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12 Papers

Feature Selection for Stock Market Prediction: A Comparison of Relief and Information Gain Methods

Topics: Knowledge Management; Applications of Expert Systems; Big Data, Data Science and Analytics

Authors: Humberto Bragança 1 , Rafael Berri 1 , Bruno Dalmazo 1 , Eduardo Borges 1 , Viviane D. de Mattos 1 , Richard Pinto 1 , Fabian Cardoso 2 and Giancarlo Lucca 3

Affiliations: 1 Federal University of Rio Grande (FURG), Rio Grande, Brazil , 2 University of Rio Verde (UniRV), Rio Verde, Brazil , 3 Catholic University of Pelotas (UCPel), Pelotas, Brazil

Digital Object Identifier: 10.5220/0013481300003929

Proceedings of ICEIS 2025, ISBN: 978-989-758-749-8

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This study explores an approach to predictive analysis in the financial market, using a data set composed of financial information from different companies listed on the stock market, which provides a more detailed and contextualized view of the behavior of shares. Based on these indicators, feature selection methods, such as Relief and Information Gain, are applied to identify the most relevant variables for building predictive models. One of the main contributions of this work is the use of cross-validation to evaluate attribute selection, a technique that has not yet been explored in this context with this dataset. The results show that the combination of new financial indicators and cross-validation offers a solid basis for more accurate analysis, with important implications for investors, financial analysts and policymakers in the stock market. This work expands the boundaries of the literature on feature selection and opens possibilities for future research in emerging markets.

Robotic Visual Attention Architecture for ADAS in Critical Embedded Systems for Smart Vehicles

Topics: Image-Based Modeling and 3D Reconstruction; Machine Learning Technologies for Vision; Object Detection and Localization; Tracking and Visual Navigation; Deep Learning for Tracking

Authors: Diego Bruno 1 , William Martins 1 , Rafael Berri 2 and Fernando Osório 3

Affiliations: 1 Sao Paulo State University (UNESP), Brazil , 2 Federal University of Rio Grande (FURG), Brazil , 3 University of Sao Paulo (USP), Brazil

Digital Object Identifier: 10.5220/0013362600003912

Proceedings of VISAPP 2025, ISBN: 978-989-758-728-3

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This paper presents the development of a perception architecture for Advanced Driver Assistance Systems (ADAS) capable of integrating (a) external and (b) internal vehicle perception to evaluate obstacles, traffic signs, pedestrians, navigable areas, potholes and deformations in road, as well as monitor driver behavior, respectively. For external perception, in previous works we used advanced sensors, such as the Velodyne LIDAR-64, the Bumblebee 3D camera for object depth analysis, but in this work, focusing on reducing hardware, processing and time costs, we apply 2D cameras with depth estimation generated by the Depth-Anything V2 network model. Internal perception is performed using the Kinect v2 and the Jetson Nano in conjunction with a SVM (Support Vector Machine) model, allowing the identification of driver posture characteristics and the detection of signs of drunkenness, drowsiness or disrespect for traffic laws. The motivation for this system lies in the fact that more than 90% of traffic accidents in Brazil are caused by human error, while only 1% are detected by surveillance means. The proposed system offers an innovative solution to reduce these rates, integrating cutting-edge technologies to provide advanced road safety. This perception architecture for ADAS offers a solution for road safety, alerting the driver and allowing corrective actions to prevent accidents. The tests carried out demonstrated an accuracy of more than 92% for external and internal perception, validating the effectiveness of the proposed approach.

