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Dr. Mazin Mohammed
University of Anbar

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Research Keywords & Expertise

0 Artifical Intelligence
0 Machine and Deep Learning
0 Fog-Cloud Computing
0 Medical imaging processing, sensors,
0 IoT in healthcare

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Machine and Deep Learning
IoT in healthcare

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Original article
Published: 13 August 2021 in Expert Systems
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Ultrasound imaging (US) is one of the most common diagnostic imaging tools for producing images of the human body in clinical practice. This work is devoted to studying ultrasound images collected from gynaecological tests for medical purposes regarding ovarian and breast defects. The study revolves around (i) Enhancing the texture of the image by applying a new effective framework that can help in reducing the speckle noise from the image while preserving the most important information; (ii) Extracting the most prominent features using the histogram of oriented gradients (HOG) and; (iii) Fusing the features that are produced by the edge operators and using them as an input to the ANN classifier to generate three trained classifiers. The fusion technique has been used to get an effective decision by using the whole features. The experimental results of the proposed method for the breast cancer and ovarian tumour using the second experiment achieved 97.96% accuracy, 96.05% sensitivity, and 99.17% specificity by utilizing the breast cancer information set. Overall, 95.87% precision, 97.01% sensitivity, and 93.33% specificity have been achieved for the ovarian tumour data collection. Consequently, the proposed method has been improved to validate the output of modern computerized and automated technologies. This method analyzes the gynaecological ultrasound images to identify suspicious objects or cases with health consequences for women.

ACS Style

Ihsan Jasim Hussein; Mohd Aboobaider Burhanuddin; Mazin Abed Mohammed; Narjes Benameur; Marwah Suliman Maashi; Mashael S. Maashi. Fully‐automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients ( HOG ). Expert Systems 2021, e12789 .

AMA Style

Ihsan Jasim Hussein, Mohd Aboobaider Burhanuddin, Mazin Abed Mohammed, Narjes Benameur, Marwah Suliman Maashi, Mashael S. Maashi. Fully‐automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients ( HOG ). Expert Systems. 2021; ():e12789.

Chicago/Turabian Style

Ihsan Jasim Hussein; Mohd Aboobaider Burhanuddin; Mazin Abed Mohammed; Narjes Benameur; Marwah Suliman Maashi; Mashael S. Maashi. 2021. "Fully‐automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients ( HOG )." Expert Systems , no. : e12789.

Journal article
Published: 01 August 2021 in Diagnostics
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Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.

ACS Style

S. Priya; Arockia Rani; M. Subathra; Mazin Mohammed; Robertas Damaševičius; Neha Ubendran. Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals. Diagnostics 2021, 11, 1395 .

AMA Style

S. Priya, Arockia Rani, M. Subathra, Mazin Mohammed, Robertas Damaševičius, Neha Ubendran. Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals. Diagnostics. 2021; 11 (8):1395.

Chicago/Turabian Style

S. Priya; Arockia Rani; M. Subathra; Mazin Mohammed; Robertas Damaševičius; Neha Ubendran. 2021. "Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals." Diagnostics 11, no. 8: 1395.

Review
Published: 28 July 2021 in Expert Systems
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

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. 2021. "Review on COVID ‐19 diagnosis models based on machine learning and deep learning approaches." Expert Systems , no. : e12759.

