Pub Date : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935582
S. Loh, You Hong Liew, J. Sim
Throughout the design life cycle of an integrated circuit (IC), verification plays a crucial part in affirming the functionalities of the features implemented based on the architecture used. In the case of a processor with advanced microarchitectural features implemented, a simulation-based approach is taken for its functional verification. Functional verification increases the level of confidence in conformance of the processor design to its specification. This paper presents the approach that could be utilized in the VLSI design course in Universiti Tunku Abdul Rahman (UTAR). More specifically, Universal Verification Methodology (UVM) is utilized for the verification methodology of the RISC-V processor implementation in this work.
{"title":"VLSI Design Course with Verification of RISC-V Design using Universal Verification Methodology (UVM)","authors":"S. Loh, You Hong Liew, J. Sim","doi":"10.1109/ICCSCE54767.2022.9935582","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935582","url":null,"abstract":"Throughout the design life cycle of an integrated circuit (IC), verification plays a crucial part in affirming the functionalities of the features implemented based on the architecture used. In the case of a processor with advanced microarchitectural features implemented, a simulation-based approach is taken for its functional verification. Functional verification increases the level of confidence in conformance of the processor design to its specification. This paper presents the approach that could be utilized in the VLSI design course in Universiti Tunku Abdul Rahman (UTAR). More specifically, Universal Verification Methodology (UVM) is utilized for the verification methodology of the RISC-V processor implementation in this work.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132720726","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935658
Muhammad Zaim Bin Kamal Abdul, Badrul Amin Nayan, S. Z. Yahaya, R. Boudville, K. A. Ahmad, Nurul Izza Husin
This paper presents the development of air quality monitoring system based on internet of things, IOT application to control the concentration of carbon dioxide gas and carbon monoxide gas in car cabin. Carbon monoxide is one most dangerous gas and nowadays there were many fatal cases happen caused by the gas leakage, while increasing of the concentration carbon dioxide will reduce oxygen level in the air, thus can cause the driver to be fatigue, and sleepiness. Moreover, gas detector usually not installed in nowadays car. To develop smart air quality monitoring system, Arduino MKR 1010, CJMCU 811(carbon dioxide sensor), MQ-7(carbon monoxide sensor), Adafruit io application, IFTTT, Webhooks, Telegram, and MATLAB fuzzy logic toolbox were used. Furthermore, Fuzzy Logic Controller (FLC) was used to analyze the input and produce the system's output decisions on the level of gas toxicities. The five levels of gas toxicities are very safe, safe, warning, high, and very high. The decision of gas level will be sent to IOT application which is Adafruit and then triggered the three types of LED indicator, power window motor, buzzer, and signal to webhook to send the Telegram. Besides, Adafruit IO also act as the platform to present the data of gases graph pattern. In addition, MATLAB fuzzy logic toolbox was used to make comparison between the result of Arduino analysis with the value of simulation. Furthermore, Telegram messenger also act as a medium to notify the users. Based on the system evaluation, this system has performed well in analysing the gas concentrating inputs and show stable IOT interactions in triggering related warning and safety devices.
