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Ahmed RM, Rashid TA. Rebuttal to: Letter to the Editor. Re: "[An extensive dataset of handwritten central Kurdish isolated characters by R.M. Ahmed, T.A. Rashid, P. Fatah, A. Alsadoon & S. Mirjalili, Data in Brief, 2021, 39, 107479]". Data Brief 2024; 53:110072. [PMID: 38312988 PMCID: PMC10837471 DOI: 10.1016/j.dib.2024.110072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/11/2024] [Accepted: 01/11/2024] [Indexed: 02/06/2024] Open
Affiliation(s)
- Rebin M. Ahmed
- IT Department, Tishk International University, Erbil, Iraq
| | - Tarik A. Rashid
- Computer Science and Engineering Department, University of Kurdistan-Hawlêr, Erbil, Iraq
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Mirimoghaddam MM, Majidpour J, Pashaei F, Arabalibeik H, Samizadeh E, Roshan NM, Rashid TA. HER2GAN: Overcome the Scarcity of HER2 Breast Cancer Dataset Based on Transfer Learning and GAN Model. Clin Breast Cancer 2024; 24:53-64. [PMID: 37926662 DOI: 10.1016/j.clbc.2023.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/06/2023] [Accepted: 09/24/2023] [Indexed: 11/07/2023]
Abstract
INTRODUCTION Immunohistochemistry (IHC) is crucial for breast cancer diagnosis, classification, and individualized treatment. IHC is used to measure the levels of expression of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor receptor 2 (HER2), and other biomarkers, which are used to make treatment decisions and predict how well a patient will do. The evaluation of the breast cancer score on IHC slides, taking into account structural and morphological features as well as a scarcity of relevant data, is one of the most important issues in the IHC debate. Several recent studies have utilized machine learning and deep learning techniques to resolve these issues. MATERIALS AND METHODS This paper introduces a new approach for addressing the issue based on supervised deep learning. A GAN-based model is proposed for generating high-quality HER2 images and identifying and classifying HER2 levels. Using transfer learning methodologies, the original and generated images were evaluated. RESULTS AND CONCLUSION All of the models have been trained and evaluated using publicly accessible and private data sets, respectively. The InceptionV3 and InceptionResNetV2 models achieved a high accuracy of 93% with the combined generated and original images used for training and testing, demonstrating the exceptional quality of the details in the synthesized images.
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Affiliation(s)
| | - Jafar Majidpour
- Department of Computer Science, University of Raparin, Rania, Iraq.
| | - Fakhereh Pashaei
- Radiation Sciences Research Center (RSRC), Aja University of Medical Sciences, Tehran, Iran.
| | - Hossein Arabalibeik
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Samizadeh
- Department of Pathology, School of Medicine and Imam Reza Hospital, AJA University of Medical Sciences, Tehran, Iran
| | | | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewlêr, Erbil, Iraq
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Salih SQ, Alsewari AA, Wahab HA, Mohammed MKA, Rashid TA, Das D, Basurra SS. Multi-population Black Hole Algorithm for the problem of data clustering. PLoS One 2023; 18:e0288044. [PMID: 37406006 DOI: 10.1371/journal.pone.0288044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/16/2023] [Indexed: 07/07/2023] Open
Abstract
The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.
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Affiliation(s)
- Sinan Q Salih
- Technical College of Engineering, Al-Bayan University, Baghdad, Iraq
| | - AbdulRahman A Alsewari
- Data Analytics & AI research Group, College of Computing and Digital Technology, Faculty of Computing Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - H A Wahab
- Faculty of Computing, Kuantan, Malaysia
| | | | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq
| | - Debashish Das
- Data Analytics & AI research Group, College of Computing and Digital Technology, Faculty of Computing Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Shadi S Basurra
- Data Analytics & AI research Group, College of Computing and Digital Technology, Faculty of Computing Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
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Tao H, Jawad AH, Shather AH, Al-Khafaji Z, Rashid TA, Ali M, Al-Ansari N, Marhoon HA, Shahid S, Yaseen ZM. Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters. Environ Int 2023; 175:107931. [PMID: 37119651 DOI: 10.1016/j.envint.2023.107931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/18/2023] [Accepted: 04/11/2023] [Indexed: 05/22/2023]
Abstract
This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
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Affiliation(s)
- Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China; State Key Laboratory of Public Big Data, Guizhou University, Guizhou, Guiyang 550025, China; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
| | - Ali H Jawad
- Faculty of Applied Sciences, UniversitiTeknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
| | - A H Shather
- Dep of Computer Technology Engineering, Engineering Technical College, University of Alkitab, Iraq.
