• Title/Summary/Keyword: machine learning

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FRM: Foundation-policy Recommendation Model to Improve the Performance of NAND Flash Memory

  • Won Ho Lee;Jun-Hyeong Choi;Jong Wook Kwak
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.8
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    • pp.1-10
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    • 2023
  • Recently, NAND flash memories have replaced magnetic disks due to non-volatility, high capacity and high resistance, in various computer systems but it has disadvantages which are the limited lifespan and imbalanced operation latency. Therefore, many page replacement policies have been studied to overcome the disadvantages of NAND flash memories. Although it is clear that these policies reflect execution characteristics of various environments and applications, researches on the foundation-policy decision for disk buffer management are insufficient. Thus, in this paper, we propose a foundation-policy recommendation model, called FRM for effectively utilizing NAND flash memories. FRM proposes a suitable page replacement policy by classifying and analyzing characteristics of workloads through machine learning. As an implementation case, we introduce FRM with a disk buffer management policy and in experiment results, prediction accuracy and weighted average of FRM shows 92.85% and 88.97%, by training dataset and validation dataset for foundation disk buffer management policy, respectively.

Personalized Size Recommender System for Online Apparel Shopping: A Collaborative Filtering Approach

  • Dongwon Lee
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.8
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    • pp.39-48
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    • 2023
  • This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

Comparative Analysis of CNN Models for Leukemia Diagnosis (백혈병 진단을 위한 CNN 모델 비교 분석)

  • Lee, Yeon-Ji;Ryu, Jung-Hwa;Lee, Il-Gu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.279-282
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    • 2022
  • Acute lymphoblastic leukemia is an acute leukemia caused by suppression of bone marrow function due to overgrowth of immature lymphocytes in the bone marrow. It accounts for 30% of acute leukemia in adults, and children show a cure rate of over 80% with chemotherapy, while adults show a low survival rate of 20% to 50%. However, research on a machine learning algorithm based on medical image data for the diagnosis of acute lymphoblastic leukemia is in the initial stage. In this paper, we compare and analyze CNN algorithm models for quick and accurate diagnosis. Using four models, an experimental environment for comparative analysis of acute lymphoblastic leukemia diagnostic models was established, and the algorithm with the best accuracy was selected for the given medical image data. According to the experimental results, among the four CNN models, the InceptionV3 model showed the best performance with an accuracy of 98.9%.

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In Silico Approach for Predicting Neurotoxicity (In silico 기법을 이용한 신경독성 예측)

  • Lee, So-yeon;Yoo, Sun-yong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.270-272
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    • 2022
  • Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

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Analysis of YouTube Trending Video Dataset by Country and Category (YouTube 인기 급상승 동영상 데이터셋의 국가별-카테고리별 분석)

  • Jung, Jimin;Kim, Seungjin;Jung, Sungwook;Lee, Dongyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.209-211
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    • 2022
  • YouTube, a video platform used by millions of people worldwide, provides a rapidly growing video service. This study aims to understand the characteristics and cultural differences of each country using the Kaggle dataset, one of the public datasets, and to show the usefulness of the public dataset. For this purpose, we analyze data from 11 countries, 15 categories, and about 1.1 million trending videos. This study adopts Python to obtain the number of videos by category for data analysis, the selection period of videos rapidly increasing in popularity, and the ratio of unique videos. In the future, based on machine learning, we plan to research to help diagnose individual videos and establish channel operation plans and strategies by predicting the selection possibility and selection period based on machine learning.

