2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)最新文献_第2页

Optimization of Saleman Travelling Problem Using Genetic Algorithm with Combination of Order and Random Crossover 基于有序与随机交叉相结合的遗传算法优化销售员出行问题

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237137

Muhammad Firdaus Shafie, F. Ahmad, Muhammad Khusairi Osman, Ahmad Puad Ismail, K. A. Ahmad, S. Z. Yahaya, M. Idris, Anwar Hassan Ibrahim

引用次数: 0

Optimal Finite- Time Prescribed Performance of Servo Pneumatic Positioning with PID Control Tuning using an Evolutionary Mating Algorithm 基于进化匹配算法的PID整定伺服气动定位有限时间最优性能研究

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237170

A. Irawan, Mohd Herwan Sulaiman, M. Azahar

引用次数: 0

Plant Disease Detection and Segmentation using End-to-End YOLOv8: A Comprehensive Approach 基于端到端YOLOv8的植物病害检测与分割:一种综合方法

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237169

Syed Asif Ahmad Qadri, N. Huang, T. Wani, Showkat Ahmad Bhat

{"title":"Plant Disease Detection and Segmentation using End-to-End YOLOv8: A Comprehensive Approach","authors":"Syed Asif Ahmad Qadri, N. Huang, T. Wani, Showkat Ahmad Bhat","doi":"10.1109/ICCSCE58721.2023.10237169","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237169","url":null,"abstract":"Preventing and managing plant leaf diseases requires a dependable and precise detection method. Detecting leaf diseases in plants is a time-consuming process that has a negative impact on productivity and crop quality. By leveraging the PlantVillage and PlantDoc datasets to train the Ultralytics YOLOv8 model from end to end, this research intends to present a deep learning solution to the detection and segmentation of plant leaf disease. The YOLOv8 model, an advancement of the YOLO series, has been designed to increase detection speed without sacrificing accuracy. Its intricate architecture, composed of multiple convolutional layers, enables complex feature extraction from images, leading to precise identification of plant leaf diseases. As the model is trained end-to-end, it can effectively learn and generalize from the input data, thereby enhancing its predictive performance for unseen or novel instances of leaf diseases. The evaluation results for the YOLOv8 approach are validated by prominent statistical metrics like precision, recall, mAP50 and mAP50-95 value, and F1-score, which resulted in 99.8{%}, 99.3%, 99.5%, 96.5% and 0.999 for the bounding box and 99.1%, 99.3%, 99.3%, 98.5% and 0.992 for the segmentation mask respectively. The results demonstrate the model’s strong performance in accurately detecting and segmenting diseased regions, as indicated by high precision, recall, and mAP values. These findings highlight the effectiveness of the YOLOv8 model in plant disease detection, showcasing its potential for precision agriculture and crop management applications.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128097291","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

Emergency Rescue Alert and Notification System for Madrasah (ERANO-MAD) 伊斯兰学校紧急救援警报和通知系统(ERANO-MAD)

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237174

Nur Liyana Erny Binti Ahmad Khairan, M. M. A. Jamil, Wan Suhaimizan Wan Zaki, R. Ambar, Mohd Helmy Abd Wahab

{"title":"Emergency Rescue Alert and Notification System for Madrasah (ERANO-MAD)","authors":"Nur Liyana Erny Binti Ahmad Khairan, M. M. A. Jamil, Wan Suhaimizan Wan Zaki, R. Ambar, Mohd Helmy Abd Wahab","doi":"10.1109/ICCSCE58721.2023.10237174","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237174","url":null,"abstract":"Various incidents have occurred to Tahfiz students in madrasahs located in rural areas due to the difficulty of getting immediately rescue. Of late, this issue has increased progressively, particularly in the events of fire, bullying or sexual harassment or health problems. In some incidents, victims failed to be rescued by the emergency response team, such as ambulance, police, fire brigade or civil defense, it is challenging to get accurate information on the exact location, circumstances of the accident and other important related details. Despite the provision of various technologies, in a few cases, the students in Madrasahs could not utilize technology facilities such as mobile phones to comply with the regulations established by the Ministry of Education. Therefore, this study provides the elucidation to overcome that limitation. Thus, to implement this project, GSM (Global System for Mobile Communications) alert notifications, and GPS (Global Positioning System) modules are connected to a central system. Triple emergency push buttons were installed. Each button corresponds to different types of emergencies. The push button actives the GSM central system and GPS when triggered by the user. In turn, immediate alert messages were sent to the response teams, namely the school head, police station and fire department. The information on the exact live location and types of incidents were also disseminated.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131841774","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

