Publications

2021
VK Tripathi, AK Kamath, L Behera, NK Verma, and S Nahavandi. 2021. “An Adaptive Fast Terminal Sliding-Mode Controller With Power Rate Proportional Reaching Law for Quadrotor Position and Altitude Tracking.” IEEE Transactions on Systems, Man, and Cybernetics: Systems.Abstract
This article focuses on developing an adaptive fast terminal sliding-mode controller (AFTSMC) with power rate proportional reaching law for the position and altitude tracking of a quadrotor in the presence of parametric uncertainties and bounded external disturbance. A nonlinear fast terminal sliding surface is proposed for the fast and finite-time convergence of the tracking error despite having the system states far away from the equilibrium point. Also, a power rate proportional reaching law has been proposed that ensures fast and finite-time convergence of the sliding manifold while attenuating the chattering phenomena in the sliding phase. To avoid the problem associated with over-estimation of the unknown disturbance bound, which eventually leads to chattering, an adaptive tuning law for gain adaptation is developed based on the Lyapunov's stability theory that completely eradicates the necessity of knowing the upper bound of the disturbance a priori. The finite-time stability of a closed-loop system is investigated using the Lyapunov theory. The effectiveness of the proposed scheme is compared with an adaptive sliding-mode controller (ASMC) using extensive simulation and validated on the DJI Matrice 100 quadrotor as a proof of concept on the hardware platform.
MRC Qazani, H Asadi, M Rostami, S Mohamed, CP Lim, and S Nahavandi. 2021. “Adaptive motion cueing algorithm based on fuzzy logic using online dexterity and direction monitoring.” IEEE systems journal, Pp. 1–9.
A Koohestani, M Abdar, S Hussain, A Khosravi, D Nahavandi, S Nahavandi, and R Alizadehsani. 2021. “Analysis of Driver Performance Using Hybrid of Weighted Ensemble Learning Technique and Evolutionary Algorithms.” Arabian Journal for Science and Engineering Section B: Engineering, Pp. 1–14.Abstract
© 2021, King Fahd University of Petroleum & Minerals. Having a full situational awareness while driving is one of the most important perceptions for safe driving which can be reduced by various factors such as in-vehicle infotainment, distraction, or mental load leading. Machine learning methods are being used to optimize for the identification of these inhibiting factors. To do so, three types of data were used: biographic features, physiological signals and vehicle information of 68 participants are being utilized to identify the normal and loaded behaviors. This research, therefore, concentrates on driving behavior analysis using a new automated hybrid framework for detection of performance degradation of drivers due to distraction. The proposed model contains a hybrid of extreme learning neural network, as an ensemble learning method and evolutionary algorithms, to determine the weights of classifiers, for combining several traditional classifiers. The obtained results showcase that the proposed model yields outstanding performance than the other applied methods.
S Nahavandi. 2021. “Applications for Machine Learning.” IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE, 7, 2, Pp. 3–3. Publisher's Version
SMJ Jalali, S Ahmadian, A Kavousi-Fard, A Khosravi, and S Nahavandi. 2021. “Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting.” IEEE Transactions on Systems, Man, and Cybernetics: Systems.Abstract
Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance (GHI). A deep convolutional long short-term memory is used to extract optimal features for accurate prediction of the GHI. The performance of such deep neural networks directly depends on their architectures. To deal with this problem, a swarm evolutionary optimization method, called the sine-cosine algorithm, is applied and advanced to automatically optimize the network architecture. A three-phase modification model is proposed to increase the diversity of population and avoid premature convergence in the optimization mechanism. The performance of the proposed method is investigated using three datasets collected from three solar stations in the east of the United States. The experimental results demonstrate the superiority of the proposed method in comparison to other forecasting models.
M Hammad, RNVPS Kandala, A Abdelatey, M Abdar, M Zomorodi‐Moghadam, RS Tan, UR Acharya, J Pławiak, R Tadeusiewicz, V Makarenkov, N Sarrafzadegan, A Khosravi, S Nahavandi, AAA EL-Latif, and P Pławiak. 2021. “Automated detection of shockable ECG signals: A review.” Information Sciences.Abstract
Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.
M Abdar, MA Fahami, S Chakrabarti, A Khosravi, P Pławiak, UR Acharya, R Tadeusiewicz, and S Nahavandi. 2021. “BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification.” Information Sciences, 577, Pp. 353–378.Abstract
Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings.
A Zolfagharian, S Gharaie, J Gregory, M Bodaghi, A Kaynak, and S Nahavandi. 2021. “A Bioinspired Compliant 3D-Printed Soft Gripper.” Soft Robotics, Pp. 1–10. Publisher's VersionAbstract
A compliant three-dimensional (3D)-printed soft gripper is designed based on the bioinspired spiral spring in this study. The soft gripper is then 3D-printed using a suitable thermoplastic filament material to deliver the desired performance. The sensorless mechanism introduced in this study provides adequate compliance with a single linear actuator for interacting with delicate objects, such as manipulation of human biological materials and fruit picking. The kinematic and dynamic models of the monolithic gripper are derived analytically as well as by means of finite element analysis to synthesize its functionality. The fabricated gripper module is installed on a robot arm to demonstrate the efficacy of design for picking and placing fruits without damaging them. The presented mechanism could be customized and used in the medical and agricultural sectors with diverse geometry objects.
SMS Islam, S Ahmed, R Uddin, MU Siddiqui, M Malekahmadi, A Al Mamun, R Alizadehsani, A Khosravi, and S Nahavandi. 2021. “Cardiovascular diseases risk prediction in patients with diabetes: Posthoc analysis from a matched case-control study in Bangladesh.” Journal of Diabetes and Metabolic Disorders, 20, 1, Pp. 417–425.Abstract
Purpose: This study aimed to investigate the estimated 10-year predicted risk of developing cardiovascular diseases (CVD) among participants with and without diabetes in Bangladesh. Methods: We performed posthoc analysis from a matched case-control study conducted among 1262 participants. A total of 631 participants with diabetes (case) were recruited from a tertiary hospital, and 631 age, sex and residence matched participants (control) were recruited from the community in Dhaka, Bangladesh. Socioeconomic anthropometric, clinical and CVD risk factor data were collected from the participants. The 10-year estimated CVD risk was calculated using the Framingham Risk Score, which has reasonable validity in South Asians. Results: The mean (SD) age of the participants were 51 (10) years. Total 52.3% of cases and 17.2% of controls were at high risk for CVD. The 10-year risk of CVD increased by age and was higher among males in both groups. Among the control group, high CVD risk was more prevalent among higher education and income groups. More than 85% of the tobacco smokers and 70% of chewing tobacco users in the case group were at high risk of CVD. Prevalence of high CVD risk among non-smokers cases was 8.6%. About 35% of hypertensive participants in the control group were at high risk of CVD. Conclusion: Bangladeshi patients with diabetes showed a significant burden of CVD risk at a relatively younger age. Strategies for reducing tobacco use and improving BP control in people with diabetes is needed for lowering future CVD risks.
F Khozeimeh, D Sharifrazi, NH Izadi, JH Joloudari, A Shoeibi, R Alizadehsani, JM Gorriz, S Hussain, ZA Sani, H Moosaei, A Khosravi, S Nahavandi, and SMS Islam. 2021. “Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.” Scientific Reports, 11, 1.Abstract
AbstractCOVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
A Shoeibi, N Ghassemi, R Alizadehsani, M Rouhani, H Hosseini-Nejad, A Khosravi, M Panahiazar, and S Nahavandi. 2021. “A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals.” Expert Systems with Applications, 163, 113788, Pp. 113788–113788.Abstract
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on people’s quality of life. Diagnosis of epileptic seizures is commonly performed on electroencephalography (EEG) signals, and by using computer-aided diagnosis systems (CADS), neurologists can diagnose epileptic seizure stages more accurately. In these systems, a mandatory stage is feature extraction, performed by handcrafting features or learning them, ordinarily by a deep neural net. While researches in this field commonly show the value of a group of limited features, yet an accurate comparison between different suggested features is essential. In this article, first, a comparison between the importance of 50 different handcrafted features for seizure detection is presented. Additionally, the computational complexity of features is investigated as well. Then the best features based on Fisher scores are picked to classify signals on a benchmark dataset for evaluation. Additionally, a convolutional autoencoder with five layers is applied to learn features in order to have a complete comparison among feature extraction approaches. Finally, a hybrid method is employed, which combines handcrafted features and encoding of autoencoder to reach high performance in seizure detection in EEG signals.
R Alizadehsani, A Khosravi, M Roshanzamir, M Abdar, N Sarrafzadegan, D Shafie, F Khozeimeh, A Shoeibi, S Nahavandi, M Panahiazar, A Bishara, RE Beygui, R Puri, S Kapadia, RS Tan, and UR Acharya. 2021. “Coronary artery disease detection using artificial intelligence techniques: a survey of trends, geographical differences and diagnostic features 1991–2020.” Computers in Biology and Medicine, 128, 104095, Pp. 1–16. Publisher's Version
AA Kekha Javan, A Shoeibi, A Zare, N Hosseini Izadi, M Jafari, R Alizadehsani, P Moridian, A Mosavi, UR Acharya, and S Nahavandi. 2021. “Design of adaptive-robust controller for multi-state synchronization of chaotic systems with unknown and time-varying delays and its application in secure communication.” Sensors (Switzerland), 21, 1, Pp. 1–21.Abstract
In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.
AA Kekha Javan, A Shoeibi, A Zare, N Hosseini Izadi, M Jafari, R Alizadehsani, P Moridian, A Mosavi, UR Acharya, and S Nahavandi. 2021. “Design of adaptive-robust controller for multi-state synchronization of chaotic systems with unknown and time-varying delays and its application in secure communication.” Sensors (Switzerland), 21, 1, Pp. 1–21.Abstract
In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.
VK Tripathi, SC Yogi, AK Kamath, L Behera, N K. Verma, and S Nahavandi. 2021. “A Disturbance Observer-Based Intelligent Finite-Time Sliding Mode Flight Controller Design for an Autonomous Quadrotor.” IEEE Systems Journal.Abstract
This article presents an intelligent control strategy for the position and attitude tracking of a quadrotor using a nonsingular fast-terminal sliding mode and disturbance observer. The quadrotor system is divided into an underactuated position and the fully-actuated attitude subsystem. A single hidden layer feed-forward neural network is used for the estimation of unknown dynamics associated with the attitude subsystem. Moreover, a nonlinear disturbance observer is designed to tackle the unknown bounded external disturbances acting on the attitude subsystem by supplying its estimation in the control laws that help in mitigating the chattering phenomena. The proposed methodology ensures the finite-time convergence of the tracking error and does not prone to the singularity problem in control. The closed-loop finite-time stability is investigated using the Lyapunov theory. Besides, an expression for the convergence time has been derived. The effectiveness of the proposed scheme is initially assessed using numerical simulations for the way-point tracking as well as circle tracking tasks and then validated in real-time using DJI Matrice 100 by performing a lemniscate tracking task. To show the superiority of the proposed methodology over existing methods, a comparative study has been done and found the improved tracking performance in the proposed approach.
N Mohajer, Z Najdovski, and S Nahavandi. 2021. “An Efficient Design Solution for a Low-Cost High-G Centrifuge System.” IEEE/ASME Transactions on Mechatronics, 26, 1, Pp. 134–145. Publisher's Version
A Shoeibi, M Khodatars, N Ghassemi, M Jafari, P Moridian, R Alizadehsani, M Panahiazar, F Khozeimeh, A Zare, H Hosseini-Nejad, A Khosravi, AF Atiya, D Aminshahidi, S Hussain, M Rouhani, S Nahavandi, and UR Acharya. 2021. “Epileptic seizures detection using deep learning techniques: A review.” International Journal of Environmental Research and Public Health, 18, 11.Abstract
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
D Sharifrazi, R Alizadehsani, M Roshanzamir, JH Joloudari, A Shoeibi, M Jafari, S Hussain, ZA Sani, F Hasanzadeh, F Khozeimeh, A Khosravi, S Nahavandi, M Panahiazar, A Zare, SM Shariful Islam, and UR Acharya. 2021. “Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.” Biomedical signal processing and control, 68, 102622, Pp. 1–14.
MS Ali, G Narayanan, S Nahavandi, JL Wang, and J Cao. 2021. “Global Dissipativity Analysis and Stability Analysis for Fractional-Order Quaternion-Valued Neural Networks With Time Delays.” IEEE Transactions on Systems, Man, and Cybernetics: Systems.Abstract
This article studies dissipativity analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with time delays. Two specific activation functions are considered along with common bounded and activation functions of Lipschitz-kind. Since quaternion multiplication is not commutative, we must divide the model, which is evaluated by quaternion, into four elements that are real-valued elements. On the basis of the construction of novel Lyapunov functional, and applying fractional-calculus theory, new criteria for the test of the global dissipativity and exponential stability of FOQVNNs model are established. FOQVNNs have also been suggested to provide global dissipativity and exponential stability, whereas nonlinear complex activation functions are constrained by the usage of linear matrix inequality methods, which utilize quaternion matrices and positive quaternion definite matrices. Finally, the effectiveness and superiority of the proposed approach is validated through numerical examples.
MS Ali, G Narayanan, S Nahavandi, JL Wang, and J Cao. 2021. “Global Dissipativity Analysis and Stability Analysis for Fractional-Order Quaternion-Valued Neural Networks With Time Delays.” IEEE Transactions on Systems, Man, and Cybernetics: Systems.Abstract
This article studies dissipativity analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with time delays. Two specific activation functions are considered along with common bounded and activation functions of Lipschitz-kind. Since quaternion multiplication is not commutative, we must divide the model, which is evaluated by quaternion, into four elements that are real-valued elements. On the basis of the construction of novel Lyapunov functional, and applying fractional-calculus theory, new criteria for the test of the global dissipativity and exponential stability of FOQVNNs model are established. FOQVNNs have also been suggested to provide global dissipativity and exponential stability, whereas nonlinear complex activation functions are constrained by the usage of linear matrix inequality methods, which utilize quaternion matrices and positive quaternion definite matrices. Finally, the effectiveness and superiority of the proposed approach is validated through numerical examples.

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