2023 15th International Conference on Computer and Automation Engineering (ICCAE)最新文献_第10页

FireBot - An Autonomous Surveillance Robot for Fire Prevention, Early Detection and Extinguishing FireBot -一种用于火灾预防、早期发现和灭火的自主监视机器人

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111251

J. Balen, Davor Damjanović, P. Maric, Krešimir Vdovjak, Matej Arlovic, Goran Martinović

{"title":"FireBot - An Autonomous Surveillance Robot for Fire Prevention, Early Detection and Extinguishing","authors":"J. Balen, Davor Damjanović, P. Maric, Krešimir Vdovjak, Matej Arlovic, Goran Martinović","doi":"10.1109/ICCAE56788.2023.10111251","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111251","url":null,"abstract":"Every year, fire is responsible for numerous deaths, as well as huge material losses. Therefore, prevention and early detection of fire have become a priority for society, as well as the main research and development issue for many scientists and various industries. This paper describes our work in the development of FireBot, an autonomous surveillance robot. The Firebot is equipped with modern technologies and state-of-the-art navigational and computer vision methods that enable autonomous navigation, obstacle avoidance, video surveillance, fire prevention and detection, and fire extinguishing. It utilizes both infrared thermal (IRT) and RGB cameras paired with a modern convolutional neural network (CNN) for fault and fire detection, as well as various other sensors for analyzing air composition, processing of surrounding sounds, and detecting irregularities in its environment in general. The best performing CNN was implemented and tested in real-world environments for fire detection purposes, the results of which are presented in this paper. A state-of-the-art SLAM algorithm paired with LiDAR and a depth camera is used for mapping and navigation. The architecture presented in this paper, along with all functionalities planned for future work, represents an innovative autonomous surveillance system that will make a great contribution in the field of fire prevention and detection.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126262428","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: 5

Determination of Sugar Apple Ripeness via Image Processing Using Convolutional Neural Network 基于卷积神经网络图像处理的糖苹果成熟度测定

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111204

Rhys B. Sanchez, Jose Angelo C. Esteves, N. Linsangan

{"title":"Determination of Sugar Apple Ripeness via Image Processing Using Convolutional Neural Network","authors":"Rhys B. Sanchez, Jose Angelo C. Esteves, N. Linsangan","doi":"10.1109/ICCAE56788.2023.10111204","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111204","url":null,"abstract":"One type of fruit that is seasonally available in the Philippines is the sugar apple which is known as \"Atis.\" Specifically, no technological advancements regarding sugar apples ripeness classification are created. Sugar apples are manually separated based on their ripeness when harvested. This research focuses on using image processing through CNN to determine the ripeness of sugar apples, which will benefit the sugar apple fruit farmers and harvesters. The researchers created a machine prototype which is able to capture the image of a sugar apple and determine the ripeness classification in the image detected. The researchers found that the use of image processing in determining the ripeness of the sugar apple is adequate and accurate based on the datasets that the machine is trained to recognize. Looking at the gathered results of the images when compared to the manual inspection of the harvesters in each image taken, the researchers were able to achieve an accuracy of 86.84% in determining the ripeness level of sugar apples using convolutional neural network.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129199987","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 Lightweight Hybrid Framework for Real-Time Detection of Process Related Anomalies in Industrial Time Series Data Generated by Online Industrial IoT Sensors 用于实时检测在线工业物联网传感器生成的工业时间序列数据中过程相关异常的轻量级混合框架

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111201

Atish Bagchi, S. Chandrasekaran

{"title":"A Lightweight Hybrid Framework for Real-Time Detection of Process Related Anomalies in Industrial Time Series Data Generated by Online Industrial IoT Sensors","authors":"Atish Bagchi, S. Chandrasekaran","doi":"10.1109/ICCAE56788.2023.10111201","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111201","url":null,"abstract":"Industrial Manufacturing plays an important role in the global economy, and estimates suggest that approximately 27 hours per month are lost in any major facility due to unplanned stoppages. The advent of Industrial IoT has seen manufacturing facilities deploy low-cost sensors with the hope of gaining improved visibility and thereby reducing unplanned machine stoppages and wastages. This paper introduces a lightweight, fast, easy-to-deploy framework that can be used for reliable and accurate identification of anomalies in real-time operational systems. The framework is holistic and includes data acquisition and data persistence modules to ensure that it can be deployed to a working production facility. The anomaly detection and contextualisation module is hybrid and uses statistical techniques and machine learning methods to provide fast responses while requiring minimal intervention. The framework was tested at a large metals manufacturing facility in New South Wales, Australia and the results show an accuracy of over 97% in anomaly detection.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128905024","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

IoT-based Monitoring of Temperature and Humidity with Fuzzy Control in Cherry Tomato Greenhouses 基于物联网的樱桃番茄大棚温湿度模糊监测

