2023 15th International Conference on Computer and Automation Engineering (ICCAE)最新文献_第5页

Student Management Model Integrating E-Commerce Based on Blockchain Technology 基于区块链技术集成电子商务的学生管理模型

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111175

K. Vo, Thu Nguyen, Thu-Thuy Ta, Tu-Anh Nguyen-Hoang, N. Dinh

{"title":"Student Management Model Integrating E-Commerce Based on Blockchain Technology","authors":"K. Vo, Thu Nguyen, Thu-Thuy Ta, Tu-Anh Nguyen-Hoang, N. Dinh","doi":"10.1109/ICCAE56788.2023.10111175","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111175","url":null,"abstract":"Blockchain is one of the most prominent technologies today. With a combination of cryptography, distributed computing, consensus algorithms, and peer-to-peer networking, blockchain technology is a distributed network with strong security. This technology opens up a new opportunity to improve the performance of other sectors such as supply chain, finance, smart city, smart health, education, e-commerce, and more. For education, blockchain technology offers the same application potential as for other professions. E-commerce is no exception to the influence of this technology. Therefore, we design a student management model integrating e-commerce based on blockchain technology (SMM-EB). This model is maintained by tokens. At the same time, this model uses blockchain technology to store student information and distribute rewards in cryptocurrency. Students can use that currency in exchanges and transactions within the scope of a school or an education on the e-commerce platform. Our method makes the process of verifying student personal information and achievements quick and accurate. At the same time, this method also overcomes fraud in e-commerce. Thanks to blockchain technology, this model-based system ensures the transparency and integrity of student information and transactions on the e-commerce platform. Therefore, the system is more secure and efficient than the centralized system and does not depend on third-party management. The proposed architecture can be replicated with the benefits of current technology.","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":"130229738","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

Implementation of FLC-Based Adaptive Droop Control in a DC Microgrid with Generation Uncertainties 具有发电不确定性的直流微电网中基于flc的自适应下垂控制的实现

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111420

Harish Doll, Kyle Miguel M. Ignacio, M. Pacis

引用次数: 0

Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset 基于ct扫描图像数据集的Covid-19病毒深度学习检测与预测

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111295

A. Agrawal, Asadi Srinivasulu

{"title":"Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset","authors":"A. Agrawal, Asadi Srinivasulu","doi":"10.1109/ICCAE56788.2023.10111295","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111295","url":null,"abstract":"As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"37 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":"114766441","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

Path Planning and Mapping of an Autonomous Agricultural Robot Using Robot Operating System (ROS) and Gazebo 基于机器人操作系统和Gazebo的自主农业机器人路径规划与映射

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111297

B. Gilliam, Quinn Sahai, B. Chandrasekaran

引用次数: 0

Automated Grapevine Inflorescence Counting in a Vineyard Using Deep Learning and Multi-object Tracking 使用深度学习和多目标跟踪在葡萄园中自动计数葡萄花序

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111243

Umme Fawzia Rahim, T. Utsumi, Yohei Iwaki, H. Mineno

{"title":"Automated Grapevine Inflorescence Counting in a Vineyard Using Deep Learning and Multi-object Tracking","authors":"Umme Fawzia Rahim, T. Utsumi, Yohei Iwaki, H. Mineno","doi":"10.1109/ICCAE56788.2023.10111243","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111243","url":null,"abstract":"To adjust management practices and improve wine marketing strategies, accurate vineyard yield estimation early in the growing season is essential. Conventional methods for yield forecasting rely on phenotypic features’ manual assessment, which is time- and labor-intensive and often destructive. We combined a deep object segmentation method, mask region-based convolutional neural network (Mask R-CNN), with two potential multi-object tracking algorithms, simple online and real-time tracking (SORT) and intersection-over-union (IOU) trackers to develop a complete visual system that can automatically detect and track individual inflorescences, enabling the assessment of the number of inflorescences per vineyard row from vineyard video footage. The performance of the two tracking algorithms was evaluated using our vineyard dataset, which is more challenging than conventional tracking benchmark datasets owing to environmental factors. Our evaluation dataset consists of videos of four vineyard rows, including 221 vines that were automatically acquired under unprepared field conditions. We tracked individual inflorescences across video image frames with a 92.1% multi-object tracking accuracy (MOTA) and an 89.6% identity F1 score (IDF1). This allowed us to estimate inflorescence count per vineyard row with a 0.91 coefficient of determination (R2) between the estimated count and manual-annotated ground truth count. The impact of leaf occlusions on inflorescence visibility was lessened by processing multiple successive image frames with minimal displacements to construct multiple camera views. This study demonstrates the use of deep learning and multi-object tracking in creating a low-cost (requiring only an RGB camera), high-throughput phenotyping system for precision viticulture.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"27 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":"134164505","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

Two-Port UWB MIMO Antenna Based on the Neutralization Line Approach for Automotive Applications 基于中和线方法的汽车用双端口UWB MIMO天线

