2021 4th International Conference of Computer and Informatics Engineering (IC2IE)最新文献_第6页

Real Time-based Skin Cancer Detection System using Convolutional Neural Network and YOLO 基于卷积神经网络和YOLO的实时皮肤癌检测系统

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649224

Hasna Fadhilah Hasya, Hilal Hudan Nuha, M. Abdurohman

引用次数: 8

Disturbance Storm Time Index Prediction using Long Short-Term Memory Machine Learning 基于长短期记忆机器学习的扰动风暴时间指数预测

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649119

Wihayati, H. Purnomo, S. Trihandaru

{"title":"Disturbance Storm Time Index Prediction using Long Short-Term Memory Machine Learning","authors":"Wihayati, H. Purnomo, S. Trihandaru","doi":"10.1109/ic2ie53219.2021.9649119","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649119","url":null,"abstract":"The cosmic matter that has the most influence on space weather on earth is greatly influenced by solar activity. Abnormal solar activity often affects the intensity of the solar wind into space, which is known as the geomagnetic storm phenomenon. One of the impacts caused by this phenomenon is the disruption of the satellite navigation system. In determining solar activity that affects the earth, observing the CME (Coronal Mass Eject) and flares continuously is necessary. One of the references for measuring the level of geomagnetic storms is the disturbance storm time index (Dst-index). This paper predicts the Dst-index based on data from the OMNI web obtained from NASA’s Advanced Composition Explorer (ACE) satellite. This paper aims to predict the disturbance storm time index using long short-term memory (LSTM). The results of the LSTM model were then evaluated using the root mean square error (RMSE) from the training results and testing results for comparative analyses of data with prediction to determine the error level. The best LSTM model for the Dst-index prediction shows the RMSEs are around the value of 3 for the training and testing.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128038969","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

5G Network Performance: A Study using Stationary and Mobility Tests on Sukhumvit Line – BTS Skytrain in Bangkok 5G网络性能:曼谷素坤逸线- BTS轻轨的静止和移动测试研究

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649216

Therdpong Daengsi, Pana Ungkap, Pongpisit Wuttidittachotti

{"title":"5G Network Performance: A Study using Stationary and Mobility Tests on Sukhumvit Line – BTS Skytrain in Bangkok","authors":"Therdpong Daengsi, Pana Ungkap, Pongpisit Wuttidittachotti","doi":"10.1109/ic2ie53219.2021.9649216","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649216","url":null,"abstract":"This paper presents a study of 5G efficientcy using field tests, covering both stationary and mobility tests, to investigate the performance of 5G networks that are deployed nationwide in Thailand. In this study the longest and busiest route of the BTS Skytrain in the heart of Bangkok was selected. Then, the data were collected using the Speedtest application and a 5G smartphone with 2 SIM cards provided by two major mobile service providers. After the analysis, it has been found that the download speeds from the stationary test was 297.5 Mbps and the upload speed was 71.2 Mbps, while the download and upload speeds from the mobility tests were 273.0 Mbps and 48.8 Mbps respectively. However, after using t-test technique for analysis, it has been found that the upload speed from the mobility tests is significantly lower than the speed from the stationary tests (p-value < 0.001). This means that mobility impacts 5G speeds. In conclusion, the overall download speed in Bangkok was 289.6 Mbps and the overall upload speed was 64.0 Mbps. The result from this study can be the new baseline for 5G deployment in other areas of Thailand.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"15 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133511140","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

Identification Post-Stroke of Motor Imagery and Asynchrony of Channel Pairs using Multiple RNN 基于多RNN的脑卒中后运动图像和通道对异步识别

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649392

Fajariani Amalia, E. C. Djamal

{"title":"Identification Post-Stroke of Motor Imagery and Asynchrony of Channel Pairs using Multiple RNN","authors":"Fajariani Amalia, E. C. Djamal","doi":"10.1109/ic2ie53219.2021.9649392","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649392","url":null,"abstract":"Stroke can cause disability, so the patient needs rehabilitation, and it is necessary to measure its effectiveness. The electroencephalogram (EEG) can capture the electrical activity in the brain, which can be real-time in post-stroke rehabilitation monitoring. EEG signal consists of several variables, including motor imagery and asynchronous from the symmetric channel. Both are features of post-stroke patients that are frequently used from previous studies, among other variables. EEG signals recorded from many channels can enrich the information on activity in the brain, including stroke. Motor imagery as a variable that reflects the stroke also be dominant in specific channels. Likewise, the asymmetry of the channel pair is too. Each channel or channel pair has its characteristics that are useful in identification. Meanwhile, a suitable method for identifying interconnected signals in time sequences is Recurrent Neural Networks (RNN). Therefore, to maintain the connectivity and take advantage of the EEG signal from multichannel, this paper proposed the Multiple RNN method in which each channel was processed by one network connected by a fusion function. The two variables - motor imagery variable and asynchronous of the symmetric channel pair are obtained from the Wavelet transform. The motor imagery feature involves FC5 and FC6 channels, while the asynchronous channel involves the AF3-AF4, F7-F8, F3-F4, FC5-FC6, T7-T8, P7-P8, and O1-O2 channel pairs. Both variables were obtained from the EEG signal using Wavelet at 1–7 Hz for asynchronous channel pairs and 8 – 30 Hz for motor imagery. The results showed that the Multiple RNN provided an accuracy of 88.04%, which increased by 8% compared to a Single RNN which obtained an accuracy of 80.09%. The results also showed the importance of choosing a learning rate to get the best accuracy.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451440","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

Time Series Analysis of Infected COVID-19 Cases in the Zamboanga Peninsula, Philippines using Long Short-Term Memory Neural Networks 基于长短期记忆神经网络的菲律宾三宝颜半岛新冠肺炎感染病例时间序列分析

