2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)最新文献_第9页

1D-Convnet Model for Detection of Antidepressant Drugs 抗抑郁药物检测的1D-Convnet模型

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865476

Gracia Rizka Pasfica, Nur Ghaniaviyanto Ramadhan, Faisal Dharma Adhinata

{"title":"1D-Convnet Model for Detection of Antidepressant Drugs","authors":"Gracia Rizka Pasfica, Nur Ghaniaviyanto Ramadhan, Faisal Dharma Adhinata","doi":"10.1109/CyberneticsCom55287.2022.9865476","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865476","url":null,"abstract":"A drug is a substance or mixture of materials to be used in determining the diagnosis, preventing, reducing, eliminating, curing disease or symptoms of disease, bodily or spiritual injury or disorder in humans or animals, including to beautify the body or parts of the human body. Problems begin to arise when a patient is wrong in consuming the target drug used, which is not by the type of disease suffered. For example, suppose a person suffers from a psychological disorder that requires taking different types of drugs, if it turns out that the type of drug consumed is not by the disease, it is very dangerous. This problem is certainly very dangerous because it can cause death for those who consume it. Currently, many researchers are using the deep learning Convolutional Neural Network (CNN) model for drug detection problems. The CNN model has a higher level, namely 1D-Convolutional Neural Network (1D-Convnet) which is still rarely used for drug detection problems. So, the purpose of this study was to detect the classification of atypical antidepressants and SSRIs antidepressants using a deep learning model of the 1D-Convolutional Network (1D-Convnet) type. The results obtained using this model are 98.3% with the most influential parameter, namely dropout. The proposed research model also produces higher accuracy than the Naive Bayes supervised learning model.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132782856","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

Design and Development of Sugarcane Maturity Identifier through Phenotypes via Image Processing 基于图像处理的甘蔗表型成熟标识的设计与开发

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865245

Alnie Mae Aderes, Harold Combalicer, Jose Rico Garcia, Alyssa Miranda, Hannah Nicole Pedrosa, Arjay Yabut, Rommel M. Anacan, Josephine L. Bagay

{"title":"Design and Development of Sugarcane Maturity Identifier through Phenotypes via Image Processing","authors":"Alnie Mae Aderes, Harold Combalicer, Jose Rico Garcia, Alyssa Miranda, Hannah Nicole Pedrosa, Arjay Yabut, Rommel M. Anacan, Josephine L. Bagay","doi":"10.1109/CyberneticsCom55287.2022.9865245","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865245","url":null,"abstract":"Most of the world's sugar demand comes from sugarcane. Sugarcane is the most produced crop globally and one of the major crops in the Philippines. The Philippines' sugarcane industry shows a decrease in the total annual production. Maturity is one factor that affects yield and, eventually, production. Sugarcane must be harvested at the proper age (peak maturity) to maximize sugar output. Among the different approaches to identify maturity, use the physical and physiological aspects. Approaches effects lead to the design and development of a system that will determine maturity through phenotypes via image processing. The system will process images of the sugarcane varieties of the Philippines, using Raspberry Pi and send/receive them using Long Range Wide Area Network (LoRa WAN). Pythons' object detection algorithm, specifically Faster Region-based Convolutional Neural Network (Faster R-CNN) and pre-trained models in TensorFlow, are used to identify maturity. The results have shown that the system performs well in identifying maturity and has excellent potential to be used in sugarcane production, which eventually increases sugar production.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134255407","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

Hybrid SVD-ARIMA Method for Sales Forecasting with Sparse Data on E-Commerce Products 基于稀疏数据的电子商务产品销售预测混合SVD-ARIMA方法

