• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• Clinical and Experimental Pediatrics
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• 제56권4호
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• pp.151-158
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• 2013

Purpose: We investigated the mRNA levels of peroxisome proliferator-activated receptor (PPAR)-${\alpha}$, PPAR-${\gamma}$, adipokines, and cytokines in the lung tissue of lean and obese mice with and without ovalbumin (OVA) challenge, and the effect of rosiglitazone, a PPAR-${\gamma}$ agonist. Methods: We developed 6 mice models: OVA-challenged lean mice with and without rosiglitazone; obese mice with and without rosiglitazone; and OVA-challenged obese mice with and without rosiglitazone. We performed real-time polymerase chain reaction for leptin, leptin receptor, adiponectin, vascular endothelial growth factor (VEGF), tumor necrosis factor (TNF)-${\alpha}$, transforming growth factor (TGF)-${\beta}$, PPAR-${\alpha}$ and PPAR-${\gamma}$ from the lung tissue and determined the cell counts and cytokine levels in the bronchoalveolar lavage fluid. Results: Mice with OVA challenge showed airway hyperresponsiveness. The lung mRNA levels of PPAR${\alpha}$ and PPAR-${\gamma}$ increased significantly in obese mice with OVA challenge compared to that in other types of mice and decreased after rosiglitazone administeration. Leptin and leptin receptor expression increased in obese mice with and without OVA challenge and decreased following rosiglitazone treatment. Adiponectin mRNA level increased in lean mice with OVA challenge. Lung VEGF, TNF-${\alpha}$, and TGF-${\beta}$ mRNA levels increased in obese mice with and without OVA challenge compared to that in the control mice. However, rosiglitazone reduced only TGF-${\beta}$ expression in obese mice, and even augmented VEGF expression in all types of mice. Rosiglitazone treatment did not reduce airway responsiveness, but increased neutrophils and macrophages in the bronchoalveolar lavage fluid. Conclusion: PPAR-${\alpha}$ and PPAR-${\gamma}$ expressions were upregulated in the lung tissue of OVA-challenged obese mice however, rosiglitazone treatment did not downregulate airway inflammation in these mice.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.65-70
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• 2021

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

• Genomics & Informatics
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• 제20권2호
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• pp.22.1-22.9
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• 2022

The rise of newer coronavirus disease 2019 (COVID-19) variants has brought a challenge to ending the spread of COVID-19. The variants have a different fatality, morbidity, and transmission rates and affect vaccine efficacy differently. Therefore, the impact of each new variant on the spread of COVID-19 is of interest to governments and scientists. Here, we proposed mathematical SEIQRDVP and SEIQRDV3P models to predict the impact of the Omicron variant on the spread of the COVID-19 situation in South Korea. SEIQEDVP considers one vaccine level at a time while SEIQRDV3P considers three vaccination levels (only one dose received, full doses received, and full doses + booster shots received) simultaneously. The omicron variant's effect was contemplated as a weighted sum of the delta and omicron variants' transmission rate and tuned using a hyperparameter k. Our models' performances were compared with common models like SEIR, SEIQR, and SEIQRDVUP using the root mean square error (RMSE). SEIQRDV3P performed better than the SEIQRDVP model. Without consideration of the variant effect, we don't see a rapid rise in COVID-19 cases and high RMSE values. But, with consideration of the omicron variant, we predicted a continuous rapid rise in COVID-19 cases until maybe herd immunity is developed in the population. Also, the RMSE value for the SEIQRDV3P model decreased by 27.4%. Therefore, modeling the impact of any new risen variant is crucial in determining the trajectory of the spread of COVID-19 and determining policies to be implemented.

• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• 한국응용약물학회:학술대회논문집
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• 한국응용약물학회 2001년도 추계학술대회 및 정기총회
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• pp.23-31
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• 2001

MBST at 4hr and SST at 3hr after oral administration remarkably inhibited histamine release from rat mast cells in a dose-dependent manner. MBST treated GPs failed to show biphasic phenomena which indicated to reduce nasal volume as leukotriene antagonists. Both groups of patients who took MBST and SST for 1 week or 2weeks showed significant decreased symptom severity index(SSI) from treatment week 2(p<0.05). The percent volume change after challenge of the antigen was decreased in patients took the extracts for tweets. We abstained longer suppression of the symptom than antihistamine.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.140-148
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• 2010

The Taihu Basin is located in the east coast of China, where the threats of frequent floods have induced construction of massive, complex, hierarchical flood defense systems over the interconnected river networks. Digital modeling of flooding processes and quantitative damage assessment still remain challenging due to such complexity. The current research uses an integrated approach to meet this challenge by combining multiple types of models within a GIS platform. A new algorithm is introduced to simulate the impacts of the flood defense systems, especially the large number of polders, on floods distributions and damages.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제63차 동계학술대회논문집 29권1호
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• pp.13-15
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• 2021

텍스트에 나타나는 감성을 분석하는 NLP task 중 하나인 감성분석에 자주 사용되는 한국어와 외국어 데이터들에 대해 다양한 BERT 모델들을 적용한 결과를 고성능 순서로 정리한 사이트(Paper with code)와 Github를 통해 준수한 성능을 보이는 BERT 모델들을 분석하고 실행해보며 성능향상을 통한 차별성을 가지는 것이 목표이다.

• Asian Journal of Innovation and Policy
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• 제6권1호
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• pp.1-22
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• 2017

The essence of inclusive innovation is to serve poor, marginalized and underprivileged sections of society to improve their livelihoods and enable them to climb up the socio-economic ladder. In this article, we explore the contemporary Indian landscape. There is a diversity of institutions and institutional approaches, multiple methodologies and goals in promoting inclusive innovations in this landscape. There are grassroots innovation institutions. All these institutions and groups have demonstrated how to improve the living conditions of poor people and enhance their income. They have developed different methodologies of inclusive innovation to intervene, build capacities and capabilities of poor people towards bridging informal and formal sectors of economy. Indian landscape can now boast of some successful models and a "social laboratory" for inclusive innovation. The challenge, however, remains to replicate and multiply these models to impact other sectors of Indian informal economy.

• Structural Engineering and Mechanics
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• 제48권2호
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• pp.151-171
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• 2013

The current paper presents the numerical blind prediction of nonlinear seismic response of two full-scale, three dimensional, one-story reinforced concrete structures subjected to bidirectional earthquake simulations on shaking table. Simulations were carried out at the laboratories of LNEC (Laboratorio Nacional de Engenharia Civil) in Lisbon, Portugal. The study was motivated by participation in the blind prediction contest of shaking table tests, organized by the challenge committee of the 15th World Conference on Earthquake Engineering. The test specimens, geometrically identical, designed for low and high ductility levels, were subjected to subsequent earthquake motions of increasing intensity. Three dimensional nonlinear analytical models were implemented and subjected to the input base motions. Reasonably accurate reproduction of the measured displacement response was obtained through appropriate modeling. The goodness of fit between analytical and measured results depended on the details of the analytical models.