| | - Zainab Al-Khafaji
- Department of Building and Construction Technologies Engineering, AL-Mustaqbal University College, Hillah 51001, Iraq.
| | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, KR, Iraq.
| | - Mumtaz Ali
- UniSQ College, University of Southern Queensland, QLD 4350, Australia.
| | - Nadhir Al-Ansari
- Dept. of Civil, Environmental and Natural Resources Engineering, Lulea Univ. of Technology, Lulea T3334, Sweden.
| | - Haydar Abdulameer Marhoon
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq; College of Computer Sciences and Information Technology, University of Kerbala, Karbala, Iraq.
| | - Shamsuddin Shahid
- Department of Hydraulics and Hydrology, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudia, Johor, Malaysia.
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
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Abdullah JM, Rashid TA, Maaroof BB, Mirjalili S. Multi-objective fitness-dependent optimizer algorithm. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08332-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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Abdulkhaleq MT, Rashid TA, Hassan BA, Alsadoon A, Bacanin N, Chhabra A, Vimal S. Fitness dependent optimizer with neural networks for COVID-19 patients. Comput Methods Programs Biomed Update 2022; 3:100090. [PMID: 36591535 PMCID: PMC9792427 DOI: 10.1016/j.cmpbup.2022.100090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 11/22/2022] [Accepted: 12/26/2022] [Indexed: 06/16/2023]
Abstract
The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.
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Affiliation(s)
- Maryam T Abdulkhaleq
- Department of Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KR, Iraq
| | - Tarik A Rashid
- Department of Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KR, Iraq
| | - Bryar A Hassan
- Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, KR, Iraq
- Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, KR, Iraq
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
- Information Technology Department, Asia Pacific International College (APIC), Sydney, Australia
| | - Nebojsa Bacanin
- Singidunum University, Danijelova 32, 11000, Belgrade, Serbia
| | - Amit Chhabra
- Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India
| | - S Vimal
- Data Analytics Lab Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, North Venganallur Village, Rajapalayam - 626 117 Virudhunagar District Tamilnadu, India
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Ahmed RM, Rashid TA, Fattah P, Alsadoon A, Bacanin N, Mirjalili S, Vimal S, Chhabra A. Kurdish Handwritten character recognition using deep learning techniques. Gene Expr Patterns 2022; 46:119278. [PMID: 36195308 DOI: 10.1016/j.gep.2022.119278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/04/2022]
Abstract
Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.
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Affiliation(s)
- Rebin M Ahmed
- IT Department, Faculty of Aplied Science, Tishk International University, Erbil, Iraq.
| | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq.
| | - Polla Fattah
- Software and Informatics Engineering, Salahaddin University-Erbil, Erbil, Iraq.
| | - Abeer Alsadoon
- School of Computing Engineering and Mathematics, Western Sydney University, Sydney City Campus, Australia; Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia; Kent Institute Australia, Information Technology Department, Sydney, Australia.
| | | | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
| | - S Vimal
- Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, North Venganallur Village, Rajapalayam 626 117, Virudhunagar District, Tamilnadu, India.
| | - Amit Chhabra
- Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar India.