A Computational Model for Predicting Cryptocurrencies Using Exogenous Variables

Topics: Neural Network Software and Applications; Data Mining; Deep Learning

Authors: Eduardo Gonçalves , Eduardo Nunes Borges , Bruno Dalmazo , Rafael Alceste Berri , Giancarlo Lucca and Vinicius M. de Oliveira

Affiliation: Center for Computational Sciences, Universidade Federal do Rio Grande, Rio Grande, Brazil

Digital Object Identifier: 10.5220/0011852000003467

Proceedings of ICEIS 2023, ISBN: 978-989-758-648-4

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The recent growth of cryptocurrencies caused worldwide interest due to capitalization power and geographic expansion. In this universe, Bitcoin is the main actor. Taking this into consideration, this paper aims to analyze the behavior of Bitcoin during the time. To do so, we use techniques already studied in the literature to perform the predictions and comparisons between methods jointly with exogenous variables to boost the results. An evaluation has been performed and the best results were achieved using the Long-Short-Term-Memory (LSTM) neural network model. Also, the experiments were carried out in different scenarios, using datasets with more than five years of daily records and exogenous variables to improve the performance of the models.

ADAS Classifier for Driver Monitoring and Driving Qualification using Both Internal and External Vehicle Data

Topics: Image Formation, Acquisition Devices and Sensors; Machine Learning Technologies for Vision; Event and Human Activity Recognition; Vision for Robotics

Authors: Rafael Alceste Berri 1 , Diego Renan Bruno 2 , Eduardo Borges 1 , Giancarlo Lucca 1 and Fernando Santos Osorio 3

Affiliations: 1 Center for Computational Science, Federal University of Rio Grande (FURG), Rio Grande, RS, Brazil , 2 São Paulo State Faculty of Technology, São Paulo, Brazil , 3 University of São Paulo, São Paulo, Brazil

Digital Object Identifier: 10.5220/0010880100003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

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In this paper, we present an innovative safety system for driver monitoring and quality of how a vehicle is being controlled by a human driver. The main objective of this work is linked to the goal of detecting human failures in the task of driving, improving the predictions of human failures. In this work, we used 3D information of the driver’s posture and also the vehicles’ behavior on the road. Our proposal is able to act when human inappropriate behaviors are detected by applying a set of automatic routines to minimize their consequences. It is also possible to produce safety alarms/warnings in order to re-educate the driver to maintain good posture practices and to avoid dangerous driving using only few seconds (2.5s) of data capture. This can help to improve traffic, drivers’ education, and benefits with the reduction of accidents. When a highly dangerous behavior/situation is detected, using 140 seconds of recorded data, an autonomous parking system is activated, parking the vehicle in a safe position. We present in this paper new classifiers for ADAS (Advanced Systems of Driver Assistance) based on Machine Learning. Our classifiers are based on Artificial Neural Nets (ANN), furthermore, the values set to adjust input features, neuron activation functions, and network topology/training parameters were optimized and selected using a Genetic Algorithm. The proposed system achieved results of 79.65% of accuracy in different alarm levels (short and long term), for joint detection of risk in situations of cellphone usage, drunkenness, or regular driving. Only 1.8% of normal situations have wrong predictions (false positive alarms) in Naturalistic Driver Behavior Dataset frames, contributing to the driver’s comfort when he/she is using the system. In the near future we aim to improve these results even more.

Analyzing the Determinant Characteristics for a Good Performance at ENADE Brazilian Exam Stratified by Teaching Modality: Face-to-face versus Online

Topics: Data Mining; Databases in the Cloud

Authors: Eric Gondran 1 , Giancarlo Lucca 1 , Rafael Berri 1 , Helida Santos 1 ; 2 and Eduardo N. Borges 1

Affiliations: 1 C3, Universidade Federal do Rio Grande, Av. Itália km 08, Rio Grande, Brazil , 2 ISC, Universidad Publica de Navarra, Campus Arrosadia, Pamplona, Spain

Digital Object Identifier: 10.5220/0011070600003179

Proceedings of ICEIS 2022, ISBN: 978-989-758-569-2

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The National Student Performance Exam (ENADE) annually evaluates different Brazilian higher education courses. This exam considers both face-to-face and distance learning courses. Distance learning is growing increasingly, especially during the coronavirus (COVID-19) pandemic. This study applies different techniques for selecting ENADE 2018 database characteristics, like information gain, gain rate, symmetric uncertainty, Pearson correlation, and relief F. The objective of the work is to discover which personal and socioeconomic characteristics are decisive for the student’s performance at ENADE, whether the student is in the context of Distance Education or face-to-face. It can be concluded, among other results, that: the father’s level of education directly influences performance; the higher the income, the better the performance; and white students have better performance than black and brown-skinned ones. Thus, the results obtained in this study may initiate analyzes of public policies towards improving performance at ENADE.

