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International Conference on Marketing and technologies, ICMarktech 2023

作者： Coutinho, Evelina Maria Oliveira Au-Yong-Oliveira, Manuel Department of Economics Management Industrial Engineering and Tourism Campus Universitário de Santiago University of Aveiro Aveiro3810-193 Portugal Research Unit On Governance Competitiveness and Public Policies Campus Universitário de Santiago University of Aveiro Aveiro3810-193 Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (纸本)9789819736973

In recent decades, the concept of sustainability has taken on a key role in the agendas of political decision-makers, businesses, and society. Brands have incorporated attributes associated with the concept of sustainability, making it essential to understand how these attributes are perceived and how they influence purchasing decisions. Young consumers tend to be well informed about social and environmental aspects and are more sensitive than previous generations to environmental issues. Although Gen Z’s purchasing intentions are influenced by the sustainable practices of organizations, their preferences are not fully understood. Recent studies have shown that the sustainability component associated with the brand influences Gen Z’s purchase intentions, and that social and economic aspects are at the root of this, emphasizing the importance of corporate social responsibility and transparent brand communication in terms of sustainability. Understanding how the sustainability component of brands influences the purchasing decisions of young consumers is fundamental for defining future branding strategies. The aim of this paper is to describe the state of the art of the relationship between the sustainability component of the brand and the purchase intention of Gen Z, based on the literature. The topic has not yet been widely studied, so it leaves clues for future development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sustainable development goals

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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Miguel Simões Tania Pereira Francisco Silva José Carlos Machado Hélder P. Oliveira INESC TEC - Institute for Systems and Computer Engineering Technology and Science FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP - Faculty of Science University of Porto Porto Portugal IPATIMUP - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde FMUP - Faculty of Medicine University of Porto Porto Portugal

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

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A Simulation-Based Approach to Reduce Waiting Times in Emergency Departments 2nd

A Simulation-Based Approach to Reduce Waiting Times in Eme...

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2nd International Scientific Conference on Innovation in engineering, ICIE 2022

作者： Teymourifar, Aydin CEGE - Centro de Estudos em Gestão e Economia Católica Porto Business School Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9783031093845

The goal of this work is to utilize a simulation model to reduce waiting times before examinations in emergency departments. Different types of arrivals to an emergency department, basic processes, and possible queues are explained. The implementation is conducted in the Rockwell Arena software and some related details are provided. The validity of the simulation model is demonstrated. Then scenarios are developed based on limited boosting of resources to reduce waiting times. It is analyzed how waiting times can be reduced. Also, it is discussed that which resources should be increased for this aim. The Process Analyzer software is used for the analysis of the scenarios, whose details are given. It is also analyzed how the utilization rate of resources changes in scenarios. Data from the literature are used for experimental results. Since the analyzes are based on generalizable approaches, they can be applied in similar emergency departments. The results show that with a reasonable increase in resources, it is possible to significantly reduce waiting times. The implications that may be useful in the management of emergency departments are presented. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete event simulation

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Ovarian Structures Detection using Convolutional Neural Networks

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Procedia computer science 2022年 196卷 542-549页

作者： Diego Wanderley Carlos Ferreira Aurélio Campilho Jorge Silva Faculty of Engineering of the University of Porto (FEUP) Porto Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

The detection of ovarian structures from ultrasound images is an important task in gynecological and reproductive medicine. An automatic detection system of ovarian structures can work as a second opinion for less experienced physicians or complex ultrasound interpretations. This work presents a study of three popular CNN-based object detectors applied to the detection of healthy ovarian structures, namely ovary and follicles, in B-mode ultrasound images. The Faster R-CNN presented the best results, with a precision of 95.5% and a recall of 94.7% for both classes, being able to detect all the ovaries correctly. The RetinaNet showed competitive results, exceeding 90% of precision and recall. Despite being very fast and suitable for real-time applications, YOLOv3 was ineffective in detecting ovaries and had the worst results detecting follicles. We also compare CNN results with classical computer vision methods presented in the ovarian follicle detection literature.

关键词： follicle detection ovary detection ovarian structures gynecological ultrasound convolutional neural networks

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Analysis of the Portuguese and Spanish NECPs Using the CEVESA MIBEL Market Model

Analysis of the Portuguese and Spanish NECPs Using the CEVES...

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International Conference on European Electricity Market, EEM

作者： André Rodrigues de Oliveira José Villar Collado Salvador Doménech Martínez João Abel Peças Lopes João Tomé Saraiva Fco. Alberto Campos INESC TEC – Institute for Systems and Computer Engineering Technology and Science Faculdade de Engenharia da Universidade do Porto Portugal Institute for Research in Technology Technical School of Engineering Comillas Pontifical University Madrid Spain

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

The member states of the European Union (EU) are actively reassessing their National Energy and Climate Plans (NECPs) [1] to jointly address climate challenges and the impacts of the COVID pandemic and gas supply crisis. This study extends the analyses described in [2] by assessing the impact of the updated NECP drafts for Portugal and Spain [3], [4] on the Iberian Electricity Market (MIBEL). For this, we use CEVESA, a market model for the long-term planning and operation of MIBEL that computes the joint dispatch of energy and secondary reserve of the two interconnected single-price zones. Departing from the expected evolution of the electricity generation technologies and demand available in the NECP drafts, joint scenarios for Portugal and Spain are built with the latest CO 2 allowances and fuel prices projections and the latest available historical data of hydro and renewable generation profiles. Simulations provide estimates for the expected market prices, technology generation dispatch, and the usage of the capacity of the interconnection lines between both countries, highlighting potential concerns and knowledge on future NECPs.