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111404

A. Yumang, Lemuel Aldwin P. Garcia, Gerome A. Mandapat

{"title":"IoT-based Monitoring of Temperature and Humidity with Fuzzy Control in Cherry Tomato Greenhouses","authors":"A. Yumang, Lemuel Aldwin P. Garcia, Gerome A. Mandapat","doi":"10.1109/ICCAE56788.2023.10111404","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111404","url":null,"abstract":"Temperature and Humidity are two environmental factors that heavily impact the growth of a plant. The unpredictability of external environmental conditions in traditional farming present difficulties in obtaining stable fruit yields. This can be avoided in enclosed environments through the implementation of an automated temperature and humidity controller. The study performed in this paper shows the effect of maintaining temperature and humidity levels within a greenhouse environment on cherry tomato fruit yield. Changes to internal temperature and humidity levels were done through sensors and controlling devices responsible for influencing internal readings to remain within a certain threshold. The implementation of fuzzy logic in the decision making automated the actions required to maintain optimal conditions within the greenhouse. To determine whether the controlled greenhouse temperature and humidity levels introduced a positive effect on cherry tomato yield, harvests from an outside plot and the greenhouse plot were recorded and compared using the two-sample t-test statistical treatment. Under the null hypothesis, the assumption that no increase in fruit yield was made, whereas the assumption that an increase in fruit yield was made under the alternate hypothesis. With a calculated value of t = 1.7271 and a critical value of 1.701 which was found using a degree of freedom of 28 and a confidence interval of 95%, the alternate hypothesis was been accepted. Thus, showing that the fuzzy logic controller has been effective in making the appropriate decisions to maintain optimal temperature and humidity levels for fruit production.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128953706","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: 3

Automated Detection of Biases within the Healthcare System Using Clustering and Logistic Regression 使用聚类和逻辑回归的医疗系统中偏差的自动检测

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111425

Jyoti Prakhar, M. T. U. Haider

引用次数: 0

Harvesting Renewable Energy from Foot Pedal Using Rotational Electromagnetic Induction Flywheel 利用旋转电磁感应飞轮从脚踏收集可再生能源

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111281

Patricia Angelica R. Montalban, Nyxzel S. Pizarra, G. Magwili

引用次数: 0

A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19 一种CNN迁移学习-心电图信号预测新冠肺炎的方法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111114

John Irungu, T. Oladunni, Max Denis, Esther Ososanya, Ruth Muriithi

{"title":"A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19","authors":"John Irungu, T. Oladunni, Max Denis, Esther Ososanya, Ruth Muriithi","doi":"10.1109/ICCAE56788.2023.10111114","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111114","url":null,"abstract":"Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114640241","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: 2

Age Range Classification Through Facial Recognition Using Keras Model 基于Keras模型的面部识别年龄范围分类

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111149

April D. Logronio, Rona Christine Reyes, N. Linsangan

引用次数: 0

Predict Market Fluctuations Based on the TSI and the Sentiment of Financial Video News Sites via Machine Learning 基于TSI和金融视频新闻网站情绪的机器学习预测市场波动

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111493

Faten Alzazah, Xiaochun Cheng, Xiaohong Gao

{"title":"Predict Market Fluctuations Based on the TSI and the Sentiment of Financial Video News Sites via Machine Learning","authors":"Faten Alzazah, Xiaochun Cheng, Xiaohong Gao","doi":"10.1109/ICCAE56788.2023.10111493","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111493","url":null,"abstract":"Scientists have long been interested in forecasting stock market fluctuations. Traditional data like financial textual news, stock prices, and comments are simply no longer sufficient because they don't provide a comprehensive picture. In this study, the efficacy of using financial video news stories versus the use of conventional text news stories to forecast the stock market is examined. We used the Granger causality test to evaluate the robustness of the causal connection between share prices, text news sentiment, video news sentiments, and the Twitter sentiment index.Several models for sentiment analysis of S&P 500 stock were assessed using LR, SVM, LSTM, ATT-LSTM, and CNN models. This study is distinctive because it compares the use of financial video news stories, conventional text news stories, and the Twitter Sentiment Index to forecast stock market movements. The experimental results suggest that there is a stronger causal connection between video news sentiment and stock market fluctuation compared to conventional text news sentiments. The result shows that we can more accurately predict market changes using video news than we can with traditional news.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133956902","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

Classifying DDoS Attack in Industrial Internet of Services Using Machine Learning 基于机器学习的工业服务互联网DDoS攻击分类

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111178

Ghazia Qaiser, S. Chandrasekaran, R. Chai, Jinchuan Zheng

{"title":"Classifying DDoS Attack in Industrial Internet of Services Using Machine Learning","authors":"Ghazia Qaiser, S. Chandrasekaran, R. Chai, Jinchuan Zheng","doi":"10.1109/ICCAE56788.2023.10111178","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111178","url":null,"abstract":"There were various research articles proposed different IIoT and Industrial Internet of Services (IIoS) techniques for Industry 4.0 innovative practices. At the same time, the concept of IIoS is considered a critical enabler for smart industries. Therefore, IIoT has evolved over time into an IIoS, which introduces servitization processes to measure product or service quality. The idea behind IIoS is to strategically use the Internet as a platform to assemble new value for the services sector in different industries. The IIoS is a vital aspect to consider for improving the final production line. However, at the same time, the internet’s inherent vulnerability puts it at risk of cyber security attacks, particularly DDoS attacks.This research is focused on investigating DDoS vulnerabilities that can negatively impact IIoS. The study evaluates six machine learning algorithms in terms of their ability to detect DDoS attacks. The mentioned ML algorithms are renowned for data traffic classification within the existing literature. Moreover, this research can assist users in diagnosing potential DDoS threats and optimizing production lines.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"79 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506126","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