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Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients’ brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time–entropy–frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.
Noor Kamal Al-Qazzaz; Zaid Abdi Alkareem Alyasseri; Karrar Hameed Abdulkareem; Nabeel Salih Ali; Mohammed Nasser Al-Mhiqani; Christoph Guger. EEG Feature Fusion for Motor Imagery: A New Robust Framework Towards Stroke Patients Rehabilitation. Computers in Biology and Medicine 2021, 104799 .
AMA StyleNoor Kamal Al-Qazzaz, Zaid Abdi Alkareem Alyasseri, Karrar Hameed Abdulkareem, Nabeel Salih Ali, Mohammed Nasser Al-Mhiqani, Christoph Guger. EEG Feature Fusion for Motor Imagery: A New Robust Framework Towards Stroke Patients Rehabilitation. Computers in Biology and Medicine. 2021; ():104799.
Chicago/Turabian StyleNoor Kamal Al-Qazzaz; Zaid Abdi Alkareem Alyasseri; Karrar Hameed Abdulkareem; Nabeel Salih Ali; Mohammed Nasser Al-Mhiqani; Christoph Guger. 2021. "EEG Feature Fusion for Motor Imagery: A New Robust Framework Towards Stroke Patients Rehabilitation." Computers in Biology and Medicine , no. : 104799.
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
Zaid Abdi Alkareem Alyasseri; Mohammed Azmi Al‐Betar; Iyad Abu Doush; Mohammed A. Awadallah; Ammar Kamal Abasi; Sharif Naser Makhadmeh; Osama Ahmad Alomari; Karrar Hameed Abdulkareem; Afzan Adam; Robertas Damasevicius; Mazin Abed Mohammed; Raed Abu Zitar. Review on COVID ‐19 diagnosis models based on machine learning and deep learning approaches. Expert Systems 2021, e12759 .
AMA StyleZaid Abdi Alkareem Alyasseri, Mohammed Azmi Al‐Betar, Iyad Abu Doush, Mohammed A. Awadallah, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Osama Ahmad Alomari, Karrar Hameed Abdulkareem, Afzan Adam, Robertas Damasevicius, Mazin Abed Mohammed, Raed Abu Zitar. Review on COVID ‐19 diagnosis models based on machine learning and deep learning approaches. Expert Systems. 2021; ():e12759.
Chicago/Turabian StyleZaid Abdi Alkareem Alyasseri; Mohammed Azmi Al‐Betar; Iyad Abu Doush; Mohammed A. Awadallah; Ammar Kamal Abasi; Sharif Naser Makhadmeh; Osama Ahmad Alomari; Karrar Hameed Abdulkareem; Afzan Adam; Robertas Damasevicius; Mazin Abed Mohammed; Raed Abu Zitar. 2021. "Review on COVID ‐19 diagnosis models based on machine learning and deep learning approaches." Expert Systems , no. : e12759.
Waste generation is a continuous process that needs to be managed effectively to ensure environmental safety and public health. The recent circular economy (CE) practices have brought a new shape for the waste management industry, creating value from the generated waste. The shift to a CE represents one of the most significant challenges, particularly in sorting and classifying generated waste. Addressing these challenges would facilitate the recycling industry and helps in promoting remanufacturing. But in the COVID times, most of the generated waste is getting mixed with conventional waste types, especially in the global south. The pandemic has resulted in colossal infectious waste generation. Its handling became the most significant challenge raising fears and concerns over sorting and classifying. Hence, this study proposes an Artificial Intelligence (AI) based automated solution for sorting COVID related medical waste streams from other waste types and, at the same time, ensures data-driven decisions for recycling in the context of CE. Metal, paper, glass waste categories, including the polyethylene terephthalate (PET) waste from the pandemic, are considered. The waste type classification is done based on the image-texture-dependent features, which provided an accurate sorting and classification before the recycling process starts. The features are fused using the proposed decision-level feature fusion scheme. The classification model based on the support vector machine (SVM) classifier performs best (with 96.5 % accuracy, 95.3 % sensitivity, and 95.9 % specificity) in classifying waste types in the context of circular manufacturing and exhibiting the abilities to manage the COVID related medical waste mixed.