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AI-based smart water environment management service platform development (AI기반 스마트 수질환경관리 서비스 플랫폼 개발)

  • Kim, NamHo
    • Smart Media Journal
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    • v.11 no.9
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    • pp.56-63
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    • 2022
  • Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

Long Short-Term Memory Neural Network assisted Peak to Average Power Ratio Reduction for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication

  • Waleed, Raza;Xuefei, Ma;Houbing, Song;Amir, Ali;Habib, Zubairi;Kamal, Acharya
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.1
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    • pp.239-260
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    • 2023
  • The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

Seoul Local Brand Alley Commercial Area Recommendation System Design Using Machine Learning (머신러닝 기반 서울시 로컬브랜드 골목상권 추천시스템 설계)

  • Jiyeon, Kim;Hyoseon, Jang;Minseo, Park
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.1
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    • pp.101-109
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    • 2023
  • According to data released by the Covid 19 Self-Employed Emergency Response Committee, 95.6% of small business sales due to Covid 19 have decreased over the past two years, and the damage has further increased due to social distancing for quarantine. However, as all social distancing guidelines have rebeen lifted, and the commercial district has been revitalized, the Seoul Metropolitan Government is pushing for a project to foster local brand commercial districts so that small business owners or prospective founders who have closed their businesses due to the prolonged COVID-19. Therefore, this study propose the model that recommends alley commercial districts suitable for founders among the five alley commercial districts selected for the project to foster local brand commercial districts in Seoul. The Seoul Metropolitan Government's local brand alley commercial recommendation system recommends major population age groups and major industries in the commercial district by combining the population perspective model using Xgboost and the commercial district characteristic model using Decision Tree.

Detection of Depression Trends in Literary Cyber Writers Using Sentiment Analysis and Machine Learning

  • Faiza Nasir;Haseeb Ahmad;CM Nadeem Faisal;Qaisar Abbas;Mubarak Albathan;Ayyaz Hussain
    • International Journal of Computer Science & Network Security
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    • v.23 no.3
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    • pp.67-80
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    • 2023
  • Rice is an important food crop for most of the population in Nowadays, psychologists consider social media an important tool to examine mental disorders. Among these disorders, depression is one of the most common yet least cured disease Since abundant of writers having extensive followers express their feelings on social media and depression is significantly increasing, thus, exploring the literary text shared on social media may provide multidimensional features of depressive behaviors: (1) Background: Several studies observed that depressive data contains certain language styles and self-expressing pronouns, but current study provides the evidence that posts appearing with self-expressing pronouns and depressive language styles contain high emotional temperatures. Therefore, the main objective of this study is to examine the literary cyber writers' posts for discovering the symptomatic signs of depression. For this purpose, our research emphases on extracting the data from writers' public social media pages, blogs, and communities; (3) Results: To examine the emotional temperatures and sentences usage between depressive and not depressive groups, we employed the SentiStrength algorithm as a psycholinguistic method, TF-IDF and N-Gram for ranked phrases extraction, and Latent Dirichlet Allocation for topic modelling of the extracted phrases. The results unearth the strong connection between depression and negative emotional temperatures in writer's posts. Moreover, we used Naïve Bayes, Support Vector Machines, Random Forest, and Decision Tree algorithms to validate the classification of depressive and not depressive in terms of sentences, phrases and topics. The results reveal that comparing with others, Support Vectors Machines algorithm validates the classification while attaining highest 79% f-score; (4) Conclusions: Experimental results show that the proposed system outperformed for detection of depression trends in literary cyber writers using sentiment analysis.

Development of a Polytropic Index-Based Reheat Gas Turbine Inlet Temperature Calculation Algorithm (폴리트로픽 지수 기반의 재열 가스터빈 입구온도 산출 알고리즘 개발)

  • Young-Bok Han;Sung-Ho Kim;Byon-Gon Kim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.3
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    • pp.483-494
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    • 2023
  • Recently, gas turbine generators are widely used for frequency control of power systems. Although the inlet temperature of a gas turbine is a key factor related to the performance and lifespan of the device, the inlet temperature is not measured directly for reasons such as the turbine structure and operating environment. In particular, the inlet temperature of the reheating gas turbine is very important for stable operation management, but field workers are experiencing a lot of difficulties because the manufacturer does not provide information on the calculation formula. Therefore, in this study, we propose a method for estimating the inlet temperature of a gas turbine using a machine learning-based linear regression analysis method based on a polytropic process equation. In addition, by proposing an inlet temperature calculation algorithm through the usefulness analysis and verification of the inlet temperature calculation model obtained through linear regression analysis, it is intended to help to improve the level of reheat gas turbine combustion tuning technology.