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Nuno Pombo
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2020 – today
- 2024
- [j26]Nasrin Tavakolizadeh
, Hamzeh Mohammadigheymasi
FaultQuake: An open-source Python tool for estimating Seismic Activity Rates in faults. Comput. Geosci. 191: 105659 (2024) - 2023
- [j25]Carlos Augusto S. Cunha, Nuno Pombo
Automated Reusable Tests for Mitigating Secure Pattern Interpretation Errors. IEEE Access 11: 52938-52948 (2023) - [j24]Virginie Felizardo
Hypoglycaemia prediction using information fusion and classifiers consensus. Eng. Appl. Artif. Intell. 123(Part A): 106194 (2023) - [c46]Manuel Magalhães, Ângelo Morgado
Unlocking the Potential of Dynamic Languages: An Exploration of Automated Unit Test Generation Techniques. AITest 2023: 122-126 - [c45]Joana Branco, Nuno Pombo
Augmented Reality for Programming Teaching: An Exploratory Study. EDUCON 2023: 1-7 - [c44]M. Luqman Jamil
Challenges of Profiling Offenders for Recidivism Risk. ICDM (Workshops) 2023: 235-244 - [c43]Faustino Muetunda
Improving Mental Disorder Predictions using Feature-Based Machine Learning Techninques. ICDM (Workshops) 2023: 1279-1288 - [c42]Faustino Muetunda
Exploring the Role of Artificial Intelligence in Mental Health. ICDM (Workshops) 2023: 1289-1298 - [c41]David Carmo, Luísa Gonçalves, Ana Dias
Improved Flaky Test Detection with Black-Box Approach and Test Smells. ISCC 2023: 245-251 - [c40]M. Luqman Jamil
An Approach for Detecting Dangerous Events from Online Text Using Transformers. WI/IAT 2023: 256-262 - [i9]Mehran Pourvahab, Seyed Jalaleddin Mousavirad, Virginie Felizardo, Nuno Pombo, Henriques Zacarias, Hamzeh Mohammadigheymasi
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification. CoRR abs/2305.02731 (2023) - 2022
- [j23]Virginie Felizardo
Hypoglycaemia Prediction Models With Auto Explanation. IEEE Access 10: 57930-57941 (2022) - [j22]Leonice Souza-Pereira
Software quality: Application of a process model for quality-in-use assessment. J. King Saud Univ. Comput. Inf. Sci. 34(7): 4626-4634 (2022) - [c39]Alicia Montoro-Lendínez, Nuno Pombo, Bruno M. C. Silva, Macarena Espinilla Estévez, Javier Medina Quero:
Classification of Food Types in a Box with Gas Sensors Using a Machine Learning Method. Case Study of Intelligent Electronic Nose. ARTIIS (1) 2022: 500-510 - [c38]Nuno Pombo
Game-Based Learning for Young Children: A Case Study. EDUCON 2022: 133-138 - [c37]Geraldo Cangondo, Nuno Pombo
Computer Science Education in Angola: The Key Challenges. EDUCON 2022: 139-147 - [c36]Henriques Zacarias
Gender Classification Using nonstandard ECG Signals - A Conceptual Framework of Implementation. HealthyIoT 2022: 108-120 - [c35]Tiago Mendes, Nuno Pombo
Evaluation of Deep Learning Techniques in Human Activity Recognition. IntelliSys (1) 2022: 114-123 - 2021
- [j21]David F. Q. Melo
Internet of Things Assisted Monitoring Using Ultrasound-Based Gesture Recognition Contactless System. IEEE Access 9: 90185-90194 (2021) - [j20]Aleksandar Jevremovic
Keeping Children Safe Online With Limited Resources: Analyzing What is Seen and Heard. IEEE Access 9: 132723-132732 (2021) - [j19]Virginie Felizardo, Nuno M. Garcia
Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction - A systematic literature review. Artif. Intell. Medicine 118: 102120 (2021) - [j18]Leonice Souza-Pereira
Quality-in-use characteristics for clinical decision support system assessment. Comput. Methods Programs Biomed. 207: 106169 (2021) - [j17]Igor Matias
Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review. Comput. Sci. Rev. 39: 100334 (2021) - [j16]Leonice Souza-Pereira
A process model for quality in use evaluation of clinical decision support systems. J. Biomed. Informatics 123: 103917 (2021) - [c34]Nuno Pombo
Test Driven Development in Action: Case Study of a Cross-Platform Web Application. EUROCON 2021: 352-356 - [c33]Nuno Pombo, Miguel Rodrigues, Zdenka Babic, Magdalena Punceva, Nuno C. Garcia:
Computerised Sentiment Analysis on Social Networks. Two Case Studies: FIFA World Cup 2018 and Cristiano Ronaldo Joining Juventus. WorldCIST (2) 2021: 126-140 - 2020
- [j15]Nuno Pombo
Classifier Precision Analysis for Sleep Apnea Detection Using ECG Signals. IEEE Access 8: 200477-200485 (2020) - [j14]Leonice Souza-Pereira
