2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)最新文献_第4页

VLSI Design Course with Verification of RISC-V Design using Universal Verification Methodology (UVM) 使用通用验证方法(UVM)验证RISC-V设计的VLSI设计课程

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935582

S. Loh, You Hong Liew, J. Sim

引用次数: 1

Design and Development of Fuzzy Logic Based Car Cabin Pollution Monitoring System 基于模糊逻辑的汽车舱室污染监测系统的设计与开发

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935658

Muhammad Zaim Bin Kamal Abdul, Badrul Amin Nayan, S. Z. Yahaya, R. Boudville, K. A. Ahmad, Nurul Izza Husin

{"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":"23 1","pages":"0"},"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}

引用次数: 0

Towards Latency Aware Multi-joint Optimization Method for VNF Placement and SFC Routing Via Swarm Intelligence 基于群智能的VNF布局和SFC路由延迟感知多联合优化方法

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935663

Zahida Sharif, Muhammed Basheer Jasser, K. Yau, A. Amphawan

{"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":"55 1","pages":"0"},"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}

引用次数: 1

Local Honey (Tualang & Kelulut) Effect on Breast Cancer Cell (MCF-7) 当地蜂蜜对乳腺癌细胞(MCF-7)的影响

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935580

M. M. A. Jamil, Nur Adilah Abd Rahman, Mohd Nazib Adon, R. Ambar, S. Saidin

引用次数: 2

A New Regression Method for Diagnosis of Lung Cancer Disease 一种新的肺癌诊断回归方法

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935634

Nurul Najiha Jafery, S. N. Sulaiman, M. K. Osman, N. Karim, M. F. Abdullah, I. Isa

{"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":"1 1","pages":"0"},"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}

引用次数: 0

A Convolutional Neural Network Model for Image Enhancement of Extremely Dense Breast Tissue in Digital Breast Tomosynthesis Images 基于卷积神经网络的数字乳腺断层合成图像中极致密乳腺组织图像增强模型

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935647

S. N. Sulaiman, Muhammad Hanzalah Normazli, N. A. Harron, N. Karim, K. A. Ahmad, Z. H. C. Soh

{"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":"44 1","pages":"0"},"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}

引用次数: 0

Design and Enforcements of Wireless Based Matrix LED Message Moving Display by HD-W60 Microcontroller 基于HD-W60单片机的无线矩阵式LED信息移动显示的设计与实现

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935632

K. K. Hasan, M. Dham

引用次数: 0

Systematic Literature Review of Security Control Assessment Challenges 安全控制评估挑战的系统文献综述

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935661

N. Othman, A. Norman, M. L. M. Kiah

引用次数: 0

CNN Comparative Study for Apple Quality Classification CNN苹果质量分类的比较研究

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 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

{"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":"37 1","pages":"0"},"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}

引用次数: 1

Cervical Pre-cancerous Cell Image Classification System Using Histogram of Oriented Gradients and K-Nearest Neighbor Algorithms 基于梯度直方图和k近邻算法的宫颈癌前细胞图像分类系统

2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2022-10-21 DOI: 10.1109/ICCSCE54767.2022.9935660

Y. Jusman, Maryza Intan Rahmawati, S. N. Sulaiman

{"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":"1 1","pages":"0"},"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}

引用次数: 0