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Abdulkhaleq MT, Rashid TA, Alsadoon A, Hassan BA, Mohammadi M, Abdullah JM, Chhabra A, Ali SL, Othman RN, Hasan HA, Azad S, Mahmood NA, Abdalrahman SS, Rasul HO, Bacanin N, Vimal S. Harmony search: Current studies and uses on healthcare systems. Artif Intell Med 2022; 131:102348. [DOI: 10.1016/j.artmed.2022.102348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/08/2022] [Accepted: 06/30/2022] [Indexed: 11/29/2022]
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Saeed AM, Hussein SR, Ali CM, Rashid TA. Medical dataset classification for Kurdish short text over social media. Data Brief 2022; 42:108089. [PMID: 35392621 PMCID: PMC8980624 DOI: 10.1016/j.dib.2022.108089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 11/26/2022] Open
Abstract
The Facebook application is used as a resource for collecting the comments of this dataset, The dataset consists of 6756 comments to create a Medical Kurdish Dataset (MKD). The samples are comments of users, which are gathered from different posts of pages (Medical, News, Economy, Education, and Sport). Six steps as a preprocessing technique are performed on the raw dataset to clean and remove noise in the comments by replacing characters. The comments (short text) are labeled for positive class (medical comment) and negative class (non-medical comment) as text classification. The percentage ratio of the negative class is 55% while the positive class is 45%.
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Salih KOM, Rashid TA, Radovanovic D, Bacanin N. A Comprehensive Survey on the Internet of Things with the Industrial Marketplace. Sensors (Basel) 2022; 22:s22030730. [PMID: 35161476 PMCID: PMC8840330 DOI: 10.3390/s22030730] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/01/2022] [Accepted: 01/06/2022] [Indexed: 12/12/2022]
Abstract
There is no doubt that new technology has become one of the crucial parts of most people's lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.
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Affiliation(s)
| | - Tarik A. Rashid
- Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil 44001, KRG, Iraq
- Correspondence: (T.A.R.); (N.B.)
| | - Dalibor Radovanovic
- Departman of Informatics and Computing, Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia;
| | - Nebojsa Bacanin
- Departman of Informatics and Computing, Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia;
- Correspondence: (T.A.R.); (N.B.)
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Rahman CM, Rashid TA, Ahmed AM, Mirjalili S. Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06811-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Shrestha A, Ali SI, Alwan AA, Salahuddin ABE, Siddiqi M, Rashid TA. GPS Navigated Drones to Deliver Emergency Medical Aid Post Catastrophic Event. Advances in Intelligent Systems and Computing 2022:84-92. [DOI: 10.1007/978-3-031-14054-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Hassan AA, Rashid TA. A Proposed Hybrid Algorithm for detecting COVID-19 Patients. KJAR 2021. [DOI: 10.24017/science.2021.2.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.
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Wang X, Gong C, Khishe M, Mohammadi M, Rashid TA. Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer. Wirel Pers Commun 2021; 124:1355-1374. [PMID: 34873379 PMCID: PMC8635480 DOI: 10.1007/s11277-021-09410-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/14/2021] [Indexed: 06/12/2023]
Abstract
The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT's procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.
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Affiliation(s)
- Xusheng Wang
- Xi’an University of Technology, Xi’an, 710048 Shaanxi China
| | - Cunqi Gong
- Department of Clinical Laboratory, Jining No.1 People’s Hospital, Jining, 272011 Shandong China
| | - Mohammad Khishe
- Department of Electronic Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region Iraq
| | - Tarik A. Rashid
- Computer Science and Engineering Department, Science and Engineering School, University of Kurdistan Hewler, Erbil, KRG Iraq
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Hassan BA, Rashid TA, Hamarashid HK. A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star. Comput Biol Med 2021; 138:104866. [PMID: 34598065 PMCID: PMC8445768 DOI: 10.1016/j.compbiomed.2021.104866] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/08/2021] [Accepted: 09/08/2021] [Indexed: 12/16/2022]
Abstract
With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms.
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Affiliation(s)
- Bryar A. Hassan
- Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, 46001, Iraq,Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani 46001, Iraq,Corresponding author. Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani 46001, Iraq
| | - Tarik A. Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Iraq
| | - Hozan K. Hamarashid
- Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaimani 46001, Iraq
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16
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Ahmed RM, Rashid TA, Fatah P, Alsadoon A, Mirjalili S. An extensive dataset of handwritten central Kurdish isolated characters. Data Brief 2021; 39:107479. [PMID: 34712756 PMCID: PMC8529099 DOI: 10.1016/j.dib.2021.107479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/29/2021] [Accepted: 10/05/2021] [Indexed: 11/20/2022] Open
Abstract
To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil.
