2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)最新文献_第2页

Detection of Obfuscated Malware by Engineering Memory Functions Applying ELM 基于ELM的工程记忆函数模糊恶意软件检测

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10226058

Leonardo Igor Moraga, Juan Pablo Rivelli Malcó, David Zabala-Blanco, Roberto Ahumada-García, César A. Azurdia-Meza, A. D. Firoozabadi

{"title":"Detection of Obfuscated Malware by Engineering Memory Functions Applying ELM","authors":"Leonardo Igor Moraga, Juan Pablo Rivelli Malcó, David Zabala-Blanco, Roberto Ahumada-García, César A. Azurdia-Meza, A. D. Firoozabadi","doi":"10.1109/ColCACI59285.2023.10226058","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10226058","url":null,"abstract":"Memory analysis is critical to detecting malicious processes, as it can capture various characteristics and behaviors. However, although it is a field in full research, there are still some major obstacles in malware detection, such as optimizing the detection rate and countering advanced malware obfuscation. Since advanced malware uses obfuscation and other techniques to hide from detection methods, there is a great need for an efficient framework that focuses on combating obfuscation and detecting hidden malware. This work proposes an extreme learning machine (ELM) trained with a database of viruses, classified into families of Trojans, spyware, and ransomware. The performance of different ELMs will be implemented and analyzed, among them, the standard ELM, regularized ELM, unbalanced ELM I and II. Its performance will be studied both in binary classification and in multiple classifications, in order to train an antivirus capable of combating the aforementioned difficulties. Prior to obtaining the results, the operating principle of these autonomous learning methods and the methodology to be followed are explained. Finally, the results obtained for each learning method are compared.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"19 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":"134432238","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

Self-Supervised Deep-Learning Segmentation of Corneal Endothelium Specular Microscopy Images 角膜内皮镜面显微镜图像的自监督深度学习分割

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10226148

S. Sánchez, Kevin Mendoza, Fernando J. Quintero, A. M. Prada, A. Tello, V. Galvis, L. Romero, A. Marrugo

引用次数: 0

Intra-day Electricity Price Forecasting Based on a Time2Vec-LSTM Neural Network Model 基于Time2Vec-LSTM神经网络模型的日内电价预测

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10225803

Sergio Cantillo-Luna, Ricardo Moreno-Chuquen, Jesus A. Lopez Sotelo

{"title":"Intra-day Electricity Price Forecasting Based on a Time2Vec-LSTM Neural Network Model","authors":"Sergio Cantillo-Luna, Ricardo Moreno-Chuquen, Jesus A. Lopez Sotelo","doi":"10.1109/ColCACI59285.2023.10225803","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10225803","url":null,"abstract":"This paper presents the development of a deep neu-ral network architecture based on stacked LSTM and $T$ ime2Vec layers for predicting electricity prices several steps ahead (8 hours) to feed future decision-making tools. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with some state-of-art time series based statistical forecasting models as SARIMA and Holt-Winters. The results showed that the proposed model outperformed these techniques by modeling nonlinearity and explicitly characterizing the data behavior. Specifically, the pro-posed model was able to capture complex patterns and depen-dencies in the data, resulting in more accurate price predictions. The Time2Vec layer was particularly useful in characterizing the temporal relationships between the input and output variables. The proposed architecture has the potential to significantly improve the accuracy of electricity price predictions, which can have important implications for decision-making in the energy sector.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"216 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":"115512089","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

Predicting COVID-19 Cases using Deep LSTM and CNN Models 使用深度LSTM和CNN模型预测COVID-19病例

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10226084

Felipe Puente, Noel Pérez, D. Benítez, Felipe Grijalva, Daniel Riofrío, Maria Baldeon-Calisto, Yovani Marrero-Ponce

{"title":"Predicting COVID-19 Cases using Deep LSTM and CNN Models","authors":"Felipe Puente, Noel Pérez, D. Benítez, Felipe Grijalva, Daniel Riofrío, Maria Baldeon-Calisto, Yovani Marrero-Ponce","doi":"10.1109/ColCACI59285.2023.10226084","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10226084","url":null,"abstract":"The COVID-19 pandemic has had a profound and far-reaching impact on society. In order to effectively address this crisis, the timely implementation of necessary measures is crucial and accurate forecasting plays a vital role. In this context, this paper aims to use and compare deep learning techniques, specifically Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN), for predicting the number of confirmed cases of COVID-19. To achieve this, the study examines the performance of CNN and LSTM architectures in forecasting the number of infected cases, both for one-day and seven-day predictions. Evaluation of these methods is based on the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics, providing a comprehensive assessment of their effectiveness. The findings demonstrate that the CNN model proposed in this study exceeds the LSTM model, exhibiting superior prediction accuracy. Specifically, the CNN model achieves a mean MAPE score of 0.91 for one-day predictions and 4.85 for seven-day predictions, employing a ten-fold prediction time series split. These results highlight that both LSTM and CNN architectures are well-suited for forecasting tasks. The CNN model, in particular, shows excellent prediction efficiency, making it a promising approach for accurately estimating the number of cases of COVID-19 in the future.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"59 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":"129363604","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

Use of Convolutional Neural Network for Detection of Intracranial Hemorrhage 应用卷积神经网络检测颅内出血

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10225763

Karla Yamile Osorio Jacome, Jose Gerardo Chacon, Oscar J. Suarez, Anderson Smith Florez

