ECTI Transactions on Computer and Information Technology (ECTI-CIT)最新文献

On-Chain Verifiable Credential with Applications in Education 应用于教育领域的链上可验证证书

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-07-13 DOI: 10.37936/ecti-cit.2024183.256091

N. Chotikakamthorn, Aye Mi San, C. Sathitwiriyawong

{"title":"On-Chain Verifiable Credential with Applications in Education","authors":"N. Chotikakamthorn, Aye Mi San, C. Sathitwiriyawong","doi":"10.37936/ecti-cit.2024183.256091","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024183.256091","url":null,"abstract":"A verifiable credential (VC) has been standardized and applied in various domains, including education. Due to its immutability, blockchain has been considered and used for credential issuance and verification. Most existing methods, however, are not compatible with the W3C VC standard. In this paper, an on-chain VC issuance and verification method has been described. The method is based on the standard VC data model and applicable to any credential type. It decomposes a VC document into a VC template and the corresponding value array(s). This allows a VC to be issued on-chain in the Bitcoin BTC network, which has a limited data embedding capacity. The proposed method reduces blockchain resource consumption due to the reusability of a VC template. In addition, it allows the use of a concise VC fingerprint format instead of a full VC for credential exchange. Two issuance modes, namely the full on-chain and partial on-chain, are proposed targeting different use cases. The proposed method has been applied for issuing and verifying two learning credential types. The method was evaluated on the Bitcoin Testnet to measure time and space complexities. With the reduced-size VC fingerprint, the proposed method can embed a VC on a traditional paper-based credential as a compact-sized QR code. The proposed method offered faster VC issuance and verification than an existing standard-based verifiable credential method.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651046","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

Stacking Ensemble Learning with Regression Models for Predicting Damage from Terrorist Attacks 堆叠集合学习与回归模型，预测恐怖袭击造成的损失

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-05-18 DOI: 10.37936/ecti-cit.2024173.255276

Thitipong Kawichai

{"title":"Stacking Ensemble Learning with Regression Models for Predicting Damage from Terrorist Attacks","authors":"Thitipong Kawichai","doi":"10.37936/ecti-cit.2024173.255276","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024173.255276","url":null,"abstract":"Terrorist attacks can cause unexpectedly enormous damage to lives and property. To prevent and mitigate damage from terrorist activities, governments and related organizations must have suitable measures and efficient tools to cope with terrorist attacks. This work proposed a new method based on stacking ensemble learning and regression for predicting damage from terrorist attacks. First, two-layer stacking classifiers were developed and used to indicate if a terrorist attack can cause deaths, injuries, and property damage. For fatal and injury attacks, regression models were utilized to forecast the number of deaths and injuries, respectively. Consequently, the proposed method can efficiently classify casualty terrorist attacks with an average area under precision-recall curves (AUPR) of 0.958. Furthermore, the stacking model can predict property damage attacks with an average AUPR of 0.910. In comparison with existing methods, the proposed method precisely estimates the number of fatalities and injuries with the lowest mean absolute errors of 1.22 and 2.32 for fatal and injury attacks, respectively. According to the superior performance shown, the stacking ensemble models with regression can be utilized as an efficient tool to support emergency prevention and management of terrorist attacks.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"102 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124885","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

A Study on Comparison between Thermal and Hydro-thermal ELD Using Metaheuristics Technique 利用元启发式技术对热式和水热式 ELD 进行比较的研究

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-05-04 DOI: 10.37936/ecti-cit.2024182.254733

D. Santra, A. Mukherjee, S. Mondal

引用次数: 0

Hybrid Approaches for Efficient Simulations of 3-Qubit Quantum Fourier Transform (QFT) Circuit Using Quick Quantum Circuit Simulation (QQCS) 利用快速量子电路仿真 (QQCS) 高效模拟 3ubit 量子傅里叶变换 (QFT) 电路的混合方法

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-04-09 DOI: 10.37936/ecti-cit.2024182.253574

Thea Mayen Malimban, Kyle Reece Oropesa, Carlo Z. Geron, Jade Kristine Comia, R. Ado, Orland D. Tubola

引用次数: 0

Ladybug: An Automated Cultivation Robot for Addressing the Manpower Shortage in the Agricultural Industry 瓢虫解决农业劳动力短缺问题的自动栽培机器人

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-03-29 DOI: 10.37936/ecti-cit.2024182.254769

