• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.350-358
• /
• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• Asian Journal for Public Opinion Research
• /
• v.8 no.4
• /
• pp.478-492
• /
• 2020

The COVID-19 global health crisis has affected the mental and psychological health and well-being of the people around the world. However, little is known about the impact of COVID-19 among Filipino teachers. This study was conducted to determine the impact of the COVID-19 pandemic on the quality of life (QoL) of professional teachers in the Philippines. A descriptive cross-sectional study was used involving 139 licensed professional teachers. The COVID-19 Impact on Quality of Life (COV19-QoL) was the primary measure used in this study. Descriptive statistics, t-test, and one-way ANOVA were the statistical tools employed to analyze the data. Results indicated a moderate COVID-19 impact on the QoL of the teachers. There was a significant difference in the impact of COVID-19 on QoL by degree program. However, the impact of COVID-19 on QoL did not significantly differ by age, sex, marital status, employment status, monthly salary, presence of a COVID-19 case near their residence, personal knowledge of someone who was infected or died of COVID-19, presence of a medical condition, and perceived threat. The psychological well-being and QoL of teachers must be recognized and teachers must be provided with support as they continue to adapt to the impact brought by the COVID-19 pandemic. This study contributes to the growing literature on the impact of the pandemic.

• Advances in concrete construction
• /
• v.13 no.4
• /
• pp.291-305
• /
• 2022

The present investigation deals with the production of the innovative lightweight fly ash angular aggregates (FAA) first time in India using local class 'F' fly ash, its characterization, and exploring the potential for its utilization as alternative coarse aggregates in structural concrete applications. Two types of aggregates are manufactured using two different kinds of binders. The manufacturing process involves mixing fly ash, binder, and water, followed by the briquetting process, sintering and crushing them into suitable size aggregates. Tests are conducted on fly ash angular aggregates to measure their physical properties such as crushing value, impact value, specific gravity, water absorption, bulk density, and percentage of voids. Study shows that the physical parameters are significantly enhanced as compared to commercially available fly ash pellets (FAP). The developed FAA are used in concrete vis-à-vis conventional granite aggregates and FAP to determine their compressive, split tensile and flexural strengths. Although being lightweight, the strength parameters for concrete containing FAA are well compared with conventional concrete. This might be due to the high pozzolanic reaction between fly ash angular aggregates and cement paste. Also, RCC beams are cast and the load-deflection behaviour and ultimate load carrying capacity signify that FAA can be suitably used for RCC construction. Hence, the utilization of fly ash as angular aggregates can reduce the dead load of the structure and at the same time serves as a solution for fly ash disposal and mineral depletion problem.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.348-358
• /
• 2022

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.261-273
• /
• 2022

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3889-3903
• /
• 2022

To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.

• Asia-Pacific Journal of Business
• /
• v.12 no.3
• /
• pp.361-378
• /
• 2021

Purpose - The purpose of this study is to classify KOSPI listed companies according to their organizational character type based on MBTI. Design/methodology/approach - This study collected 109,989 reviews from an online firm review website, Jobplanet. Using these reviews and the descriptions about organizational character, we conducted document similarity analysis. Doc2Vec technique was hired for the analysis. Findings - First, there are more companies belonging to Extraversion(E), Intuition(N), Feeling(F), and Judging(J) than Introversion(I), Sensing(S), Thinking(T), and Perceiving(P) as organizational character types of MBTI. Second, more companies have EJ and EP as the behavior type and NT and NF as the decision-making type. Third, the top-3 organizational character type of which firms have among 16 types are ENTJ, ENFP, and ENFJ. Finally, companies belonging to the same industry group were found to have similar organizational character. Research implications or Originality - This study provides a noble way to measure organizational character type using firm review big data and document similarity analysis technique. The research results can be practically used for firms in their organizational diagnosis and organizational management, and are meaningful as a basic study for various future studies to empirically analyze the impact of organizational character.

• Korean Journal of Childcare and Education
• /
• v.18 no.4
• /
• pp.1-16
• /
• 2022

Objective: The purpose of this study is to test the psychometric properties of the Maternal Emotion Coaching Questionnaire (MECQ, Lim et al., 2018) in order to measure emotion coaching in mothers of preschoolers. Methods: A total of 316 preschoolers and their mothers participated in this study. Maternal emotion coaching was assessed by self-report and child emotion regulation ability was evaluated by the teacher. Data were analyzed with chi-square tests, reliability analysis, confirmatory factor analysis, latent profile analysis, and F-test. Results: Each item of the MECQ showed proper discriminative power. The MECQ and each subscale demonstrated adequate internal consistency and split-half reliability. Evidence of construct validity was provided by confirmatory factor analysis. The five-factor model including maternal attention, awareness, acceptance, empathy, and guidance showed a good fit. Results of the latent profile analysis revealed three profiles of emotion coaching: excellent, good, and poor. Preschoolers with mothers in the poor coaching profile showed significantly lower emotion regulation ability compared to those in the excellent or good coaching profiles, which suggested discriminative validity of the MECQ. Conclusion/Implications: The MECQ presents a reliable and valid tool to assess emotion coaching in mothers of preschool children and can thus be effectively used for mothers of preschoolers.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.628-634
• /
• 2009

It is a common practice in Hong Kong for the main contractors of local building projects to sublet most of the work to subcontractors. Consequently their roles have gradually transformed from a constructor to a manager of subcontractors. The outcomes of a project therefore depend heavily on the subcontractors' performance. However, most of the subcontractors complain that they are unable to efficiently and effectively operate due to site coordination problems, such as inaccurate site reference lines, caused by main contractors. The site problems may consume significant amounts of resources if practical solutions cannot be agreed by the project participants early enough. Rahim Organizational Conflict Inventory-II (ROCI-II) model was developed by M.A. Rahim that measure five types of conflict management style including Integrating, Obliging, Dominating, Avoiding and Compromising. This paper presents the questionnaire survey based on the ROCI-II model to rank the preference on the conflict management style adopted by the project representatives of the subcontractors in handling the site coordination problems and its impact to the time used to agree the solutions to the different types of site coordination problems with main contractor. The survey results show that most of the subcontractors' project representatives preferred to adopt the Compromising style to tackle the site coordination problems and the time used to agree the solutions with main contractor was influenced by the conflict management style adopted.

• ETRI Journal
• /
• v.45 no.1
• /
• pp.18-27
• /
• 2023

We present an English-Korean speech translation corpus, named EnKoST-C. End-to-end model training for speech translation tasks often suffers from a lack of parallel data, such as speech data in the source language and equivalent text data in the target language. Most available public speech translation corpora were developed for European languages, and there is currently no public corpus for English-Korean end-to-end speech translation. Thus, we created an EnKoST-C centered on TED Talks. In this process, we enhance the sentence alignment approach using the subtitle time information and bilingual sentence embedding information. As a result, we built a 559-h English-Korean speech translation corpus. The proposed sentence alignment approach showed excellent performance of 0.96 f-measure score. We also show the baseline performance of an English-Korean speech translation model trained with EnKoST-C. The EnKoST-C is freely available on a Korean government open data hub site.