{"title":"Design and Development of Fuzzy Logic Based Car Cabin Pollution Monitoring System","authors":"Muhammad Zaim Bin Kamal Abdul, Badrul Amin Nayan, S. Z. Yahaya, R. Boudville, K. A. Ahmad, Nurul Izza Husin","doi":"10.1109/ICCSCE54767.2022.9935658","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935658","url":null,"abstract":"This paper presents the development of air quality monitoring system based on internet of things, IOT application to control the concentration of carbon dioxide gas and carbon monoxide gas in car cabin. Carbon monoxide is one most dangerous gas and nowadays there were many fatal cases happen caused by the gas leakage, while increasing of the concentration carbon dioxide will reduce oxygen level in the air, thus can cause the driver to be fatigue, and sleepiness. Moreover, gas detector usually not installed in nowadays car. To develop smart air quality monitoring system, Arduino MKR 1010, CJMCU 811(carbon dioxide sensor), MQ-7(carbon monoxide sensor), Adafruit io application, IFTTT, Webhooks, Telegram, and MATLAB fuzzy logic toolbox were used. Furthermore, Fuzzy Logic Controller (FLC) was used to analyze the input and produce the system's output decisions on the level of gas toxicities. The five levels of gas toxicities are very safe, safe, warning, high, and very high. The decision of gas level will be sent to IOT application which is Adafruit and then triggered the three types of LED indicator, power window motor, buzzer, and signal to webhook to send the Telegram. Besides, Adafruit IO also act as the platform to present the data of gases graph pattern. In addition, MATLAB fuzzy logic toolbox was used to make comparison between the result of Arduino analysis with the value of simulation. Furthermore, Telegram messenger also act as a medium to notify the users. Based on the system evaluation, this system has performed well in analysing the gas concentrating inputs and show stable IOT interactions in triggering related warning and safety devices.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124008012","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935663
Zahida Sharif, Muhammed Basheer Jasser, K. Yau, A. Amphawan
A coherent placement of virtual network functions (VNFs) permits the efficient forwarding of data flows (routing). VNFs' placement and service function chain (SFC) routing (VNF-PSFCR) in an optimized way to minimize latency has been reported as NP-hard problem. The review of related work highlights the limitations of this domain, which revolve around high time complexity, high delays, and the ignorance of utilization of bandwidth and power consumption. The main consideration of this paper is to solve joint optimization of VNF-PSFCR and envisage the latency requirements of 5G networks. The optimal selection of VNFs and a new routing strategy are required to solve this multi-joint optimization problem via swarm intelligence. Swarm intelligence algorithms, as inspired by the collective behaviors of swarms that offer robust and high-quality solutions, have the viability to solve the mentioned problem effectively compared to conventional algorithms. A novel fuzzy heuristic and swarm intelligence-based algorithm named latency aware multi-joint placement and traffic routing-grey wolf optimizer (LAMPTR-GWO) is proposed in this work to solve the latency minimization problem. The proposed algorithm comprises two phases; the first is the efficient placement of VNFs in the graph, and the second phase is SFC routing. The Takagi Sugeno Kang system (TSK) is employed to guide the wolves to potentially explore the search space and to handle the uncertainties inherent in the NFV infrastructure. It is an effective combination that can achieve the required balance between the exploration and the exploitation of GWO. The proposed algorithm is expected to have the ability to surpass the performance of other swarm algorithms via overcoming the limitations of the GWO for solving the VNF-PSFCR problem.
{"title":"Towards Latency Aware Multi-joint Optimization Method for VNF Placement and SFC Routing Via Swarm Intelligence","authors":"Zahida Sharif, Muhammed Basheer Jasser, K. Yau, A. Amphawan","doi":"10.1109/ICCSCE54767.2022.9935663","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935663","url":null,"abstract":"A coherent placement of virtual network functions (VNFs) permits the efficient forwarding of data flows (routing). VNFs' placement and service function chain (SFC) routing (VNF-PSFCR) in an optimized way to minimize latency has been reported as NP-hard problem. The review of related work highlights the limitations of this domain, which revolve around high time complexity, high delays, and the ignorance of utilization of bandwidth and power consumption. The main consideration of this paper is to solve joint optimization of VNF-PSFCR and envisage the latency requirements of 5G networks. The optimal selection of VNFs and a new routing strategy are required to solve this multi-joint optimization problem via swarm intelligence. Swarm intelligence algorithms, as inspired by the collective behaviors of swarms that offer robust and high-quality solutions, have the viability to solve the mentioned problem effectively compared to conventional algorithms. A novel fuzzy heuristic and swarm intelligence-based algorithm named latency aware multi-joint placement and traffic routing-grey wolf optimizer (LAMPTR-GWO) is proposed in this work to solve the latency minimization problem. The proposed algorithm comprises two phases; the first is the efficient placement of VNFs in the graph, and the second phase is SFC routing. The Takagi Sugeno Kang system (TSK) is employed to guide the wolves to potentially explore the search space and to handle the uncertainties inherent in the NFV infrastructure. It is an effective combination that can achieve the required balance between the exploration and the exploitation of GWO. The proposed algorithm is expected to have the ability to surpass the performance of other swarm algorithms via overcoming the limitations of the GWO for solving the VNF-PSFCR problem.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693654","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935580
M. M. A. Jamil, Nur Adilah Abd Rahman, Mohd Nazib Adon, R. Ambar, S. Saidin
Based on the findings of this study, natural sources may become an integral part of cancer treatment. Honey is a well-known natural substance that has been utilised in traditional medicine for centuries. MCF-7 cells are a sample of eukaryotic cells. It is comprised of breast cancer cells. This experiment is conducted for 24 hrs with Tualang (wild bee) and Kelulut (stingless bee) honey of varying concentrations (100, 75, 50, 25, and 10 percent). This experiment revealed that Tualang extract has anti-cancer capabilities; increasing the concentration of the extract reduces the viability of cancer cells. In contrast, Kelulut displays the opposite. Kelulut outperformed Tualang in terms of wound closure time during the scratch test. It is highly possible that this recent finding will have impact on biomedical research which cannot be understated.
