Pigmented Dermatological Lesions Classification Using Convolutional Neural Networks Ensemble Mediated by Multilayer Perceptron Network Jean Phelipe de Oliveira Lima, Luiz Carlos Silva de Araújo Filho, Fábio Santos da Siva, Carlos Maurício Seródio Figueiredo IEEE Latin America Transactions, 2019 Skin cancer has the highest occurrence rate compared to other types of cancer. This paper presents the development of a Deep Learning model, trained from the Skin Cancer MNIST database (HAM10000). It is able to perform Classification of Pigmented Dermatological Lesions using Convolutional Neural Networks techniques by proposing an ensemble with Multilayer Perceptron Neural Networks. In order to evaluate the Convolutional Networks, the metrics Accuracy, Precision, Revocation and F1-Score were taken into consideration. The ensemble implementation was based on a Grid Search with Cross Validation and evaluated according to Accuracy. The obtained results show the relevance of the research and the consolidation of the techniques used in the development of Artificial Intelligence solutions applied to clinical images analysis. Accuracy and Recall reached 0.93 and Precision and F1-Score 0.92, which is superior performance to specialists and related researches.
A sampling algorithm for intermittently connected delay tolerant wireless sensor networks Israel L. C. Vasconcelos, David H. S. Lima, Carlos M. S. Figueiredo, Andre L. L. Aquino Proceedings IEEE Symposium on Computers and Communications, 2016 This work presents a sampling solution applied to intermittently connected delay tolerant wireless sensor networks (ICDT-WSNs). In such networks, when the storage capacity of a node is limited compared to the amount of data to collect, it is common to apply a packet drop strategy based on network layer parameters only. However, such strategies are not suitable for monitoring applications of WSNs where data quality is essential. Alternatively, we propose a data-aware drop strategy which applies a sampling algorithm over the collected data in order to reduce the amount of stored data while keeping an overall data quality to represent the monitored area. In order to evaluate our solution, we executed and compared it with drop strategies in literature. We modeled and simulated scenarios where different events occur. The results show that the sampling algorithm is, approximately, twice better than common drop strategies in all evaluated scenarios.
Characterizing the communication in the Amazon rainforest: Towards a realistic simulation Afonso D. Ribas, Antonio R. Carvalho, Carlos M. S. Figueiredo, Eduardo F. Nakamura Journal of the Brazilian Computer Society, 2013 Many literature papers evaluate solutions in wireless sensor networks by simulation experiments. However, little attention is given to the adequacy of the simulator propagation models to the environment in which such solutions are employed. This can lead to imprecise or inconsistent results in relation to the real world. This paper presents a methodology for adjusting the parameters of these models. In particular, we present experimental results for rainforest environments, which can be the goal of many sensor networks monitoring applications. The impact of the proposed approach is shown by evaluating a localization solution. The results show that this procedure is necessary for a higher fidelity of simulation experiments.
Reducing the impact of location errors for target tracking in wireless sensor networks Éfren L. Souza, Eduardo F. Nakamura, Horácio A. B. F. de Oliveira, Carlos M. S. Figueiredo Journal of the Brazilian Computer Society, 2013 In wireless sensor networks (WSNs), target tracking algorithms usually depend on geographical information provided by localization algorithms. However, errors introduced by such algorithms affect the performance of tasks that rely on that information. A major source or errors in localization algorithms is the distance estimation procedure, which often is based on received signal strength indicator measurements. In this work, we use a Kalman Filter to improve the distance estimation within localization algorithms to reduce distance estimation errors, ultimately improving the target tracking accuracy. As a proof-of-concept, we chose the recursive position estimation and directed position estimation as the localization algorithms, while Kalman and Particle filters are used for tracking a moving target. We provide a deep performance assessment of these combined algorithms (localization and tracking) for WSNs are used. Our results show that by filtering multiple distance estimates in the localization algorithms we can improve the tracking accuracy, but the associate communication cost must not be neglected.
Compressive sensing for efficiently collecting wildlife sounds with wireless sensor networks Javier J. M. Diaz, Juan G. Colonna, Rodrigo B. Soares, Carlos M. S. Figueiredo, Eduardo F. Nakamura 2012 21st International Conference on Computer Communications and Networks ICCCN 2012 Proceedings, 2012 Wildlife sounds provide relevant information for non-intrusive environmental monitoring when Wireless Sensor Networks (WSNs) are used. Thus, collecting such audio data, while maximizing the network lifetime, is a key challenge for WSNs. In this work, we propose a methodology that applies Compressive Sensing (CS) aiming at collecting as little data as possible to allow the signal reconstruction, so that the reconstructed signal is still representative. The key issue is to determine a sparse base that best represents the audio information used for identifying the target species. As a proof-of-concept, we focus on anuran (frogs and toads) calls, but the methodology can be applied for other animal families and species. The reason for that choice is that long-term anuran monitoring has been used by biologists as an early indicator for ecological stress. By using real wild anuran calls, we show that 98% classification rate can be achieved by using as little as 10% of the original data. We also use simulation to evaluate the impact of our solution on the network performance (energy consumption, delivery rate, and network delay).
