• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.3
• /
• pp.830-860
• /
• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Women's Health Nursing
• /
• v.7 no.2
• /
• pp.121-130
• /
• 2001

Although the general concept of suffering care includes palliative care technology for terminally ill person to alleviate his pain, it is much more holistic including emotional, spiritual and other life dimension. This inclusive concept of caring can be possible with the fundamental reflection on the human suffering. Far from the concept of pain understood in the context of materialist medical approach, human suffering has many dimensions including aesthetic, psychological, and religious: its meaning is holistic. With this perspective, the experience of the suffering client must be reconsidered before one starts with an objective side or a subjective side of suffering. Indeed, the actual strategies of suffering care can be different depending on the definition of human suffering accepted by practicians. In this caring perspective, the body, mind and spirit are integrated so the objectivity and subjectivity can merge; the extended awareness with inner resource or energy, and the positive thinking about the God is meaningful especially for dying person, his family members and the caring team. Despite this impending importance of the inclusive understanding of human suffering, the actual nursing practice still does not reflect this growing understanding of human suffering. This approach, which tried to pursuit the more fundamental meaning of human suffering, can contribute to the development of nursing education and practice which pay attention to the more inclusive view of human suffering.

• Asian-Australasian Journal of Animal Sciences
• /
• v.8 no.3
• /
• pp.295-300
• /
• 1995

Data from purebred and crossbred cattle involving Holstein and the Local breed in Bangladesh were used to estimate the genetic effects on average daily milk yield and birth weight A total of 877 records on average daily milk yield for 4 types of breed groups and a total of 418 records on birth weight for 5 breed groups were analyzed. Two different methods were applied in this study; the least squares analysis of variance approach and the linear regression approach. Breed group effects were highly significant for both average daily milk yield and birth weight. The result showed that straightbred Holstein produced the highest milk yield and the 7/8 crosses ranked highest in birth weight For the two traits, the additive breed effect was highly significant, whereas the individual heterosis effect was not significant. Furthermore, this study showed a negative maternal heterosis for average daily milk yields and a positive maternal heterosis for birth weight Comparing the breed least squares means obtained from the linear regression approach revealed that straightbred Holstein produced the highest average milk yield and the 3/4 crosses were predicted to have the largest birth weight. It is indicated that the linear regression approach can adequately separate the genetic component of performance, estimate unknown crossbreeding parameters and predict unknown performance of crosses which are not include in the original data.

• Journal of Korea Technology Innovation Society
• /
• v.6 no.3
• /
• pp.279-297
• /
• 2003

The purpose of this paper is to analyze the determinants of company R＆D investment with zero observations by using the data of R＆D Scoreboard published by Ministry of Science and Technology(2002). Conventional parametric approach to dealing with zero investments is not robust to heteroscedastic and/or non-normal error structure. Thus, this study applies symmetrically trimmed least squares(STLS) estimation as a semi-parametric approach to dealing with zero R&D investments. The result of specification test indicates the semi-parametric approach outperforms the parametric approach significantly. Moreover, the results of the study provide various implications as summarized below. The R&D investment of IT company is larger than that of non-IT company. The R&D investment has a positive relation to foreigners' investment ratio. The higher degree of financial self-reliance is, the larger the R&D investment is. Firm size variables such as sales amount and the number of workers are positively related to R&D investment. The sales elasticity of R&D investment is larger than one. However, the workers elasticity of R&D investment is smaller than one.

• Korean Journal of Human Ecology
• /
• v.18 no.3
• /
• pp.631-639
• /
• 2009

This study examines how the project approach utilizing beans as the subject matter influences young children's scientific research capabilities and scientific attitudes. This examination ultimately aims at developing effective teaching methods and programs that can promote children's scientific research capabilities and scientific attitudes. Thirty six children at H kindergarten in Gunsan, Jeollabuk-do were selected as subjects of this study. The children aged five were divided into an experiment group and a comparison group, with eighteen for each group. Before the experiment, a pre-test was conducted on the children's scientific research capabilities and scientific attitudes. The pre-test results were subject to a t test to identify whether there were differences between the two groups in age as well as the levels of scientific research capabilities and attitudes. A post-test was also conducted to determine the differences between the two groups in these categories. These results have led to the conclusion that the project approach utilizing beans as the subject matter has a positive impact on improving young children's scientific research capabilities and scientific attitudes.

• Journal of Information Processing Systems
• /
• v.1 no.1 s.1
• /
• pp.14-21
• /
• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.9
• /
• pp.4788-4813
• /
• 2019

In this paper, we introduce concepts of optimal and near optimal secret data hiding schemes. We present a new digital image steganography approach based on the Galois field $GF(p^m)$ using graph and automata to design the data hiding scheme of the general form ($k,N,{\lfloor}{\log}_2p^{mn}{\rfloor}$) for binary, gray and palette images with the given assumptions, where k, m, n, N are positive integers and p is prime, show the sufficient conditions for the existence and prove the existence of some optimal and near optimal secret data hiding schemes. These results are derived from the concept of the maximal secret data ratio of embedded bits, the module approach and the fastest optimal parity assignment method proposed by Huy et al. in 2011 and 2013. An application of the schemes to the process of hiding a finite sequence of secret data in an image is also considered. Security analyses and experimental results confirm that our approach can create steganographic schemes which achieve high efficiency in embedding capacity, visual quality, speed as well as security, which are key properties of steganography.

• International Journal of Computer Science & Network Security
• /
• v.23 no.5
• /
• pp.163-171
• /
• 2023

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

• Educational Technology International
• /
• v.19 no.2
• /
• pp.175-197
• /
• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• Journal of Distribution Science
• /
• v.22 no.6
• /
• pp.1-10
• /
• 2024

Purpose: This study's objective is to examine the impact of firm-specific and macroeconomic factors on the business performance of non-cyclical and cyclical sectors in Indonesian listed firms. The evaluation of business performance holds paramount importance for the achievement and long-term viability of a company. Research Design Data and Methodology: The data for 61 non-cyclicals sector companies and 57 cyclicals sector companies was gathered over a 4-year period from 2018-2021. The model integrates firm size, leverage, and sales growth as firm-specific factors, with real GDP growth and inflation rate as macroeconomic variables. ROA and ROE are indicators of a firm's business performance. The regression models are estimated using the distribution of a dynamic approach with Arellano-Bond Panel Generalized Method of Moments (GMM) estimation. Results: The results of the pooled sample indicate that the historical ROA and ROE have a positive relationship with the business performance of all sectors, including both non-cyclical and cyclical industries. The ROE of non-cyclical enterprises is primarily influenced by firm-specific characteristics and macroeconomic influences. Conclusion: To ensure the successful implementation of the distribution of a dynamic approach towards enhancing corporate business performance, organizations need to take into account a combination of firm-specific factors and macroeconomic factors.