{"title":"Local Honey (Tualang & Kelulut) Effect on Breast Cancer Cell (MCF-7)","authors":"M. M. A. Jamil, Nur Adilah Abd Rahman, Mohd Nazib Adon, R. Ambar, S. Saidin","doi":"10.1109/ICCSCE54767.2022.9935580","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935580","url":null,"abstract":"Based on the findings of this study, natural sources may become an integral part of cancer treatment. Honey is a well-known natural substance that has been utilised in traditional medicine for centuries. MCF-7 cells are a sample of eukaryotic cells. It is comprised of breast cancer cells. This experiment is conducted for 24 hrs with Tualang (wild bee) and Kelulut (stingless bee) honey of varying concentrations (100, 75, 50, 25, and 10 percent). This experiment revealed that Tualang extract has anti-cancer capabilities; increasing the concentration of the extract reduces the viability of cancer cells. In contrast, Kelulut displays the opposite. Kelulut outperformed Tualang in terms of wound closure time during the scratch test. It is highly possible that this recent finding will have impact on biomedical research which cannot be understated.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128596621","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935634
Nurul Najiha Jafery, S. N. Sulaiman, M. K. Osman, N. Karim, M. F. Abdullah, I. Isa
A radiologist typically diagnoses lung cancer by visually inspecting Computed Tomography (CT) scan images. The procedure is time-consuming, tedious, and prone to errors. Aside from that, variations in intensity in CT scan images, as well as anatomical structure misjudgment by doctors and radiologists, may make identifying cancerous cells difficult. Internationally, doctors and radiologists use the TNM (Tumor, Nodule, Metastases) method to describe the stage of lung cancer. The purpose of this study is to propose an image processing method for detecting Primary Tumour (T) stages of lung cancer by introducing new regression features extraction method for lung cancer in CT scan images. This will aid medical professionals in diagnosing and treating patients. To accomplish this, lung CT scans are processed to isolate. First, lung region with its background then the lesion region and later extract relevant features from the segmented lesion region. The study begins by proposing a new segmentation procedure for lung CT images that can segment lesion and non-lesion. Then a new regression feature of lesion and non-lesion will be extracted. This study's expected outcome is that a new regression feature can help in classifying lung cancer T staging.