A coverage-based drop-policy in wireless sensor network with disruptive connections Daniel Frazao Luiz, Carlos M. S. Figueiredo, Eduardo F. Nakamura Proceedings IEEE Symposium on Computers and Communications, 2012 Many applications in Wireless Sensor Networks (WSNs) consider remote and large scale monitoring. For those scenarios, the whole network is difficultly fully connected all the time. A possible vision is the union of WSNs and Disruptive Tolerant Network(DTNs) concepts, where mobile nodes make collect data in sparse networks and deliver them to a base station. This work presents a buffer management strategy, which is a basic principle in DTN networks. The proposed solution considers the global coverage to choose which messages are dropped, therefore, minimizing the impact on monitoring application. Such solutions are important for WSNs applications with limited resources. We show through simulation that the proposed Coverage-Based Drop-Policy in Wireless Sensor Network with Disruptive Connections (CBDP) improves the network coverage compared to traditional DTN drop policies like Drop Last Packet (DL) and Drop First Packet (DF).
VCARP: Vehicular Ad-hoc Networks context-aware routing protocol Rodrigo B. Soares, Eduardo F. Nakamura, Carlos M. S. Figueiredo, Antonio A. F. Loureiro Proceedings IEEE Symposium on Computers and Communications, 2012 Vehicular Networks routing protocols must deal with several issues such as high mobility, high speed and, consequently, high disconnection rate among the network nodes. In this paper, we propose VCARP, a geocast routing protocol for Vehicular Networks that takes into account context informations of the network (such as nodes location and destination) to make routing decisions. It consists of a shared cache mechanism used to avoid packets loss due to full caches, and a flow-based routing to reduce network overhead caused by unnecessary packet retransmissions. Simulations show that the proposed protocol can increase packet delivery rate by 40% and decrease overhead by up to 89%, compared to another geocast protocol of the literature. In addition, we have studied the influence of cache size and neighbors discovery period duration on the packet delivery rate.
Similarity clustering for data fusion in Wireless Sensor Networks using k-means Afonso D. Ribas, Juan G. Colonna, Carlos M. S. Figueiredo, Eduardo F. Nakamura Proceedings of the International Joint Conference on Neural Networks, 2012 Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, in-network data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the application of value fusion (for clusters of nodes with correlated measurements) and decision fusion (combining the local decisions of the clusters) is a common strategy. In this work, we propose an algorithm for properly selecting the groups of nodes with correlated measurements. Experiments show that our algorithm is 30% better than a solution that considers only the spatial coherence regions.
On the design of UPnP gateways for service discovery in wireless sensor networks Bruno da Silva Campos, Eduardo Freire Nakamura, Carlos Mauricio S. Figueiredo, Joel J. P. C. Rodrigues Proceedings IEEE Symposium on Computers and Communications, 2011 In this paper, it is presented the design of a Universal Plug and Play (UPnP) gateway for wireless sensor networks (WSNs), capable of discovering all the services of a WSN, and creating a unique UPnP device to advertise them to interested UPnP control points. Besides, an application protocol for WSN is designed to exchange UPnP-related data between the WSN and the gateway. Experiments in an indoor environment with restricted sensor nodes confirm the feasibility of this solution.
