Pub Date : 2024-07-13DOI: 10.37936/ecti-cit.2024183.256091
N. Chotikakamthorn, Aye Mi San, C. Sathitwiriyawong
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.
{"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}
Pub Date : 2024-05-18DOI: 10.37936/ecti-cit.2024173.255276
Thitipong Kawichai
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.
{"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}
Pub Date : 2024-05-04DOI: 10.37936/ecti-cit.2024182.254733
D. Santra, A. Mukherjee, S. Mondal
This paper presents for the first-time, application of Moth Flame Optimization and Bat Algorithm (MFO-BA) for optimal scheduling of thermal and hydro-thermal systems in a simulated environment. Results of three test systems (4-unit, 5-unit and 6-unit) comprising seven test cases as different combinations of fixed-head hydro units and thermal units with and without losses are presented to demonstrate the performance of the hybrid MFO-BA algorithm. The test results comprehensively establish the advan- tage and overall effectiveness of the hydro-thermal system over thermal-only system in terms of load dispatch and economy of generation cost and transmission loss. The present study can help find the most economic scheduling of hydro-thermal generating units using hybrid soft computing approach.
{"title":"A Study on Comparison between Thermal and Hydro-thermal ELD Using Metaheuristics Technique","authors":"D. Santra, A. Mukherjee, S. Mondal","doi":"10.37936/ecti-cit.2024182.254733","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024182.254733","url":null,"abstract":"This paper presents for the first-time, application of Moth Flame Optimization and Bat Algorithm (MFO-BA) for optimal scheduling of thermal and hydro-thermal systems in a simulated environment. Results of three test systems (4-unit, 5-unit and 6-unit) comprising seven test cases as different combinations of fixed-head hydro units and thermal units with and without losses are presented to demonstrate the performance of the hybrid MFO-BA algorithm. The test results comprehensively establish the advan- tage and overall effectiveness of the hydro-thermal system over thermal-only system in terms of load dispatch and economy of generation cost and transmission loss. The present study can help find the most economic scheduling of hydro-thermal generating units using hybrid soft computing approach.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141013873","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}
Pub Date : 2024-04-09DOI: 10.37936/ecti-cit.2024182.253574
Thea Mayen Malimban, Kyle Reece Oropesa, Carlo Z. Geron, Jade Kristine Comia, R. Ado, Orland D. Tubola
The research devised efficient methods for simulating 3-qubit Quantum Fourier Transform (QFT) circuits using Quick Quantum Circuit Simulation (QQCS). The hybrid methodologies suggested as a solution for efficiently simulating the circuit involve the combination of decision diagrams and property exploitation techniques. This paper incorporated two methods based on decision diagrams: the reordering trick and decision diagram approximations, template-based optimization, and linear reversible circuit synthesis for property exploitation. The proposed approaches significantly improved and optimized quantum algorithms and hardware by aiming to simulate quantum circuits accurately and quickly. Simulations using QQCS proved the effectiveness of these strategies, which were then compared to the original circuit. The results yielded valuable insights into enhancing simulation efficiency while upholding circuit accuracy.
{"title":"Hybrid Approaches for Efficient Simulations of 3-Qubit Quantum Fourier Transform (QFT) Circuit Using Quick Quantum Circuit Simulation (QQCS)","authors":"Thea Mayen Malimban, Kyle Reece Oropesa, Carlo Z. Geron, Jade Kristine Comia, R. Ado, Orland D. Tubola","doi":"10.37936/ecti-cit.2024182.253574","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024182.253574","url":null,"abstract":"The research devised efficient methods for simulating 3-qubit Quantum Fourier Transform (QFT) circuits using Quick Quantum Circuit Simulation (QQCS). The hybrid methodologies suggested as a solution for efficiently simulating the circuit involve the combination of decision diagrams and property exploitation techniques. This paper incorporated two methods based on decision diagrams: the reordering trick and decision diagram approximations, template-based optimization, and linear reversible circuit synthesis for property exploitation. The proposed approaches significantly improved and optimized quantum algorithms and hardware by aiming to simulate quantum circuits accurately and quickly. Simulations using QQCS proved the effectiveness of these strategies, which were then compared to the original circuit. The results yielded valuable insights into enhancing simulation efficiency while upholding circuit accuracy.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"16 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723165","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}
Pub Date : 2024-03-29DOI: 10.37936/ecti-cit.2024182.254769
Apirak Tooltham, Suchart Khummanee, C. Jareanpon, Montree Nonphayom
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.