{"title":"A New Regression Method for Diagnosis of Lung Cancer Disease","authors":"Nurul Najiha Jafery, S. N. Sulaiman, M. K. Osman, N. Karim, M. F. Abdullah, I. Isa","doi":"10.1109/ICCSCE54767.2022.9935634","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935634","url":null,"abstract":"A radiologist typically diagnoses lung cancer by visually inspecting Computed Tomography (CT) scan images. The procedure is time-consuming, tedious, and prone to errors. Aside from that, variations in intensity in CT scan images, as well as anatomical structure misjudgment by doctors and radiologists, may make identifying cancerous cells difficult. Internationally, doctors and radiologists use the TNM (Tumor, Nodule, Metastases) method to describe the stage of lung cancer. The purpose of this study is to propose an image processing method for detecting Primary Tumour (T) stages of lung cancer by introducing new regression features extraction method for lung cancer in CT scan images. This will aid medical professionals in diagnosing and treating patients. To accomplish this, lung CT scans are processed to isolate. First, lung region with its background then the lesion region and later extract relevant features from the segmented lesion region. The study begins by proposing a new segmentation procedure for lung CT images that can segment lesion and non-lesion. Then a new regression feature of lesion and non-lesion will be extracted. This study's expected outcome is that a new regression feature can help in classifying lung cancer T staging.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128648752","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935647
S. N. Sulaiman, Muhammad Hanzalah Normazli, N. A. Harron, N. Karim, K. A. Ahmad, Z. H. C. Soh
Almost half of all mammograms performed on women over 40 reveal dense breasts. Breast density has been associated with both low BMI and the use of postmenopausal hormone replacement therapy. Breast cancer is more common in women with dense breasts than in women with fatty breasts, and the risk increases with increasing breast density. Mammograms may be more challenging to read in women with dense breasts than in women with fatty breasts. This is because dense breast tissue and pathological breast alterations, such as calcifications and tumours, appear as white regions on mammography. As a result, this study aims to create a fast and accurate approach for enhancing Digital Breast Tomosynthesis (DBT) images using a deep learning convolution neural network (CNN). The results then show that the very deep super-resolution (VDSR) approach produces the best results compared to the BICUBIC. Finally, this research will compare the obtained results for VDSR with the traditional method, BICUBIC. This study can conclude that the project successfully obtained a satisfactory result.
{"title":"A Convolutional Neural Network Model for Image Enhancement of Extremely Dense Breast Tissue in Digital Breast Tomosynthesis Images","authors":"S. N. Sulaiman, Muhammad Hanzalah Normazli, N. A. Harron, N. Karim, K. A. Ahmad, Z. H. C. Soh","doi":"10.1109/ICCSCE54767.2022.9935647","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935647","url":null,"abstract":"Almost half of all mammograms performed on women over 40 reveal dense breasts. Breast density has been associated with both low BMI and the use of postmenopausal hormone replacement therapy. Breast cancer is more common in women with dense breasts than in women with fatty breasts, and the risk increases with increasing breast density. Mammograms may be more challenging to read in women with dense breasts than in women with fatty breasts. This is because dense breast tissue and pathological breast alterations, such as calcifications and tumours, appear as white regions on mammography. As a result, this study aims to create a fast and accurate approach for enhancing Digital Breast Tomosynthesis (DBT) images using a deep learning convolution neural network (CNN). The results then show that the very deep super-resolution (VDSR) approach produces the best results compared to the BICUBIC. Finally, this research will compare the obtained results for VDSR with the traditional method, BICUBIC. This study can conclude that the project successfully obtained a satisfactory result.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132818692","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935632
K. K. Hasan, M. Dham
The HD-W60 microcontroller is an open source prototype electronic platform using HD2016 software package. The proposed system consists of a Light Emitting Diode (LED) array, as well as a circuit for connecting to a computer through USB or wireless connectivity, it also comprises a computer application for controlling the display of characters or graphics on the screen. Because the display is made up of small independent modules, this work proposes an efficient and scalable solution in which the size of the display can be extended along the rows and columns without changing the circuit. This paper will deliberate on displaying a scrolling text message on a 96×32 LED dot matrix display which consists of 24 boards of (8×16 LED matrix). The developed system is determined to be suitable for a variety of other Wi Fi applications, as well as the additional features that may be incorporated in Hypertext (Graphics Interchange Format GIF - Picture Format, Animation Character, (shock wave flash) SWF Video) further designs.