Multivariate reduction in wireless sensor networks Orlando Silva Junior, Andre L. L. Aquino, Raquel A. F. Mini, Carlos M. S. Figueiredo Proceedings IEEE Symposium on Computers and Communications, 2009
Algorithms for Mobile Ad Hoc Networks Azzedine Boukerche, Daniel Câmara, Antonio A. F. Loureiro, Carlos M. S. Figueiredo Algorithms and Protocols for Wireless and Mobile Ad Hoc Networks, 2008
Data stream based algorithms for wireless sensor network applications Andr L.L. de Aquino, Carlos M.S. Figueiredo, Eduardo F. Nakamura, Luciana S. Buriol, Antonio A.F. Loureiro, et al. Proceedings International Conference on Advanced Information Networking and Applications AINA, 2007
Policy-based adaptive routing in autonomous WSNs Carlos M. S. Figueiredo, Aldri L. dos Santos, Antonio A. F. Loureiro, José M. Nogueira Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Gender Identification in Brazilian Portuguese Product Reviews: A Comparative Study of Classical Models, BERT, and LLMs T de Melo, CMS Figueiredo Proceedings of the 17th International Conference on Computational Processing … , 2026 2026
Discovery of Legal Patterns in Civil Petitions via LLM-Based Fact Extraction and Density Clustering R Esashika, CMS Figueiredo, T de Melo Proceedings of the 17th International Conference on Computational Processing … , 2026 2026
Rating–Text Mismatch in Brazilian Portuguese Reviews: How Reliable Are Zero-Shot LLMs? E Marreira, CMS Figueiredo, T de Melo Proceedings of the 17th International Conference on Computational Processing … , 2026 2026
Detectando Incoerências Avaliativas em E-commerce com LLMs-Um Estudo de Caso na Amazon Brasil E Marreira, T de Melo, M Oliveira, C Maurício Brazilian Symposium on Multimedia and the Web (WebMedia), 535-539 , 2025 2025
Rating Prediction in Brazilian Portuguese: A Benchmark of Large Language Models E Marreira, T de Melo, M de Oliveira, CMS Figueiredo Journal of the Brazilian Computer Society 31 (1), 827-838 , 2025 2025
Comparing LLMs for Sentiment Analysis in Financial Market News LE Pereira Teles, C Figueiredo arXiv e-prints, arXiv: 2510.15929 , 2025 2025
A Multimodal Approach for Music Genre Classification Using Audio and Lyrics Embeddings JML Silva, CMS Figueiredo, EB Guedes, T De Melo Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 1293-1304 , 2025 2025
Structuring Information from Initial Petitions Using LLMs: A Study in Brazilian Courts of Justice R Esashika, CMS Figueiredo, T de Melo Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 546-557 , 2025 2025
Monitoramento Inteligente de Tempo Real para Tráfego Urbano em Cidades Brasileiras C Loureiro, EB Guedes, CMS Figueiredo Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP), 41-50 , 2025 2025
Explorando o uso de VLMs para classificação Zero-Shot de Imagens CMS Figueiredo, TE de Melo Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP), 1-10 , 2025 2025
OneTrack-M: A multitask approach to transformer-based MOT models L de Araujo, C Figueiredo arXiv preprint arXiv:2502.04478 , 2025 2025
Explorando a Eficácia das Linguagens Generativas em Tarefas de Análise de Sentimentos no Português Brasileiro G de Araújo, T de Melo, CMS Figueiredo Linguamática 16 (2), 41-58 , 2024 2024 Citations: 1
A Digital Twin-Based Framework for Smart Spaces VR dos Santos, EB Guedes, CMS Figueiredo Congresso Brasileiro de Automática-CBA 4 (1) , 2024 2024
Anomaly Detection in Sound Activity with Generative Adversarial Network Models WA de Oliveira Neto, EB Guedes, CMS Figueiredo Journal of Internet Services and Applications 15 (1), 313-324 , 2024 2024 Citations: 3
OneTrack-An End2End approach to enhance MOT with Transformers L Araujo, C Figueiredo Journal of Internet Services and Applications 15 (1), 302-312 , 2024 2024
OneTrack-Modelos Baseados em Transformers e Eficientes em Tempo de Inferência para Rastreamento de Múltiplos Objeto LC Araújo Filho Universidade Federal do Amazonas , 2024 2024
Desenvolvimento de um Sistema de Monitoramento de Exercícios Fisioterápicos com Auto-Encoder LSTM LHC Evangelista, CMS Figueiredo, EB Guedes Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP), 101-110 , 2024 2024
Is chatgpt an effective solver of sentiment analysis tasks in portuguese? a preliminary study G de Araujo, T de Melo, CMS Figueiredo Proceedings of the 16th International Conference on Computational Processing … , 2024 2024 Citations: 9