Nallapaneni Manoj Kumar; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Robertas Damasevicius; Salama A. Mostafa; Mashael S. Maashi; Shauhrat S. Chopra. Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice. Process Safety and Environmental Protection 2021, 152, 482 -494.
AMA StyleNallapaneni Manoj Kumar, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Robertas Damasevicius, Salama A. Mostafa, Mashael S. Maashi, Shauhrat S. Chopra. Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice. Process Safety and Environmental Protection. 2021; 152 ():482-494.
Chicago/Turabian StyleNallapaneni Manoj Kumar; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Robertas Damasevicius; Salama A. Mostafa; Mashael S. Maashi; Shauhrat S. Chopra. 2021. "Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice." Process Safety and Environmental Protection 152, no. : 482-494.
The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.
Abdullah Lakhan; Mazin Mohammed; Ahmed Rashid; Seifedine Kadry; Thammarat Panityakul; Karrar Abdulkareem; Orawit Thinnukool. Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System. Sensors 2021, 21, 4093 .
AMA StyleAbdullah Lakhan, Mazin Mohammed, Ahmed Rashid, Seifedine Kadry, Thammarat Panityakul, Karrar Abdulkareem, Orawit Thinnukool. Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System. Sensors. 2021; 21 (12):4093.
Chicago/Turabian StyleAbdullah Lakhan; Mazin Mohammed; Ahmed Rashid; Seifedine Kadry; Thammarat Panityakul; Karrar Abdulkareem; Orawit Thinnukool. 2021. "Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System." Sensors 21, no. 12: 4093.
Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.
Sheeba Lal; Saeed Rehman; Jamal Shah; Talha Meraj; Hafiz Rauf; Robertas Damaševičius; Mazin Mohammed; Karrar Abdulkareem. Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition. Sensors 2021, 21, 3922 .
AMA StyleSheeba Lal, Saeed Rehman, Jamal Shah, Talha Meraj, Hafiz Rauf, Robertas Damaševičius, Mazin Mohammed, Karrar Abdulkareem. Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition. Sensors. 2021; 21 (11):3922.
Chicago/Turabian StyleSheeba Lal; Saeed Rehman; Jamal Shah; Talha Meraj; Hafiz Rauf; Robertas Damaševičius; Mazin Mohammed; Karrar Abdulkareem. 2021. "Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition." Sensors 21, no. 11: 3922.
Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
Mohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability 2021, 13, 5406 .
AMA StyleMohd Abd Ghani, Nasir Noma, Mazin Mohammed, Karrar Abdulkareem, Begonya Garcia-Zapirain, Mashael Maashi, Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability. 2021; 13 (10):5406.
Chicago/Turabian StyleMohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. 2021. "Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques." Sustainability 13, no. 10: 5406.
Using gestures can help people with certain disabilities in communicating with other people. This paper proposes a lightweight model based on YOLO (You Only Look Once) v3 and DarkNet-53 convolutional neural networks for gesture recognition without additional preprocessing, image filtering, and enhancement of images. The proposed model achieved high accuracy even in a complex environment, and it successfully detected gestures even in low-resolution picture mode. The proposed model was evaluated on a labeled dataset of hand gestures in both Pascal VOC and YOLO format. We achieved better results by extracting features from the hand and recognized hand gestures of our proposed YOLOv3 based model with accuracy, precision, recall, and an F-1 score of 97.68, 94.88, 98.66, and 96.70%, respectively. Further, we compared our model with Single Shot Detector (SSD) and Visual Geometry Group (VGG16), which achieved an accuracy between 82 and 85%. The trained model can be used for real-time detection, both for static hand images and dynamic gestures recorded on a video.
Abdullah Mujahid; Mazhar Awan; Awais Yasin; Mazin Mohammed; Robertas Damaševičius; Rytis Maskeliūnas; Karrar Abdulkareem. Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model. Applied Sciences 2021, 11, 4164 .
