2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)最新文献_第4页

Developed Histogram of Oriented Gradients-based Feature Extraction for Covid-19 X-Ray Image Classification 基于定向梯度的直方图特征提取用于Covid-19 x射线图像分类

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180423

Y. Jusman, Wikan Tyassari, Wignyo Nindita, Alif Jamil Hussein Harahap, Akbar Maulana Ismail

引用次数: 0

Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques 利用人工神经网络(ANN)和其他预测技术建模和预测沙特原油产量

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180990

Ali Alarjani, Teg Alam, A. Kineber

{"title":"Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques","authors":"Ali Alarjani, Teg Alam, A. Kineber","doi":"10.1109/ISMODE56940.2022.10180990","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180990","url":null,"abstract":"Forecasting models are essential for economic development and making appropriate policy decisions. The purpose of this study is to forecast crude oil production in Saudi Arabia for the following year. Our study is aimed at predicting Saudi Arabia’s crude oil production using Artificial Neural Networks (ANN), Holt-Winters Exponential Smoothing (HW), and Autoregressive Integrated Moving Averages (ARIMA). Based on 1993-2022 crude oil production (million barrels per day) data, this study applies statistical analysis to forecast time series data based on said models over a period. The study also analyzes the forecast model’s accuracy using a variety of measures. As a result of the analysis, this study found that ANNs are the most effective at predicting crude oil production. Thus, among other models analyzed in this study, the ANN model can accurately predict Saudi Arabia’s crude oil production in the future. In addition, the study aims to clarify the current situation of crude oil production in the kingdom. Researchers will be able to better understand crude oil production forecasts as a result of this study. This study can also provide guidance for developing a strategic plan for government entities.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116330732","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

Vision-based Real-Time Disaster Recognition Monitoring System using Raspberry Pi and Deep Learning Model 基于树莓派和深度学习模型的视觉实时灾害识别监测系统

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180915

Daryl B. Valdez, Rey Anthony G. Godmalin, Allan Josephus M. Bunga

{"title":"Vision-based Real-Time Disaster Recognition Monitoring System using Raspberry Pi and Deep Learning Model","authors":"Daryl B. Valdez, Rey Anthony G. Godmalin, Allan Josephus M. Bunga","doi":"10.1109/ISMODE56940.2022.10180915","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180915","url":null,"abstract":"Natural disasters are destructive forces that greatly affect all people around the world. In the Philippines, earthquakes, tropical cyclones, and floods are some of the most frequent disasters that struck the country in recent years. Several studies utilizing technology have been conducted with varying degrees of success to reduce the impact of these uncontrollable events. Different from others, this paper investigates the use of a Deep Learning model deployed in a Raspberry Pi 3b for on-the-ground, real-time, automated disaster recognition and monitoring. It aims to empower emergency responders and people in the community to easily detect disasters as they happen in real-time, reducing the loss of life and damage to property. To this end, a novel low-cost monitoring system is proposed. Experiments and a survey made to emergency responders were conducted to validate the system’s feasibility and acceptability. Results revealed that the proposed system detects disasters with a high degree of performance. Also, it utilizes a low CPU and memory footprint while achieving seven frames per second processing rate during disaster recognition. In addition, the respondents find the system clear, helpful, innovative, and easy to use. Hence, the system is capable of recognizing disasters in real-time, proving acceptable and beneficial to people in the community.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124822343","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

Robot Boat Prototype System Based on Image Processing for Maritime Patrol Area 基于图像处理的海上巡逻区域机器船原型系统

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10181007

A. N. Handayani, Ferina Ayu Pusparani, D. Lestari, I. M. Wirawan, Osamu Fukuda, Aqil Aqthobirrobbany

引用次数: 0

Intelligent Optimization Using Craziness Particle Swarm on Permanent Magnet Synchronous Motor 基于疯狂粒子群的永磁同步电机智能优化

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180931

Machrus Ali, M. Djalal, Hidayatul Nurohmah, Rukslin

{"title":"Intelligent Optimization Using Craziness Particle Swarm on Permanent Magnet Synchronous Motor","authors":"Machrus Ali, M. Djalal, Hidayatul Nurohmah, Rukslin","doi":"10.1109/ISMODE56940.2022.10180931","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180931","url":null,"abstract":"A Proportional Integral Derivative (PID) controller in a synchronous motor is widely used because of its simple structure, robustness, strength and ease of use. The use of a PID controller requires proper parameter settings for optimal performance on the motor. The solution often used is the trial-error method to determine the correct parameters for the PID, but the results obtained do not make the PID controller optimal. Recently there have been many studies to optimize PID controllers wrong with intelligent methods. For this reason, this research will use the Craziness Particle Swarm Optimization (CRPSO) optimization method to optimize and determine the proper parameters of the PID. The CRPSO method is a method that provides an innovation to the velocity function of the particles distributed in the PSO method. From the simulation results, CRPSO performance is more optimal than PSO. From the correct PID parameter tuning results, a minimum overshoot response is obtained with several speed variations. In addition, an increase was also obtained in PMSM starting torque using CRPSO.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121189293","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 Usage of Ensemble Model Output Statistics for Calibration and Short-term Weather Forecast 集成模型输出统计量在校准和短期天气预报中的应用