Journal article
Published: 17 July 2021 in Journal of Cleaner Production
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Mechanization in agriculture involves all stages of cultivating and preparing innovations, from basic and essential hand devices and tools to more complex and mechanized implements. Fundamentally, such devices and tools enable agricultural activities to initiate different crop yields in many different neighborhoods in the world's ecological system. Due to the unstable nature of fuel prices, market demands, and damaging effects of tillage machinery vibrations on the operator, exploring efficient methods of preventing these situations has become indispensable. There are many existing methods related to tillage optimization, including fast, high quality, and cost-effective tilling. However, none of the existing methods perform real-time and dynamic recommendations due to the inability to provide real-time tillage data. Another issue is the unavailability of a tillage recommendation model that acts based on the changes of a fixed set of tillage parameters and provides the necessary guidance to the tractor driver during the tillage operation. This paper proposes a Tillage Operations Quality Optimization (TOQO) model that aims to improve the efficiency of tillage operations. The model provides real-time tillage data through integrating the Internet of Things (IoT) technology and a dynamic Decision Support System (DSS) for tillage machinery operation and process optimization. The proposed model has been successfully implemented, tested, and evaluated in real-world tillage operations. The results of this TOQO study clearly show improvements in the tillage operations. This is because the TOQO gives online recommendations in real-time based on dynamic measurements of six tillage performance evaluation parameters. The parameters are vibration, bulk density, slippage ratio, fuel consumption, real tillage depth, and field efficiency. The TOQO model successfully handles more than one tillage machinery tractor by using cloud computing services. Vibration increases when using the system on the X-axis at 46.63843 rms, Y-axis at 23.23612 rms, and the Z-axis at 51.47240 rms. Vibration fractures the soil, reducing soil cohesiveness when using an oscillating tillage tool and producing smaller soil aggregates than a non-oscillating one. When using the system, the value of the virtual density increases by 0.23821 g/cm3. Most of the bulk density values for the optimized phase are within the recommended or required growth range. There is a slight increase in depth by 0.4279 cm and a decrease in slippage ratio by 0.64364%. Tractor fuel consumption decreases by 1.2169 L per hour, indicating a reduction in tractor wheel slippage. Reduced fuel consumption and wheel slippage of tillage operation are essential parameters to increase field efficiency and reduce tillage operation costs.

ACS Style

Haider Fawzi; Salama A. Mostafa; Desa Ahmed; Nayef Alduais; Mazin Abed Mohammed; Mohamed Elhoseny. TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System. Journal of Cleaner Production 2021, 316, 128263 .

AMA Style

Haider Fawzi, Salama A. Mostafa, Desa Ahmed, Nayef Alduais, Mazin Abed Mohammed, Mohamed Elhoseny. TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System. Journal of Cleaner Production. 2021; 316 ():128263.

Chicago/Turabian Style

Haider Fawzi; Salama A. Mostafa; Desa Ahmed; Nayef Alduais; Mazin Abed Mohammed; Mohamed Elhoseny. 2021. "TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System." Journal of Cleaner Production 316, no. : 128263.

Original research paper
Published: 02 July 2021 in IET Communications
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The evolution of 4G telecommunication propagated various resource-crunched clients to experience rate-effective resources at ease. However, it extends its underlying centralised architecture, which arouses various challenges correlated with network data availability, network information protection, and operational infrastructure charges. With the recent revolution of telecommunication, 5G networks promised to provide credible schemes like the high quality of service, ultra-low latency, and much security over the pre-existing architecture. However, the deployment of end-to-end 5G network cutting-edge systems in the present heterogeneous world limits its core idea of extensive data privacy, native interoperability, risk-free interference, and radio spectrum sharing. Perhaps, to achieve its true capability, improved versions of blockchain technology could be aligned to strengthen various real-time complex applications at a flourishing rate. One of the multiplexed real-time enterprise applications is a keyword search engine where the integrity of user data files and keyword searches are bound to come under cyber hackers. On the one hand, it was found that when a 5G-based blockchain emulated network gets deployed with intact encryption techniques, the entire system facilitates to give reliable, efficient, and risk-free keyword search over variegated 5G network data and its complex computational calculations. Consequently, the use of blockchain-based decentralised cloud orchestration scheme at various levels enabled the architecture to remain incorruptible and protects all the confidential files and keywords in a fully controlled file access environment. The results of the simulation kernel shows that proposed architecture which, when combined with blockchain-based decentralised cloud orchestration network system, justify all the essential characteristics and effectuates the optimal use of 5G network sharing by each network entity.

ACS Style

Poongodi M; Mohit Malviya; Mounir Hamdi; Vijayakumar V; Mazin Abed Mohammed; Hafiz Tayyab Rauf; Kawther A. Al‐Dhlan. 5G based Blockchain network for authentic and ethical keyword search engine. IET Communications 2021, 1 .

AMA Style

Poongodi M, Mohit Malviya, Mounir Hamdi, Vijayakumar V, Mazin Abed Mohammed, Hafiz Tayyab Rauf, Kawther A. Al‐Dhlan. 5G based Blockchain network for authentic and ethical keyword search engine. IET Communications. 2021; ():1.

Chicago/Turabian Style

Poongodi M; Mohit Malviya; Mounir Hamdi; Vijayakumar V; Mazin Abed Mohammed; Hafiz Tayyab Rauf; Kawther A. Al‐Dhlan. 2021. "5G based Blockchain network for authentic and ethical keyword search engine." IET Communications , no. : 1.