AMA StyleAbdullah Mujahid, Mazhar Awan, Awais Yasin, Mazin Mohammed, Robertas Damaševičius, Rytis Maskeliūnas, Karrar Abdulkareem. Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model. Applied Sciences. 2021; 11 (9):4164.
Chicago/Turabian StyleAbdullah Mujahid; Mazhar Awan; Awais Yasin; Mazin Mohammed; Robertas Damaševičius; Rytis Maskeliūnas; Karrar Abdulkareem. 2021. "Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model." Applied Sciences 11, no. 9: 4164.
Cardiac arrhythmias impose a significant burden on the healthcare environment due to the increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is a common form of cardiac arrhythmia which begins from the lower chamber of the heart, and frequent occurrence of PVC beat might lead to mortality. ECG signals are the noninvasive and primary tool used to identify the actual life threat related to the heart. Nowadays, in society, the computer-assisted technique reduces doctors' burden to evaluate heart disease and heart arrhythmia automatically. Regardless of well-equipped and well-developed health facilities that are available for monitoring the cardiac condition, the success stories are yet unsatisfactorily due to the complexity of the cardiac disorder. The most challenging part in ECG signal analysis is to extract the accurate features relevant to the arrhythmia for classification due to the inter-patient variation. There are many morphological changes present in the ECG signals. Hence, there is a gap in the usage of appropriate methods for the extraction of features and classification models, which reduce the biased diagnosis of PVC arrhythmia. To predict PVC arrhythmia accurately is a quite challenging task owing to (a) QRS negative (b) long compensatory pause (c) p-wave (d) biased diagnosis of PVC detection due to the small feature set. This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R–R wave interval, QRS duration, and verification of P-wave whether it is present or absent using threshold technique. We propose the machine learning-data mining MACDM integrated approach using five different models of multiple logistic regression and four classifiers, namely, Random Forest (RF), K-Nearest Neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB). The experiment was conducted on the public benchmark MIT-BIH-AR to evaluate the performance of our proposed MACDM technique. The multiple logistic regression models constructed as a function of all independent variables achieved an accuracy of 99.96%, sensitivity 98.9%, specificity 99.20%, PPV 99.25%, and Youden's index parameter 98.24%. Thus, it is proved that this computer-aided method helps our medical practitioners improve the efficiency of their services.
Qurat-Ul-Ain Mastoi; Muhammad Suleman Memon; Abdullah Lakhan; Mazin Abed Mohammed; Mumtaz Qabulio; Fadi Al-Turjman; Karrar Hameed Abdulkareem. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Computing and Applications 2021, 33, 11703 -11719.
AMA StyleQurat-Ul-Ain Mastoi, Muhammad Suleman Memon, Abdullah Lakhan, Mazin Abed Mohammed, Mumtaz Qabulio, Fadi Al-Turjman, Karrar Hameed Abdulkareem. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Computing and Applications. 2021; 33 (18):11703-11719.
Chicago/Turabian StyleQurat-Ul-Ain Mastoi; Muhammad Suleman Memon; Abdullah Lakhan; Mazin Abed Mohammed; Mumtaz Qabulio; Fadi Al-Turjman; Karrar Hameed Abdulkareem. 2021. "Machine learning-data mining integrated approach for premature ventricular contraction prediction." Neural Computing and Applications 33, no. 18: 11703-11719.
The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.
Sharif Makhadmeh; Mohammed Al-Betar; Zaid Alyasseri; Ammar Abasi; Ahamad Khader; Robertas Damaševičius; Mazin Mohammed; Karrar Abdulkareem. Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer. Electronics 2021, 10, 447 .
AMA StyleSharif Makhadmeh, Mohammed Al-Betar, Zaid Alyasseri, Ammar Abasi, Ahamad Khader, Robertas Damaševičius, Mazin Mohammed, Karrar Abdulkareem. Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer. Electronics. 2021; 10 (4):447.
