Article
Computer Science, Artificial Intelligence
Ahmed S. Eltrass, Mazhar B. Tayel, Abeer Ammar
Summary: The study proposes a novel hybrid approach combining deep neural networks with linear and nonlinear features extracted from ECG and HRV to enhance the performance of ECG diagnosis. By optimizing deep learning features and aggregating ECG features and HRV measures effectively, the system outperforms other state-of-the-art systems in diagnosing various heart disorders.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Biomedical
Sihem Nita, Salim Bitam, Matthieu Heidet, Abdelhamid Mellouk
Summary: In this paper, a new data augmentation convolutional neural network (CNN) for human emotion recognition based on ECG signal is proposed. By enriching the ECG dataset and using a seven-layer CNN classifier, high accuracy rates were achieved for valence, arousal, and dominance detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Gawsalyan Sivapalan, Koushik Kumar Nundy, Alex James, Barry Cardiff, Deepu John
Summary: This article proposes an explainable rule-mining strategy for prioritizing abnormal class detection in ECG data using an artificial neural network and rule-based system. The proposed model achieves high accuracy and sensitivity in detecting abnormal heartbeats through a comprehensive offline rule-mining process. It is suitable for healthcare applications due to its explainability, lower complexity, and real-time flexibility when deployed in IoT-enabled wearable edge sensors.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Mathematics
Anton Romanov, Julia Stroeva, Aleksey Filippov, Nadezhda Yarushkina
Summary: Modern decision support systems require components for storing knowledge and supporting fuzzy inference, using ontologies for complex structures. Developers must use specific libraries for ontology features and cannot use them in non-Java programming languages. Therefore, it is necessary to develop ontology services for fuzzy inference and ontology operations.
Article
Veterinary Sciences
Luca Turini, Francesca Bonelli, Antonio Lanata, Valentina Vitale, Irene Nocera, Micaela Sgorbini, Marcello Mele
Summary: This study evaluated the feasibility of using smart textiles technology to assess HRV in small ruminants and found that it is comparable to a standard base-apex ECG. The smart textiles technology is simple to use and does not require glue or shaving the sheep's wool, reducing animal handling and stress.
FRONTIERS IN VETERINARY SCIENCE
(2022)
Article
Chemistry, Analytical
Herag Arabian, Tamer Abdulbaki Alshirbaji, Ramona Schmid, Verena Wagner-Hartl, J. Geoffrey Chase, Knut Moeller
Summary: Emotional intelligence aims to bridge the gap between human and machine interactions, and its application in digital health has gained prominence. This study presents a system that utilizes physiological signal data, such as electrodermal activity and electrocardiogram, to identify and classify emotional reactions, as well as measure their arousal strength. The system demonstrates good performance in emotion detection and can be integrated into therapeutic settings to monitor and guide patients' emotional responses.
Article
Automation & Control Systems
Chang-Shing Lee, Yi-Lin Tsai, Mei-Hui Wang, Sheng-Hui Huang, Marek Reformat, Naoyuki Kubota
Summary: This paper introduces an Adaptive Fuzzy Neural Agent (AFNA) for human and machine co-learning, utilizing a Patch Learning Mechanism and a Fuzzy Machine-Learning Model to construct regression models for students and robots.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Mathematics, Interdisciplinary Applications
Rogelio Pina-Vega, Martin Valtierra-Rodriguez, Carlos A. Perez-Ramirez, Juan P. Amezquita-Sanchez
Summary: This paper introduces a new methodology for automatically predicting sudden cardiac death (SCD) events by combining fractal dimension (FD) algorithms and a fuzzy logic system. By evaluating the geometrical complexity of electrocardiogram signals, the FD-based methodology can predict an SCD event up to 60 minutes before onset with an accuracy of 91.54%.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2021)
Article
Mathematics
Vadim Moshkin, Dmitry Kurilo, Nadezhda Yarushkina
Summary: This paper proposes an approach to detecting time series anomalies by integrating LSTM neural network and Fuzzy OWL ontology. The method involves mathematical anomaly search using LSTM network and inference using fuzzy ontology. The proposed software system architecture is presented, and computational experiments on drilling rigs data are conducted. The high efficiency of the approach is demonstrated, but further improvements are planned in terms of neural network architecture and automatic inference rule generation.