Article
Published: 20 June 2021 in Cluster Computing
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These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications.

ACS Style

Abdullah Lakhan; Muhammad Suleman Memon; Qurat-Ul-Ain Mastoi; Mohamed Elhoseny; Mazin Abed Mohammed; Mumtaz Qabulio; Mohamed Abdel-Basset. Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing 2021, 1 -23.

AMA Style

Abdullah Lakhan, Muhammad Suleman Memon, Qurat-Ul-Ain Mastoi, Mohamed Elhoseny, Mazin Abed Mohammed, Mumtaz Qabulio, Mohamed Abdel-Basset. Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing. 2021; ():1-23.

Chicago/Turabian Style

Abdullah Lakhan; Muhammad Suleman Memon; Qurat-Ul-Ain Mastoi; Mohamed Elhoseny; Mazin Abed Mohammed; Mumtaz Qabulio; Mohamed Abdel-Basset. 2021. "Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network." Cluster Computing , no. : 1-23.

Journal article
Published: 19 June 2021 in Process Safety and Environmental Protection
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Nallapaneni 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.

Journal article
Published: 18 June 2021 in Expert Systems with Applications
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There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which reduces the performance of the classifier. Therefore, different nature-inspired metaheuristic algorithms are integrated with the NN training algorithms to provide derivative-free solutions for complex classification problems. Consequently, this paper proposes the integration of a particle swarm optimization (PSO) algorithm with an improved Elman recurrent neural network (ERNN) to form a PSO-ERNN metaheuristic model. The key contribution of this study is the development of a new dimensional equation for ERNN architecture and the integration of PSO in ERNN learning to produce the PSO-ERNN model. The PSO is constructed to train the NN and ERNN models to achieve a fast convergence rate and avoid local minima problems. The PSO-ERNN model is validated by comparing it against the standard PSO-NN metaheuristic model and similar models from the literature. The PSO-NN and PSO-ERNN models are tested and evaluated using ten benchmark classification problems of breast cancer, heart, hepatitis, liver, wine, iris, lung cancer, yeast, Pima Indians diabetes, and ionosphere datasets. In the training phase, the results show that the PSO-ERNN model performs better than the PSO-NN model when the training set has a bigger size of samples. In the testing phase, the PSO-ERNN model outperforms the PSO-NN model for all the tested datasets except the lung cancer and yeast datasets, in which the accuracy percentage slightly decreases. In the validation phase, the PSO-ERNN model shows better performance quality in terms of accuracy percentage in six of the tested datasets. The average percentage of the training, testing, and validation accumulation show that the PSO-NN performs better than the PSO-ERNN in the lung cancer (87.27, 83.32), and heart (73.56, 70.64) datasets. On the other hand, the PSO-ERNN performs better than the PSO-NN in the iris (88.18, 86.74), hepatitis (88.60, 87.93), wine (89.16, 86.08), liver (73.56, 70.64), ionosphere (83.98, 78.94), and breast cancer (94.84, 91.17). PSO-NN and PSO-ERNN produce the same average results in the Pima Indians diabetes (84.00, 84.00) and yeast (91.31, 91.30) dataset. These results show clearly that the PSO-ERNN generally outperforms the PSO-NN when considering the overall results of the ten datasets. Nevertheless, the combinations of the PSO-NN and PSO-ERNN are proven to represent consistent and robust classification methods. The computational efficiencies of the training processes in both the PSO-NN and PSO-ERNN models are highly improved when coupled with the PSO.

ACS Style

Mohamad Firdaus Ab Aziz; Salama A Mostafa; Cik Feresa Mohd. Foozy; Mazin Abed Mohammed; Mohamed Elhoseny; Abedallah Zaid Abualkishik. Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Systems with Applications 2021, 183, 115441 .

AMA Style

Mohamad Firdaus Ab Aziz, Salama A Mostafa, Cik Feresa Mohd. Foozy, Mazin Abed Mohammed, Mohamed Elhoseny, Abedallah Zaid Abualkishik. Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Systems with Applications. 2021; 183 ():115441.

Chicago/Turabian Style

Mohamad Firdaus Ab Aziz; Salama A Mostafa; Cik Feresa Mohd. Foozy; Mazin Abed Mohammed; Mohamed Elhoseny; Abedallah Zaid Abualkishik. 2021. "Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets." Expert Systems with Applications 183, no. : 115441.

Journal article
Published: 14 June 2021 in Sensors
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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.