FSCS with Error Sharing based on Alpha Weights for Improving Accuracy of Reconstructed Images 基于Alpha权值误差共享的FSCS提高重建图像精度

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237146

Eri Suzuki, Takuto Yamauchi, Kenji Tei

{"title":"FSCS with Error Sharing based on Alpha Weights for Improving Accuracy of Reconstructed Images","authors":"Eri Suzuki, Takuto Yamauchi, Kenji Tei","doi":"10.1109/ICCSCE58721.2023.10237146","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237146","url":null,"abstract":"Automatic layer decomposition, primarily in the field of image editing, has garnered substantial interest. The prevalent technique is soft color segmentation. Fast Soft Color Segmentation (FSCS), a novel neural network-based method, has been proposed to accelerate the processing time by learning the iterative optimization process responsible for the slow processing time of traditional methods. However, the reconstructed image–obtained by reconstructing the decomposed layers–does not match the original image in terms of saturation and coloring. Therefore, we introduced post-processing involving error sharing based on alpha weights to FSCS (FSCS-ESAW) to improve the agreement between reconstructed and original images. We define the “alpha weight” as the ratio of each alpha layer value corresponding to each color layer to the total value of each alpha layer. FSCS-ESAW shares the reconstruction error–the error that occurs between the reconstructed image and the original image–with each color layer based on alpha weights, thereby improving the accuracy of each decomposed layer. FSCS-ESAW is characterized by its complete independence from FSCS itself and enables getting more accurate images by adding a simple and lowcost post-processing step to FSCS. Experimental results validated the efficacy of FSCS-ESAW, demonstrating superior agreement between the original and reconstructed images compared to FSCS.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673520","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

Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study 使用机器学习方法的统计特征提取的疼痛分类:一项初步研究

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237153

Sudimanto, A. Trisetyarso, I. H. Kartowisastro, W. Budiharto

{"title":"Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study","authors":"Sudimanto, A. Trisetyarso, I. H. Kartowisastro, W. Budiharto","doi":"10.1109/ICCSCE58721.2023.10237153","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237153","url":null,"abstract":"Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people’s psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from shimmersensing.com. The shimmersensing.com data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from shimmersensing.com will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"82 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131590933","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

Comparison of Prostate Cell Image Classification Using CNN: ResNet-101 and VGG-19 使用CNN: ResNet-101和VGG-19进行前列腺细胞图像分类的比较

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237088

Y. Jusman

{"title":"Comparison of Prostate Cell Image Classification Using CNN: ResNet-101 and VGG-19","authors":"Y. Jusman","doi":"10.1109/ICCSCE58721.2023.10237088","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237088","url":null,"abstract":"Being the most common disease in men, prostate cancer attacks the urinary system. Men with higher androgen levels have a greater risk of developing prostate cancer. This cancer occurs in the prostate gland of the male reproductive tract. This cancer appears when it begins to mutate and reproduce uncontrollably. Risk factors for prostate cancer include age, race and family history. This study classified prostate cell images based on their severity. Along with today’s technological advancement, especially research on image classification, it will be simpler for medical personnel to educate the public on how to recognize the severity of prostate cancer through a system. This image classification system utilized the preserved models of the deep learning method from the Convolutional Neural Network (CNN) algorithms: ResNet-101 and VGG-19. It aims to discover the most appropriate algorithm in image classification, determined from the training graph and confusion matrix calculation results. The measurements of the models’ reliability encompassed accuracy, computation time, graph stability, and confusion matrix calculation results. ResNet-101 appeared as the model with the highest training data accuracy and fastest computation time. The confusion matrix calculation also unveiled that ResNet-101 acquired the greatest results with an average accuracy of 97.70%, precision of 93.19%, recall of 93.25%, specificity of 98.62% and F-score of 93.11%, demonstrating its superiority over VGG-19 in classifying prostate cell images based on testing data.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"6 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120835875","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

User’s Acceptance and Intention of Blockchain Technology with UTAUT 3 Model: Beneficial or Detrimental in the Indonesian Banking Industry 用户对UTAUT 3模型区块链技术的接受程度和意图:对印尼银行业有利还是有害

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237147

Sintya Lasma Putri Br Panjaitan, Stevi Chartinie Valiyenty, Ignatius Edward Riantono

