2023 15th International Conference on Computer and Automation Engineering (ICCAE)最新文献_第7页

Detection of Anthracnose on Mango Tree Leaf Using Convolutional Neural Network 基于卷积神经网络的芒果叶片炭疽病检测

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111489

A. Yumang, Christian Joseph N. Samilin, John Christian P. Sinlao

{"title":"Detection of Anthracnose on Mango Tree Leaf Using Convolutional Neural Network","authors":"A. Yumang, Christian Joseph N. Samilin, John Christian P. Sinlao","doi":"10.1109/ICCAE56788.2023.10111489","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111489","url":null,"abstract":"Mangoes have been one of the most important products that are being produced mostly within tropical regions here in the Philippines. Anthracnose is the most common and serious disease that can occur on mango crops in the country. It is a disease caused by a fungus called Colletotrichum gloeosporioides, which targets leaves, fruits, twigs, and flowering panicles of the crop. For this study, the researchers' aim is to detect anthracnose disease in mango leaves and classify them as healthy or unhealthy. The system will implement (You Only Look Once, Version 3) YOLOv3, which uses the features learned in Convolutional Neural Network to detect a specific object, live videos, and even lesions of plants. The training package comprises around 80.282% of the photographs, while the trial package contains approximately 19.718% of the images. This is done by randomly splitting the data into two sets. This ratio distribution is frequently used in neural network applications. The PDF format was used on the images with 600 dpi for a better resolution. After training the system it obtained 60.680% mean average precision (mAP), 7.79fps, and a lower total validation loss of 20.93. After training the system and using the confusion matrix an accuracy of 83.33% was obtained.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"48 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":"130936765","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

Leaf Classification of Costus Plant Species Using Convolutional Neural Network 基于卷积神经网络的木香植物叶片分类

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111389

R. Cruz, Simon Guevarra, J. Villaverde

引用次数: 0

Fault-Tolerant Broadcast Algorithm for Tree Networks 树形网络的容错广播算法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111474

M. Karaata, Aysha Dabees

引用次数: 0

Impact of AIServiceOps on Organizational Resilience 服务运营对组织弹性的影响

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111409

Harsha Vijayakumar, A. Seetharaman, K. Maddulety

{"title":"Impact of AIServiceOps on Organizational Resilience","authors":"Harsha Vijayakumar, A. Seetharaman, K. Maddulety","doi":"10.1109/ICCAE56788.2023.10111409","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111409","url":null,"abstract":"Artificial Intelligence (AI) has been significant technology of the 21st century. This technology is changing every aspect of modern enterprise technology tooling, from strategies to selecting and implementing to adopting digital AI transformation. The rapid development of Artificial Intelligence has prompted many changes in the field of Information Technology (IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on how Artificial intelligence (AI) Powered IT Service Operations impact Organization Resilience and can help IT build optimized resilience by creating value in complex and ever-changing environments as product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps shape an organization's resilience in terms of value creation and sustainability, basically how AIServiceOps makes the IT staff liberation from a low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation tool to increase organizational resilience.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"146 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":"128501464","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

Level-Set Method for Limited-Data Reconstruction in CT using Dictionary-Based Compressed Sensing 基于字典压缩感知的CT有限数据重建水平集方法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111292

Haytham A. Ali, H. Kudo

{"title":"Level-Set Method for Limited-Data Reconstruction in CT using Dictionary-Based Compressed Sensing","authors":"Haytham A. Ali, H. Kudo","doi":"10.1109/ICCAE56788.2023.10111292","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111292","url":null,"abstract":"Compressed sensing using a dictionary is known to be effective for reconstructing CT images from incomplete projection data (eg. limited-angle CT and sparse-view CT) and its practical applications are increasing. However, when the measurement conditions are insufficient, its performance in image quality is still insufficient and the computational time is long. In this paper, to overcome these limitations, we propose a new method that can dramatically improve the performance by using a priori knowledge about the gray levels of the image to be reconstructed. But, the main problem with using prior information is that a standard formulation leads to a non-convex optimization problem that is difficult to solve. In this study, we succeeded in overcoming this problem based on deep theoretical consideration. Specifically, we formulate a convex optimization problem that can be stably and successfully solved based on an image model that expresses the boundary of the images as a level-set function consisting of linear combinations of the dictionary elements. We create the dictionary that determines the performance by preparing a small number of basic shapes followed by applying the geometric transformations to each shape to construct the dictionary elements. The simulation results for synthetic images and real data shown that the proposed method compared favorably to Total Variation, DART and Dual problem.","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":"125914014","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

Attention Based Evolutionary Approach for Image Classification 基于注意力的图像分类进化方法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111236

Ajay Prem, Anirudh Joshi, Haritha Madana, Jaywanth J, Arti Arya

{"title":"Attention Based Evolutionary Approach for Image Classification","authors":"Ajay Prem, Anirudh Joshi, Haritha Madana, Jaywanth J, Arti Arya","doi":"10.1109/ICCAE56788.2023.10111236","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111236","url":null,"abstract":"Lately, evolutionary algorithms have gained traction due to their ability to produce state-of-the-art deep learning architectures for a given data set, even though they require considerable amount of compute resources, they are a heavily researched domain because of the complexities involved in designing deep learning architectures. Currently, none of the evolutionary approaches available have incorporated the attention mechanism, which is a proven technique to improve the performance of image classification and language models. This paper posits a neuroevolutionary technique coupled with the use of Convolution Block Attention Module for image classification. As technology progresses, it’s inevitable that there will be massive advancements leading to cheaper and more available computing making evolutionary approaches a promising avenue to develop task specific deep learning models. The proposed approach evolves a topology that achieves a high fitness of 87.44%, using fewer parameters as compared to previous approaches. This results in a superior fitness score compared to most past approaches, despite being evolved for just few generations.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"27 2 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":"125682719","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