ACS Style

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 Style

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 (12):4093.

Chicago/Turabian Style

Abdullah 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.

Journal article
Published: 07 June 2021 in IEEE Access
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IoT has facilitated predominant advancements in cancer research by incorporating Artificial intelligence (AI) that enables human decision-makers to achieve a better decision. Recently, Least Absolute Shrinkage and Selection Operator (LASSO) classifier has launched in predicting recurrence cancer genes in the cervix. At the initial phase, the recurrence gene expression of lncRNA is collected from Geo Datasets. Secondly, data imputation, accomplished with Mode and Mean Missing method (MMM-DI). Thirdly, feature selection is compassed using Hilbert-Schmidt independence criterion with Diversity based Artificial Fish Swarm (HSDAFS). In the HSDA.FS algorithm, the diversity parameter is added based on the gene value, and their risk score of the lncRNAs is computed using the Artificial intelligence (AI) technique. Finally, recurrence prediction, an ENSemble Classification Framework (ENSCF), is proposed based on recurrent neural networks. The prognostic factor is computed with a risk score of nine lncRNA signatures for 300 samples taken from GSE44001. The Chi-Square method has been used to obtain statistical results. The survival of the patient with recurrence cervical cancer is shown using the proposed model.

ACS Style

Geeitha Senthilkumar; Jothilakshmi Ramakrishnan; Jaroslav Frnda; Manikandan Ramachandran; Deepak Gupta; Prayag Tiwari; Mohammad Shorfuzzaman; Mazin Abed Mohammed. Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer. IEEE Access 2021, 9, 1 -1.

AMA Style

Geeitha Senthilkumar, Jothilakshmi Ramakrishnan, Jaroslav Frnda, Manikandan Ramachandran, Deepak Gupta, Prayag Tiwari, Mohammad Shorfuzzaman, Mazin Abed Mohammed. Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Geeitha Senthilkumar; Jothilakshmi Ramakrishnan; Jaroslav Frnda; Manikandan Ramachandran; Deepak Gupta; Prayag Tiwari; Mohammad Shorfuzzaman; Mazin Abed Mohammed. 2021. "Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer." IEEE Access 9, no. : 1-1.

Journal article
Published: 07 June 2021 in Sensors
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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.

ACS Style

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 Style

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 (11):3922.

Chicago/Turabian Style

Sheeba 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.

Journal article
Published: 21 May 2021 in IEEE Access
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In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database. In other words, the systems use one vowel, such as /a/, and ignore sentences and other vowels when analyzing the audio signal. Other key issues that should be considered in the systems are accuracy and time consumption of an algorithm. Online Sequential Extreme Learning Machine (OSELM) is one of the machine learning algorithms that can be regarded as a rapid and accurate algorithm in the classification process. Therefore, this paper presents a voice pathology detection and classification system by using OSELM algorithm as a classifier, and Mel-frequency cepstral coefficient (MFCC) as a featured extraction. In this work, the voice samples were taken from the Saarbrücken voice database (SVD). This system involves two parts of the database; the first part includes all voices in SVD with sentences and vowels /a/, /i/, and /u/, which are uttered in high, low, and normal pitches; and the second part utilizes voice samples of the common three types of pathologies (cyst, polyp, and paralysis) based on the vowel /a/ that is produced in normal pitch. The experimental results have shown that OSELM was able to achieve the highest accuracy up to 91.17%, 94% of precision, and 91% of recall. Furthermore, OSELM obtained 87%, 87.55%, and 97.67% for f-measure, G-mean, and specificity, respectively. The proposed system also presents a high ability to achieve detection and classification results in real-time clinical applications.

ACS Style

Fahad Taha Al-Dhief; Marina Mat Baki; Nurul Mu'azzah Abdul Latiff; Nik Noordini Nik Abd. Malik; Naseer Sabri Salim; Musatafa Abbas Abbood Albader; Nor Muzlifah Mahyuddin; Mazin Abed Mohammed. Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine. IEEE Access 2021, 9, 77293 -77306.

AMA Style

Fahad Taha Al-Dhief, Marina Mat Baki, Nurul Mu'azzah Abdul Latiff, Nik Noordini Nik Abd. Malik, Naseer Sabri Salim, Musatafa Abbas Abbood Albader, Nor Muzlifah Mahyuddin, Mazin Abed Mohammed. Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine. IEEE Access. 2021; 9 ():77293-77306.

