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2. SAMSEF: An Agile Software Maintenance Leveraging Scrum Framework for Improved Efficiency and Effectiveness

Author

Ali, Muhammad, Cheema, Sehrish Munawar, Naz, Ammerha, Pires, Ivan Miguel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Poniszewska-Marańda, Aneta, editor

Published
2024
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4. Self-reporting Tool for Cardiovascular Patients

Author

Denysyuk, Hanna Vitaliyivna, Amado, João, Gonçalves, Norberto Jorge, Zdravevski, Eftim, Garcia, Nuno M., Pires, Ivan Miguel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Alvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Colla, Valentina, editor

Published
2024
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11. An IoT-Based Framework for Sustainable Supply Chain Management System

Author

Ali, Muhammad, Cheema, Sehrish Munawar, Pires, Ivan Miguel, Naz, Ammerha, Aslam, Zaheer, Ayub, Nasir, Coelho, Paulo Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moniz, Nuno, editor, Vale, Zita, editor, Cascalho, José, editor, Silva, Catarina, editor, and Sebastião, Raquel, editor

Published
2023
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12. A Framework to Improve the Confidence of Restaurants User Reviews Based on Facial Analysis

Author

Melo, Pedro, Pereira, Pedro, Saraiva, Luzia, Henriques, João, Pires, Ivan Miguel, Wanzeller, Cristina, Caldeira, Filipe, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, de la Iglesia, Daniel H., editor, de Paz Santana, Juan F., editor, and López Rivero, Alfonso J., editor

Published
2023
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13. Smart Wearables Data Collection and Analysis for Medical Applications: A Preliminary Approach for Functional Reach Test

Author

Duarte, João, Francisco, Luís, Pires, Ivan Miguel, Coelho, Paulo Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas Ruiz, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2023
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14. Preliminary Study on the Identification of Diseases by Electrocardiography Sensors’ Data

Author

Pinto, Rui João, Silva, Pedro Miguel, Duarte, Rui Pedro, Marinho, Francisco Alexandre, Gouveia, António Jorge, Gonçalves, Norberto Jorge, Coelho, Paulo Jorge, Zdravevski, Eftim, Lameski, Petre, Garcia, Nuno M., Pires, Ivan Miguel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas Ruiz, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2023
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15. Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection

Author

Freitas, Mauricio, de Aquino Piai, Vinicius, Dazzi, Rudimar, Teive, Raimundo, Parreira, Wemerson, Fernandes, Anita, Pires, Ivan Miguel, Leithardt, Valderi Reis Quietinho, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Longfei, Shangguan, editor, and Bodhi, Priyantha, editor

Published
2023
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16. Medical and Healthcare Information Systems in Portugal: Short Literature Review

Author

Rodrigues, Bruna, Matos, Rita, Guedes, Silvana, Pires, Ivan Miguel, Gouveia, António Jorge, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Cunha, António, editor, M. Garcia, Nuno, editor, Marx Gómez, Jorge, editor, and Pereira, Sandra, editor

Published
2023
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17. Preliminary Study on Gender Identification by Electrocardiography Data

Author

Bastos, Eduarda Sofia, Duarte, Rui Pedro, Marinho, Francisco Alexandre, Pimenta, Luís, Gouveia, António Jorge, Gonçalves, Norberto Jorge, Coelho, Paulo Jorge, Zdravevski, Eftim, Lameski, Petre, Garcia, Nuno M., Pires, Ivan Miguel, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Spinsante, Susanna, editor, Iadarola, Grazia, editor, Paglialonga, Alessia, editor, and Tramarin, Federico, editor

Published
2023
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27. An Efficient Data Imputation Technique for Human Activity Recognition

Author

Pires, Ivan Miguel, Hussain, Faisal, Garcia, Nuno M., and Zdravevski, Eftim

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset., Comment: 8 Pages, 8 Figures, 1 Table. Accepted in 14th Multi Conference on Computer Science and Information Systems 2020 (MCCSIS 2020)

Published
2020

28. An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Author

Hussain, Faisal, Umair, Muhammad Basit, Ehatisham-ul-Haq, Muhammad, Pires, Ivan Miguel, Valente, Tânia, Garcia, Nuno M., and Pombo, Nuno

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier., Comment: 6 pages, SENSORDEVICES 2018, the Ninth International Conference on Sensor Device Technologies and Applications, Venice, Italy, 16-20 September 2018