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10181009

Fajar Dwi Cahyoko, Sutikno, Purhadi

{"title":"The Usage of Ensemble Model Output Statistics for Calibration and Short-term Weather Forecast","authors":"Fajar Dwi Cahyoko, Sutikno, Purhadi","doi":"10.1109/ISMODE56940.2022.10181009","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10181009","url":null,"abstract":"Numerical Weather Prediction is a weather forecasting method that is translated into a system of mathematical equations that are solved by numerical methods. The transformation of the basic theory of NWP into computer code still produces errors. To reduce errors and increase the accuracy of the prediction results of the NWP model, statistical postprocessing can be performed using the Model Output Statistics (MOS) method. The use of model output statistics for weather prediction still has a deficiency, namely, it still produces high bias. To increase the accuracy of the prediction model, it can use the ensemble model output statistics (EMOS). This approach is set out from the ensemble prediction system (EPS) which has an understanding as a model consisting of a combination of two or more single prediction models that are verified at the same time. This technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables. The ensemble members in this study consist of prediction from PLS, PCR, and Ridge Regression. In these performances, EMOS offers predictive PDF and CDF from an ensemble forecast of a continuous weather variable, but it is not considered spatial correlation. For the training period over 20,30 and 40 days, EMOS temperature forecast at 3 sites into good and fair ones. Based on weather prediction assessment indicators like RMSE and CRPS, EMOS is better than raw ensemble in terms of accuracy and precision.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126737564","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

IPCA-SAMKNN: A Novel Network IDS for Resource Constrained Devices IPCA-SAMKNN:一种资源受限设备的新型网络标识

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180926

P. Agbedanu, R. Musabe, James Rwigema, Ignace Gatare, Yanis Pavlidis

{"title":"IPCA-SAMKNN: A Novel Network IDS for Resource Constrained Devices","authors":"P. Agbedanu, R. Musabe, James Rwigema, Ignace Gatare, Yanis Pavlidis","doi":"10.1109/ISMODE56940.2022.10180926","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180926","url":null,"abstract":"Intrusion Detection Systems (IDSs) in traditional computing systems have played a significant role in detecting and preventing cyber-attacks. Unsurprisingly, the same technology is used to detect and prevent cyber attacks in Internet of Things (IoT) environments. However, due to the computational constraints of IoT devices, traditional computing-based IDS is challenging to deploy on IoT devices. Moreover, IDS for IoT environments should have high classification performance, low complexity models, and small model sizes. Despite numerous advances in IoT-based intrusion detection, developing models that achieve high classification performance while being less complex and smaller in size remains difficult. This study proposes a novel IDS for resource-constrained devices like IoT systems by using a blend of incremental principal component analysis (IPCA) and Self Adjusting Memory KNN (SAM-KNN) to develop a lightweight machine learning model to detect intrusions in IoT systems. The proposed system was deployed on a Raspberry Pi Model B, representing a resource-constrained device, and evaluated using the UNSW-NB15 dataset. The experimental results show a superior accuracy of 98.91%, a memory overhead of 1.4%, 1.6% and 2% overhead for CPU and energy, respectively.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130733174","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

Power Monitoring System for 3-Phase Electric Motors Using IoT-Based Current Transformers and Potential Transformers 基于物联网电流互感器和电位互感器的三相电动机功率监测系统