An Efficiency Frontier based Model for Cloud Computing Provider Selection and Ranking

Topics: Strategic Decision Support Systems; Modeling of Distributed Systems; Internet Agents

Authors: Lucas Borges de Moraes 1 , Pedro Cirne 2 , Fernando Matos 3 , Rafael Stubs Parpinelli 1 and Adriano Fiorese 1

Affiliations: 1 Santa Catarina State University (UDESC), Brazil , 2 Instituto de Telecomunicações, Portugal , 3 Federal University of Paraíba (UFPB)

Digital Object Identifier: 10.5220/0006775705430554

Proceedings of ICEIS 2018, ISBN: 978-989-758-298-1

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Cloud computing become a successful service model that allows hosting and distribution of computational resources all around the world, via Internet and on demand. This success leveraged and popularized its adoption into all major IT companies. Based on this success, a large number of new companies were competitively created as providers of cloud computing services. This fact can difficult the customer ability to choose among those several cloud providers the most appropriate one to attend their requirements and computing needs. Therefore, this work aims to propose a model capable of selecting and ranking cloud providers according the analysis on the most efficient ones using a popular Multicriteria Decision Analysis (MCDA) method called Data Envelopment Analysis (DEA), that calculates efficiency using Linear Programming (LP) techniques. To accomplish that, the efficiency modeling is based on the analysis of each Performance Indicator (PI) values desired by the customer and the available ones in the cloud provider database. An example of the method’s usage is given to illustrate the model operation, selection results and final provider ranking for five hypothetical customer requests and for ten providers.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Artificial Intelligence and Decision Support Systems

Internet Agents

Software Agents and Internet Computing

Enterprise Information Systems

Modeling of Distributed Systems

Strategic Decision Support Systems
Information Systems Analysis and Specification

A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection

Topics: Virtual Enterprises; Collaborative Computing; Measurements, Metrics and Evaluation of EA Artefacts and Processes; Databases in the Cloud

Authors: Lucas Borges de Moraes 1 , Adriano Fiorese 1 and Fernando Matos 2

Affiliations: 1 Santa Catarina State University (UDESC), Brazil , 2 Federal University of Paraíba (UFPB)

Digital Object Identifier: 10.5220/0006289305880599

Proceedings of ICEIS 2017, ISBN: 978-989-758-248-6

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Cloud computing is a service model that allows hosting and on demand distribution of computing resources all around the world, via Internet. Thus, cloud computing has become a successful paradigm that has been adopted and incorporated into virtually all major known IT companies (e.g., Google, Amazon, Microsoft). Based on this success, a large number of new companies were competitively created as providers of cloud computing services. This fact hindered the clients’ ability to choose among those several cloud computing providers the most appropriate one to attend their requirements and computing needs. This work aims to specify a logical/mathematical multi-criteria scoring method able to select the most appropriate(s) cloud computing provider(s) to the user (customer), based on the analysis of performance indicator values desired by the customer and associated with every cloud computing provider that supports the demanded requirements. The method is a three stages algorithm that evaluates, scores, sorts and selects different cloud providers based on the utility of their performance indicators for each specific user of the method. An example of the method’s usage is given in order to illustrate its operation.
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Collaborative Computing

Software Agents and Internet Computing

Virtual Enterprises
Enterprise Information Systems

A Trust Reputation Architecture for Virtual Organization Integration in Cloud Computing Environment

Topics: Modeling of Distributed Systems; Virtual Enterprises; Simulation; Collaborative Computing