Apirak Tooltham, Suchart Khummanee, C. Jareanpon, Montree Nonphayom

{"title":"Ladybug: An Automated Cultivation Robot for Addressing the Manpower Shortage in the Agricultural Industry","authors":"Apirak Tooltham, Suchart Khummanee, C. Jareanpon, Montree Nonphayom","doi":"10.37936/ecti-cit.2024182.254769","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024182.254769","url":null,"abstract":"The agricultural sector is projected to need more labor as a result of declining interest in careers within this domain. Despite the escalating demand for agricultural goods, previous endeavors to mitigate this challenge through the deployment of robotic prototypes have encountered hindrances such as issues pertaining to automation, adaptability to varying tasks, and the financial burdens associated with development. To address this exigency, we have developed an automated cultivation robot utilizing advancements in the Internet of Things (IoT), Image Processing, and artificial Intelligence (AI) for seeding in pots. The robot demonstrates the capacity to sow seeds in 257 pots per hour, accomplish a mission within 12.53 minutes, traverse at a velocity of 360 meters per hour, and seed pots at a rate of 13.37 seconds per pot. It possesses an operational duration of approximately two hours, completing nine cycles and seeding 486 pots on a single charge. Notably, the robot exhibits a mission success rate of 1.00 and a seeding accuracy 0.78. Moreover, it features an adaptable workspace and a lightweight frame weighing 20 kg, rendering it a cost-effective solution for mass production.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"62 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367828","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

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification 基于迁移学习的 InceptionV3 和 VGG16 模型组合用于水稻叶病分类

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-02-10 DOI: 10.37936/ecti-cit.2024181.254501

Sowmiya Baskar, Saminathan K, Chithra Devi M

{"title":"An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification","authors":"Sowmiya Baskar, Saminathan K, Chithra Devi M","doi":"10.37936/ecti-cit.2024181.254501","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.254501","url":null,"abstract":"Paddy is a crucial food crop providing essential nutrients and energy and serving more than half the global population. Diagnosing and preventing plant diseases at an early stage is crucial for the health and productivity of crops. Automated disease diagnosis eliminates the need for experts and delivers accurate outcomes. This research will diagnose paddy leaf diseases with Deep Learning technology. The diseases such as bacterial blight, blast, tungro, brown spot, and healthy leaf classes are diagnosed and classified in this study. The dataset contains 160 images from each class with 800 images. Our proposed model is an ensemble of transfer-learned InceptionV3 and VGG16 architectures, which utilizes the strength of individual models to improve overall performance. The use of transfer-learned ensemble deep learning architectures achieved impressive accuracy rates of 97.03%, 94.97%, and 98.87% for training, validation and testing respectively. The results indicating that model is not overfit and generalizes well to unseen data. The model's performance is evaluated with confusion matrix with the parameters like precision, recall, F1-score, and support. We also tested the model's performance against other proposed deep learning techniques with and without transfer learning techniques. Moreover, this research advances reliable automated disease detection systems, fostering sustainable agriculture and enhancing global food security.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846342","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

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification 基于迁移学习的 InceptionV3 和 VGG16 模型组合用于水稻叶病分类

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-02-10 DOI: 10.37936/ecti-cit.2024181.254501

Sowmiya Baskar, Saminathan K, Chithra Devi M

{"title":"An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification","authors":"Sowmiya Baskar, Saminathan K, Chithra Devi M","doi":"10.37936/ecti-cit.2024181.254501","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.254501","url":null,"abstract":"Paddy is a crucial food crop providing essential nutrients and energy and serving more than half the global population. Diagnosing and preventing plant diseases at an early stage is crucial for the health and productivity of crops. Automated disease diagnosis eliminates the need for experts and delivers accurate outcomes. This research will diagnose paddy leaf diseases with Deep Learning technology. The diseases such as bacterial blight, blast, tungro, brown spot, and healthy leaf classes are diagnosed and classified in this study. The dataset contains 160 images from each class with 800 images. Our proposed model is an ensemble of transfer-learned InceptionV3 and VGG16 architectures, which utilizes the strength of individual models to improve overall performance. The use of transfer-learned ensemble deep learning architectures achieved impressive accuracy rates of 97.03%, 94.97%, and 98.87% for training, validation and testing respectively. The results indicating that model is not overfit and generalizes well to unseen data. The model's performance is evaluated with confusion matrix with the parameters like precision, recall, F1-score, and support. We also tested the model's performance against other proposed deep learning techniques with and without transfer learning techniques. Moreover, this research advances reliable automated disease detection systems, fostering sustainable agriculture and enhancing global food security.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786579","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

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":"40 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846742","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

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning 利用计算机视觉和机器学习技术开发可提高可靠性的自主组件测试系统

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-02-10 DOI: 10.37936/ecti-cit.2024181.253854

Hoang Anh Phan, Van Tan Duong, Mai Nguyen Thi, Anh Nguyen Thi, Hang Khuat Thi Thu, Thang Luu Duc, Van Hieu Dang, Huu Quoc Dong Tran, Thi Thanh Van Nguyen, Thanh Tung Bui

引用次数: 0

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning 利用计算机视觉和机器学习技术开发可提高可靠性的自主组件测试系统

ECTI Transactions on Computer and Information Technology (ECTI-CIT) Pub Date : 2024-02-10 DOI: 10.37936/ecti-cit.2024181.253854

Hoang Anh Phan, Van Tan Duong, Mai Nguyen Thi, Anh Nguyen Thi, Hang Khuat Thi Thu, Thang Luu Duc, Van Hieu Dang, Huu Quoc Dong Tran, Thi Thanh Van Nguyen, Thanh Tung Bui

引用次数: 0