引用次数: 0

Model Agnostic Approach for NLP Backdoor Detection NLP后门检测的模型不可知方法

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10226144

Hema Karnam Surendrababu

引用次数: 0

Detection of Pedaling Tasks through EEG Using Extreme Learning Machine for Lower-Limb Rehabilitation Brain-Computer Interfaces 基于极限学习机的下肢康复脑机接口脑电检测踏车任务

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10225911

Cristian Felipe Blanco-Díaz, C. D. Guerrero-Méndez, T. Bastos-Filho, A. F. Ruiz-Olaya, S. Jaramillo-Isaza

{"title":"Detection of Pedaling Tasks through EEG Using Extreme Learning Machine for Lower-Limb Rehabilitation Brain-Computer Interfaces","authors":"Cristian Felipe Blanco-Díaz, C. D. Guerrero-Méndez, T. Bastos-Filho, A. F. Ruiz-Olaya, S. Jaramillo-Isaza","doi":"10.1109/ColCACI59285.2023.10225911","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10225911","url":null,"abstract":"Brain-Computer Interfaces (BCI) are systems that may function as communication channels between people and external devices through brain information. BCIs using Electroencephalography (EEG) combined with robotic systems, such as minibikes, have enabled the rehabilitation of stroke patients by decoding their actions and executing a motor task. However, the Signal-to-Noise Ratio (SNR) of EEG is low, and there is intersubject variability for pedaling detection through brain information, which reduces the Accuracy of the rehabilitation devices. Additionally, in real-time BCIs, it is necessary to maintain a good ratio of detection and execution times. In this work, it is proposed a methodology based on an Extreme Learning Machine (ELM) to identify when the subject is executing pedaling tasks through EEG, which allows efficient detection with a maximum Accuracy of 0.85 and a False Positive Rate of 0.07. Additionally, four different frequency bands in the filtering stage were evaluated, and the results allowed concluding that the most discriminant information was available between two frequency bands: 3–7 Hz and 7–13 Hz, with an area under the ROC curve average of 0.71. The results indicate that the proposed method is suitable for the detection of pedaling tasks using EEG, which could be used for the control of a real-time BCI for lower-limb rehabilitation.","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":"122208582","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

Modular Robotic Arm for Teaching Braille to Children with Normal Visual Acuity 用于正常视力儿童盲文教学的模块化机械臂

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10226151

Santiago S. Puentes G., M. C. Moreno, Brayan Daniel Sarmiento, Oscar J. Suarez

{"title":"Modular Robotic Arm for Teaching Braille to Children with Normal Visual Acuity","authors":"Santiago S. Puentes G., M. C. Moreno, Brayan Daniel Sarmiento, Oscar J. Suarez","doi":"10.1109/ColCACI59285.2023.10226151","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10226151","url":null,"abstract":"This paper introduces the design and development of a modular robotic arm with four degrees of freedom (DoF) intended for implementation in educational environments as an interactive robotics tool for teaching the Braille system. This project arose from the need for inclusive and accessible education, using robotics to bring Braille to a broader audience and encourage collaborative learning. Integrating direct and inverse kinematics, as well as dynamics and a cubic spline interpolator, ensures accurate and efficient movements. Additionally, a neural network model was developed and trained to calculate the kinematic inverse of the robotic arm, demonstrating a high level of accuracy and reliability in solving the kinematic inverse problem. The last link is modular, which allows switching between a writing tool to teach the Braille alphabet to people with normal visual acuity, a gripper, and a holding mechanism that will allow didactically placing different pieces to form any letter of the alphabet in Braille, as well as other classroom uses.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"106 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":"132150335","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

Cognitive Search: A Free Information Retrieval Web Service to Coronavirus Scientific Papers 认知搜索:冠状病毒科学论文的免费信息检索网络服务

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/ColCACI59285.2023.10225924

C. M. Villalobos, L. Mendoza, Renato Sayão da Rocha, Jose Eduardo Ruiz, H. D. de Mello Junior, M. Pacheco

{"title":"Cognitive Search: A Free Information Retrieval Web Service to Coronavirus Scientific Papers","authors":"C. M. Villalobos, L. Mendoza, Renato Sayão da Rocha, Jose Eduardo Ruiz, H. D. de Mello Junior, M. Pacheco","doi":"10.1109/ColCACI59285.2023.10225924","DOIUrl":"https://doi.org/10.1109/ColCACI59285.2023.10225924","url":null,"abstract":"Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. With millions of people affected by the COVID-19 pandemic, many questions arise concerning transmission, diagnosis, treatment, development of vaccines, and viral pathogens. Bearing that in mind, the dangers of wrong and inaccurate information represent a socio-economic could do more damage than the disease itself. To help fight this ongoing outbreak, we present Cognitive Search - a friendly deployed service application IR exploring the latest language processing advance. The service provides access to CORD-19, a resource of scholarly articles about COVID-19 and related coronaviruses. The system allows rending documents retrieval by Term-Frequency and Semantic Neural Search and the Hybrid Term-Neural. The retrieval performance can often be significantly improved by using several different retrieval algorithms and allowing the user to combine the results instead of just one. Additionally, the Hybrid Term-Neural approach supports the exploitation of temporal information in documents and the usage of such information to anchor search results along a well-defined timeline. So it can generate insights through an intuitive and easy-to-use interface.","PeriodicalId":206196,"journal":{"name":"2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)","volume":"157 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":"116055394","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

Copyright Page 版权页

2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) Pub Date : 2023-07-26 DOI: 10.1109/colcaci59285.2023.10226019

引用次数: 0