Pub Date : 2022-06-25DOI: 10.1109/i2cacis54679.2022.9815478
Lu Min, Tan Xiao Jian, Khairul Shakir Ab Rahman, Teoh Leong Hoe, Quah Yi Hang, Wong Chung Yee, Yip Sook Yee, W. Z. A. Wan Muhamad, Teoh Chai Ling
Based on the Nottingham Histopathology Grading system, mitosis count is one of the important criteria that contribute to the overall grade of breast cancer. Over the years, many automated mitosis detection methods have been proposed. Nonetheless, the ever-increasing demand for quality detection continues by seeking optimization in each stage in the detection pipeline. This paper aims to focus on the optimization of the non-mitosis cells reduction stage by proposing three semantic descriptors: solidity, eccentricity, and area to eliminate the non-mitosis cells in breast histopathology images. The proposed method consists of three stages: (1) color normalization, (2) nucleus segmentation, and (3) non-mitosis reduction and its performance was evaluated using 40 histopathology images. The proposed three semantic descriptors were found to be useful and effective in reducing non-mitosis cells, achieving 96.18% (with standard deviation tabulated at ±1.6374%) across the dataset.
{"title":"A Non-Mitosis Reduction Method using Semantic Descriptors for Breast Cancer Mitosis Detection Application","authors":"Lu Min, Tan Xiao Jian, Khairul Shakir Ab Rahman, Teoh Leong Hoe, Quah Yi Hang, Wong Chung Yee, Yip Sook Yee, W. Z. A. Wan Muhamad, Teoh Chai Ling","doi":"10.1109/i2cacis54679.2022.9815478","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815478","url":null,"abstract":"Based on the Nottingham Histopathology Grading system, mitosis count is one of the important criteria that contribute to the overall grade of breast cancer. Over the years, many automated mitosis detection methods have been proposed. Nonetheless, the ever-increasing demand for quality detection continues by seeking optimization in each stage in the detection pipeline. This paper aims to focus on the optimization of the non-mitosis cells reduction stage by proposing three semantic descriptors: solidity, eccentricity, and area to eliminate the non-mitosis cells in breast histopathology images. The proposed method consists of three stages: (1) color normalization, (2) nucleus segmentation, and (3) non-mitosis reduction and its performance was evaluated using 40 histopathology images. The proposed three semantic descriptors were found to be useful and effective in reducing non-mitosis cells, achieving 96.18% (with standard deviation tabulated at ±1.6374%) across the dataset.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134151443","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 : 2022-06-25DOI: 10.1109/i2cacis54679.2022.9815273
Faisal Al Thobiani, Muneer Ammami, A. Shamekh, A. Altowati
This paper presents a Weighted version of the Latent Variable Model Predictive Control (WLV-MPC) to address the control solution instability of the original LV-MPC algorithm that is related to the loading matrix decomposition. The suggested idea is firstly applied in a system identification framework where a modified version of an iterative Least Squares (LS) technique supported with the Upper Diagonal (UD) factorization algorithm is implemented in model parameter optimization. The second part illustrates the derivation of the WLV-MPC through penalizing the loading matrices that form the basis of the designed cost function. The use of the D matrix to penalize the formulated Hessian matrix in Quadratic Programming (QP) has significantly improved the solution stability. The performance of the proposed approach has been verified through a numerical example and in the temperature control of a batch process benchmark.
{"title":"Application of Weighted Latent Variable Model Predictive Control in Batch Process Temperature Control","authors":"Faisal Al Thobiani, Muneer Ammami, A. Shamekh, A. Altowati","doi":"10.1109/i2cacis54679.2022.9815273","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815273","url":null,"abstract":"This paper presents a Weighted version of the Latent Variable Model Predictive Control (WLV-MPC) to address the control solution instability of the original LV-MPC algorithm that is related to the loading matrix decomposition. The suggested idea is firstly applied in a system identification framework where a modified version of an iterative Least Squares (LS) technique supported with the Upper Diagonal (UD) factorization algorithm is implemented in model parameter optimization. The second part illustrates the derivation of the WLV-MPC through penalizing the loading matrices that form the basis of the designed cost function. The use of the D matrix to penalize the formulated Hessian matrix in Quadratic Programming (QP) has significantly improved the solution stability. The performance of the proposed approach has been verified through a numerical example and in the temperature control of a batch process benchmark.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121210613","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 : 2022-06-25DOI: 10.1109/i2cacis54679.2022.9815485
S.Anitha Rajathi, Arun Sahayadhas, A. Melvin
In the agriculture domain, the main issues determined are knowledge deficiencies regarding alterations in climate. As each crop poses its own climatic features, but the issues confronted in agriculture are managed using precise farming methods. The precision farming assists in fulfilling the demand for food, maintaining crop productivity, and increasing the yield rate. In India, the emerging needs require sustainable agriculture. The two main issues in agriculture are the selection of crops and altering climatic conditions, which are solved by examining prediction and monitoring techniques, but there is no solution for the crop suggestion. This survey examines several techniques based on crop prediction in the IoT network. This study utilizes 25 research papers focused on several methods, and review of researches based on the classical technique is devised. The assessment is done based on publication year, research technique, implementation tools, performance measures and achievement of the research methodologies towards crop prediction techniques in IoT network. At the end, the research gaps and issues of the existing techniques are devised in such a way that the motivation for developing an effective method for crop prediction techniques in IoT network is revealed.
{"title":"Study and Analysis of Various Crop Prediction Techniques in IoT Network: An Overview","authors":"S.Anitha Rajathi, Arun Sahayadhas, A. Melvin","doi":"10.1109/i2cacis54679.2022.9815485","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815485","url":null,"abstract":"In the agriculture domain, the main issues determined are knowledge deficiencies regarding alterations in climate. As each crop poses its own climatic features, but the issues confronted in agriculture are managed using precise farming methods. The precision farming assists in fulfilling the demand for food, maintaining crop productivity, and increasing the yield rate. In India, the emerging needs require sustainable agriculture. The two main issues in agriculture are the selection of crops and altering climatic conditions, which are solved by examining prediction and monitoring techniques, but there is no solution for the crop suggestion. This survey examines several techniques based on crop prediction in the IoT network. This study utilizes 25 research papers focused on several methods, and review of researches based on the classical technique is devised. The assessment is done based on publication year, research technique, implementation tools, performance measures and achievement of the research methodologies towards crop prediction techniques in IoT network. At the end, the research gaps and issues of the existing techniques are devised in such a way that the motivation for developing an effective method for crop prediction techniques in IoT network is revealed.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114941339","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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