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Affiliation(s)
- Rebin M. Ahmed
- IT Department, Tishk International University, Erbil, Iraq
| | - Tarik A. Rashid
- Computer Science and Engineering Department, University of Kurdistan-Hawlêr, Erbil, Iraq
- Corresponding author.
| | | | - Abeer Alsadoon
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
- Kent Institute Australia, Sydney, Australia
- Asia Pacific International College (APIC), Sydney, Australia
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
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17
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Shrestha K, Alsadoon OH, Alsadoon A, Rashid TA, Ali RS, Prasad P, Jerew OD. A novel solution of an elastic net regularisation for dementia knowledge discovery using deep learning. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1970237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Kshitiz Shrestha
- School of Computing and Mathematics, Charles Sturt University (Csu), Wagga Wagga, Australia
| | | | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University (Csu), Wagga Wagga, Australia
- School of Computer Data and Mathematical Sciences, University of Western Sydney (Uws), Australia
- Kent Institute Australia, Information Technology Department, Sydney, Australia
- Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia
| | - Tarik A. Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, IRAQ
| | - Rasha S. Ali
- Department of Computer Techniques Engineering, Al Nisour University College, Baghdad, Iraq
| | - P.W.C. Prasad
- School of Computer Data and Mathematical Sciences, University of Western Sydney (Uws), Australia
- Kent Institute Australia, Information Technology Department, Sydney, Australia
| | - Oday D. Jerew
- Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia
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18
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Rahman CM, Rashid TA, Alsadoon A, Bacanin N, Fattah P, Mirjalili S. A survey on dragonfly algorithm and its applications in engineering. Evol Intel 2021. [DOI: 10.1007/s12065-021-00659-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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19
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Thapa A, Alsadoon A, Prasad PWC, Bajaj S, Alsadoon OH, Rashid TA, Ali RS, Jerew OD. Deep learning for breast cancer classification: Enhanced tangent function. Comput Intell 2021. [DOI: 10.1111/coin.12476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Ashu Thapa
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
- School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia
- Kent Institute Australia Sydney Australia
- Asia Pacific International College (APIC) Sydney Australia
| | - P. W. C. Prasad
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
| | - Simi Bajaj
- School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia
| | | | - Tarik A. Rashid
- Computer Science and Engineering University of Kurdistan Hewler Erbil KRG IRAQ
| | - Rasha S. Ali
- Department of Computer Techniques Engineering AL Nisour University College Baghdad Iraq
| | - Oday D. Jerew
- Asia Pacific International College (APIC) Sydney Australia
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20
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Hassan BA, Rashid TA, Mirjalili S. Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets. Data Brief 2021; 36:107044. [PMID: 33981821 PMCID: PMC8086011 DOI: 10.1016/j.dib.2021.107044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/22/2021] [Accepted: 04/02/2021] [Indexed: 11/24/2022] Open
Abstract
This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.
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Affiliation(s)
- Bryar A. Hassan
- Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq
- Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
| | - Tarik A. Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, Korea
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21
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Hu T, Khishe M, Mohammadi M, Parvizi GR, Taher Karim SH, Rashid TA. Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm. Biomed Signal Process Control 2021; 68:102764. [PMID: 33995562 PMCID: PMC8112401 DOI: 10.1016/j.bspc.2021.102764] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/07/2021] [Accepted: 05/09/2021] [Indexed: 12/29/2022]
Abstract
Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.
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Affiliation(s)
- Tianqing Hu
- College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo City, Henan Province, China
| | - Mohammad Khishe
- Department of Electronic Engineering Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, Lebanese French University, Erbil, KRG, Iraq
| | | | | | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, KRG, Iraq
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22
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23
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Maharjan N, Alsadoon A, Prasad PWC, Abdullah S, Rashid TA. A novel visualization system of using augmented reality in knee replacement surgery: Enhanced bidirectional maximum correntropy algorithm. Int J Med Robot 2021; 17:e2223. [PMID: 33421286 DOI: 10.1002/rcs.2223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIM Image registration and alignment are the main limitations of augmented reality (AR)-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. METHODOLOGY Markerless image registration method was used for AR-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right-to-left direction and non-border occlusion from left-to-right direction. RESULTS This study has improved video precision to 0.57-0.61 mm alignment error. Furthermore, with the use of bidirectional points, that is, forward and backward directional cloud point, the iteration on image registration was decreased. This has led to improve the processing time as well. The processing time of video frames was improved to 7.4-11.74 frames per second. CONCLUSIONS It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by the movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed AR system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels and so forth.