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10181008

Moh. Afandy, Muh. Alif Nur, Abdul Haris Mubarak

{"title":"Power Monitoring System for 3-Phase Electric Motors Using IoT-Based Current Transformers and Potential Transformers","authors":"Moh. Afandy, Muh. Alif Nur, Abdul Haris Mubarak","doi":"10.1109/ISMODE56940.2022.10181008","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10181008","url":null,"abstract":"This study presents the results of the IOT-based 3-phase electric motor monitoring system design in the Nickel smelting industry. Conventional measurement methods have a high level of risk and are very dangerous for field workers. The use of automatic measurement methods is considered better because the accuracy of measurement results can reach 98% with measurement data that can be well documented in the storage system. The use of voltage and current sensors in this design uses Current Transformer (CT) and Potential Transformer (PT) which are industry standard devices. The measurement signal obtained from the CT and PT components is processed using a signal conditioning circuit. Setting the AC to DC voltage in the signal conditioning circuit is the initial stage in signal management, then the results are amplified using signal amplification, which is needed to increase the accuracy of readings in the control circuit using OP-AMP amplification. The results of reading the current, voltage, and power measurement data from a 3-phase electric motor will be displayed on the HMI using the website. The measurement results are in the form of data stored in database storage to facilitate the evaluation process of daily, weekly, and even annual power usage. From these results, the power of using a 3-phase electric motor with an additional generator load of 3212, 563W is obtained. In processing the value of the current measurement results in each phase, the difference in the value of the phase current is obtained, namely 5.11A, 4.36A, 4.06A. Voltage measurements in each phase are also obtained through data processing voltages of 232.13 V, 242.03 V, 238.16 V for the R, S, and T phases. Finally, the monitoring system design this time can be implemented.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131328333","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

Estimation of Air Quality Parameters using Lightweight Machine Learning on Low-cost Edge-IoT Architectures 在低成本边缘物联网架构上使用轻量级机器学习估计空气质量参数

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180952

J. Gamazo-Real, Raúl Torres Fernández, Adrián Murillo Armas

{"title":"Estimation of Air Quality Parameters using Lightweight Machine Learning on Low-cost Edge-IoT Architectures","authors":"J. Gamazo-Real, Raúl Torres Fernández, Adrián Murillo Armas","doi":"10.1109/ISMODE56940.2022.10180952","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180952","url":null,"abstract":"The vast increase in connected Internet of Things (IoT) devices have revolutionised how data are processed. This fact, coupled with the current trend from cloud to edge computing paradigms, has resulted in the need for efficient and reliable data processing near to data sources using resource-constrained devices. In this article, low-cost edge-IoT architectures are implemented to deploy lightweight Machine Learning (ML) models for air quality estimation, such as Polynomial Regression and Artificial Neural Networks (ANN). ML models are deployed in wireless centralised and distributed parallel architectures with common modules such as sensor fusion for luminosity, temperature, humidity, CO2, and other gases. The centralised architecture uses a Graphic Processing Unit (GPU) and the Message Queuing Telemetry Transport (MQTT) protocol, but low-performance processing devices and the Message Passing Interface (MPI) protocol are used in the distributed one. The training and testing of models are attained with appropriate datasets obtained from multiple peak, step, and transient test cases for each air quality parameter. The results for temperature forecasting, and similar ones for other parameters, supports that the distributed parallel architecture could achieve a slightly better estimation metrics and a better performance in power consumption compared to the centralised architecture despite using low-cost general purpose devices.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130851361","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

Design of Smartdoor for Live Face Detection Based on Image Processing Using Physiological Motion Detection 基于生理运动检测图像处理的实时人脸检测智能门设计

2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) Pub Date : 2022-12-22 DOI: 10.1109/ISMODE56940.2022.10180411

Rizky Naufal Perdana, Budhi Irawan, C. Setianingsih, Dian Rezky Wulandari, Ivan Satrio Pamungkas, Fajri Nurfauzan, Adinda Ophelia Putri Sakinah, Muhammad Raihan Ramadhan

{"title":"Design of Smartdoor for Live Face Detection Based on Image Processing Using Physiological Motion Detection","authors":"Rizky Naufal Perdana, Budhi Irawan, C. Setianingsih, Dian Rezky Wulandari, Ivan Satrio Pamungkas, Fajri Nurfauzan, Adinda Ophelia Putri Sakinah, Muhammad Raihan Ramadhan","doi":"10.1109/ISMODE56940.2022.10180411","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180411","url":null,"abstract":"In the current era, technology is developing very rapidly, especially in the field of image processing, technological developments can help and facilitate human work. The purpose of image processing is to learn how to process images to detect objects. This project has the aim of implementing real face detection based on image processing in the smart door design. The physiological motion detection system can recognize the difference between real faces and photo imitations based on facial reflexes in the eyes and mouth. The methods used for face detection are Histogram Oriented Gradient and Haar-Cascade, motion detection of facial reflexes using the Support Vector Machine. The result of this project concludes that the smart door system with physiological motion detection that has been designed successfully performs real face detection, the average accuracy rate is 93.5% and photo imitation faces have an average of 90.7% based on eye reflex motion detection. and mouth.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134629702","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