关键词： Analytical models Schedules Renewable energy sources Electricity Europe Production Pricing

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Data Fusion Approach for Unmodified UAV Tracking with Vision and mmWave Radar

Data Fusion Approach for Unmodified UAV Tracking with Vision...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Guilherme Amaral João J. Martins Pedro Martins André Dias José Almeida Eduardo Silva INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

The knowledge of the precise 3D position of a target in tracking applications is a fundamental requirement. The lack of a low-cost single sensor capable of providing the three-dimensional position (of a target) makes it necessary to use complementary sensors together. This research presents a Local Positioning System (LPS) for outdoor scenarios, based on a data fusion approach for unmodified UAV tracking, combining a vision sensor and mmWave radar. The proposed solution takes advantage of the radar's depth observation ability and the potential of a neural network for image processing. We have evaluated five data association approaches for radar data cluttered to get a reliable set of radar observations. The results demonstrated that the estimated target position is close to an exogenous ground truth obtained from a Visual Inertial Odometry (VIO) algorithm executed onboard the target UAV. Moreover, the developed system's architecture is prepared to be scalable, allowing the addition of other observation stations. It will increase the accuracy of the estimation and extend the actuation area. To the best of our knowledge, this is the first work that uses a mmWave radar combined with a camera and a machine learning algorithm to track a UAV in an outdoor scenario.

关键词： Visualization Target tracking Radar clutter Airborne radar Data integration Radar imaging Vision sensors Radar tracking Autonomous aerial vehicles Millimeter wave communication

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Automating the Annotation of Medical Images in Capsule Endoscopy Through Convolutional Neural Networks and CBIR 12th

Automating the Annotation of Medical Images in Capsule Endos...

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Fernandes, Rodrigo Salgado, Marta Paçal, Ishak Cunha, António UTAD—University of Trás-os-Montes and Alto Douro Vila Real5001-801 Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Centro Hospitalar Universitário de Santo António Porto4099-001 Portugal Igdir University Iğdır Turkey

ISBN: (纸本)9783031606649

This research addresses the significant challenge of automating the annotation of medical images, with a focus on capsule endoscopy videos. The study introduces a novel approach that synergistically combines Deep Learning and Content-Based Image Retrieval (CBIR) techniques to streamline the annotation process. Two pre-trained Convolutional Neural Networks (CNNs), MobileNet and VGG16, were employed to extract and compare visual features from medical images. The methodology underwent rigorous validation using various performance metrics such as accuracy, AUC, precision, and recall. The MobileNet model demonstrated exceptional performance with a test accuracy of 98.4%, an AUC of 99.9%, a precision of 98.2%, and a recall of 98.6%. On the other hand, the VGG16 model achieved a test accuracy of 95.4%, an AUC of 99.2%, a precision of 97.3%, and a recall of 93.5%. These results indicate the high efficacy of the proposed method in the automated annotation of medical images, establishing it as a promising tool for medical applications. The study also highlights potential avenues for future research, including expanding the image retrieval scope to encompass entire endoscopy video databases. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Endoscopy

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A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things

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Cluster Computing 2023年第1期26卷 599-612页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja’fari, Forough Pinto, Pedro Zhang, Weizhe Balasubramanian, Sudha Department of Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal School of Computing Science and Engineering VIT University Vellore India Universidade da Maia Maia4475-690 Portugal INESC TEC Porto4200-465 Portugal

Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Cloud computing

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Deep Spatio-Temporal Ensemble to Improve Wind Speed Forecasting

SSRN

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SSRN 2023年

作者： Raynaud-Sanchez, Jaime Garcia-Gutierrez, Jorge Gonçalves-Seco, Luis Division of Computer Science University of Seville Seville41012 Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Maia Porto4475-690 Portugal

The accuracy of wind speed forecasting is critical for effectively integrating renewable energy sources, such as wind power, into the electrical grid. In recent years, deep learning techniques have gained popularity for forecasting renewable energy, especially in the field of spatio-temporal wind forecasting due to their ability to uncover the spatial and temporal correlations in wind speed data. In this context, an ensemble of three Recurrent Neural Networks is proposed for improving spatio-temporal wind speed forecasting, aiming to determine the efficacy of an ensemble approach in enhancing the precision of a state-of-the-art spatio-temporal wind speed forecasting method obtained from the literature. To establish solid results, experiments were conducted on 31 spatio-temporal wind speed datasets, obtained from the National Renewable Energies Laboratory, representing various locations worldwide. With the proposed approach, the ensemble algorithm presented demonstrates the ability to enhance the forecasting performance of the state-of-the-art technique from the literature upon which it is based, reducing its Mean Absolute Error on average by 45.82% and producing statistically significant results. © 2023, The Authors. All rights reserved.

关键词： Wind power

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