Application of Digital Image Processing in a Deepfake Detection Network LM Da Rosa, CMS Figueiredo Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 501-509 , 2023 2023 Citations: 1
Modelos geradores para detecções de anomalias em atividades sonoras WA Oliveira Neto Universidade Federal do Amazonas , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Algorithms for Mobile and Ad Hoc Networks A Boukerche, D Camara, C Figueiredo Algorithms and protocols for wireless and mobile ad hoc networks 77, 1-19 , 2008 2008 Citations: 805
Redes de sensores sem fio AAF Loureiro, JMS Nogueira, LB Ruiz, RAF Mini, EF Nakamura, ... Simpósio Brasileiro de Redes de Computadores (SBRC) 21, 179-226 , 2003 2003 Citations: 323
Computação móvel: Novas oportunidades e novos desafios CMS Figueiredo, E Nakamura T&C Amazônia 1 (2), 21 , 2003 2003 Citations: 111
Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach T de Melo, CMS Figueiredo JMIR Public Health and Surveillance 7 (2), e24585 , 2021 2021 Citations: 109
Bluepass: An indoor bluetooth-based localization system for mobile applications JJM Diaz, RA Maues, RB Soares, EF Nakamura, CMS Figueiredo The IEEE symposium on Computers and Communications, 778-783 , 2010 2010 Citations: 90
Arquiteturas para redes de sensores sem fio LB Ruiz, LHA Correia, LFM Vieira, DF Macedo, EF Nakamura, ... Tutorial of the simpósio Brasileiro de Redes de Computadores e Sistemas … , 2004 2004 Citations: 69
Model to integration of RFID into wireless sensor network for tracking and monitoring animals DP Pereira, WRA Dias, M de Lima Braga, R da Silva Barreto, ... 2008 11th IEEE International Conference on Computational Science and … , 2008 2008 Citations: 51
Using information fusion to assist data dissemination in wireless sensor networks EF Nakamura, FG Nakamura, CMS Figueiredo, AAF Loureiro Telecommunication Systems 30 (1-3), 237-254 , 2005 2005 Citations: 48
Design and construction of wireless sensor network gateway with IPv4/IPv6 support B da Silva Campos, JJPC Rodrigues, LDP Mendes, EF Nakamura, ... 2011 IEEE international conference on communications (ICC), 1-5 , 2011 2011 Citations: 40
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection JP de Oliveira Lima, CMS Figueiredo Inteligencia Artificial 24 (67), 40-50 , 2021 2021 Citations: 36
On the use of scrum for the management of practcal projects in graduate courses L Pinto, R Rosa, C Pacheco, C Xavier, R Barreto, V Lucena, M Caxias, ... 2009 39th IEEE Frontiers in Education Conference, 1-6 , 2009 2009 Citations: 36
A comprehensive framework for industrial sticker information recognition using advanced OCR and object detection techniques G Monteiro, L Camelo, G Aquino, RA Fernandes, R Gomes, A Printes, ... Applied Sciences 13 (12), 7320 , 2023 2023 Citations: 35
Data stream based algorithms for wireless sensor network applications ALL De Aquino, CMS Figueiredo, EF Nakamura, LS Buriol, AAF Loureiro, ... 21st International Conference on Advanced Information Networking and … , 2007 2007 Citations: 35
An integrated approach for density control and routing in wireless sensor networks IG Siqueira, CMS Figueiredo, AAF Loureiro, JM Nogueira, LB Ruiz Proceedings 20th IEEE International Parallel & Distributed Processing … , 2006 2006 Citations: 35
A first public dataset from Brazilian twitter and news on COVID-19 in Portuguese T DE MELO, CMS FIGUEIREDO Data in Brief 32 , 2020 2020 Citations: 34
Assessing the communication performance of wireless sensor networks in rainforests CMS Figueiredo, EF Nakamura, AD Ribas, TRB de Souza, RS Barreto 2009 2nd IFIP Wireless Days (WD), 1-6 , 2009 2009 Citations: 32
A Novel Methodology for Developing Troubleshooting Chatbots Applied to ATM Technical Maintenance Support N Azevedo, G Aquino, L Nascimento, L Camelo, T Figueira, J Oliveira, ... Applied Sciences 13 (11), 6777 , 2023 2023 Citations: 30
Architectures for wireless sensor networks LB Ruiz, LHA Correia, LFM Vieira, DF Macedo, EF Nakamura, ... Proceedings of the 22nd Brazilian Symposium on Computer Networks (SBRC’04 … , 2004 2004 Citations: 26
Similarity clustering for data fusion in wireless sensor networks using k-means AD Ribas, JG Colonna, CMS Figueiredo, EF Nakamura The 2012 International Joint Conference on Neural Networks (IJCNN), 1-7 , 2012 2012 Citations: 25
Diffuse: A topology building engine for wireless sensor networks EF Nakamura, CMS Figueiredo, FG Nakamura, AAF Loureiro Signal Processing 87 (12), 2991-3009 , 2007 2007 Citations: 24
