Proceedings of the 2020 12th International Conference on Computer and Automation Engineering最新文献_第3页

Combination of Acoustic and Vibration Sensor Data Using Support Vector Machines and One-Versus-All Technique Data Fusion for Detecting Objects 基于支持向量机和单对全技术的声学和振动传感器数据融合检测目标

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384626

A. Yumang, G. Cruz, Llanz Adeo Fontanilla

{"title":"Combination of Acoustic and Vibration Sensor Data Using Support Vector Machines and One-Versus-All Technique Data Fusion for Detecting Objects","authors":"A. Yumang, G. Cruz, Llanz Adeo Fontanilla","doi":"10.1145/3384613.3384626","DOIUrl":"https://doi.org/10.1145/3384613.3384626","url":null,"abstract":"This paper aims to create a device that will be able to detect the presence of an object and classify the object into human, animal, or vehicle by using the information obtained from acoustic and seismic signals. The specific objectives are to develop a hardware device based from Raspberry Pi Minicomputer with seismic and acoustic sensors and transmit sensor signals to a computer for feature extraction and data fusion, to develop a software using Python, MATLAB, and use Data Fusion with the use of Support Vector Machine with One-Versus-All technique, to accurately classify the object into human, animal (canine), or vehicle, to use statistical treatment using multi-class confusion matrix to determine the F-score or accuracy of the classifiers, as an aid for answering the formulated hypotheses. In the testing phase, blind test was performed for the classifiers, using different gathered samples. The F-score of the human, animal, and vehicle classifiers were, respectively, 93.549%, 98.305%, and 100%. The researchers recommend a ground-mounted seismic sensor for comparison of its F-score contribution with the used seismic sensor. Training the SVM models with different parameters could also lead to potential increase in accuracy, such as the number of k-fold cross validations. SVM can as well be compared to other classifier models.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117226963","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

Algae biomass and radius prediction based on ARMA-BP neural network combination model 基于ARMA-BP神经网络组合模型的藻类生物量和半径预测

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384641

Shigang Cui, Y. Liu, Yongli Zhang, Lin He, Xingli Wu

引用次数: 1

Hybrid Artificial Neural Network and Genetic Algorithm Model for Multi-Objective Strength Optimization of Concrete with Surkhi and Buntal Fiber Surkhi和Buntal纤维混凝土多目标强度优化的混合人工神经网络和遗传算法模型

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384617

D. Silva, K. L. D. Jesus, Bernard S. Villaverde, E. Adina

{"title":"Hybrid Artificial Neural Network and Genetic Algorithm Model for Multi-Objective Strength Optimization of Concrete with Surkhi and Buntal Fiber","authors":"D. Silva, K. L. D. Jesus, Bernard S. Villaverde, E. Adina","doi":"10.1145/3384613.3384617","DOIUrl":"https://doi.org/10.1145/3384613.3384617","url":null,"abstract":"Fiber-reinforced concrete (FRC) is one of the efficient innovation in concrete industry that has the ability to enhance the mechanical properties significantly. To cope up with the increase in infrastructural activities which resulted in greater demand in production of different construction materials have a negative impact on the environment, this study aims to determine the mechanical performance of the optimum compressive and flexural strength of buntal fiber-reinforced concrete with surkhi as partial replacement for sand (BFRC-SS). Using 28th-day compressive and flexural strength, several mixtures were experimentally tested to derive a mix proportion that gave the best mechanical properties of BFRC-SS. From the results, best hybrid models of compressive and flexural strength were formulated using Artificial Neural Network (ANN). Results showed that ANN was able to establish the effects of surkhi and buntal (Corypha utan Lam) fiber to the mechanical properties of BFRC-SS. Furthermore, the multi-objective Genetic Algorithm (GA) model generated the optimum proportion for the best compressive and flexural strength. Fuzzy Inference System (FIS) and Multi-Linear Regression Analysis (MLRA) were also utilized to assess and validate the hybrid model through surface imaging. Utilizing least percent error, ANN hybrid model showed the most significant predictive model compared to other models generated by MLRA and FIS. This study adoptied the fusion of 4.0 Industrial Revolution and favoring creativity and integrity through artificial intelligence.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124677630","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: 20

Solving Hierarchical Soft Constraints with an SMT Solver 用SMT求解器求解分层软约束

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384654

H. Hosobe

引用次数: 0

Real-time Motion Artifacts and Low-Frequency Drift Correction for Functional Near-infrared Spectroscopy 功能近红外光谱的实时运动伪影和低频漂移校正