Published
2019

32. Premises Based Smart Door Chains System Using IoT Cloud

Author

Hannan, Abdul, Hussain, Faisal, Cheema, Sehrish Munawar, Pires, Ivan Miguel, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Hara, Takahiro, editor, and Yamaguchi, Hirozumi, editor

Published
2022
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33. Mobile 4G Network: Signal Power and Quality vs Bandwidth Throughput

Author

Oliveira, Luis, Henriques, Joao, Pires, Ivan Miguel, Wanzeller, Cristina, Caldeira, Filipe, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, de Paz Santana, Juan F., editor, de la Iglesia, Daniel H., editor, and López Rivero, Alfonso José, editor

Published
2022
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41. The New Era of Technology Applied to Cardiovascular Patients: State-of-the-Art and Questionnaire Applied for a System Proposal

Author

Villasana, María Vanessa, Sá, Juliana, Pires, Ivan Miguel, Albuquerque, Carlos, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Pires, Ivan Miguel, editor, Spinsante, Susanna, editor, Zdravevski, Eftim, editor, and Lameski, Petre, editor

Published
2021
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42. Management Technology for Institutional Environment in Pandemic Times

Author

Aragão, Maria Eduarda, Lopes, Maria Alice, Miranda, Gustavo Neves, Morgado, José, Morgado, Francisco Miguel, Pires, Ivan Miguel, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Pires, Ivan Miguel, editor, Spinsante, Susanna, editor, Zdravevski, Eftim, editor, and Lameski, Petre, editor

Published
2021
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43. Biometric Data Capture as a Way to Identify Lack of Physical Activity in Daily Life

Author

Marques, Luís, Lopes, Luca, Ferreira, Miguel, Henriques, Joao, Pires, Ivan Miguel, Caldeira, Filipe, Wanzeller, Cristina, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Pires, Ivan Miguel, editor, Spinsante, Susanna, editor, Zdravevski, Eftim, editor, and Lameski, Petre, editor

Published
2021
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44. COVID-19 Next Day Trend Forecast

Author

Costa, Marcelo, Rodrigues, Margarida, Baptista, Pedro, Henriques, João, Pires, Ivan Miguel, Wanzeller, Cristina, Caldeira, Filipe, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Pires, Ivan Miguel, editor, Spinsante, Susanna, editor, Zdravevski, Eftim, editor, and Lameski, Petre, editor

Published
2021
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45. CoviHealth: A Pilot Study with Teenagers in Schools of Centre of Portugal

Author

Villasana, María Vanessa, Pires, Ivan Miguel, Sá, Juliana, Garcia, Nuno M., Zdravevski, Eftim, Chorbev, Ivan, Lameski, Petre, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Goleva, Rossitza, editor, Garcia, Nuno Ricardo da Cruz, editor, and Pires, Ivan Miguel, editor

Published
2021
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47. User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

Author

Pires, Ivan Miguel, Garcia, Nuno M., Pombo, Nuno, and Flórez-Revuelta, Francisco

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Physics - Data Analysis, Statistics and Probability
Abstract

The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing activities of daily living. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition, data processing, data fusion, and artificial intelligence methods. The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.

Published
2017

48. Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer

Author

Pires, Ivan Miguel, Garcia, Nuno M., Pombo, Nuno, Flórez-Revuelta, Francisco, and Spinsante, Susanna

Subjects
Computer Science - Computers and Society, Physics - Data Analysis, Statistics and Probability
Abstract

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.

Published
2017

49. Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Author

Pires, Ivan Miguel, Garcia, Nuno M., Pombo, Nuno, Flórez-Revuelta, Francisco, and Spinsante, Susanna

Subjects
Computer Science - Computers and Society, Computer Science - Computer Vision and Pattern Recognition, Physics - Data Analysis, Statistics and Probability
Abstract

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors data, reports an accuracy of 89.51%., Comment: arXiv admin note: substantial text overlap with arXiv:1711.00124, arXiv:1711.00104; text overlap with arXiv:1711.00096

Published
2017

50. A Multiple Data Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data

Author

Pires, Ivan Miguel, Garcia, Nuno M., Pombo, Nuno, Flórez-Revuelta, Francisco, Teixeira, Maria Canavarro, Zdravevski, Eftim, and Spinsante, Susanna

Subjects
Computer Science - Computers and Society, Physics - Data Analysis, Statistics and Probability
Abstract

Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.

Published
2017

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