Article
Public, Environmental & Occupational Health
Xinxia Li, Weiwei Zhu, Xiaofan Sui, Aizhi Zhang, Lijie Chi, Lu Lv
Summary: This study aims to measure workplace stress of nurses using heart rate variability (HRV) analysis. The results showed that work shifts and posture had significant effects on the HRV of nurses. The findings support the use of HRV as a tool for investigating workplace stress and identifying stress-related illnesses among nurses.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Cardiac & Cardiovascular Systems
Emanuele Pizzo, Silvia Berrettoni, Ridhima Kaul, Daniel O. Cervantes, Valeria Di Stefano, Sudhir Jain, Jason T. Jacobson, Marcello Rota
Summary: The study found that myocardial infarction in rodents is associated with changes in heart rate variability, indicating sympathetic excitation and parasympathetic inhibition. The importance of mouse strain in evaluating autonomic function through HRV analysis was also confirmed.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Multidisciplinary Sciences
M. Murugappan, L. Murugesan, S. Jerritta, Hojjat Adeli
Summary: This study aims to predict SCA using the Rpeak to T-end (R-T-end) feature in ECG signals, extracting four nonlinear features and classifying them using three classifiers. The combination of sample entropy feature and support vector machine classifier can effectively predict the onset of SCA with the highest classification accuracy.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Chang-Shing Lee, Mei-Hui Wang, Sheng-Hui Huang, Fu-Jie Yang, Chun-Hao Tsai, Ling-Qing Wang
Summary: This article discusses the importance of applying fuzzy ontology concepts and the Heart Sutra to artificial intelligence in English and fuzzy markup language learning for students. By using an intelligent agent on the AI-FML Metaverse platform, students can experience computational intelligence applications and combine fuzzy ontology, AI-FML, and the principles of the Heart Sutra. After evaluating its implementation, the intelligent agent proves to be effective in teaching, and students' interest and performance show an increasing tendency.
2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2022)
Article
Biology
Sanjay Kumar, Abhishek Mallik, Akshi Kumar, Javier Del Ser, Guang Yang
Summary: Electrocardiogram (ECG) is a widely used non-invasive technique to diagnose cardiovascular diseases. In this work, a deep learning and fuzzy clustering based approach (Fuzz-ClustNet) is proposed for Arrhythmia detection from ECG signals. The collected ECG signals are denoised and segmented, followed by data augmentation and feature extraction using a CNN. Fuzzy clustering algorithm is then used to classify the ECG signals for their respective cardio diseases. The proposed approach shows better performance compared to other contemporary algorithms.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Quanan Yang, Lang Zou, Keming Wei, Guanzheng Liu
Summary: This study proposed a new method for detecting obstructive sleep apnea (OSA) using a one-dimensional squeeze-and-excitation (SE) residual group network to thoroughly extract the complementary information between heart rate variability (HRV) and ECG-derived respiration (EDR). The method showed higher accuracy, sensitivity, and specificity compared to existing methods during testing.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Environmental Sciences
Annalisa Mele, Autilia Vitiello, Manuela Bonano, Andrea Miano, Riccardo Lanari, Giovanni Acampora, Andrea Prota
Summary: This paper proposes a strategy for the preliminary identification and ranking of critical buildings in a built environment, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence techniques. By applying the DBSCAN algorithm to the SBAS-DInSAR products, the automatic clustering of buildings and deformation analysis are achieved, leading to the identification and ranking of critical buildings.