Clinical decision support systems for chronic diseases: A Systematic literature review. Comput. Methods Programs Biomed. 195: 105565 (2020) - [c32]Sofia Ouhbi
Software Engineering Education: Challenges and Perspectives. EDUCON 2020: 202-209 - [c31]Fábio Machado Gil, Nuno M. Garcia, Bárbara Matos, Nuno Pombo, Rossitza Goleva, Ciprian Dobre:
Identifying Packet Loss and Reordering Packets in Keyed UDP Transmissions. GLOBECOM (Workshops) 2020: 1-5 - [c30]Tomás Jerónimo, Bruno M. C. Silva, Nuno Pombo
An IoMT System for Healthcare Emergency Scenarios. IntelliSys (3) 2020: 545-558 - [c29]Melaku Girma, Nuno M. Garcia, Eftim Zdravevski
Analysis of trends in scientific publications by an NLP toolkit: A case study in Software Development Methods for Enhanced Living Environment. SDS 2020: 59-66 - [i8]Fábio Machado Gil, Nuno M. Garcia, Bárbara Matos, Nuno Pombo, Rossitza Goleva, Ciprian Dobre:
Identifying Packet Loss and Reordering Packets in Keyed UDP Transmissions. CoRR abs/2007.00127 (2020)
2010 – 2019
- 2019
- [j13]Nuno M. Garcia
Keyed User Datagram Protocol: Concepts and Operation of an Almost Reliable Connectionless Transport Protocol. IEEE Access 7: 18951-18963 (2019) - [j12]Andre Miguel da Silva Pinho, Nuno Pombo
Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection. Appl. Soft Comput. 83 (2019) - [j11]Sari Merilampi
Implementing Mobile Games into Care Services - Service Models for Finnish and Chinese Elderly Care. Inf. 10(2): 55 (2019) - [c28]Nuno Pombo
How to Get a Badge? Unlock Your Mind : Motivation through Student Empowerment. EDUCON 2019: 115-119 - [c27]Lina Xu, Nuno Pombo
Is the Overfitting in a Neural Network a Reliable Model for the Recognition of Activities of Daily Living? GOODTECHS 2019: 223-226 - [c26]Salome Oniani, Ivan Miguel Pires
A review of frameworks on continuous data acquisition for e-Health and m-Health. GOODTECHS 2019: 231-234 - [c25]Vasco Ponciano, Ivan Miguel Pires
Smartphone-based automatic measurement of the results of the Timed-Up and Go test. GOODTECHS 2019: 239-242 - [c24]María Vanessa Villasana
CoviHealth: Novel approach of a mobile application for nutrition and physical activity management for teenagers. GOODTECHS 2019: 261-266 - [c23]Miguel Mendonça, Tomás Jerónimo, Mauro Julião, João Santos, Nuno Pombo
An IoT-Based Healthcare Ecosystem for Home Intelligent Assistant Services in Smart Homes. HealthyIoT 2019: 142-155 - [c22]Sandeep Pirbhulal
Towards Machine Learning Enabled Security Framework for IoT-based Healthcare. ICST 2019: 1-6 - [c21]Lina Xu, Nuno Pombo
Human Behavior Prediction Though Noninvasive and Privacy-Preserving Internet of Things (IoT) Assisted Monitoring. WF-IoT 2019: 773-777 - [p1]Eftim Zdravevski
Automation in Systematic, Scoping and Rapid Reviews by an NLP Toolkit: A Case Study in Enhanced Living Environments. Enhanced Living Environments 2019: 1-18 - [i7]Faisal Hussain
An Efficient Machine Learning-based Elderly Fall Detection Algorithm. CoRR abs/1911.11976 (2019) - [i6]Ivan Miguel Pires
Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer. PeerJ Prepr. 7: e27225 (2019) - 2018
- [j10]Ivan Miguel Pires
Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer. J. Ambient Intell. Smart Environ. 10(4): 315-326 (2018) - [j9]Ivan Miguel Pires
Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices. Pervasive Mob. Comput. 47: 78-93 (2018) - [j8]Ivan Miguel Pires
Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review. Sensors 18(1): 160 (2018) - [j7]Ivan Miguel Pires
Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices. Sensors 18(2): 640 (2018) - [c20]Ivan Miguel Pires
Framework for the Recognition of Activities of Daily Living and Their Environments in the Development of a Personal Digital Life Coach. DATA 2018: 163-170 - [c19]Igor Matias
Scaffolding students on connecting STEM and interaction design: Case study in Tallinn University Summer School. EDUCON 2018: 979-987 - [c18]Ivan Miguel Pires, Nuno M. Garcia
Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People. ICT4AWE 2018: 269-275 - [c17]Ivan Miguel Pires, Diogo Marques
Measurement of the Reaction Time in the 30-S Chair Stand Test using the Accelerometer Sensor Available in off-the-Shelf Mobile Devices. ICT4AWE 2018: 293-298 - [c16]Ivan Miguel Pires, Nuno Pombo
Multi-Sensor Mobile Platform for the Recognition of Activities of Daily Living and their Environments based on Artificial Neural Networks. IJCAI 2018: 5850-5852 - [c15]Igor Matias, Nuno Pombo
