Pub Date : 2022-06-25DOI: 10.1109/i2cacis54679.2022.9815475
Wong Chung Yee, Tan Xiao Jian, Khairul Shakir Ab Rahman, Teoh Leong Hoe, Lu Juei Min, Quah Yi Hang, Teoh Chai Ling
Color normalization in histopathology is a prominent research topic in the image processing field as color in histopathology images plays a crucial role in diagnosis. As computer-aided diagnosis emerged, color normalization is much crucial as it becomes the foundation of medical image processing to assure algorithm precision and accuracy. In this paper, the main objective is to perform an analysis on three commonly used color normalization methods, namely histogram matching, histogram equalization, and stain unmixing methods. 60 breast histopathology images were used for testing purposes. Four statistical metrics were calculated to measure and determine the applicability of the color normalization methods: Structural similarity index measure (SSIM), Pearson’s correlation coefficient (PCC), visual saliency-induced index (VSI), and Gradient similarity (GS). Based on the outputs, it is found that the stain unmixing method demonstrates better than that of the histogram matching and histogram equalization methods with higher values in SSIM and VSI, and comparable values in PCC and GS.
{"title":"Performance Analysis of Color Normalization Methods in Histopathology Images","authors":"Wong Chung Yee, Tan Xiao Jian, Khairul Shakir Ab Rahman, Teoh Leong Hoe, Lu Juei Min, Quah Yi Hang, Teoh Chai Ling","doi":"10.1109/i2cacis54679.2022.9815475","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815475","url":null,"abstract":"Color normalization in histopathology is a prominent research topic in the image processing field as color in histopathology images plays a crucial role in diagnosis. As computer-aided diagnosis emerged, color normalization is much crucial as it becomes the foundation of medical image processing to assure algorithm precision and accuracy. In this paper, the main objective is to perform an analysis on three commonly used color normalization methods, namely histogram matching, histogram equalization, and stain unmixing methods. 60 breast histopathology images were used for testing purposes. Four statistical metrics were calculated to measure and determine the applicability of the color normalization methods: Structural similarity index measure (SSIM), Pearson’s correlation coefficient (PCC), visual saliency-induced index (VSI), and Gradient similarity (GS). Based on the outputs, it is found that the stain unmixing method demonstrates better than that of the histogram matching and histogram equalization methods with higher values in SSIM and VSI, and comparable values in PCC and GS.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"43 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":"134217761","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/i2cacis54679.2022.9815480
Teoh Leong Hoe, T. Jian, Khairul Shakir Ab Rahman, Lu Juei Min, Quah Yi Hang, Wong Chung Yee, Thien Yee Von, L. C. Chin, Teoh Chai Ling
Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection. Cell nuclei segmentation is a fundamental yet crucial step in pleomorphism detection based on the Nottingham Histopathology Grading (NHG) system. The information of the segmented nuclei such as size and morphology properties can be used to determine the scoring decision of the breast histopathology images in terms of pleomorphism. The main purpose of this project is to segment the cell nuclei using Hematoxylin and Eosin (H&E) stained breast histopathology images. In this paper, noise removal associated with Fuzzy C-Mean (FCM) clustering algorithm is introduced which can extract information about each object and then revised and eliminate those irrelevant regions. The RGB input images would be first pre-processed to normalize the color of the input images. The color normalization stage is essential to facilitate the segmentation stage in the later step. For the segmentation process, FCM clustering techniques are applied to better allocate similar pixels into the same clustering while having significant differences between each cluster. Next, the noise region reduction method is performed to discard those pixels which are not related to the properties of cell nuclei. The proposed method is measured by Performance Matrix and the experimental result shows that it demonstrates more desirable performance than the convention FCM clustering method which has average accuracy of 77.30% (±3.332).