Chicago/Turabian StyleSharif Makhadmeh; Mohammed Al-Betar; Zaid Alyasseri; Ammar Abasi; Ahamad Khader; Robertas Damaševičius; Mazin Mohammed; Karrar Abdulkareem. 2021. "Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer." Electronics 10, no. 4: 447.
The recent seen intelligent technologies like the internet of things (IoT), computer vision etc. facilitates farming activities and also provides flexible farm operations. On the other side, farming has become feasible even in urban areas, especially building roofs, open gardens, and indoor agriculture. In this context, farm management and appropriate monitoring of farm parameters are now indispensable for productive farming in smart cities or rural areas. In this paper, an IoT based Smart AgroTech system is proposed in the context of urban farming that considers humidity, temperature, and soil moisture as necessary farming parameters. The proposed system decides whether the irrigation action should begin or stop depending on the farming land condition and provides the monitoring facility and remote control to the farm owner. The system's reliability is verified by determining the error percentage between actual data and observed data at different observations. The average error rate for humidity and soil moisture is below 3% and for temperature is below 1.5%. The system ascertains a feasible Smart AgroTech system that provides advantages to the farming activities in future cities than other conventional methods.
Amit Kumer Podder; Abdullah Al Bukhari; Sayemul Islam; Sujon Mia; Mazin Abed Mohammed; Nallapaneni Manoj Kumar; Korhan Cengiz; Karrar Hameed Abdulkareem. IoT based smart agrotech system for verification of Urban farming parameters. Microprocessors and Microsystems 2021, 82, 104025 .
AMA StyleAmit Kumer Podder, Abdullah Al Bukhari, Sayemul Islam, Sujon Mia, Mazin Abed Mohammed, Nallapaneni Manoj Kumar, Korhan Cengiz, Karrar Hameed Abdulkareem. IoT based smart agrotech system for verification of Urban farming parameters. Microprocessors and Microsystems. 2021; 82 ():104025.
Chicago/Turabian StyleAmit Kumer Podder; Abdullah Al Bukhari; Sayemul Islam; Sujon Mia; Mazin Abed Mohammed; Nallapaneni Manoj Kumar; Korhan Cengiz; Karrar Hameed Abdulkareem. 2021. "IoT based smart agrotech system for verification of Urban farming parameters." Microprocessors and Microsystems 82, no. : 104025.
The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
Mazhar Javed Awan; Mohd Mohd Rahim; Naomie Salim; Mazin Mohammed; Begonya Garcia-Zapirain; Karrar Abdulkareem. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics 2021, 11, 105 .
AMA StyleMazhar Javed Awan, Mohd Mohd Rahim, Naomie Salim, Mazin Mohammed, Begonya Garcia-Zapirain, Karrar Abdulkareem. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics. 2021; 11 (1):105.
Chicago/Turabian StyleMazhar Javed Awan; Mohd Mohd Rahim; Naomie Salim; Mazin Mohammed; Begonya Garcia-Zapirain; Karrar Abdulkareem. 2021. "Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach." Diagnostics 11, no. 1: 105.
Mazin Abed Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Salama A. Mostafa; Mashael S. Maashi; Alaa S. Al-Waisy; Mohammed Ahmed Subhi; Ammar Awad Mutlag; Dac-Nhuong Le. A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images. Computers, Materials & Continua 2021, 66, 3289 -3310.
AMA StyleMazin Abed Mohammed, Karrar Abdulkareem, Begonya Garcia-Zapirain, Salama A. Mostafa, Mashael S. Maashi, Alaa S. Al-Waisy, Mohammed Ahmed Subhi, Ammar Awad Mutlag, Dac-Nhuong Le. A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images. Computers, Materials & Continua. 2021; 66 (3):3289-3310.
Chicago/Turabian StyleMazin Abed Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Salama A. Mostafa; Mashael S. Maashi; Alaa S. Al-Waisy; Mohammed Ahmed Subhi; Ammar Awad Mutlag; Dac-Nhuong Le. 2021. "A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images." Computers, Materials & Continua 66, no. 3: 3289-3310.
