ECTI Transactions on Computer and Information Technology (ECTI-CIT)最新文献_第2页

Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification 基于深度学习的常规子宫颈抹片图像分类集合方法

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-02-10 DOI: 10.37936/ecti-cit.2024181.254621

Paisit Khanarsa, Satanat Kitsiranuwat

{"title":"Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification","authors":"Paisit Khanarsa, Satanat Kitsiranuwat","doi":"10.37936/ecti-cit.2024181.254621","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.254621","url":null,"abstract":"Cervical cancer screening allows the early signs of precancerous abnormalities in the cervix before they develop into invasive cancer. The Pap Smear is a widely used screening for early detection and prevention of cervical cancer. In many remote areas, the number of cytologists available to interpret pap smear screening tests is insufficient. This lack of personnel makes the test interpretation very time-consuming. To address this, deep learning techniques have been employed to detect cervical cancer cells and support cytologists. Therefore, an integrative approach with deep learning models and the ensemble techniques such as the maximum occurrence and the maximum probability score of cervical cells was proposed. The multi-cell assessment of the Pap smear slide allowed aggregate predictions of single cervical cell images using the proposed method. The classification results between pre-trained deep learning models and the proposed method were compared. In the experimental results, the proposed method can achieve an accuracy score of more than 97%, while the best pre-trained deep learning model can attain an accuracy score of more than 85%. Hence, the proposed method may have the potential to assist physicians or cytologists in the classification of cervical cell types for Pap Smear images.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feature Extraction of Risk Group and Electricity Theft by using Electrical Profiles and Physical Data for Classification in the Power Utilities 利用电力档案和物理数据对电力公司中的风险群体和窃电行为进行特征提取分类

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-01-20 DOI: 10.37936/ecti-cit.2024181.252738

Supakan Janthong, Rakkrit Duangsoithong, K. Chalermyanont

{"title":"Feature Extraction of Risk Group and Electricity Theft by using Electrical Profiles and Physical Data for Classification in the Power Utilities","authors":"Supakan Janthong, Rakkrit Duangsoithong, K. Chalermyanont","doi":"10.37936/ecti-cit.2024181.252738","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.252738","url":null,"abstract":"Non-technical loss (NTL) is one of the problems that has been a major issue in lost revenue for many years. Electricity distributors have attempted to reduce NTL by detecting electricity theft using various methods. Some events are difficult to detect that conventional meters inspection is inadequate. Moreover, many anomaly patterns found are very complex, confusing in identifying or distinguishing what types of electricity customers are at abnormal risk or energy theft that affects NTL. This paper proposes five key feature extraction methods and six classifying electricity customers using supervised learning. The main problem was studied and collected information, including kilowatt meters, electronic meters, TOU meters, and AMR meters, which cover four customer types that were recorded in the Provincial Electricity Authority (PEA) of Thailand. An electrical profile to be extracted for in-depth analysis of the behavior of each type of electricity customer, combined with the information of physical data to help enhance and increase efficiency. All features examined the relationships in each feature using Pearson correlation and handled unbalanced data using random oversampling (ROS). Then, the extracted data has been trained, validated, and tested to classify three classes: normal, risk, and theft, where we evaluate the results with performance metrics. The results show that random forest (RF) outperforms the rest of the classifiers by achieving a precision-recall area under the curve of 90% and a receiver operating characteristic curve of 78%. Significantly, the results were compared to previous studies and benchmark datasets, which revealed that the proposed method gave better results than other techniques.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"32 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-Destructive Inspection of Tile Debonding by DWT and MFCC of Tile-Tapping Sound with Machine versus Deep Learning Models 利用机器学习与深度学习模型，通过瓦片敲击声的 DWT 和 MFCC 对瓦片脱胶进行无损检测

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-01-20 DOI: 10.37936/ecti-cit.2024181.253800