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865590

Vania Putri Minarso, T. B. Adji, N. A. Setiawan

{"title":"Hybrid SVD-ARIMA Method for Sales Forecasting with Sparse Data on E-Commerce Products","authors":"Vania Putri Minarso, T. B. Adji, N. A. Setiawan","doi":"10.1109/CyberneticsCom55287.2022.9865590","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865590","url":null,"abstract":"In online business, predictive analytics or forecasting is often used to improve performance effectiveness. One of the forecastings that play an important role in most businesses is sales forecasting. The results of sales forecasting are used to make stock planning and the right decisions for the future. Several previous studies on forecasting preferred to use available methods. Besides, there were also some studies that combined or compared several forecasting methods to produce higher accuracy. However, in the testing process, those studies were still carried out with non-sparse data. Therefore, the Hybrid method between Singular Value Decomposition (SVD) and Autoregressive Integrative Moving Average (ARIMA) is used to do sales forecasting in this study. SVD method is used to predict sparse data. The ARIMA method is then used to forecast sales based on data from the SVD method. The research results on monthly forecasting using sparse data of 40% have an average RMSE and MAE values improvement of 0.308 and 0.352, respectively. For monthly forecasts that use 50% sparse data, the average RMSE and MAE values improvement are 0.279 and 0.28, respectively. For daily forecasting using sparse data of 40%, the average RMSE and MAE values improvement are 0.021 and 0.014, respectively. For daily forecasting using 50% sparse data, the average RMSE and MAE values improvement are 0.017 and 0.009, respectively. The accuracy results show that the Hybrid SVD-ARIMA method can perform forecasts better than the ARIMA method. However, in daily forecasting, the Hybrid SVD-ARIMA method still has a high forecasting error.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134559132","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

Data-Driven Modelling For Tsunami Forecasting Using Computational Intelligence 基于计算智能的海啸预报数据驱动模型

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865565

Michael Siek, Alfriyadi Rafles

{"title":"Data-Driven Modelling For Tsunami Forecasting Using Computational Intelligence","authors":"Michael Siek, Alfriyadi Rafles","doi":"10.1109/CyberneticsCom55287.2022.9865565","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865565","url":null,"abstract":"Numerous tsunami disasters have happened in Indonesia as located across Ring of Fire, and it brings casualties to both the economy and welfare of the people in the event of their occurrence. Frequent tectonic earthquakes in the oceanic areas may lead to tsunami disasters that can cause significant damages to the infrastructures and people. Therefore, the development and implementation of a significantly improved early warning system's performance is essential. This paper presents the research on finding an appropriate machine learning algorithm for provisioning fast and accurate tsunami forecasts using spatiotemporal data of tsunami event in Aceh occurred on December 26th, 2004. A mixture of two modelling paradigms: physically based and data-driven modelling was explored and developed by utilizing 3D numerical models with essential measurement data. The outputs of numerical computations are in the form of time series datasets with various time windows and forecast horizons. Three machine learning algorithms namely fully connected neural network (FCNN), convolutional neural networks (CNN), and recurrent neural network (CNN) with long short-term memory (LSTM) were employed and compared to achieve accurate tsunami wave forecasts, evaluated according to specific set of evaluation metrics. The model forecast comparison with window size of 15 minutes and forecast horizon of 1 minute indicate that FCNN model outperform the CNN and RNN with LSTM models, with RMSE of 0.299. This modelling results show that the proposed modelling framework has been able to support in provisioning towards fast and accurate tsunami early warning system.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133752172","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

Investigating the Key Factors on XYZ Generations' Higher-Order Thinking Skills in E-Learning 影响XYZ世代在线学习高阶思维技能的关键因素研究

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865409

Erwin Ardianto Halim, Kevin, Hans Kristian, Marylise Hebrard

引用次数: 0

Capitalization Feature and Learning Rate for Improving NER Based on RNN BiLSTM-CRF 基于RNN BiLSTM-CRF改进NER的资本化特征和学习率

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865660

Warto, Muljono, Purwanto, E. Noersasongko

{"title":"Capitalization Feature and Learning Rate for Improving NER Based on RNN BiLSTM-CRF","authors":"Warto, Muljono, Purwanto, E. Noersasongko","doi":"10.1109/CyberneticsCom55287.2022.9865660","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865660","url":null,"abstract":"Entity extraction in the natural language processing research field is still a widely researched topic. It can be a data source for the next NLP stage, such as text summarization, sentiment analysis, chatbot, machine translation, information retrieval, opinion mining, speech recognition, etc. Named Entity Recognition (NER) is the task of detecting named entities on the corpus. The detection process of entities can use various features, one of which is capital letters. Capital letters that appear at the beginning of a sentence indicate the name of a person, place, organization, geolocation, etc. The experiment uses the deep learning approach with Recurrent Neural Network Bidirectional Long Short Term Conditional Random Field (RNN-BiLSTM-CRF). Our comparing three optimization algorithms: Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), and Adadelta, with the CoNLL2003 dataset. The experiment results using capital letter features showed an increase in the value of F1-Score by 2.9 higher compared to test results that did not use capital letter features. The highest F1-score score was 92.82 in testing using Adam's algorithm, with a 0.001 learning rate.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134192161","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