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
Alaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing 2020, 1 -16.
AMA StyleAlaa S. Al-Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, Muhammad Arif, Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing. 2020; ():1-16.
Chicago/Turabian StyleAlaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. 2020. "COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images." Soft Computing , no. : 1-16.
Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.
Mohammed Al-Mhiqani; Rabiah Ahmad; Z. Abidin; Warusia Yassin; Aslinda Hassan; Karrar Abdulkareem; Nabeel Ali; Zahri Yunos. A Review of Insider Threat Detection: Classification, Machine Earning Techniques, Datasets, Open Challenges, and Recommendations. Applied Sciences 2020, 10, 5208 .
AMA StyleMohammed Al-Mhiqani, Rabiah Ahmad, Z. Abidin, Warusia Yassin, Aslinda Hassan, Karrar Abdulkareem, Nabeel Ali, Zahri Yunos. A Review of Insider Threat Detection: Classification, Machine Earning Techniques, Datasets, Open Challenges, and Recommendations. Applied Sciences. 2020; 10 (15):5208.
Chicago/Turabian StyleMohammed Al-Mhiqani; Rabiah Ahmad; Z. Abidin; Warusia Yassin; Aslinda Hassan; Karrar Abdulkareem; Nabeel Ali; Zahri Yunos. 2020. "A Review of Insider Threat Detection: Classification, Machine Earning Techniques, Datasets, Open Challenges, and Recommendations." Applied Sciences 10, no. 15: 5208.
This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.
Ahmed Albahri; A.A. Zaidan; B.B. Zaidan; Karrar Hameed Abdulkareem; Z.T. Al-Qaysi; A.H. Alamoodi; A.M. Aleesa; M.A. Chyad; R.M. Alesa; L.C. Kem; Muhammad Modi Lakulu; A.B. Ibrahim; Nazre Abdul Rashid. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health 2020, 13, 1381 -1396.
AMA StyleAhmed Albahri, A.A. Zaidan, B.B. Zaidan, Karrar Hameed Abdulkareem, Z.T. Al-Qaysi, A.H. Alamoodi, A.M. Aleesa, M.A. Chyad, R.M. Alesa, L.C. Kem, Muhammad Modi Lakulu, A.B. Ibrahim, Nazre Abdul Rashid. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health. 2020; 13 (10):1381-1396.
Chicago/Turabian StyleAhmed Albahri; A.A. Zaidan; B.B. Zaidan; Karrar Hameed Abdulkareem; Z.T. Al-Qaysi; A.H. Alamoodi; A.M. Aleesa; M.A. Chyad; R.M. Alesa; L.C. Kem; Muhammad Modi Lakulu; A.B. Ibrahim; Nazre Abdul Rashid. 2020. "Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects." Journal of Infection and Public Health 13, no. 10: 1381-1396.
People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods. The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between ‘serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria’ and ‘patient list based on novel MCDM method known as subjective and objective decision by opinion score method’. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix. An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments. The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.
O.S. Albahri; Jameel R. Al-Obaidi; A.A. Zaidan; B.B. Zaidan; Mahmood M. Salih; Abdulhadi Qays; K.A. Dawood; R.T. Mohammed; Karrar Hameed Abdulkareem; A.M. Aleesa; A.H. Alamoodi; M.A. Chyad; Che Zalina Zulkifli. Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Computer Methods and Programs in Biomedicine 2020, 196, 105617 -105617.
AMA StyleO.S. Albahri, Jameel R. Al-Obaidi, A.A. Zaidan, B.B. Zaidan, Mahmood M. Salih, Abdulhadi Qays, K.A. Dawood, R.T. Mohammed, Karrar Hameed Abdulkareem, A.M. Aleesa, A.H. Alamoodi, M.A. Chyad, Che Zalina Zulkifli. Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Computer Methods and Programs in Biomedicine. 2020; 196 ():105617-105617.