Augmented Reality Campus Exploration Application Incorporating Equity, Diversity, and Inclusion 融合公平、多元和包容的增强现实校园探索应用

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111189

Pranshi Jindal, Andrew J. Park, E. Hwang

{"title":"Augmented Reality Campus Exploration Application Incorporating Equity, Diversity, and Inclusion","authors":"Pranshi Jindal, Andrew J. Park, E. Hwang","doi":"10.1109/ICCAE56788.2023.10111189","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111189","url":null,"abstract":"Augmented Reality (AR) is one of the most promising technologies with many practical applications, enhancing/augmenting the real world with virtual information/objects. With advances in AR technology and affordable AR hardware, AR has widely been used in training, classroom education, entertainment, tourism, public safety, retail, and many others. This paper presents an AR application that provides real-time information for campus exploration incorporating Equity, Diversity, and Inclusion (EDI). Many mobile AR applications have been developed for campus tours to help new students and visitors identify campus buildings and their services. However, from the User Experience (UX) design perspective, simply providing general information about each building may not be sufficient for students who need specific information based on their backgrounds and particular needs. The mobile AR application, TWUExplorAR, presented in this paper, incorporates the concept of EDI by overlaying the landmark information tailored to each user’s background and needs, thus improving UX. The paper outlines the background and related work, describes the development of the AR application, illustrates the incorporation of EDI in the application, summarizes the usability tests, and suggests future improvements.","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":"128950818","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

The Effect of Using Augmented Image in the Identification of Human Nail Abnormality using Yolo3 增强图像在Yolo3人体指甲异常识别中的应用

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111315

R. Pellegrino, Jethro Hoyt T. Lacuesta, Carl Ferione L. Dela Cuesta

{"title":"The Effect of Using Augmented Image in the Identification of Human Nail Abnormality using Yolo3","authors":"R. Pellegrino, Jethro Hoyt T. Lacuesta, Carl Ferione L. Dela Cuesta","doi":"10.1109/ICCAE56788.2023.10111315","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111315","url":null,"abstract":"Human-nail abnormality manifests the status of nail’s health and human health in general. Terry’s nail is common to people with severe liver disease. Spoon nail can be found in people with diabetes and heart diseases. High cholesterol causes the Splinter Hemorrhage abnormality in nail. Although studies on Human-nail have been developed, there is still a lack of datasets to further the study on nail to serve as an additional tool for diagnostic purposes on specific abnormalities: Splinter Hemorrhages, Terry's nail, and Spoon nail. This study aims to determine the effect of using augmented images in training and testing nail image dataset to identify nail abnormality. The study compares three models: an unaugmented model, an on-the-fly model, and a manually augmented model using the open-source python image augmentation library imgaug, to identify Splinter Hemorrhage, Terry's nail, and Spoon nail abnormalities with its associated diseases using Yolov3 on a Raspberry Pi 4 model B with libraries like OpenCV, Keras, and TensorFlow. The manually augmented model achieved the highest accuracy of 91% which is 5.58% higher than the on-the-fly model and 13.92% higher accuracy than the unaugmented model..","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"79 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":"129515630","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

Development of Anemia Cells Recognition System Using Raspberry Pi 利用树莓派开发贫血细胞识别系统

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111486

R. Pellegrino, Aubrey C. Tarrobago, Dave Lester B. Zulueta

引用次数: 1

Performance Comparison of Machine Learning Algorithms for Identification of Physiological Maturity of Pineapple using Optical Property 利用光学特性识别菠萝生理成熟度的机器学习算法性能比较

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111216

R. Lapcharoensuk, Noppadon Phannote, Dimae Kasetyangyunsapa

{"title":"Performance Comparison of Machine Learning Algorithms for Identification of Physiological Maturity of Pineapple using Optical Property","authors":"R. Lapcharoensuk, Noppadon Phannote, Dimae Kasetyangyunsapa","doi":"10.1109/ICCAE56788.2023.10111216","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111216","url":null,"abstract":"Pineapple is important fruit of Thailand which is consumed in its fresh state or in processed products. Typically, harvested dates affected to quality of pineapple fresh. Identification of pineapple harvested dates at raw material receiving state in factory is very difficult. This research aims to determination of appropriate machine learning algorithm for Identifying maturity of pineapple using optical property. Color of pineapple fruits and fresh was measured by portable colorimeter on CIE system (L*, a* and b* values). The ten algorithms were fit to the training set including naive Bayes (NB), linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB) and adaptive boosting (AB). The best model for pineapple fruit were established from ANN while DT showed highest performance for pineapple fresh. The accuracy of ANN and DT for fruit and fresh models were 83 and 92% respectively. This finding point is novel technique for identification of pineapple according to harvested dates which it can apply to quality control and assurance in pineapple industries.","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":"130015396","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