• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 한국물환경학회지
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• 제35권5호
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• pp.432-438
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• 2019

The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, $R^2$, % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, $R^2$ is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.358-365
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• 2016

We propose a novel application of random forest, a machine learning-based general classification algorithm, to analyze the influence of design attributes on the silicon-to-SPICE (S2S) gap. To improve modeling accuracy, we introduce magnification of learning data as well as randomization for the counting of design attributes to be used for each tree in the forest. From the automatically generated decision trees, we can extract the so-called importance and impact indices, which identify the most significant design attributes determining the S2S gap. We apply the proposed method to actual silicon data, and observe that the identified design attributes show a clear trend in the S2S gap. We finally unveil 10nm key fin-shaped field effect transistor (FinFET) structures that result in a large S2S gap using the measurement data from 10nm test vehicles specialized for model-hardware correlation.

• 자원환경지질
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• 제56권1호
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.

• 한국정보시스템학회지:정보시스템연구
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• 제33권1호
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• pp.31-44
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• 2024

Purpose The purpose of this study is to use climate data to find the algorithm with the highest Hallabong production prediction ability and to predict future Hallabong production in areas where Hallabong cultivation is expected to be possible. Design/methodology/approach The research is conducted in two stages. In the first step, find the algorithm with the highest predictive power among XGBoost, Random Forest, SVM, and LSTM methodologies. In the second stage, the algorithm found in the first stage is applied to predict future Hallabong production in three regions where Hallabong production is expected to be possible. Findings As with many prediction studies, we found that XGBoost showed the highest prediction power. Even in areas where Hallabong production is expected to be possible, Hallabong production was predicted to be highest in Hongcheon, Gangwon-do, which has the highest latitude.

• 한국정보전자통신기술학회논문지
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• 제16권6호
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• pp.385-391
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• 2023

최근 탄소배출을 최소화하기 위해 전기자동차의 사용이 증가함에 따라 핵심 부품인 리튬이온 배터리의 상태 및 성능 분석의 중요성이 대두되고 있다. 따라서 배터리의 상태 및 성능에 영향을 줄 수 있는 배터리의 전압, 전류 및 온도뿐만 아니라 전기 자동차의 운행 데이터 및 충전 패턴 데이터를 활용한 종합적인 분석이 필요하다. 따라서 전기적 이동 수단에서 수집되는 배터리 데이터 수집 및 데이터 전처리, 단순 배터리 데이터에 추가적인 운전자 운전 습관에 대한 데이터 수집 및 전처리, 분석된 영향인자를 기반으로 인공지능 알고리즘 세부 설계 및 수정, 해당 알고리즘을 기반으로 하는 배터리 분석 및 평가 모델 설계하였다. 본 논문에서는 실시간 전기버스를 대상으로 운행 데이터와 배터리 데이터를 수집하여 Random Forest 알고리즘 활용하여 학습시킨 후, XAI 알고리즘을 통해 배터리 상태 중요 영향인자로 배터리의 상태, 운행 및 충전 패턴 데이터 등을 종합적으로 고려하여 운행 패턴에서 급가속, 급 감속, 급정지와 충 방전 패턴에서 일 주행횟수, 일일 누적 DOD와 셀 방전에서 셀 전압 차 , 셀 최대온도, 셀 최소온도의 요소가 배터리 상태에 많은 영향을 미치는 인자로 확인되었으며, Random Forest 알고리즘 기반으로 배터리 분석 및 평가 모델을 설계하고 평가하였다.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.103-111
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• 2021

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.267-274
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• 2021

Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye's classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized.

• 융합보안논문지
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• 제23권5호
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• pp.73-79
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• 2023

사이버 위협은 기술의 발전에 따라 진화되고 정교해지고 있으며, DDoS 공격으로 인한 서비스 장애를 발생 이슈들이 증가하고 있다. 최근 DDoS 공격은 특정 서비스나 서버의 도메인 주소에 대량의 트래픽을 유입시켜 서비스 장애를 발생시키는 유형이 많아지고 있다. 본 논문에서는 대역폭 소진 공격의 대표적인 공격 유형인 Syn Flooding 공격의 데이터를 생성 후, 효과적인 공격 탐지를 위해 Random Forest, Decision Tree, Multi-Layer Perceptron, KNN 알고리즘을 사용하여 비교 분석하였고 최적의 알고리즘을 도출하였다. 이 결과를 토대로 Syn Flooding 공격 탐지 정책을 위한 기법으로 효과적인 활용이 가능할 것이다.