• International Journal of Computer Science & Network Security
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• v.23 no.8
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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 %.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1288-1293
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• 2004

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach of adopting the genetic algorithm as an initial value selector, whereas using the conjugate-gradient method and Newton method to reduce their dependence on the initial value.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.413-418
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• 2012

This paper presents a multi-robot path planner for environment exploration and monitoring. Robotics systems are being widely used as data measurement tools, especially in dangerous environment. For large scale environment monitoring, multiple robots are required in order to save time. The path planner should not only consider the collision avoidance but efficient coordination of robots for optimal measurements. Nonlinear spring force based planning algorithm is integrated with the spatial gradient following path planner. Perturbation/Correlation based estimation of spatial gradient is applied. An algorithm of tuning the stiffness for robot coordination is presented. The performance of the proposed algorithm is discussed with simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.178-182
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• 1995

In this paper, the fuzzy approximator and nonlinear inversion control scheme are considered. An adaptive nonlinear control is proposed based on the speed gradient algorithms proposed by Fradkov. This proposed control scheme is that three types of adaptive law is utilized to approximate the unknown function f by fuzzy logic system in designing the nonlinear inversion controller for the nonlinear system. In order to reduce the approximation errors, the differences of nonlinear function and fuzzy approximator, another three types of adaptive law is also introduced and the stability of proposed control scheme are proven with SG algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.117-126
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• 2009

The recent interest in sensor networks has led to a number of routing schemes that use the limited resources available at sensor nodes more efficiently. These schemes typically try to find the minimum energy path to optimize energy usage at a node. Some schemes, however, are prone to unbalance of the traffic and energy. To solve this problem, we propose a novel solution: a gradient-field approach which takes account of the minimum cost data delivery, energy consumption balancing, and traffic equalization. We also modify the backoff-based cost field setup algorithm to establish our gradient-field based sensor network and give the algorithm. Simulation results show that the overhead of routing establishment obtained by our algorithm is much less than the one obtained by Flooding. What's more, our approach guarantees the basic Quality of Service (QoS) without extra spending.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.10
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• pp.702-709
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• 2008

In this paper, the optimal thermal storage time of an air-conditioning system with slab thermal storage in office building was analyzed on the basis of the gradient method algorithm. The sum of room temperature deviation and heat extraction rate was set to the criterion function. It was calculated that four hours is the optimal thermal storage time under the standard evaluation criterion. Furthermore, some case studies were executed by controlling ratio of weight functions of room temperature deviation and heat extraction rate in criterion function. It is possible to design many kinds of optimal operation of an air-conditioning system with slab thermal storage by controlling ratio of the weight functions in criterion function.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2754-2768
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• 2013

Video fingerprints generated from global features are usually vulnerable against general geometric transformations. In this paper, a novel video fingerprinting algorithm is proposed, in which a new spatio-temporal gradient is designed to represent the spatial and temporal information for each frame, and a new partition scheme, based on concentric circle and rings, is developed to resist the attacks efficiently. The centroids of spatio-temporal gradient orientations (CSTGO) within the circle and rings are then calculated to generate a robust fingerprint. Our experiments with different attacks have demonstrated that the proposed approach outperforms the state-of-the-art methods in terms of robustness and discrimination.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.731-740
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• 2003

Since motion estimation remove the redundant data to employ the temporal correlations between adjacent frames in a video sequence, it plays an important role in digital video coding. And in the block matching algorithm, search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image quality. In this paper, we propose a new fast block matching algorithm using the small-cross search pattern and the block-based gradient descent search pattern. Our algorithm first finds the motion vectors that are close to the center of search window using the small-cross search pattern, and then quickly finds the other motion vectors that are not close to the center of search window using the block-based gradient descent search pattern. Through experiments, compared with the block-based gradient descent search algorithm(BBGDS), the proposed search algorithm improves as high as 26-40% in terms of average number of search point per motion vector estimation.