{"title":"Nuclei Segmentation in Breast Histopathology Images using FCM","authors":"Teoh Leong Hoe, T. Jian, Khairul Shakir Ab Rahman, Lu Juei Min, Quah Yi Hang, Wong Chung Yee, Thien Yee Von, L. C. Chin, Teoh Chai Ling","doi":"10.1109/i2cacis54679.2022.9815480","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815480","url":null,"abstract":"Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection. Cell nuclei segmentation is a fundamental yet crucial step in pleomorphism detection based on the Nottingham Histopathology Grading (NHG) system. The information of the segmented nuclei such as size and morphology properties can be used to determine the scoring decision of the breast histopathology images in terms of pleomorphism. The main purpose of this project is to segment the cell nuclei using Hematoxylin and Eosin (H&E) stained breast histopathology images. In this paper, noise removal associated with Fuzzy C-Mean (FCM) clustering algorithm is introduced which can extract information about each object and then revised and eliminate those irrelevant regions. The RGB input images would be first pre-processed to normalize the color of the input images. The color normalization stage is essential to facilitate the segmentation stage in the later step. For the segmentation process, FCM clustering techniques are applied to better allocate similar pixels into the same clustering while having significant differences between each cluster. Next, the noise region reduction method is performed to discard those pixels which are not related to the properties of cell nuclei. The proposed method is measured by Performance Matrix and the experimental result shows that it demonstrates more desirable performance than the convention FCM clustering method which has average accuracy of 77.30% (±3.332).","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"148 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":"132535350","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/I2CACIS54679.2022.9815493
Changhong Weng, Yip Sook Yee, W. Muzammil, M. Ismail
This paper presents a standalone solar power generation with energy storage management developed for an Eco-tourism centre in Sabah. The site is isolated and located far away from the power grid, and hence, it motivates the development of an onsite solar power generation for sustainable and efficient power usage. A perturbed and observed maximum power point tracker is deployed to ensure the solar system always generates the maximum power with solar irradiance. It is integrated with energy storage to preserve the continuity of power supply to the tourists, which is controlled by the energy management system. The power flow of the entire system is managed by regulating the charging and discharging process of the battery. The performance of the proposed system is verified using MATLAB Simulink with the load demand and solar irradiance profile collected from the site. The results show that the proposed system can balance power generation and utilization by alleviating the mismatch between solar power and the load demand for all the case studies. Besides, the EMS prolongs the battery lifespan by preventing it from over-charging and over-discharging, regulating its state of charge within the maximum and minimum state of charge at 10% and 90%, respectively. With the system's deployment, approximately 0.89 tonnes of carbon dioxide can be reduced annually. A preliminary economic analysis shows that the payback period is 12 years which guarantees the secure investment for the onsite green energy generation system.
{"title":"Standalone Solar Power Generation with Dynamic Error-driven PI-based Energy Management System for Rural Electrification in Malaysia","authors":"Changhong Weng, Yip Sook Yee, W. Muzammil, M. Ismail","doi":"10.1109/I2CACIS54679.2022.9815493","DOIUrl":"https://doi.org/10.1109/I2CACIS54679.2022.9815493","url":null,"abstract":"This paper presents a standalone solar power generation with energy storage management developed for an Eco-tourism centre in Sabah. The site is isolated and located far away from the power grid, and hence, it motivates the development of an onsite solar power generation for sustainable and efficient power usage. A perturbed and observed maximum power point tracker is deployed to ensure the solar system always generates the maximum power with solar irradiance. It is integrated with energy storage to preserve the continuity of power supply to the tourists, which is controlled by the energy management system. The power flow of the entire system is managed by regulating the charging and discharging process of the battery. The performance of the proposed system is verified using MATLAB Simulink with the load demand and solar irradiance profile collected from the site. The results show that the proposed system can balance power generation and utilization by alleviating the mismatch between solar power and the load demand for all the case studies. Besides, the EMS prolongs the battery lifespan by preventing it from over-charging and over-discharging, regulating its state of charge within the maximum and minimum state of charge at 10% and 90%, respectively. With the system's deployment, approximately 0.89 tonnes of carbon dioxide can be reduced annually. A preliminary economic analysis shows that the payback period is 12 years which guarantees the secure investment for the onsite green energy generation system.","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":"127209525","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/i2cacis54679.2022.9815464
Z. M. Hussin, S. S. Saaddin, S. Mohammad, N. A. M. Azmi, S. Salim
Drainage automated system is an important monitoring device to make sure that the city is always clean and safe. Manual monitoring inside the manhole is incompetent and workers could be exposed to many problems during the monitoring. Therefore, an automated drainage system is proposed to enhance the operational and monitoring of the drainage system. The focus is on the effectiveness and real-time monitoring of the drainage system using internet of thing (IoT) applications. The system is comprised of Arduino microcontroller, gas sensors and ultrasonic sensors used for the purpose of manipulating the raw signals. Global System for Mobile Communication (GSM) is also used in the system to send alert messages to the authorities or personnel. The data from the sensors will be delivered and posted to ThingSpeak in the form of graph where the data will also be stored in the web server and cloud using ESP 8266.
