediction

This study proposes new method to overcome sparsity problem of document clustering.We build combined method using dimension reduction, K-means clustering, and SVC.In particular, we attempt to overcome the sparseness in patent document clustering.First, we conduct experiment using news data from UCI machine learning repository.Second, using retrieved patent documents, we carry out patent clustering. Many studies on developing technologies have been published as articles, papers, or patents. We use and analyze these documents to find scientific and technological trends. In this paper, we consider document clustering as a method of document data analysis. In general, we have trouble analyzing documents directly because document data are not suitable for statistical and machine learning methods of analysis. Therefore, we have to transform document data into structured data for analytical purposes. For this process, we use text mining techniques. The structured data are very sparse, and hence, it is difficult to analyze them. This study proposes a new method to overcome the sparsity problem of document clustering. We build a combined clustering method using dimension reduction and K-means clustering based on support vector clustering and Silhouette measure. In particular, we attempt to overcome the sparseness in patent document clustering. To verify the efficacy of our work, we first conduct an experiment using news data from the machine learning repository of the University of California at Irvine. Second, using patent documents retrieved from the United States Patent and Trademark Office, we carry out patent clustering for technology forecasting.

Document clustering method using dimension reduction and support vector clustering to overcome sparseness

Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development) efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.

https://www.mdpi.com/2071-1050/7/10/13126/pdf

A Network Analysis Model for Selecting Sustainable Technology

The rapid pace of technological advances creates many difficulties for R&D practitioners in analyzing emerging technologies. Patent information analysis is an effective tool in this situation. Conventional patent information analysis has focused on the extraction of vacant, promising, or core technologies and the monitoring of technological trends. From a technology management perspective, the ultimate purpose of R&D is technology commercialization. The core of technology commercialization is the technology transfer phase. Although a great number of patents are filed, publicized, and registered every year, many commercially relevant patents are filtered through registration processes that examine novelty, creativity, and industrial applicability. Despite the efforts of these selection processes, the number of patents being transferred is low when compared with total annual patent registrations. To deal with this problem, this study proposes a predictive model for technology transfer using patent analysis. In the predictive model, patent analysis is conducted to reveal the quantitative relations between technology transfer and a range of variables included in the patent data.

A Predictive Model of Technology Transfer Using Patent Analysis

These days, technological advances are being made through technological conversion. Following this trend, companies need to adapt and secure their own sustainable technological strategies. Technology transfer is one such strategy. This method is especially effective in coping with recent technological developments. In addition, universities and research institutes are able to secure new research opportunities through technology transfer. The aim of our study is to provide a technology transfer prediction model for the sustainable growth of companies. In the proposed method, we first collected patent data from a Korean patent information service provider. Next, we used latent Dirichlet allocation, which is a topic modeling method used to identify the technical field of the collected patents. Quantitative indicators on the patent data were also extracted. Finally, we used the variables that we obtained to create a technology transfer prediction model using the AdaBoost algorithm. The model was found to have sufficient classification performance. It is expected that the proposed model will enable universities and research institutes to secure new technology development opportunities more efficiently. In addition, companies using this model can maintain sustainable growth in line, coping with the changing pace of society.

Ensemble Modeling for Sustainable Technology Transfer

Many studies have predicted the future performance of companies for the purpose of making investment decisions. Most of these are based on the qualitative judgments of experts in related industries, who consider various financial and firm performance information. With recent developments in data processing technology, studies have started to use machine learning techniques to predict corporate performance. For example, deep neural network-based prediction models are again attracting attention, and are now widely used in constructing prediction and classification models. In this study, we propose a deep neural network-based corporate performance prediction model that uses a company’s financial and patent indicators as predictors. The proposed model includes an unsupervised learning phase and a fine-tuning phase. The learning phase uses a restricted Boltzmann machine. The fine-tuning phase uses a backpropagation algorithm and a relatively up-to-date training data set that reflects the latest trends in the relationship between predictors and corporate performance.

https://www.mdpi.com/2071-1050/9/6/899/pdf

Sangsung Park

Papers

Document clustering method using dimension reduction and support vector clustering to overcome sparseness

A Network Analysis Model for Selecting Sustainable Technology

A Predictive Model of Technology Transfer Using Patent Analysis

Ensemble Modeling for Sustainable Technology Transfer

Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability