Article
Acoustics
Sachin Taran, Varun Bajaj, G. R. Sinha, Kemal Polat
Summary: The paper proposes detecting sleep apnea using Lampel-Ziv complexity of EEG signals, achieving high accuracy in identifying apnea events through TQWT, KW test, and ensemble classification technique.
Article
Neurosciences
Fangzhou Xu, Jinzhao Zhao, Ming Liu, Xin Yu, Chongfeng Wang, Yitai Lou, Weiyou Shi, Yanbing Liu, Licai Gao, Qingbo Yang, Baokun Zhang, Shanshan Lu, Jiyou Tang, Jiancai Leng
Summary: In this study, a functional connection network was constructed using phase-locked value (PLV) to analyze the interaction of brain regions in different sleep stages. The experimental results showed that when the EEG signal was divided into multiple sub-periods and PLV was used for feature fusion, the best classification effect was achieved in the alpha (8-13 Hz) frequency band. The classification result after 10-fold cross-validation reached 92.59%.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Souhir Khessiba, Ahmed Ghazi Blaiech, Khaled Ben Khalifa, Asma Ben Abdallah, Mohamed Hedi Bedoui
Summary: Electroencephalography (EEG) is commonly used for studying brain electrical activity. Deep learning networks can accurately predict individuals' states of vigilance based on EEG signals. Experimental results demonstrate that the proposed 1D-UNet and 1D-UNet-LSTM models perform well in stabilizing training and recognizing vigilance states, indicating the effectiveness of the proposed methods.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Hui Wen Loh, Chui Ping Ooi, Shivani G. Dhok, Manish Sharma, Ankit A. Bhurane, U. Rajendra Acharya
Summary: Visual sleep stages scoring by human experts is the current gold standard for sleep analysis, but it is tedious, time-consuming, error-prone, and unable to detect microstructures like CAP. This study proposes a deep learning model based on 1D-CNN for accurate CAP detection and 3-class sleep stages classification, achieving good performance.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Shreeya Garg, Urvashi Prakash Shukla, Linga Reddy Cenkeramaddi
Summary: The global increase in Major Depressive Disorder (MDD) cases is concerning. Electroencephalography (EEG) is used for depression analysis, but current Machine Learning (ML) methods require manual annotations and consume a lot of time. This article proposes an unsupervised learning method for identifying MDD using extracted features and spectral clustering.
Article
Computer Science, Artificial Intelligence
Supriya Supriya, Siuly Siuly, Hua Wang, Yanchun Zhang
Summary: This study proposes a scheme for automatic classification of sleep stages using edge strength of visibility graph technique from single-channel EEG signals. Through simulation analysis, the proposed technique achieves better performance than other related work for the two standard groups of sleep stages: Rechtschaffen and Kales standard and American Academy of Sleep Medicine.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2021)
Article
Chemistry, Analytical
Debadyuti Mukherjee, Koustav Dhar, Friedhelm Schwenker, Ram Sarkar
Summary: The study focuses on detecting Obstructive Sleep Apnea (OSA) from Electrocardiogram (ECG) signals obtained through body sensors, using different ensemble techniques on three deep learning models. The experiments conducted on the PhysioNet Apnea-ECG Database achieved a highest OSA detection accuracy of 85.58% with an MLP based ensemble approach, surpassing many state-of-the-art methods.
Article
Engineering, Biomedical
Hyungjik Kim, Seung Min Lee, Sunwoong Choi
Summary: Sleep efficiency is crucial for a healthy life. This study proposes a multi-level fusion method that utilizes multiple signals to accurately classify sleep stages, resulting in improved performance compared to existing methods.
BIOMEDICAL ENGINEERING LETTERS
(2022)
Article
Chemistry, Multidisciplinary
Young-Keun Yoo, Chae-Won Jung, Hyun-Chool Shin
Summary: This paper proposes a unsupervised method using biosignals obtained from a 61 GHz single FMCW radar to detect the three stages of sleep-wake, REM sleep, and non-REM sleep. Non-learning techniques were used to extract the characteristic information of each sleep stage from radar signals to detect the subject's sleep stages. Experimental results based on clinical data show an average of 68% similarity with actual sleep stages observed in PSG, indicating the feasibility of using FMCW radar as an alternative to conventional PSG-based sleep-stage detection with multiple limitations.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Lei Zhang, Dan Chen, Peilu Chen, Weiguang Li, Xiaoli Li
Summary: The study developed a deep learning framework for multi-modal sleep scoring, using dual-CNN to process different forms of inputs and reinforcing temporal correlations through an RNN layer. The final results were fine-tuned by a customized Markov chain model, achieving a high accuracy rate in sleep scoring against a public Sleep-EDF dataset.
Article
Medicine, General & Internal
Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Elizabeth Emma Palmer, Edward J. Ciaccio, U. Rajendra Acharya
Summary: In this research, a hand-modeled classification model using a new ternary motif pattern (TMP) has been proposed for separating healthy versus ADHD individuals based on noisy EEG signals. The model utilizes the Tunable Q Wavelet Transform (TQWT) for feature extraction and applies neighborhood component analysis (NCA) and k-nearest neighbor (kNN) classifier for feature selection and classification. The model achieved high classification accuracies of 95.57% and 77.93% using 10-fold and leave one subject out (LOSO) cross-validations, respectively.
Article
Engineering, Biomedical
Raed Mohammed Hussein, Loay E. George, Firas Sabar Miften
Summary: This study aims to investigate the possibility of automating the classification of sleep stages using wavelet transform and residue decomposition. The classification is done using least square support vector machine classifier. Numerical computer simulations show good classification results with the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Nursena Baygin, Emrah Aydemir, Prabal D. Barua, Mehmet Baygin, Sengul Doganm, Turker Tuncer, Ru-San Tann, U. Rajendra Acharya
Summary: We developed a machine learning model to quantify mental performance in mental arithmetic tasks, which accurately discriminated between bad counters and good counters. Our model used a novel multilevel feature extraction method and a distance-based pooling function for signal decomposition, along with feature selection and result classification algorithms. The model achieved high classification accuracies in both cross-validations, showing its effectiveness in mental arithmetic tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Aayesha, Muhammad Bilal Qureshi, Muhammad Afzaal, Muhammad Shuaib Qureshi, Muhammad Fayaz
Summary: This paper focuses on extracting distinguishing features of seizure EEG recordings to develop an approach that employs both fuzzy-based and traditional machine learning algorithms for epileptic seizure detection. The obtained results show that K-Nearest Neighbor (KNN) and Fuzzy Rough Nearest Neighbor (FRNN) give the highest classification accuracy scores, with improved sensitivity and specificity percentages.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Xuebin Xu, Chen Chen, Kan Meng, Longbin Lu, Xiaorui Cheng, Haichao Fan
Summary: Sleep is crucial for human health and poor sleep conditions can lead to various physical ailments. Automatic sleep stage classification is of great medical practice significance and this paper proposes a deep model architecture called NAMRTNet to address the challenges in sleep stage classification. The experimental results demonstrate the superiority of the network in different evaluation metrics and the shorter training time compared to other models.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohammed Diykh, Shahab Abdulla, Ravinesh C. Deo, Siuly Siuly, Mumtaz Ali
Summary: This study proposes a hybrid approach combining hierarchical dispersion entropy (HDE) and Adaptive Boosting with convolutional neural networks (AdaB_CNN) for human activity recognition (HAR) from sensor data. The experimental results demonstrate that the proposed model outperforms most current methods in HAR. This research has the potential for practical applications and further development.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Green & Sustainable Science & Technology
Ravinesh C. Deo, A. A. Masrur Ahmed, David Casillas-Perez, S. Ali Pourmousavi, Gary Segal, Yanshan Yu, Sancho Salcedo-Sanz
Summary: Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy.
Article
Computer Science, Information Systems
Sagthitharan Karalasingham, Ravinesh C. Deo, David Casillas-Perez, Nawin Raj, Sancho Salcedo-Sanz
Summary: This paper proposes a new image super-resolution deep learning model based on convolutional neural network to generate high resolution spatial representations of surface albedo from coarse resolution remote sensing-based data. The proposed model outperforms alternative deep learning, super-resolution approaches in terms of mean square error, signal-to-noise ratio, and structural similarity index.
Article
Green & Sustainable Science & Technology
Lionel P. Joseph, Ravinesh C. Deo, Ramendra Prasad, Sancho Salcedo-Sanz, Nawin Raj, Jeffrey Soar
Summary: This research proposes a novel hybrid bidirectional LSTM model for near real-time wind speed forecasting. The model utilizes wind speed and selected climate indices to predict wind speed, and applies a 3-stage feature selection to extract significant input features. The proposed hybrid BiLSTM algorithm outperforms other tested algorithms in wind speed prediction.
Article
Chemistry, Analytical
Farina Riaz, Shahab Abdulla, Hajime Suzuki, Srinjoy Ganguly, Ravinesh C. Deo, Susan Hopkins
Summary: Quantum machine learning has been the focus of research in the past decade, and multiple models have been developed to demonstrate its practical applications. In this study, the authors propose two quantum models, QuanvNN and NNQE, that improve the image classification accuracy of neural networks on MNIST and CIFAR-10 datasets. The proposed methods show promising results but degrade in accuracy on the GTSRB dataset, prompting further research on suitable quantum circuits for complex data.
Article
Thermodynamics
Raed Al-Rbaihat, Khalid Saleh, Ray Malpress, David Buttsworth
Summary: This study focuses on improving ejector performance in variable conditions by developing a radial flow variable geometry radial ejector (VGRE). The improved VGRE achieved higher entrainment ratio and critical compression ratio compared to a fixed geometry design. The concept of the improved VGRE could be applied to a wide range of applications in the future.
APPLIED THERMAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Salvin S. Prasad, Ravinesh C. Deo, Sancho Salcedo-Sanz, Nathan J. Downs, David Casillas-Perez, Alfio V. Parisi
Summary: This research aims to design an artificial intelligence-inspired early warning tool for short-term forecasting of UV index (UVI) in Australian hotspots. The proposed model outperformed all benchmarked models and its predictions are influenced by factors such as ozone effect and cloud conditions. The UVI prediction system reaffirms its benefits for providing real-time UV alerts and reducing health complications.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Shuting Xu, Ravinesh Deo, Jeffrey Soar, Prabal Datta Barua, Oliver Faust, Nusrat Homaira, Adam Jaffe, Arm Luthful Kabir, U. Rajendra Acharya
Summary: This study investigates the role of automated detection of obstructive airway diseases in reducing cost and improving diagnostic quality. Medical imaging, genetics, and physiological signals are the main sources used for disease detection. Machine Learning is more prevalent than Deep Learning in the field, with Convolutional Neural Network being a common DL classifier and Support Vector Machine being widely used in ML.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Engineering, Civil
C. Pelaez-Rodriguez, J. Perez-Aracil, L. Prieto-Godino, S. Ghimire, R. C. Deo, S. Salcedo-Sanz
Summary: A novel fuzzy-based cascade ensemble of regression models is proposed to accurately estimate extreme wind speed values. It involves partitioning the training data into fuzzy-soft clusters based on the target variable value, and training a specific regression model within each cluster. The predictions made by individual models are then integrated into a fuzzy-based ensemble using a pertinence value assigned to each model. The performance of the proposed methodology has been evaluated using real data from wind farms in Spain.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2023)
Review
Computer Science, Information Systems
S. Janifer Jabin Jui, Ravinesh C. Deo, Prabal Datta Barua, Aruna Devi, Jeffrey Soar, U. Rajendra Acharya
Summary: An automated Neurological Disorder detection system is a cost-effective and resource efficient tool for medical and healthcare applications. The concept of entropy is a promising approach for processing electroencephalogram signals. Support Vector Machines and sample entropy are the most commonly used machine learning model and entropy measure respectively.
Article
Thermodynamics
Basil Mahdi Al-Srayyih, Ahmed Al-Manea, Khalid Saleh, Azher M. Abed, Qusay Rasheed Al-Amir, Hameed K. Hamzah, Farooq H. Ali, Raed Al-Rbaihat, Ali Alahmer
Summary: This study investigates the effects of introducing oscillating elliptical obstacles on natural convection and entropy generation in a quarter-circle cavity filled with a water-based hybrid nanofluid. The results show that an increase in the length of the hot source leads to a decrease in heat transfer rate, and increasing the Rayleigh number significantly increases total entropy generation and heat transfer irreversibility. The amplitude has a greater impact on the average Nusselt number compared to the frequency, and the direction of oscillation of the elliptical obstacles has different effects on the average Nusselt number at different Rayleigh numbers.
NUMERICAL HEAT TRANSFER PART A-APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Cherie M. O'Sullivan, Ravinesh C. Deo, Afshin Ghahramani
Summary: Transfer of processed data and parameters is a common technique in water quality modelling, but the determination of catchment similarities for Dissolved Inorganic Nitrogen (DIN) is uncertain, impacting the predictive capability of DIN simulation models. This study explores the use of a neural network pattern recognition model and explainable artificial intelligence approach to match ungauged catchments to gauged ones based on proxy spatial data. The results demonstrate that discriminating training data to DIN regime improves simulation performance, highlighting the usefulness of proxy spatial data in classifying catchments with similar DIN regimes.
SCIENTIFIC REPORTS
(2023)
Article
Thermodynamics
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Perez, Sancho Salcedo-Sanz
Summary: A hybrid multi-algorithm framework incorporating Artificial Neural Networks (ANN), Encoder-Decoder Based Long Short-Term Memory (EDLSTM), and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICMD) is developed for electricity demand prediction. The hybrid ICMD-ANN-EDLSTM model outperformed other benchmark models, detecting seasonality in demand data and providing valuable insights into market analysis.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Mahesh Anil Inamdar, U. Raghavendra, Anjan Gudigar, Sarvesh Bhandary, Massimo Salvi, Ravinesh C. Deo, Prabal Datta Barua, Edward J. Ciaccio, Filippo Molinari, U. Rajendra Acharya
Summary: This paper presents an efficient gland segmentation model using digital histopathology and deep learning, which has the potential to revolutionize medicine. The study aims to develop an automated method for segmenting histopathological images of human prostate glands and compare it with other techniques, showing that our method performs better in segmentation task.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