{"title":"Development of Automated Drainage System","authors":"Z. M. Hussin, S. S. Saaddin, S. Mohammad, N. A. M. Azmi, S. Salim","doi":"10.1109/i2cacis54679.2022.9815464","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815464","url":null,"abstract":"Drainage automated system is an important monitoring device to make sure that the city is always clean and safe. Manual monitoring inside the manhole is incompetent and workers could be exposed to many problems during the monitoring. Therefore, an automated drainage system is proposed to enhance the operational and monitoring of the drainage system. The focus is on the effectiveness and real-time monitoring of the drainage system using internet of thing (IoT) applications. The system is comprised of Arduino microcontroller, gas sensors and ultrasonic sensors used for the purpose of manipulating the raw signals. Global System for Mobile Communication (GSM) is also used in the system to send alert messages to the authorities or personnel. The data from the sensors will be delivered and posted to ThingSpeak in the form of graph where the data will also be stored in the web server and cloud using ESP 8266.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"41 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":"129747449","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/i2cacis54679.2022.9815470
S. Ab-Ghani, H. Daniyal, N. Jaalam, Nur Huda Ramlan, Norhafidzah Mohd Saad
The proliferation of clean energy and environmentally friendly transportation has contributed to the development of electric vehicles (EVs) including the EV DC charger system. A dual active bridge (DAB) is a DC-DC converter that has the required features for an EV DC charger. A proportional-integral (PI) controller is a common method in power electronics applications, including DAB. However, the manual tuning of PI parameters using Ziegler-Nichols (ZN) needs a lengthy time and the tuning values are practical and well-functioning at the tuning point only. Moreover, the fixed gains in offline tuning cannot fully control the system output as needed and do not guarantee the robustness of the system. This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. The DAB performance with the proposed controller is evaluated in terms of steady-state error, eSS and dynamic performance under various reference voltages at different loads and load step changes. Comparative analysis between the proposed method and manual tuning performance are presented. A hardware-in-the-loop (HIL) experimental circuit is built to validate the simulation results. The DAB with the proposed method produces 64% higher accuracy and 40% faster response compared to manual tuning. tuning.
{"title":"Time-Variant Online Auto-Tuned PI Controller Using PSO Algorithm for High Accuracy Dual Active Bridge DC-DC Converter","authors":"S. Ab-Ghani, H. Daniyal, N. Jaalam, Nur Huda Ramlan, Norhafidzah Mohd Saad","doi":"10.1109/i2cacis54679.2022.9815470","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815470","url":null,"abstract":"The proliferation of clean energy and environmentally friendly transportation has contributed to the development of electric vehicles (EVs) including the EV DC charger system. A dual active bridge (DAB) is a DC-DC converter that has the required features for an EV DC charger. A proportional-integral (PI) controller is a common method in power electronics applications, including DAB. However, the manual tuning of PI parameters using Ziegler-Nichols (ZN) needs a lengthy time and the tuning values are practical and well-functioning at the tuning point only. Moreover, the fixed gains in offline tuning cannot fully control the system output as needed and do not guarantee the robustness of the system. This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. The DAB performance with the proposed controller is evaluated in terms of steady-state error, eSS and dynamic performance under various reference voltages at different loads and load step changes. Comparative analysis between the proposed method and manual tuning performance are presented. A hardware-in-the-loop (HIL) experimental circuit is built to validate the simulation results. The DAB with the proposed method produces 64% higher accuracy and 40% faster response compared to manual tuning. tuning.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"360 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":"124533981","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/i2cacis54679.2022.9815477
T. J. Daim, R. A. Lee
When it comes to Human-Computer Interface (HCI), Hand Gesture Recognition (HGR) is a kind of HCI that does not need users to make physical contact with the computer or instrument with which they communicate. Recently, there has been an upsurge in interest in developing methodologies for radar-based HGR which is based on Ultra-Wideband (UWB) transmission state of the art. It utilizes a transmitter-receiver pair that operates across a large frequency spectrum and has a short pulse length, making it ideal for use in urban environments. This study investigates the influence of body position on hand gesture speed recognition and classification system utilizing an IR-UWB radar sensor. Overall, the study outcomes show that the system performs differently at identifying hand gestures and the corresponding speeds in two different body positions.