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111355

Praveen Kumar, T. Ali, M. M. M. Pai

引用次数: 0

Magnetic Hysteresis Simulation by Using a Deep Neural Network for Non-sinusoidal Excitations 基于深度神经网络的非正弦激励磁滞仿真

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111116

E. Cardelli, Antonino Laudani, Francesco Riganti-Fulginei

引用次数: 0

Automated Service Robot for Catering Businesses Using Arduino Mega 2560 使用Arduino Mega 2560的餐饮企业自动服务机器人

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111103

James D. Cruz, Gian Cris B. Domingo, Ramon G. Garcia

{"title":"Automated Service Robot for Catering Businesses Using Arduino Mega 2560","authors":"James D. Cruz, Gian Cris B. Domingo, Ramon G. Garcia","doi":"10.1109/ICCAE56788.2023.10111103","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111103","url":null,"abstract":"As technology becomes more advanced, the use of robots will further increase in the near future. It is expected that most of these robots are industrial robots and are most likely to be utilized in the upcoming years. However, the use of other types of robots, such as service robots, is also expected to increase gradually in the future. A waiter robot is a type of service robot that works in a catering business by performing the roles of a waiter, such as serving meals or accepting orders depending on its function. Currently, waiter robots are facing limitations. The main factor is how it navigates and interacts within a human environment. This paper aims to create a service robot that serves orders in a catering business. Arduino Mega 2560 was used as a microcontroller, and the line-following algorithm was used to guide the robot to the location of the dining tables. Two ultrasonic sensors were used to detect obstacles from a distance. Results show that the robot moves at a speed of 0.06 m/s in controlled testing and maintains that constant speed while carrying a maximum of 15kg load. Furthermore, it is shown that the robot is capable of serving multiple orders at a single dining table. The robot was able to serve 56 customers in a restaurant.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"17 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":"122281479","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

Detection of Common Types of Eczema Using Gray Level Co-occurrence Matrix and Support Vector Machine 基于灰度共生矩阵和支持向量机的常见湿疹类型检测

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111261

Sophia Gabrielle S. Jardeleza, Jonirille C. Jose, J. Villaverde, M. A. Latina

{"title":"Detection of Common Types of Eczema Using Gray Level Co-occurrence Matrix and Support Vector Machine","authors":"Sophia Gabrielle S. Jardeleza, Jonirille C. Jose, J. Villaverde, M. A. Latina","doi":"10.1109/ICCAE56788.2023.10111261","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111261","url":null,"abstract":"Many people are being affected by eczema around the world. In the Philippines, the most common types of eczema are atopic dermatitis, contact dermatitis, and nummular dermatitis. This study covered these three types and detected them by applying image processing techniques, Gray Level Co-occurrence Matrix, and the classifier Support Vector Machine. The designed prototype is to capture a section of the skin where eczema can be present and send the image to the software for skin region detection, eczema region detection, and feature extractions. In skin region detection, the YCbCr color model identifies the skin's color to discard the non-skin pixels and detect the skin pixels, allowing isolation of those pixels. The eczema region detection uses the CIELAB color model and K-means clustering to extract eczema on the image. The feature extractions have color features composed of RGB, HSV, and YCbCr color models and texture features consisting of contrast, homogeneity, energy, and correlation of GLCM. Then the software will classify the acquired image as healthy, atopic, contact, or nummular using SVM. Next to the testing process, the results are obtained and plotted in a confusion matrix. After analyzing the results, the computed overall accuracy of the system was 83.33%.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"4 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":"117037317","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

Monitoring of Breathing Effort and Oxygen Levels for Identification of Sleep Apnea 监测呼吸力度和氧气水平以识别睡眠呼吸暂停

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111359

Amiel Nico G. Loceo, Robert Brandan Lim, R. Pellegrino

{"title":"Monitoring of Breathing Effort and Oxygen Levels for Identification of Sleep Apnea","authors":"Amiel Nico G. Loceo, Robert Brandan Lim, R. Pellegrino","doi":"10.1109/ICCAE56788.2023.10111359","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111359","url":null,"abstract":"Sleep restores human body’s energy and well-being. Sleep apnea is a very concerning disorder which occurs when a person's breathing is interrupted during sleep. Or, if a person could not breathe properly during their sleeping period. This heavily affects the amount of oxygen being received by the brain and body during sleep. Sleep problems are diagnosed in the laboratory with the monitoring wires contribute to the uncomfortable, if not deprivation of, sleep. There is a need for a portable sleep apnea diagnosing device since there are limited sleep laboratory in hospitals for diagnosis and treatment of sleep apnea. This study delves into developing a portable device to monitor sleep using only two sensors namely flex sensor and the nasal sensor and compared to Polysomnogram (PSG) multiple wired equipment used in sleep laboratory. The portable wireless device consists of the Raspberry Pi 3B+, Flex Sensor, Thermistor and Nasal Pressure Transducer to monitor respiratory effort and nasal airflow respectively. The experiment yields to no significant difference between the data obtained from the standard clinical device and the developed device. It also shows a strong Pearson’s correlation R of 0.89.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"30 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":"117089340","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