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649041

Urbano B. Patayon

{"title":"Time Series Analysis of Infected COVID-19 Cases in the Zamboanga Peninsula, Philippines using Long Short-Term Memory Neural Networks","authors":"Urbano B. Patayon","doi":"10.1109/ic2ie53219.2021.9649041","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649041","url":null,"abstract":"Infectious disease outbreaks, such as COVID-19 pandemics, exhibit patterns that can be described by the dynamics of a mathematical model This study seeks to explore the use of LSTM in order to develop models that will capture the non-linear dynamic changes of COVID-19 cases in Zamboanga Peninsula. The study uses 436 data points where the latest timestamp for the dataset is on May 29, 2021 and the oldest is on March 20, 2020. These data are taken from the DOH repositories and revalidated using the data from the DOH Regional Office. The training and testing phase results show that among the different LSTM variants, convLSTM trained using Adam and RMSProp attained the smallest RMSE result of 42.34 and 43.67 and a correlation coefficient of 0.94 0.93, respectively. ConvLSTM, when trained with Adam and RMSProp, produces the best results, as evidenced by the shortest RMSE and highest correlation coefficient. Results revealed that convLSTM appears to be a viable choice for modeling the time series of the COVID 19 infected cases in Zamboanga Peninsula Region in compared with the different variants of LSTM.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056779","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: 3

CrowdFunding Application For Waqf Donation 众筹申请Waqf捐款

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649066

Eriya, Risna Sari, Ahmad Haydar Ardabelli

引用次数: 1

Development of An Online Exam Security System using Ensemble Method 集成法在线考试安全系统的开发

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649355

Handri Santoso, Ganis Samoedra Murharyono, Theresia Herlina

引用次数: 1

Hyperparameter Tuning using GridsearchCV on The Comparison of The Activation Function of The ELM Method to The Classification of Pneumonia in Toddlers 基于网格搜索cv的超参数调优:ELM方法激活函数对幼儿肺炎分类的比较

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649207

Pirjatullah, Dwi Kartini, D. T. Nugrahadi, Muliadi, Andi Farmadi

{"title":"Hyperparameter Tuning using GridsearchCV on The Comparison of The Activation Function of The ELM Method to The Classification of Pneumonia in Toddlers","authors":"Pirjatullah, Dwi Kartini, D. T. Nugrahadi, Muliadi, Andi Farmadi","doi":"10.1109/ic2ie53219.2021.9649207","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649207","url":null,"abstract":"Pneumonia is a disease that is susceptible to attack toddlers. According to data from the Ministry of Health, the cause of under-five deaths due to pneumonia is number 2 of all under-five deaths. In Kalimantan, forest fires are one of the causes of the high number of pneumonia cases. Knowing the symptoms of the disease is very important, considering that sufferers often do not know that they have been exposed to Pneumonia because the symptoms that appear are just ordinary pain. In this study, the classification of Pneumonia and Non- Pneumonia Cough was carried out based on symptom factors. The dataset used in this study is the Poly MTBS at the Martapura Timur Health Center. The classification method used in this research is Extreme Learning Machine (ELM). The classification process starts from SMOTE upsampling, this is done to balance the classes, because the amount of data between classes used is not balanced. Then hyper tuning the parameters is done using GridsearchCV on the hidden layer neurons, to determine the best parameters that will be used as recommendations in the classification process. At the classification stage using the ELM method by comparing the activation functions of Binary Sigmoid, Sin, Hard Limit, Triangular Basis, Radial Base, Linear, and Bipolar Sigmoid by comparing test datasets 90:10, 80:20, 70:30, 60:40, and 50:50. This study provides the best performance results on the use of the Triangular Base activation function with 86.36% accuracy, 85% precision, 100% recall and 92% F1 Score, training data ratio, and testing 90:10 and 3 hidden layer neurons.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114928772","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: 13

Analytics Platform for Morphometric Grow out and Production Condition of Mud Crabs of the Genus Scylla with K-Means Scylla属泥蟹形态测定生长及生产条件的K-Means分析平台

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649163

Rossian V. Perea, E. Festijo

{"title":"Analytics Platform for Morphometric Grow out and Production Condition of Mud Crabs of the Genus Scylla with K-Means","authors":"Rossian V. Perea, E. Festijo","doi":"10.1109/ic2ie53219.2021.9649163","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649163","url":null,"abstract":"The traditional use of an electronic spreadsheet is poorly suited to operationalize and manage the visualization and analysis of datasets related to mud crab production in the Philippines because of the long process of constructing equations to produce the desired diagram. A mud crab-specific analytic platform is needed to accurately visualize and analyze the datasets that will eventually result in better and data-driven decision-making on the part of farmers and stakeholders. This paper introduces the design and development of a state-of-the-art Mud crab Analytics Platform (MAP) utilizing the K-means algorithm. A holistic system development approach was applied which consists of three phases namely; 1) capture, 2) consumption, and 3) integration and transformation. As a result, MAP was able to provide significant insights in terms of descriptive, predictive, and prescriptive analytics. This real-time and dynamic analytic platform may provide farmers and stakeholders a powerful means to conduct data analysis on a community level to obtain actionable insights.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126857078","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

Performance Comparison of Adaptive Neuro Fuzzy Inference System and Support Vector Machine Algorithm in Balanced and Unbalanced Multiclass Data Classification 自适应神经模糊推理系统与支持向量机算法在平衡与不平衡多类数据分类中的性能比较

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649423

Muhammad Irfan Saputra, Irwan Budiman, Dwi Kartini, D. T. Nugrahadi, M. Reza Faisal

引用次数: 0