Pedro Barría Valdebenito, David Zabala-Blanco, Roberto Ahumada-García, I. Soto, A. D. Firoozabadi, Marco J. Flores-Calero
{"title":"Extreme Learning Machines for Detecting the Water Quality for Human Consumption","authors":"Pedro Barría Valdebenito, David Zabala-Blanco, Roberto Ahumada-García, I. Soto, A. D. Firoozabadi, Marco J. Flores-Calero","doi":"10.1109/ColCACI59285.2023.10225820","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10225820","url":null,"abstract":"Determining the potability of water for consumption is crucial for human health. To assess the water quality, levels of minerals and ions are measured, such as pH, hardness, sodium, chloramines, sulfate, conductivity, organic carbon, tri-halomethanes, and turbidity. To achieve this more efficiently and accurately, techniques of Machine Learning (ML) and Deep Learning (DL) have been applied, with deep neural networks being one of the most popular methods. In this study, Extreme Learning Machines (ELM) are evaluated for the first time, including the standard ELM, the Regularized ELM, the weighted ELMs in configurations 1 and 2, and the multi-layer ELM. Accuracy and the G-mean were used to extensively compare the results and it was found that the weighted ELM 1 is the most recommended algorithm for the binary classification of the potability of water for human consumption, with an accuracy of 75.8% and a G-mean of 80.6%. The feasibility of using ELMs to determine the potability of water is thus demonstrated, as they offer acceptable performance and low computational cost for training.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"21 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120940686","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}
René Játiva E., Oliver Caisaluisa, Katty Beltrán, M. Gavilánez
{"title":"Radio Frequency Pattern Matching - Smart Subscriber Location in 5G mmWave Networks","authors":"René Játiva E., Oliver Caisaluisa, Katty Beltrán, M. Gavilánez","doi":"10.1109/ColCACI59285.2023.10225998","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10225998","url":null,"abstract":"Received Signal Strength measures have been collected at the Base Station antenna array of a wireless network operating at 28 GHz mmWaves, and virtually deployed using Open Street Maps and Matlab®. These radio frequency patterns imprinted by a geolocated subscriber transmitting along the campus of Universidad San Francisco de Quito, have been used to automatically discover the characteristics of the area of interest by using k-means clustering into the proposed unsupervised method. Furthermore, this technique has been integrated into supervised ML methods based on K-Nearest Neighbors, in order to provide an accurate estimation of the subscriber position by performing the match between the received RF patterns and the stored fingerprints. Results provided with this new approach improve accuracy over previous works based on supervised ML methods.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123761552","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}
César Cruz, Eduardo Grados, G. L. Rosa, J. Valdiviezo, J. Soto
{"title":"Band Reduction of the Spectral Signature for the Determination of Models Based on Machine Learning and Spectroscopy Using Hyperspectral Imaging in Cocoa Beans","authors":"César Cruz, Eduardo Grados, G. L. Rosa, J. Valdiviezo, J. Soto","doi":"10.1109/ColCACI59285.2023.10225887","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10225887","url":null,"abstract":"The agribusiness in Peru has grown significantly in the last decade and has become one of the most important producers of fine and flavor cocoa. The European Union has established quality parameters for the presence of heavy metals, one of which is cadmium due to its harmful effects on human health. To improve the quality control of cocoa, this article proposes to estimate the percentage of cadmium in a cocoa sample using the hyperspectral imaging and Machine Learning algorithms, as a non-invasive, non-destructive method that can be implemented in real time. In addition, areas of the spectral signature that contribute the most to the cadmium estimate are identified in order to significantly reduce the number of spectral bands used in the model. This reduction of spectral bands allowed to increase the R2 with the validation data from 67.92% to 72.39%, reaching an average error of 0.18 ppm using the Partial Least Square (PLS) method.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130198092","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}
Agustín Mascaró-Muñoz, Roberto Ahumada-García, David Zabala-Blanco, César A. Azurdia-Meza, I. Soto, Pablo Palacios Játiva
{"title":"Extreme Learning Machines as Equalizers on Optical OFDM Systems","authors":"Agustín Mascaró-Muñoz, Roberto Ahumada-García, David Zabala-Blanco, César A. Azurdia-Meza, I. Soto, Pablo Palacios Játiva","doi":"10.1109/ColCACI59285.2023.10226069","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10226069","url":null,"abstract":"Optical Fiber Radio (RoF) systems based on OFDM meet the needs of high transmission and reception speeds, as well as offering greater reliability in the system. These systems are exposed to various disturbances, such as the thermal and shot noise of the photodetector, the amplified emission of optical links, and the relative phase intensity in the optical oscillator. To partially address these drawbacks, techniques such as multi-carrier modulation (OFDM), pilot-assisted equalization (PAE), and typical filters have been used. Recently, Extreme Learning Machines (ELM) have been employed instead of classic digital signal processing in RoF-OFDM systems to tackle physical limitations. ELMs are learning algorithms that have low latency rates and the ability to process large volumes of data. This article presents a review and comparison of the main research studies that have utilized ELM. It should be noted that ELM-C achieved the shortest equalization time in most cases compared to other algorithms.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126194183","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}