{"title":"Design and Enforcements of Wireless Based Matrix LED Message Moving Display by HD-W60 Microcontroller","authors":"K. K. Hasan, M. Dham","doi":"10.1109/ICCSCE54767.2022.9935632","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935632","url":null,"abstract":"The HD-W60 microcontroller is an open source prototype electronic platform using HD2016 software package. The proposed system consists of a Light Emitting Diode (LED) array, as well as a circuit for connecting to a computer through USB or wireless connectivity, it also comprises a computer application for controlling the display of characters or graphics on the screen. Because the display is made up of small independent modules, this work proposes an efficient and scalable solution in which the size of the display can be extended along the rows and columns without changing the circuit. This paper will deliberate on displaying a scrolling text message on a 96×32 LED dot matrix display which consists of 24 boards of (8×16 LED matrix). The developed system is determined to be suitable for a variety of other Wi Fi applications, as well as the additional features that may be incorporated in Hypertext (Graphics Interchange Format GIF - Picture Format, Animation Character, (shock wave flash) SWF Video) further designs.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114195262","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935661
N. Othman, A. Norman, M. L. M. Kiah
The advancement of cybersecurity has called for active effective information security management. Security control (SC) assessment must be empowered to ensure that security implementation is effective and provides expected protection. Lack of comprehensive literature analysis on SC assessment compared to risk and threats assessments are concerning. This research aims to systematically review the trends of SC assessment by identifying, categorizing and analyzing the challenges and available solutions of SC assessment. 34 articles were qualitatively selected with a definite contribution in SC assessment. These articles were reviewed using thematic analysis according to Theme 1: Assessment Challenges, and Theme 2: Proposed Solution. Findings from each theme are systematically categorized to answer research questions. The results of this review are significant in identifying the issues and areas of improvement for future research and can serve as the baseline for SC assessment characteristics.
{"title":"Systematic Literature Review of Security Control Assessment Challenges","authors":"N. Othman, A. Norman, M. L. M. Kiah","doi":"10.1109/ICCSCE54767.2022.9935661","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935661","url":null,"abstract":"The advancement of cybersecurity has called for active effective information security management. Security control (SC) assessment must be empowered to ensure that security implementation is effective and provides expected protection. Lack of comprehensive literature analysis on SC assessment compared to risk and threats assessments are concerning. This research aims to systematically review the trends of SC assessment by identifying, categorizing and analyzing the challenges and available solutions of SC assessment. 34 articles were qualitatively selected with a definite contribution in SC assessment. These articles were reviewed using thematic analysis according to Theme 1: Assessment Challenges, and Theme 2: Proposed Solution. Findings from each theme are systematically categorized to answer research questions. The results of this review are significant in identifying the issues and areas of improvement for future research and can serve as the baseline for SC assessment characteristics.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115793598","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935652
Muhammad Syafiq Ibrahim, Shabinar Abdul Hamid, Z. Muhammad, N. A. M. Leh, S. Abdullah, S. J. A. Bakar, M. K. Osman, Solahuddin Yusuf Fadhlullah
A reliable and efficient automated fruit grading process is needed to meet the market's increased demand for good quality fruits. Automated systems based on image processing technology are being developed to reduce reliance on manual expertise, which is often time-consuming, expensive, and biased. The propose of this paper is to construct and evaluate different types of CNN models, including conventional CNN, GoogLeNet and AlexNet, for categorising fresh and rotten appearances using a dataset of apple images. All the datasets had performed the operations of pre-processing steps with scaling, rotating, and cropping. All models were trained and tested using the datasets provided by the KAGGLE network. The accuracy and loss performance of all the models are measured. The result shows that the GoogLeNet achieved 100% accuracy, which has better performance compared to the AlexNet and conventional CNN. Hence, the GoogLeNet model has the potential for integration into an automatic fruit grading system.