Article
Computer Science, Artificial Intelligence
Amir Pourabdollah, Giovanni Acampora, Roberto Schiattarella
Summary: Quantum computation, utilizing quantum mechanics effects, is expected to have a significant impact on the field of computing and the application of fuzzy systems. This article introduces a novel representation of fuzzy sets and operators based on quadratic unconstrained binary optimization problems to implement fuzzy inference engines on quantum computers.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Ferdinando Di Martino, Alfredo Massa, Roberto Schiattarella, Autilia Vitiello
Summary: This paper introduces the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) as a reference computational model to design innovative frameworks for quantum devices to interact and solve complex problems collaboratively. Through two case studies, a multi-threaded implementation of the D-NISQ model demonstrates greater reliability in solving problems through quantum computation.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello
Summary: The paper proposes a quantum algorithm called Quantum Genetic Sampling (QGS) to increase population diversity and reduce the possibility of convergence to low-quality solutions in genetic evolution.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Giovanni Acampora, Pasquale Trinchese, Roberto Trinchese, Autilia Vitiello
Summary: This paper highlights the importance of recognizing and collecting evidence at a crime scene and emphasizes the role of experience and training in improving the skills of forensic investigators. It proposes a serious mixed-reality game called TraceGame to support the training of novice investigators in evidence-recovery techniques.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello
Summary: This article introduces the first quantum-based fuzzy inference engine that provides exponential acceleration in fuzzy rule execution compared to classical methods, and enables quantum computers to be programmed using fuzzy linguistic rules.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Angela Chiatto, Autilia Vitiello
Summary: This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session.
PATTERN RECOGNITION LETTERS
(2023)
Article
Computer Science, Software Engineering
Francesco Amato, Matteo Cicalese, Luca Contrasto, Giacomo Cubicciotti, Gerardo D'Ambola, Antonio La Marca, Giuseppe Pagano, Fiorentino Tomeo, Gennaro Alessio Robertazzi, Gabriele Vassallo, Giovanni Acampora, Autilia Vitiello, Gemma Catolino, Giammaria Giordano, Stefano Lambiase, Valeria Pontillo, Giulia Sellitto, Filomena Ferrucci, Fabio Palomba
Summary: Machine learning is widely used by software engineering researchers to solve problems in various research fields. Quantum machine learning has recently gained attention for its potential to revolutionize program computation and enhance problem-solving capabilities. To facilitate the use of quantum computing technologies, we propose a community-based low-code platform called QUANTUMOONLIGHT, which enables researchers and practitioners to configure and experiment with quantum machine learning pipelines, compare them with traditional algorithms, and share their experiences. This article introduces the architecture and key features of QUANTUMOONLIGHT and discusses its expected impact on research and practice.
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Angela Chiatto, Autilia Vitiello
Summary: This paper discusses the application of quantum computing in optimization problems and proposes the use of genetic algorithms as gradient-free methods to optimize the parameters of Quantum Approximate Optimization Algorithm (QAOA) circuit. Experimental results on noisy quantum devices solving MaxCut problem show that the genetic algorithm outperforms other gradient-free optimizers in terms of approximation ratio.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Autilia Vitiello
Summary: Quantum computers have the potential to revolutionize computing through massive parallelism, which is particularly relevant for evolutionary algorithms. However, the limited number of qubits in current quantum processors hinders their full potential, especially for continuous optimization problems. This paper introduces a hybrid and granular approach to overcome this limitation and achieve good solutions on small quantum computers.
GRANULAR COMPUTING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Bhavesh Pandya, Amir Pourabdollah, Ahmad Lotfi, Giovanni Acampora
Summary: This study developed a cloud-based fuzzy logic system under Microsoft Azure, showing its effectiveness in serving mobile phone applications for human monitoring purposes. The study compared Mamdani and TSK fuzzy inference systems in terms of processing time and accuracy, with Mamdani system outperforming TSK system.
2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello
Summary: Genetic Algorithms are optimization methods that search for near-optimal solutions by applying selection, crossover, and mutation operations. This paper introduces a new mating operator, the Quantum Mating Operator, that utilizes the stochastic nature of quantum computation to improve the performance of genetic optimization.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Giovanni Acampora, Angela Chiatto, Autilia Vitiello
Summary: Variational Quantum Circuits (VQCs) are gaining attention for their robustness to noise in quantum devices. However, the widely used gradient descent method becomes inefficient in high-dimensional classification problems. This paper proposes using Genetic Algorithms (GAs) to train quantum classifiers, resulting in accurate solutions with reduced queries to quantum devices.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)