{"title":"The Effect of Body Position on IR-UWB Radar Sensor-based Hand Gesture Speed Recognition and Classification System","authors":"T. J. Daim, R. A. Lee","doi":"10.1109/i2cacis54679.2022.9815477","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815477","url":null,"abstract":"When it comes to Human-Computer Interface (HCI), Hand Gesture Recognition (HGR) is a kind of HCI that does not need users to make physical contact with the computer or instrument with which they communicate. Recently, there has been an upsurge in interest in developing methodologies for radar-based HGR which is based on Ultra-Wideband (UWB) transmission state of the art. It utilizes a transmitter-receiver pair that operates across a large frequency spectrum and has a short pulse length, making it ideal for use in urban environments. This study investigates the influence of body position on hand gesture speed recognition and classification system utilizing an IR-UWB radar sensor. Overall, the study outcomes show that the system performs differently at identifying hand gestures and the corresponding speeds in two different body positions.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"30 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120906939","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/i2cacis54679.2022.9815458
Muhammad Izzat Shafiq Nazruddin, Raihah Aminuddin, N. N. Abu Mangshor, Khyrina Airin Fariza Abu Samah
Real-time face mask types detection using image processing and deep learning model had seen enormous promise in real-world applications. Due to the spread of Covid-19, the practice of wearing face masks in public areas is used to safeguard people from the virus. However, to manually detect the type of face masks used can be difficult, hence this project aims to design and develop a real-time face mask detection model that can detect types of face masks worn by an individual which include 1) surgical masks, 2) KF94, 3) N95, 4) cloth or 5) double-masking. It could also identify if an individual is wearing the face masks incorrectly. This project is developed using the modified waterfall methodology. There are four phases in the methodology: (i) Requirement Analysis, (ii) Design, (iii) Implementation, and (iv) Testing. The data used for training and testing in this project was collected from available images on the internet. The data were pre-processed to remove any unwanted images and each image is then annotated with appropriate classes. The detection model was built using the You Only Look Once version 3 (YOLOv3) framework.
基于图像处理和深度学习模型的实时人脸类型检测在实际应用中有着巨大的前景。由于Covid-19的传播，在公共场所戴口罩的做法被用来保护人们免受病毒感染。然而，手动检测使用的口罩类型可能很困难，因此本项目旨在设计和开发一个实时口罩检测模型，该模型可以检测个人佩戴的口罩类型，包括1)外科口罩，2)KF94, 3) N95, 4)布口罩或5)双重口罩。它还可以识别一个人是否戴错了口罩。这个项目是使用改进的瀑布方法开发的。该方法分为四个阶段:(i)需求分析，(ii)设计，(iii)实施和(iv)测试。本项目中用于训练和测试的数据是从互联网上可用的图像中收集的。对数据进行预处理以删除任何不需要的图像，然后用适当的类对每个图像进行注释。检测模型是使用You Only Look Once version 3 (YOLOv3)框架构建的。
{"title":"Real-time Face Mask Types Detection to Monitor Standard Operating Procedure Compliance Using You Only Look Once-based Framework","authors":"Muhammad Izzat Shafiq Nazruddin, Raihah Aminuddin, N. N. Abu Mangshor, Khyrina Airin Fariza Abu Samah","doi":"10.1109/i2cacis54679.2022.9815458","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815458","url":null,"abstract":"Real-time face mask types detection using image processing and deep learning model had seen enormous promise in real-world applications. Due to the spread of Covid-19, the practice of wearing face masks in public areas is used to safeguard people from the virus. However, to manually detect the type of face masks used can be difficult, hence this project aims to design and develop a real-time face mask detection model that can detect types of face masks worn by an individual which include 1) surgical masks, 2) KF94, 3) N95, 4) cloth or 5) double-masking. It could also identify if an individual is wearing the face masks incorrectly. This project is developed using the modified waterfall methodology. There are four phases in the methodology: (i) Requirement Analysis, (ii) Design, (iii) Implementation, and (iv) Testing. The data used for training and testing in this project was collected from available images on the internet. The data were pre-processed to remove any unwanted images and each image is then annotated with appropriate classes. The detection model was built using the You Only Look Once version 3 (YOLOv3) framework.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"27 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":"115026349","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/i2cacis54679.2022.9815461
Nur Naajihah Ab Rahman, Nafrizuan Mat Yahya
This project compares the performance of a DC motor PI-PD controller with and without a pole-zero system identification method. Cohen-Coon tuning is used to fine-tune the proposed controller’s parameters. In addition, a small number of tests were performed with varying pole and zero values. The DC motor’s performance with a PI-PD controller was evaluated throughout these tests for transient response characteristics. At the end of the study, it can be concluded that system identification is required in order to enhance the performance of the DC motor. By that, the DC motor with the PI-PD controller performed better than other variants, with 0.067s rising time, 1.748s settling time, 1.170s peak time, and 42.143 percent overshoot with no steady-state error, despite having only two poles and no zeros in the proposed controller.