Chicago/Turabian Style

Fahad Taha Al-Dhief; Marina Mat Baki; Nurul Mu'azzah Abdul Latiff; Nik Noordini Nik Abd. Malik; Naseer Sabri Salim; Musatafa Abbas Abbood Albader; Nor Muzlifah Mahyuddin; Mazin Abed Mohammed. 2021. "Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine." IEEE Access 9, no. : 77293-77306.

Journal article
Published: 17 May 2021 in ACM Transactions on Multimedia Computing, Communications, and Applications
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Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.

ACS Style

Mazin Abed Mohammed; Mohamed Elhoseny; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi. A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis. ACM Transactions on Multimedia Computing, Communications, and Applications 2021, 17, 1 -22.

AMA Style

Mazin Abed Mohammed, Mohamed Elhoseny, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi. A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis. ACM Transactions on Multimedia Computing, Communications, and Applications. 2021; 17 (2s):1-22.

Chicago/Turabian Style

Mazin Abed Mohammed; Mohamed Elhoseny; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi. 2021. "A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2s: 1-22.

Journal article
Published: 12 May 2021 in Sustainability
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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.

ACS Style

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 Style

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 (10):5406.

Chicago/Turabian Style

Mohd 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.

Journal article
Published: 02 May 2021 in Applied Sciences
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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.

ACS Style

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 Style

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 (9):4164.

Chicago/Turabian Style

Abdullah 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.

Journal article
Published: 25 April 2021 in Electronics
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The authenticity and integrity of medical images in telemedicine has to be protected. Robust reversible watermarking (RRW) algorithms provide copyright protection and the original images can be recovered at the receiver’s end. However, the existing algorithms have limitations in their ability to balance the tradeoff among robustness, imperceptibility, and embedded capacity. Some of them are even not completely reversible. Besides, most medical image watermarking algorithms are not designed for color images. To improve their performance in protecting medical color image information, we propose a novel RRW scheme based on the discrete wavelet transform (DWT). First, the DWT provides a robust solution. Second, the modification of the wavelet domain coefficient guarantees the changes of integer values in the spatial domain and ensures the reversibility of the watermarking scheme. Third, the embedding scheme makes full use of the characteristics of the original image and watermarking. This reduces the modification of the original image and ensures better imperceptibility. Lastly, the selection of the Zernike moments order for geometric correction is optimized to predict attack parameters more accurately by using less information. This enhances the robustness of the proposed scheme against geometric attacks such as rotation and scaling. The proposed scheme is robust against common and geometric attacks and has a high embedding capacity without obvious distortion of the image. The paper contributes towards improving the security of medical images in remote healthcare.

ACS Style

Xiaoyi Zhou; Yue Ma; Qingquan Zhang; Mazin Mohammed; Robertas Damaševičius. A Reversible Watermarking System for Medical Color Images: Balancing Capacity, Imperceptibility, and Robustness. Electronics 2021, 10, 1024 .

AMA Style

Xiaoyi Zhou, Yue Ma, Qingquan Zhang, Mazin Mohammed, Robertas Damaševičius. A Reversible Watermarking System for Medical Color Images: Balancing Capacity, Imperceptibility, and Robustness. Electronics. 2021; 10 (9):1024.

Chicago/Turabian Style

Xiaoyi Zhou; Yue Ma; Qingquan Zhang; Mazin Mohammed; Robertas Damaševičius. 2021. "A Reversible Watermarking System for Medical Color Images: Balancing Capacity, Imperceptibility, and Robustness." Electronics 10, no. 9: 1024.

Research article
Published: 22 April 2021 in Security and Communication Networks
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Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.

ACS Style

Bashar Ahmad Khalaf; Salama A. Mostafa; Aida Mustapha; Mazin Abed Mohammed; Moamin A. Mahmoud; Bander Ali Saleh Al-Rimy; Shukor Abd Razak; Mohamed Elhoseny; Adam Marks. An Adaptive Protection of Flooding Attacks Model for Complex Network Environments. Security and Communication Networks 2021, 2021, 1 -17.

AMA Style

Bashar Ahmad Khalaf, Salama A. Mostafa, Aida Mustapha, Mazin Abed Mohammed, Moamin A. Mahmoud, Bander Ali Saleh Al-Rimy, Shukor Abd Razak, Mohamed Elhoseny, Adam Marks. An Adaptive Protection of Flooding Attacks Model for Complex Network Environments. Security and Communication Networks. 2021; 2021 ():1-17.