Optimal Sizing of BESS Considering Economic Dispatch and VRE in Thailand Generation System 考虑经济调度和VRE的泰国发电系统BESS最优规模

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865363

Audchara Yimprapai, S. Chaitusaney

{"title":"Optimal Sizing of BESS Considering Economic Dispatch and VRE in Thailand Generation System","authors":"Audchara Yimprapai, S. Chaitusaney","doi":"10.1109/CyberneticsCom55287.2022.9865363","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865363","url":null,"abstract":"The increasing proportion of renewable energy (RE) in a power system causes many problems such as duck curve phenomenon, making its load curve and conventional generator operations change. These issues have become significant concerns. If the difference of the load curve is a huge ramp, the operating costs of the conventional generation will increase. In recent years, the price of battery energy storage system (BESS) has been decreasing continuously. With its fast response characteristic, the BESS is a potential candidate to mitigate the load curve issue. Therefore, this paper proposes an economic dispatch method for minimizing the operating costs of conventional generation integrated with RE generation and the BESS. Besides, the detailed analysis based on Thailand's power generation is provided to achieve the optimal BESS size. According to Power Development Plan (PDP) 2018, the RE generation comprises photovoltaic (PV) and wind generation while the conventional generation consists of combined-cycle and coal thermal generation. The proposed method was performed using the optimization toolbox in MATLAB programming. The amount of RE generation is swept through 10 to 140 percent of its initial value while the amount of conventional generation is constant. The simulation results show that if the RE generation reaches 90 percent, the BESS is required. To obtain the lowest system operating cost, the optimal size of the BESS under the state of charge (SoC) constraint of 50 percent is 3,463.82 MWh. In summary, this proposed method helps deal with the future growth of VRE generation in Thailand's electrical system.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131393392","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 LSTM-UNet and Zero Padding technique to detect deforestation in Amazon area 基于LSTM-UNet和零填充技术的亚马逊森林砍伐检测

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865621

Irham Muhammad Fadhil, A. M. Arymurthy

引用次数: 1

Detector Face Mask using UAV-based CNN Transfer Learning of YOLOv5 基于无人机的YOLOv5 CNN迁移学习检测器面罩

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865243

Rizqi Renafasih Alinra, Satryo B Utomo, Gamma Aditya Rahardi, Khairul Anam

{"title":"Detector Face Mask using UAV-based CNN Transfer Learning of YOLOv5","authors":"Rizqi Renafasih Alinra, Satryo B Utomo, Gamma Aditya Rahardi, Khairul Anam","doi":"10.1109/CyberneticsCom55287.2022.9865243","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865243","url":null,"abstract":"Detection of the use of masks on someone is helpful in health protocols during the COVID-19 pandemic. All public services or places require people to wear masks during the pandemic. There are about three types of masks commonly used by the public today: surgical/medical masks, cloth masks, and scuba masks. This research aims to detect masks by monitoring a user using a mask through a camera. also detects the type of mask used by the community. So that it can provide convenience in implementing discipline in carrying out the COVID-19 health protocol using masks. In addition, this research proposes the detection of masks on the face by monitoring using a drone. The detection method used in this research is Transfer Learning CNN. This algorithm is a deep learning method that can classify and detect in digital image processing. The initial step of the research is to collect the types of masks on the market in the form of digital images, followed by the application before being modeled into mathematical calculations, which will later be processed using the Convolutional Neural Network method. This research compares two architectural transfer learning methods in deep learning, namely mobile net V2 with YOLOv5. The system testing process will be carried out by analyzing the recall value, precision, and accuracy. The testing process on drone camera-based devices uses the python programming language. Based on the results of the transfer learning method using YOLOv5, the results of the data training accuracy are 97% in detecting masks.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124270891","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

Day Ahead Solar Irradiation Forecasting Based on Extreme Learning Machine 基于极限学习机的日前太阳辐射预报

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865532

A. Rehiara, Sabar Setiawidayat

引用次数: 0