Chicago/Turabian StyleO.S. Albahri; Jameel R. Al-Obaidi; A.A. Zaidan; B.B. Zaidan; Mahmood M. Salih; Abdulhadi Qays; K.A. Dawood; R.T. Mohammed; Karrar Hameed Abdulkareem; A.M. Aleesa; A.H. Alamoodi; M.A. Chyad; Che Zalina Zulkifli. 2020. "Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods." Computer Methods and Programs in Biomedicine 196, no. : 105617-105617.
Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.
Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mohd Khanapi Abd Ghani; Mashael S. Maashi; Begonya Garcia-Zapirain; Ibon Oleagordia; Hosam AlHakami; Fahad Taha Al-Dhief. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences 2020, 10, 3723 .
AMA StyleMazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mohd Khanapi Abd Ghani, Mashael S. Maashi, Begonya Garcia-Zapirain, Ibon Oleagordia, Hosam AlHakami, Fahad Taha Al-Dhief. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences. 2020; 10 (11):3723.
Chicago/Turabian StyleMazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mohd Khanapi Abd Ghani; Mashael S. Maashi; Begonya Garcia-Zapirain; Ibon Oleagordia; Hosam AlHakami; Fahad Taha Al-Dhief. 2020. "Voice Pathology Detection and Classification Using Convolutional Neural Network Model." Applied Sciences 10, no. 11: 3723.
Given the rapid development of dehazing image algorithms, selecting the optimal algorithm based on multiple criteria is crucial in determining the efficiency of an algorithm. However, a sufficient number of criteria must be considered when selecting an algorithm in multiple foggy scenes, including inhomogeneous, homogenous and dark foggy scenes. However, the selection of an optimal real-time image dehazing algorithm based on standardised criteria presents a challenge. According to previous studies, a standardisation and selection framework for real-time image dehazing algorithms based on multi-foggy scenes is not yet available. To address this gap, this study proposes a new standardisation and selection framework based on fuzzy Delphi (FDM) and hybrid multi-criteria analysis methods. Experiments are also conducted in three phases. Firstly, the image dehazing criteria are standardised based on FDM. Secondly, an evaluation experiment is conducted based on standardised criteria and nine real-time image dehazing algorithms to obtain a multi-perspective matrix. Third, entropy and VIKOR methods are hybridised to determine the weight of the standardised criteria and to rank the algorithms. Three rules are applied in the standardisation process to determine the criteria. To objectively validate the selection results, mean is applied for this purpose. The results of this work can be taken into account in designing efficient methods and metrics for image dehazing.
Karrar Hameed Abdulkareem; Nureize Arbaiy; A. A. Zaidan; B. B. Zaidan; O. S. Albahri; M. A. Alsalem; Mahmood M. Salih. A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Computing and Applications 2020, 33, 1029 -1054.
AMA StyleKarrar Hameed Abdulkareem, Nureize Arbaiy, A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, Mahmood M. Salih. A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Computing and Applications. 2020; 33 (4):1029-1054.
Chicago/Turabian StyleKarrar Hameed Abdulkareem; Nureize Arbaiy; A. A. Zaidan; B. B. Zaidan; O. S. Albahri; M. A. Alsalem; Mahmood M. Salih. 2020. "A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods." Neural Computing and Applications 33, no. 4: 1029-1054.
ABSTRACT Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.
Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Alaa S. Al-Waisy; Salama A. Mostafa; Shumoos Al-Fahdawi; Ahmed Musa Dinar; Wajdi Alhakami; Abdullah Baz; Mohammed Nasser Al-Mhiqani; Hosam Alhakami; Nureize Arbaiy; Mashael S. Maashi; Ammar Awad Mutlag; Begonya Garcia-Zapirain; Isabel De La Torre Diez. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access 2020, 8, 99115 -99131.
AMA StyleMazin Abed Mohammed, Karrar Hameed Abdulkareem, Alaa S. Al-Waisy, Salama A. Mostafa, Shumoos Al-Fahdawi, Ahmed Musa Dinar, Wajdi Alhakami, Abdullah Baz, Mohammed Nasser Al-Mhiqani, Hosam Alhakami, Nureize Arbaiy, Mashael S. Maashi, Ammar Awad Mutlag, Begonya Garcia-Zapirain, Isabel De La Torre Diez. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access. 2020; 8 (99):99115-99131.