{"title":"CNN Comparative Study for Apple Quality Classification","authors":"Muhammad Syafiq Ibrahim, Shabinar Abdul Hamid, Z. Muhammad, N. A. M. Leh, S. Abdullah, S. J. A. Bakar, M. K. Osman, Solahuddin Yusuf Fadhlullah","doi":"10.1109/ICCSCE54767.2022.9935652","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935652","url":null,"abstract":"A reliable and efficient automated fruit grading process is needed to meet the market's increased demand for good quality fruits. Automated systems based on image processing technology are being developed to reduce reliance on manual expertise, which is often time-consuming, expensive, and biased. The propose of this paper is to construct and evaluate different types of CNN models, including conventional CNN, GoogLeNet and AlexNet, for categorising fresh and rotten appearances using a dataset of apple images. All the datasets had performed the operations of pre-processing steps with scaling, rotating, and cropping. All models were trained and tested using the datasets provided by the KAGGLE network. The accuracy and loss performance of all the models are measured. The result shows that the GoogLeNet achieved 100% accuracy, which has better performance compared to the AlexNet and conventional CNN. Hence, the GoogLeNet model has the potential for integration into an automatic fruit grading system.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116975282","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935660
Y. Jusman, Maryza Intan Rahmawati, S. N. Sulaiman
Cervical cancer is a dangerous disease, with more than 99% of which contain Human Papillomavirus (HPV), threatening women worldwide. The Global Burden of Cancer Study (Globocan) has recorded 36,633 cervical cancer cases, ranking second in Indonesia. Analysis of Pap smear results manually as an early detection effort possesses many weaknesses. Therefore, as an early detection step in diagnosing cervical pre-cancerous cell images, an artificial intelligence system is highly required to assist medical personnel in providing fast and accurate diagnostic evaluations. This study utilized 972 training image data and 108 testing image data, with a cervical pre-cancerous cells image classification system using Histogram of Oriented Gradients (HOG) algorithms for feature extraction and KNN machine learning for the classification system. The gray level in the contrasting images between the texture of the nucleus, cytoplasm, and background had different pixel and bit depth intensity values. Hence, HOG obtained bin orientation for each pixel in the cell. The cosine KNN model demonstrated the best matrix performance, acquiring classification results of 0.8 for accuracy, 0.8 for precision, 0.889 for recall, 0.846 for specificity, and 0.771 for f-score. Moreover, the training data generated an accuracy of 69.3% and the fastest training time of 4.2359s.
宫颈癌是一种危险的疾病，其中99%以上含有人类乳头瘤病毒(HPV)，威胁着全世界的妇女。全球癌症负担研究(Globocan)记录了36,633例宫颈癌病例，在印度尼西亚排名第二。人工分析子宫颈抹片检查结果作为早期检测工作具有许多弱点。因此，作为诊断宫颈癌前细胞图像的早期检测步骤，高度需要人工智能系统协助医护人员提供快速准确的诊断评估。本研究利用972张训练图像数据和108张测试图像数据，采用直方图定向梯度(Histogram of Oriented Gradients, HOG)算法进行特征提取，采用KNN机器学习进行分类，建立了宫颈癌前细胞图像分类系统。对比图像中细胞核、细胞质和背景纹理的灰度值具有不同的像素和比特深度强度值。因此，HOG获得了单元格中每个像素的bin方向。余弦KNN模型表现出最佳的矩阵性能，分类结果准确率为0.8，精密度为0.8，召回率为0.889，特异性为0.846,f分数为0.771。训练数据的准确率为69.3%，最快训练时间为4.2359s。
{"title":"Cervical Pre-cancerous Cell Image Classification System Using Histogram of Oriented Gradients and K-Nearest Neighbor Algorithms","authors":"Y. Jusman, Maryza Intan Rahmawati, S. N. Sulaiman","doi":"10.1109/ICCSCE54767.2022.9935660","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935660","url":null,"abstract":"Cervical cancer is a dangerous disease, with more than 99% of which contain Human Papillomavirus (HPV), threatening women worldwide. The Global Burden of Cancer Study (Globocan) has recorded 36,633 cervical cancer cases, ranking second in Indonesia. Analysis of Pap smear results manually as an early detection effort possesses many weaknesses. Therefore, as an early detection step in diagnosing cervical pre-cancerous cell images, an artificial intelligence system is highly required to assist medical personnel in providing fast and accurate diagnostic evaluations. This study utilized 972 training image data and 108 testing image data, with a cervical pre-cancerous cells image classification system using Histogram of Oriented Gradients (HOG) algorithms for feature extraction and KNN machine learning for the classification system. The gray level in the contrasting images between the texture of the nucleus, cytoplasm, and background had different pixel and bit depth intensity values. Hence, HOG obtained bin orientation for each pixel in the cell. The cosine KNN model demonstrated the best matrix performance, acquiring classification results of 0.8 for accuracy, 0.8 for precision, 0.889 for recall, 0.846 for specificity, and 0.771 for f-score. Moreover, the training data generated an accuracy of 69.3% and the fastest training time of 4.2359s.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854795","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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