Chicago/Turabian Style

Bashar Ahmad Khalaf; Salama A. Mostafa; Aida Mustapha; Mazin Abed Mohammed; Moamin A. Mahmoud; Bander Ali Saleh Al-Rimy; Shukor Abd Razak; Mohamed Elhoseny; Adam Marks. 2021. "An Adaptive Protection of Flooding Attacks Model for Complex Network Environments." Security and Communication Networks 2021, no. : 1-17.

Review
Published: 30 March 2021 in IEEE Access
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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

ACS Style

Shafiza Ariffin Kashinath; Salama A. Mostafa; Aida Mustapha; Hairulnizam Mahdin; David Lim; Moamin A. Mahmoud; Mazin Abed Mohammed; Bander Ali Saleh Al-Rimy; Mohd Farhan Md Fudzee; Tan Jhon Yang. Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis. IEEE Access 2021, 9, 51258 -51276.

AMA Style

Shafiza Ariffin Kashinath, Salama A. Mostafa, Aida Mustapha, Hairulnizam Mahdin, David Lim, Moamin A. Mahmoud, Mazin Abed Mohammed, Bander Ali Saleh Al-Rimy, Mohd Farhan Md Fudzee, Tan Jhon Yang. Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis. IEEE Access. 2021; 9 (99):51258-51276.

Chicago/Turabian Style

Shafiza Ariffin Kashinath; Salama A. Mostafa; Aida Mustapha; Hairulnizam Mahdin; David Lim; Moamin A. Mahmoud; Mazin Abed Mohammed; Bander Ali Saleh Al-Rimy; Mohd Farhan Md Fudzee; Tan Jhon Yang. 2021. "Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis." IEEE Access 9, no. 99: 51258-51276.

Review
Published: 19 March 2021 in Network Modeling Analysis in Health Informatics and Bioinformatics
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Recently, there has been an advancement in the development of innovative computer-aided techniques for the segmentation and classification of retinal vessels, the application of which is predominant in clinical applications. Consequently, this study aims to provide a detailed overview of the techniques available for segmentation and classification of retinal vessels. Initially, retinal fundus photography and retinal image patterns are briefly introduced. Then, an introduction to the pre-processing operations and advanced methods of identifying retinal vessels is deliberated. In addition, a discussion on the validation stage and assessment of the outcomes of retinal vessels segmentation is presented. In this paper, the proposed methods of classifying arteries and veins in fundus images are extensively reviewed, which are categorized into automatic and semi-automatic categories. There are some challenges associated with the classification of vessels in images of the retinal fundus, which include the low contrast accompanying the fundus image and the inhomogeneity of the background lighting. The inhomogeneity occurs as a result of the process of imaging, whereas the low contrast which accompanies the image is caused by the variation between the background and the contrast of the various blood vessels. This means that the contrast of thicker vessels is higher than those that are thinner. Another challenge is related to the color changes that occur in the retina from different subjects, which are rooted in biological features. Most of the techniques used for the classification of the retinal vessels are based on geometric and visual characteristics that set the veins apart from the arteries. In this study, different major contributions are summarized as review studies that adopted deep learning approaches and machine learning techniques to address each of the limitations and problems in retinal blood vessel segmentation and classification techniques. We also review the current challenges, knowledge gaps and open issues, limitations and problems in retinal blood vessel segmentation and classification techniques.

ACS Style

Aws A. Abdulsahib; Moamin A. Mahmoud; Mazin Abed Mohammed; Hind Hameed Rasheed; Salama A. Mostafa; Mashael S. Maashi. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics 2021, 10, 1 -32.

AMA Style

Aws A. Abdulsahib, Moamin A. Mahmoud, Mazin Abed Mohammed, Hind Hameed Rasheed, Salama A. Mostafa, Mashael S. Maashi. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics. 2021; 10 (1):1-32.

Chicago/Turabian Style

Aws A. Abdulsahib; Moamin A. Mahmoud; Mazin Abed Mohammed; Hind Hameed Rasheed; Salama A. Mostafa; Mashael S. Maashi. 2021. "Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images." Network Modeling Analysis in Health Informatics and Bioinformatics 10, no. 1: 1-32.

Original article
Published: 14 March 2021 in Neural Computing and Applications
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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.

ACS Style

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 Style

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 (18):11703-11719.

Chicago/Turabian Style

Qurat-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.