{"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}
Pub Date : 2024-02-10DOI: 10.37936/ecti-cit.2024181.254501
Sowmiya Baskar, Saminathan K, Chithra Devi M
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.
{"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}
Pub Date : 2024-02-10DOI: 10.37936/ecti-cit.2024181.254501
Sowmiya Baskar, Saminathan K, Chithra Devi M
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.
{"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}
Pub Date : 2024-02-10DOI: 10.37936/ecti-cit.2024181.254621
Paisit Khanarsa, Satanat Kitsiranuwat
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.
{"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}
Pub Date : 2024-02-10DOI: 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
This study evaluated computer vision-based models, including Histogram Analysis, Logistic Regression, Sift-SVM, and Deep learning models, in an autonomous testing system developed for smartphone camera modules. System performance was assessed in a practical factory setting with workers operating the system, and metrics such as processing time, sensitivity, specificity, accuracy, and defect rate were evaluated. Based on the results, the Sift-SVM model demonstrated the greatest potential for enhancing the reliability of the system with a processing time of 0.01578 seconds, a sensitivity of 99.811%, and a reduction in the failure rate to 1888 PPM. The study findings suggest that Sift-SVM has the potential to be practically applied in the industry, thus improving the speed and accuracy of automatic defect detection in manufacturing and reducing the defect rate.
{"title":"Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning","authors":"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","doi":"10.37936/ecti-cit.2024181.253854","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.253854","url":null,"abstract":"This study evaluated computer vision-based models, including Histogram Analysis, Logistic Regression, Sift-SVM, and Deep learning models, in an autonomous testing system developed for smartphone camera modules. System performance was assessed in a practical factory setting with workers operating the system, and metrics such as processing time, sensitivity, specificity, accuracy, and defect rate were evaluated. Based on the results, the Sift-SVM model demonstrated the greatest potential for enhancing the reliability of the system with a processing time of 0.01578 seconds, a sensitivity of 99.811%, and a reduction in the failure rate to 1888 PPM. The study findings suggest that Sift-SVM has the potential to be practically applied in the industry, thus improving the speed and accuracy of automatic defect detection in manufacturing and reducing the defect rate.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":"45 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846607","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}
Pub Date : 2024-02-10DOI: 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
This study evaluated computer vision-based models, including Histogram Analysis, Logistic Regression, Sift-SVM, and Deep learning models, in an autonomous testing system developed for smartphone camera modules. System performance was assessed in a practical factory setting with workers operating the system, and metrics such as processing time, sensitivity, specificity, accuracy, and defect rate were evaluated. Based on the results, the Sift-SVM model demonstrated the greatest potential for enhancing the reliability of the system with a processing time of 0.01578 seconds, a sensitivity of 99.811%, and a reduction in the failure rate to 1888 PPM. The study findings suggest that Sift-SVM has the potential to be practically applied in the industry, thus improving the speed and accuracy of automatic defect detection in manufacturing and reducing the defect rate.
{"title":"Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning","authors":"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","doi":"10.37936/ecti-cit.2024181.253854","DOIUrl":"https://doi.org/10.37936/ecti-cit.2024181.253854","url":null,"abstract":"This study evaluated computer vision-based models, including Histogram Analysis, Logistic Regression, Sift-SVM, and Deep learning models, in an autonomous testing system developed for smartphone camera modules. System performance was assessed in a practical factory setting with workers operating the system, and metrics such as processing time, sensitivity, specificity, accuracy, and defect rate were evaluated. Based on the results, the Sift-SVM model demonstrated the greatest potential for enhancing the reliability of the system with a processing time of 0.01578 seconds, a sensitivity of 99.811%, and a reduction in the failure rate to 1888 PPM. The study findings suggest that Sift-SVM has the potential to be practically applied in the industry, thus improving the speed and accuracy of automatic defect detection in manufacturing and reducing the defect rate.","PeriodicalId":507234,"journal":{"name":"ECTI Transactions on Computer and Information Technology (ECTI-CIT)","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786532","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}
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