Authors: Luís Felipe Bilecki 1 , Adriano Fiorese 1 and Fernando Matos 2

Affiliations: 1 Santa Catarina State University (UDESC), Brazil , 2 Federal University of Paraíba (UFPB)

Digital Object Identifier: 10.5220/0006276506950702

Proceedings of ICEIS 2017, ISBN: 978-989-758-248-6

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Virtual Organization (VO) represents a prominent collaboration initiative, where a set of entities share competencies and risks attending a common goal. Moreover, their interactions can be supported in an Internet basis, using Cloud Computing (CC) resources. The VO and CC integration brings several benefits, such as: reduction of costs and maintenance, interoperability, among others. However, there are issues related to privacy, trust and security that need to be addressed. One of the issues observed is how much trust VO members put in the cloud provider (CP), particularly, in a scenario where VO members use the resources provided by a CP to made available their services and in order to interact with other members. Thus, the proposed reputation architecture intends to assist the decision-making processes present in the VO’s life-cycle reputing CP trust. The reputed trust is based on two sources: a) objective (Quality of Service (QoS) indicators) and b) subjective (feedback from users regarding those QoS indicators). The evaluation results show that the architecture is resilient to attacks on subjective trust during the reputation calculation. Also, it is possible to note that the proposed architecture presents an acceptable average time for each one operation, and a significant role during VO’s creation and operation.
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Agents

Enterprise Information Systems

Operational Research

Artificial Intelligence

Information Systems Analysis and Specification

Simulation

Bioinformatics

Methodologies and Technologies

Software Agents and Internet Computing

Biomedical Engineering

Modeling of Distributed Systems

Virtual Enterprises
Collaborative Computing

A Pattern Recognition System for Detecting Use of Mobile Phones While Driving

Topics: Segmentation and Grouping; Machine Learning Technologies for Vision; Color and Texture Analyses; Features Extraction; Object and Face Recognition; Object Detection and Localization; Categorization and Scene Understanding

Authors: Rafael A. Berri 1 , Alexandre G. Silva 1 , Rafael S. Parpinelli 1 , Elaine Girardi 1 and Rangel Arthur 2

Affiliations: 1 Santa Catarina State University (UDESC), Brazil , 2 University of Campinas (Unicamp), Brazil

Digital Object Identifier: 10.5220/0004684504110418

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-004-8

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It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone), containing frontal images of the driver. Support Vector Machine (SVM) with Polynomial kernel is the most advantageous classification system to the features provided by the algorithm, obtaining a success rate of 91.57% for the vision system. Tests done on videos show that it is possible to use the image datasets for training classifiers in real situations. Periods of 3 seconds were correctly classified at 87.43% of cases.
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Color and Texture Analyses

Features Extraction

Segmentation and Grouping
Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

A Risk Analysis Method for Selecting Service Providers in P2P Service Overlay Networks

Topic: Virtual Enterprises

Authors: Rafael Giordano Vieira , Omir Correia Alves Junior and Adriano Fiorese

Affiliation: Santa Catarina State University, Brazil

Digital Object Identifier: 10.5220/0004865804770488

Proceedings of ICEIS 2014, ISBN: 978-989-758-028-4

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In an increasingly competitive market place, the development of collaborative networked environments has become a key factor to companies successfully leverage their business activities. Nevertheless, when these companies get involved in more volatile strategic networks, it is necessary to deal with additional risks that need to be identified, measured, and mitigated through a well defined process. In this sense, this paper aims to specify a method for risk analysis comprising a set of service providers (SPs) in a P2P Service Overlay Network (SON). In this applied, qualitative and essentially exploratory work, the proposed method assesses the level of risk present in a set of previously selected SPs using key performance indicators (KPIs), and measures the viability of a Virtual Organization (VO) formation using those selected SPs. A computational prototype was also specified and used to execute a set of tests to assess the proposed risk analysis method.
Collapse Button Related Ontology Subjects/Areas/Topics (3)
Enterprise Information Systems

Software Agents and Internet Computing

Virtual Enterprises

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