{"title":"System Identification for a Mathematical Model of DC Motor System","authors":"Nur Naajihah Ab Rahman, Nafrizuan Mat Yahya","doi":"10.1109/i2cacis54679.2022.9815461","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815461","url":null,"abstract":"This project compares the performance of a DC motor PI-PD controller with and without a pole-zero system identification method. Cohen-Coon tuning is used to fine-tune the proposed controller’s parameters. In addition, a small number of tests were performed with varying pole and zero values. The DC motor’s performance with a PI-PD controller was evaluated throughout these tests for transient response characteristics. At the end of the study, it can be concluded that system identification is required in order to enhance the performance of the DC motor. By that, the DC motor with the PI-PD controller performed better than other variants, with 0.067s rising time, 1.748s settling time, 1.170s peak time, and 42.143 percent overshoot with no steady-state error, despite having only two poles and no zeros in the proposed controller.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"05 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":"131335724","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/i2cacis54679.2022.9815465
Vinukumar Luckose, Jothish Chembath, Joe Arun Raja Ponnusamy, Sakshi Sharma, Pritpal Kaur, Sajitha Smiley
Web server maintains the essential user log files, recording every request to it. Web log is a record of events which includes all the user details from the time the web visitor initiated the session to the end of the session. The web usage pattern discovery to identify different states of the user access behavior on web. The design of web recommender system using a context-aware Cohesive Markov Model and Apriori clustering is proposed. The prediction rate of proposed algorithm is higher than conventional Markov model.
{"title":"Web Usage Pattern Detection Using Cohesive Markov Model With Apriori Algorithm","authors":"Vinukumar Luckose, Jothish Chembath, Joe Arun Raja Ponnusamy, Sakshi Sharma, Pritpal Kaur, Sajitha Smiley","doi":"10.1109/i2cacis54679.2022.9815465","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815465","url":null,"abstract":"Web server maintains the essential user log files, recording every request to it. Web log is a record of events which includes all the user details from the time the web visitor initiated the session to the end of the session. The web usage pattern discovery to identify different states of the user access behavior on web. The design of web recommender system using a context-aware Cohesive Markov Model and Apriori clustering is proposed. The prediction rate of proposed algorithm is higher than conventional Markov model.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"13 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":"130863119","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/i2cacis54679.2022.9815494
Cheng Weng Fong, H. Asmuni, Pui Huang Leong, Yet Huat Sam, Yee Yong Pang, Hiew Moi Sim
Timetabling is a task of assigning a set of events into a set of resources and satisfying predefined constraints. University timetabling is one of the most stdied timetabling problems among the timetabling domains. It is also a time consuming administrative task that need to be performed in all the academic institutions as there are many constraints needed to be considered. In this study, a zombie survival optimization (ZSO) has been applied to address university examination timetabling problem. The underlying idea of ZSO is based on the foraging behavior of zombies, where zombies represent searching agents (solutions) in searching for antidote (optimal solution). There are three modes in ZSO, namely exploration mode, hunter mode and human exploitation mode where zombies explore for solutions (randomly) in exploration mode, explore towards a human (promising search region) in hunter mode and turn into human to search (exploitation) for local optimum. The ZSO is tested on Carter’s university un-capacitated examination benchmark dataset and results demonstrated that ZSO is capable of producing promising quality of solutions when compared with the published methods in the literature. In fact, ZSO managed to record new best-known results on 3 instances of the dataset.
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