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384620

Ruisen Huang, Seong-Woo Woo, K. Hong

引用次数: 0

Crack Width Measurement Using Unified Image Processing Techniques for Aging Structures 基于统一图像处理技术的老化结构裂纹宽度测量

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384635

Jeremy A. Argosino, Marie Aila B. Capistrano, Lisette S. Salido, J. Villaverde, A. Paglinawan

引用次数: 0

Abandoned Baggage Detection & Alert System Via AI and IoT 通过人工智能和物联网的废弃行李检测和警报系统

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384614

Z. H. C. Soh, Khadijah Kamarulazizi, K. Daud, I. H. Hamzah, Zuraidi Saad, S. Abdullah

{"title":"Abandoned Baggage Detection & Alert System Via AI and IoT","authors":"Z. H. C. Soh, Khadijah Kamarulazizi, K. Daud, I. H. Hamzah, Zuraidi Saad, S. Abdullah","doi":"10.1145/3384613.3384614","DOIUrl":"https://doi.org/10.1145/3384613.3384614","url":null,"abstract":"Nowadays, piece of unattended luggage there are like tens thousands of lost or left behind in the airports in every year. Due to this issue, this research work aims to develop a camera system that can detect the baggage or luggage and human and notify the authority via media social WhatsApp application when abandoned baggage detected. On top of that, Raspberry Pi 3 model B is used as a hardware where Pi camera is installed in the hardware. This research work is using deep learning method to perform the detection of the baggage and human. The Single Shot Multibox Detector (SSD) is used as the deep learning object detection algorithms for this research work to train the object detection model. The OpenCV and Tensorflow Library is a deep learning library is installed in the Raspberry Pi 3 model B minicomputer to perform the process of detection of the human and baggage. As a result, the Abandoned Baggage Detection and Alert System (ABDAS) able to detected human and baggage using computer vision and the system will notify the authority when the system detected an abandoned baggage through WhatsApp using Twilio Internet of Things (IoT) application software.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116099869","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

Demarcation of Lung Lobes in CT Scan Images for Lung Cancer Detection using Watershed Segmentation CT扫描图像中肺叶的分水岭分割用于肺癌检测

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384624

Nur Najihah Sofia Mohd Marzuki, I. Isa, N. Karim, I. Shuaib, Z. H. C. Soh, S. N. Sulaiman

{"title":"Demarcation of Lung Lobes in CT Scan Images for Lung Cancer Detection using Watershed Segmentation","authors":"Nur Najihah Sofia Mohd Marzuki, I. Isa, N. Karim, I. Shuaib, Z. H. C. Soh, S. N. Sulaiman","doi":"10.1145/3384613.3384624","DOIUrl":"https://doi.org/10.1145/3384613.3384624","url":null,"abstract":"Lung cancer is one of the dangerous and life-threatening cancer diseases in the world. The most common ways to detect lung cancer is by using the Computed Tomography (CT) image. Nowadays, Computed Aided Diagnosis (CAD) is becoming more prominent. In medical applications, the CAD system is adopted to help doctors to perform an image analysis and make their final decisions. Therefore, the main aim of this research is to establish an image processing method for the segmentation of lung cancer from CT scan images. In order to achieve the main aims, the work is divided into two parts, the first is obtaining the lung region from CT scan images and the second is detecting the lesion of lung cancer. This paper will present the outcome of the first part. Firstly, the image will undergo the threshold, clustering and image filtering as well as the enhancement process to get better and clearer lung area images. Next, is the most important stage in this research which is the segmentation stage. In this work, modified watershed is used to demarcate the lung region from the CT scan images. Then, the performance of the segmentation process is measured using accuracy, recall, precision and F- score parameters. The outcome of this research is very helpful for the doctor to determine later the type of treatment that should be provided to the patient.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133872898","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: 6

The Graded CNN Technique to Identify Type of Food as The Preliminary Stages to Handle the Issues of Image Content Abundant 分级CNN识别食物类型技术作为处理图像内容丰富问题的初级阶段

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384649

M. Sadikin, Desi Ramayanti, A. Indrayanto

引用次数: 2

Synergetic Workspace Tracking Control for 4-DOF Robot Manipulator 四自由度机器人机械手的协同工作空间跟踪控制

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384618

R. Fareh, S. Khadraoui, Mohammed Baziyad, M. Bettayeb

引用次数: 5