Towards a Fully Automated Bracelet for Health Emergency Solution. IoTBDS 2018: 307-314 - [c14]Ivan Miguel Pires
Conceptual Definition of a Platform for the Monitoring of the Subjects with Nephrolithiasis Based on the Energy Expenditure and the Activities of Daily Living Performed. PAAMS (Workshops) 2018: 3-11 - [c13]Eftim Zdravevski
Importance of Personalized Health-Care Models: A Case Study in Activity Recognition. pHealth 2018: 185-188 - [i5]Ivan Miguel Pires
Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer. PeerJ Prepr. 6: e27225 (2018) - 2017
- [j6]Eftim Zdravevski
Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering. IEEE Access 5: 5262-5280 (2017) - [j5]Nuno Pombo
Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review. Comput. Methods Programs Biomed. 140: 265-274 (2017) - [c12]Nuno Pombo
Simulation in Medical School Education. AAATE Conf. 2017: 1034-1036 - [c11]Ivan Miguel Pires
Limitations of Energy Expenditure Calculation Based on a Mobile Phone Accelerometer. HPCS 2017: 124-127 - [i4]Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, Susanna Spinsante:
Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer. CoRR abs/1711.00096 (2017) - [i3]Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta:
A Multiple Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data. CoRR abs/1711.00104 (2017) - [i2]Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta:
User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living. CoRR abs/1711.00124 (2017) - [i1]Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, Susanna Spinsante:
Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living. CoRR abs/1711.07328 (2017) - 2016
- [j4]Ivan Miguel Pires
Validation Techniques for Sensor Data in Mobile Health Applications. J. Sensors 2016: 2839372:1-2839372:9 (2016) - [j3]Ivan Miguel Pires
From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices. Sensors 16(2): 184 (2016) - [c10]Nuno Pombo
A data fusion model to evaluate computerized pain diaries on anxiety and depression assessment. BlackSeaCom 2016: 1-5 - [c9]Sandra Reis, Virginie Felizardo
Elderly mobility analysis during Timed Up and Go test using biosignals. DSAI 2016: 240-245 - [c8]Susanna Spinsante, Laura Montanini, Ennio Gambi
Smartphones as Multipurpose Intelligent Objects for AAL: Two Case Studies. GOODTECHS 2016: 125-134 - [c7]Andre Miguel da Silva Pinho, Nuno Pombo
Sleep apnea detection using a feed-forward neural network on ECG signal. HealthCom 2016: 1-6 - [c6]Andre G. Pinto, Gil Dias
Electrocardiography, electromyography, and accelerometry signals collected with BITalino while swimming: Device assembly and preliminary results. ICCP 2016: 37-41 - [c5]Virginie Felizardo, Hugo Rodrigues, Nuno C. Garcia, Celina Alexandre, Daniel Oliveira, Paula Sousa, Nuno M. Garcia, Nuno Pombo:
Metabolic.Care: A Novel Solution Based on a Thermography for Detection of Diabetic Foot. ISAmI 2016: 113-119 - [c4]Ivan Miguel Pires
Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis. ISAmI 2016: 121-130 - [c3]Nuno Pombo
ubiSleep: An ubiquitous sensor system for sleep monitoring. WiMob 2016: 1-4 - 2015
- [c2]Nuno Pombo
Combining Data Imputation and Statistics to Design a Clinical Decision Support System for Post-Operative Pain Monitoring. CENTERIS/ProjMAN/HCist 2015: 1018-1025 - 2014
- [j2]Nuno Pombo
Knowledge discovery in clinical decision support systems for pain management: A systematic review. Artif. Intell. Medicine 60(1): 1-11 (2014) - [j1]Nuno Pombo
Applied computer technologies in clinical decision support systems for pain management: A systematic review. J. Intell. Fuzzy Syst. 26(5): 2411-2425 (2014) - [c1]Nuno Pombo
Big data reduction using RBFNN: A predictive model for ECG waveform for eHealth platform integration. Healthcom 2014: 66-70
Coauthor Index
[j24] [i9] [j23] [c36] [j20] [j19] [j17] [j15] [j14] [c31] [c29] [i8] [j13] [j12] [c28] [c27] [c26] [c25] [c24] [c22] [p1] [i7] [i6] [j10] [j9] [j8] [j7] [c20] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [i5] [j6] [j5] [c12] [c11] [i4] [i3] [i2] [i1] [j4] [j3] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c1]
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last updated on 2024-10-07 21:25 CEST by the dblp team
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