J. Panyavaraporn, Paramate Horkaew

{"title":"Non-Destructive Inspection of Tile Debonding by DWT and MFCC of Tile-Tapping Sound with Machine versus Deep Learning Models","authors":"J. Panyavaraporn, Paramate Horkaew","doi":"10.37936/ecti-cit.2024181.253800","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.253800","url":null,"abstract":"\u0000\u0000\u0000One of the essential processes of construction quality control is tile bonding inspection. Hollows beneath tile tessellation can lead to unbounded or completely broken tiles. An interior inspector typically used a hollowsounding technique. However, it relies on skill and judgment that greatly vary among individuals. Moreover, equipment and interpretation are difficult to calibrate and standardize. This paper addresses these issues by employing machine-learning strategies for tile-tapping sound classification. Provided that a tapping signal was digitally acquired, the proposed method was fully computerized. Firstly, the signal was analyzed and its wavelets and MFCC were extracted. The corresponding spectral features were then classified by SVM, k-NN, Naïve Bayes, and Logistic Regression algorithm, in turn. The results were subsequently compared against those from a previous works that employed a deep learning strategy. It was revealed that when the proposed method was properly configured, it required much less computing resources than the deep learning based one, while being able to distinguish dull from hollow sounding tiles with 93.67% accuracy.\u0000\u0000\u0000","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"68 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139611546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D-Wave Implementation of Quantum Annealing for Optimal Resource Allocation in Disaster Response Operation of Marikina City 量子退火的 D 波实现，用于马里基纳市救灾行动中的最佳资源分配

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-01-20 DOI: 10.37936/ecti-cit.2024181.253577

Allyza Ruth Cadeliña, Mori Assanty Cuevas, Marselean Kallos, Morris John Montemayor, Ronald Jay Uy, Joshua Benjamin Rodriguez, Orland D. Tubola

{"title":"D-Wave Implementation of Quantum Annealing for Optimal Resource Allocation in Disaster Response Operation of Marikina City","authors":"Allyza Ruth Cadeliña, Mori Assanty Cuevas, Marselean Kallos, Morris John Montemayor, Ronald Jay Uy, Joshua Benjamin Rodriguez, Orland D. Tubola","doi":"10.37936/ecti-cit.2024181.253577","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.253577","url":null,"abstract":"Quantum computing shows a positive approach for addressing optimization challenges in NP-hard problems such as the vehicle routing problem (VRP). This study focuses on improving the efficiency of disaster response operations by localizing the application of D-wave quantum annealing in Marikina City. This study uses the Solution Partitioning Solver (SPS) and the Quadratic Unconstrained Binary Optimization (QUBO) formulation to convert the VRP into an equation that can be solved using quantum annealing. The study demonstrates that quantum computing effectively distributes resources during emergency response operations and improves overall operational efficiency. In determining the most effective route for each vehicle, the D-wave Leap API and QUBO representation compute the distances traveled by each vehicle. These findings contribute to the practical applications of quantum computing to revolutionize various fields, including disaster management. Implementing D-wave quantum annealing in Marikina City shows relevance for future advancements in optimizing resource allocation and improving disaster response operations.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"62 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139611563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving Privacy Preservation Constraints based on K-Anonymity in conjunction with Adjacency Matrix and Weighted Graphs 基于 K 匿名与邻接矩阵和加权图实现隐私保护约束

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-01-20 DOI: 10.37936/ecti-cit.2024181.253483

Surapon Riyana, Kittikorn Sasujit, N. Homdoung

{"title":"Achieving Privacy Preservation Constraints based on K-Anonymity in conjunction with Adjacency Matrix and Weighted Graphs","authors":"Surapon Riyana, Kittikorn Sasujit, N. Homdoung","doi":"10.37936/ecti-cit.2024181.253483","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.253483","url":null,"abstract":"A well-known privacy preservation model is k-anonymity. It is simple and widely applied in several real-life systems. To achieve k-anonymity constraints in datasets, all explicit identifiers of users are removed. Furthermore, the unique quasi-identifiers of users are distorted by their less specific values to be at least k indistinguishable tuples. For this reason, after datasets are satisfied by k-anonymity constraints, they can guarantee that all possible query conditions to them always have at least k tuples that are satisfied. Aside from achieving privacy preservation constraints, the data utility and the complexity of data transformation are serious issues that must also be considered when datasets are released. Therefore, both privacy preservation models are proposed in this work. They are based on k-anonymity constraints in conjunction with the weighted graph of correlated distortion tuples and the adjacency matrix of tuple distances. The proposed models aim to preserve data privacy in datasets. Moreover, the data utility and data transform complexities are also considered in the privacy preservation constraint of the proposed models. Furthermore, we show that the proposed data transformation technique is more efficient and effective by using extensive experiments.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"136 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Smart Farming Capabilities for Small-Scale Cattle Farmers in Chiang Rai, Thailand 提高泰国清莱小型养牛场主的智能农业能力

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-01-13 DOI: 10.37936/ecti-cit.2024181.253823

Bunyarat Umsura, Kamonlak Chaidee, Kingkan Puansurin, Dueanpen Manoruang, Pornthipat Wimooktayone, Kanjana Boontasri, Wisoot Kaenmueng

引用次数: 0