Chicago/Turabian StyleMazin Abed Mohammed; Karrar Hameed Abdulkareem; Alaa S. Al-Waisy; Salama A. Mostafa; Shumoos Al-Fahdawi; Ahmed Musa Dinar; Wajdi Alhakami; Abdullah Baz; Mohammed Nasser Al-Mhiqani; Hosam Alhakami; Nureize Arbaiy; Mashael S. Maashi; Ammar Awad Mutlag; Begonya Garcia-Zapirain; Isabel De La Torre Diez. 2020. "Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods." IEEE Access 8, no. 99: 99115-99131.
Telemedicine is increasingly used in the modern health care system because it provides health care services to patients amidst distant locations. However, the prioritisation process for patients with multiple chronic diseases (MCDs) over telemedicine is becoming increasingly complex due to diverse and big data generated from multiple disease conditions. To solve such a problem, massive datasets must be collected, and high velocity must be acquired, specifically in real-time processing. This process requires decision-making (DM) regarding the emergency degree of each chronic disease for every patient. Multi-criteria decision-making (MCDM) approaches (i.e. direct aggregation, distance measurement and compromise ranking) are the main solutions for dealing with this complex situation. However, each MCDM approach provides a unique rank from those of other methods. By far, the preferred DM approach that can provide an ideal rank better than other approaches has not been established. This study proposes an extension of the technique for reorganising opinion order to interval levels (TROOIL). Such an extension is called Hybrid DM and Voting Method (HDMVM) which is based on different DM approaches (i.e. direct aggregation, distance measurement and compromise ranking). HDMVM is used to prioritise big data of patients with MCDs in real-time through the remote health-monitoring procedure. In this paper, we propose a methodology that is based on three sequential stages. The first stage illustrates how the big data of patients with MCDs can be recognised in the telemedicine environment and identifies the target telemedicine tier in this study. The second stage describes the steps of the proposed HDMVM sequentially. The third stage applies the proposed method by prioritising the case study of big data of patients with MCDs based on the above DM approaches. Moreover, dataset of remote patients with MCDs (n = 500) is adopted, which contains three diseases, namely, chronic heart diseases and high and low blood pressures. The prioritisation results vary among direct aggregation, distance measurement and compromise approaches. The proposed HDMVM effectively provides a uniform and final ranking result for big data of patients with MCDs. A statistical method (i.e. mean) is performed to objectively validate the ranking results. Significant differences between the scores of the groups are identified in the objective validation, signifying identical ranking results. The evaluation of the proposed work with the benchmark study indicates that this study has tackled issues relevant to big data and diversity of MCDM approaches in the prioritisation of patients with MCDs.
K. I. Mohammed; Jafreezal Jaafar; A. A. Zaidan; O. S. Albahri; B. B. Zaidan; Karrar Hameed Abdulkareem; Ali Najm Jasim; Ali. H. Shareef; M. J. Baqer; M. A. Alsalem; A. H. Alamoodi. A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method. IEEE Access 2020, 8, 91521 -91530.
AMA StyleK. I. Mohammed, Jafreezal Jaafar, A. A. Zaidan, O. S. Albahri, B. B. Zaidan, Karrar Hameed Abdulkareem, Ali Najm Jasim, Ali. H. Shareef, M. J. Baqer, M. A. Alsalem, A. H. Alamoodi. A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method. IEEE Access. 2020; 8 (99):91521-91530.
Chicago/Turabian StyleK. I. Mohammed; Jafreezal Jaafar; A. A. Zaidan; O. S. Albahri; B. B. Zaidan; Karrar Hameed Abdulkareem; Ali Najm Jasim; Ali. H. Shareef; M. J. Baqer; M. A. Alsalem; A. H. Alamoodi. 2020. "A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method." IEEE Access 8, no. 99: 91521-91530.