{"title":"User’s Acceptance and Intention of Blockchain Technology with UTAUT 3 Model: Beneficial or Detrimental in the Indonesian Banking Industry","authors":"Sintya Lasma Putri Br Panjaitan, Stevi Chartinie Valiyenty, Ignatius Edward Riantono","doi":"10.1109/ICCSCE58721.2023.10237147","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237147","url":null,"abstract":"The rapid development of technology has resulted in new technological innovations that continue to emerge and affect all aspects of life. Blockchain is a new technology that has attracted attention as it is considered an innovation that claims to be able to change the future of the banking side. Currently, banks have big data on the transactions that have taken place so far. Therefore, bankers also require technology to balance their job’s complexity. Here, blockchain technology is put forth as a way for bankers to stay current with changes during the industrial revolution, especially in the era of Banking 4.0. Therefore, in this study, we aim to predict the level of acceptance and intention of bankers in the Indonesian banking industry (Top ten banks in Indonesia based on Indonesia Stock Exchange (IDX) survey 2022) towards the use of blockchain technology using 8 factors in UTAUT 3 as our grand theory. This study employs quantitative methods by utilizing primary data by distributing questionnaires to accountants in the banking sector and testing hypotheses using SMARTPLS software. Based on the results of data processing, performance expectancy, social influence, habit, and personal innovativeness have a significant influence on behavioral intention. Meanwhile, effort expectancy, facilitating condition, hedonic motivation, and price value do not have a significant influence on behavioral intention. Bankers must be aware of the four variables that directly influence behavioral intention in using blockchain, including the need for increased knowledge and habits regarding the use of blockchain technology so that the adoption process will be better, in line with the benefits obtained from using blockchain technology.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132584703","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

Water Level Control in Boiler System Using Self-tuning Fuzzy PID Controller 锅炉系统水位自整定模糊PID控制

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237138

Z. Muhammad, Syahmin Hamka Ahmad Dziauddin, Shabinar Abdul Hamid, N. A. M. Leh

{"title":"Water Level Control in Boiler System Using Self-tuning Fuzzy PID Controller","authors":"Z. Muhammad, Syahmin Hamka Ahmad Dziauddin, Shabinar Abdul Hamid, N. A. M. Leh","doi":"10.1109/ICCSCE58721.2023.10237138","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237138","url":null,"abstract":"In boiler control system, water level is regarded as an important process control parameter to ensure that the boiler could function efficiently. PID controller is the most used controller in controlling the water level. However, conventional PID is unable to control efficiently for the process that have high nonlinearity and high sensitivity. In this project, fuzzy logic controller plus PID control was introduced to the water level control in boiler system. The controller named as self-tuning fuzzy PID (STFPID) controller was proposed in this project to overcome the problem occurred. The controller is implemented in MATLAB and simulated in SIMULINK to analyze the performance analysis between conventional PID controller, FLC and STFPID controller. These controllers will be comparing their performance in terms of system response that includes percent overshoot (% OS), settling time (Ts) and rise time (Tr). Based on the analysis, the STFPID controller produces better performance than the PID and fuzzy logic controller in term of rise time, settling time and overshoot.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725501","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

Luminance Based Detection of Adulterated Honey using Machine Learning 基于亮度的掺假蜂蜜的机器学习检测

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237131

Alia Qamelia Mohd Zuber, Nur Hazliza Ariffin, Sabiran Abubakar

{"title":"Luminance Based Detection of Adulterated Honey using Machine Learning","authors":"Alia Qamelia Mohd Zuber, Nur Hazliza Ariffin, Sabiran Abubakar","doi":"10.1109/ICCSCE58721.2023.10237131","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237131","url":null,"abstract":"Honey is frequently associated with adulteration. Conventional assessment of honey quality relies highly on resource-intensive laboratory-based examination of chemicals. This is particularly true in remote areas, where limited resources and logistical challenges make sample transportation to an analytical laboratory difficult. Additionally, certain instruments required for analysis are expensive, require experts to operate, and are often inaccessible to small-scale holders. Hence, this work intends to explore a simple and low-cost method to assess honey purity. The approach uses transmittance spectrum of Light Emitting Diode (LED) to analyze honey. Sugar percentage in honey influences its purity, which is evident from the difference spectral irradiance. CL-500A illuminance spectrophotometer captures the spectrum. The decision tree classifier trains the spectra dataset to determine the purity of honey or its adulteration percentage. The results show that the luminance produced by the blue LED showcases a distinct difference in the transmittance spectrum. The decision tree model successfully classifies adulteration.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133261220","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