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Affiliation(s)
- Nitish Maharjan
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney Campus, Wagga Wagga, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney Campus, Wagga Wagga, Australia.,School of Computer Data and Mathematical Sciences, University of Western Sydney (UWS), Sydney, Australia.,School of Information Technology, Southern Cross University (SCU), Sydney, Australia.,Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia.,Kent Institute Australia, Sydney, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney Campus, Wagga Wagga, Australia
| | - Salma Abdullah
- Department of Computer Engineering, University of Technology, Baghdad, Iraq
| | - Tarik A Rashid
- Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia
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24
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Maharjan A, Alsadoon A, Prasad PWC, AlSallami N, Rashid TA, Alrubaie A, Haddad S. A novel solution of using mixed reality in bowel and oral and maxillofacial surgical telepresence: 3D mean value cloning algorithm. Int J Med Robot 2021; 17:e2224. [PMID: 33426753 DOI: 10.1002/rcs.2224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 09/01/2020] [Accepted: 09/01/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND AIM Most of the mixed reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. METHODOLOGY The proposed system enhanced mean-value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the three-dimensional mean-value coordinates and improvised mean-value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. RESULTS The accuracy in terms of overlay error of the proposed solution is improved from 1.01 to 0.80 mm, whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 s from 0.211 s. The processing time and the accuracy of the proposed solution are enhanced as compared to the state-of-art solution. CONCLUSION Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.
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Affiliation(s)
- Arjina Maharjan
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia.,School of Computer Data and Mathematical Sciences, University of Western Sydney (UWS), Sydney, Australia.,School of Information Technology, Southern Cross University (SCU), Sydney, Australia.,Information Technology Department, Asia Pacific International College (APIC), Sydney, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia
| | - Nada AlSallami
- Computer Science Department, Worcester State University, Massachusetts, USA
| | - Tarik A Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, Iraq
| | - Ahmad Alrubaie
- Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Sami Haddad
- Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Australia.,Department of Oral and Maxillofacial Services, Central Coast Area Health, Australia
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25
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Chhetri S, Alsadoon A, Al‐Dala'in T, Prasad PWC, Rashid TA, Maag A. Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow. Comput Intell 2020. [DOI: 10.1111/coin.12428] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Sagar Chhetri
- School of Computing and Mathematics Charles Sturt University Sydney Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics Charles Sturt University Sydney Australia
| | - Thair Al‐Dala'in
- School of Computing and Mathematics Charles Sturt University Sydney Australia
- School of Computing Engineering and Mathematics Western Sydney University Sydney Australia
| | - P. W. C. Prasad
- School of Computing and Mathematics Charles Sturt University Sydney Australia
| | - Tarik A. Rashid
- Computer Science and Engineering University of Kurdistan Hewler Erbil, KRG IRAQ
| | - Angelika Maag
- School of Computing and Mathematics Charles Sturt University Sydney Australia
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26
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Maharjan A, Alsadoon A, Prasad PWC, AlSallami N, Rashid TA, Alrubaie A, Haddad S. A Novel Solution of Using Mixed Reality in Bowel and Oral and Maxillofacial Surgical Telepresence: 3D Mean Value Cloning algorithm. Int J Med Robot 2020:e2161. [PMID: 32886412 DOI: 10.1002/rcs.2161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 09/01/2020] [Accepted: 09/01/2020] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND AIM Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. METHODOLOGY The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region RESULTS: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Arjina Maharjan
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Nada AlSallami
- Computer Science Department, Worcester State University, MA, USA
| | - Tarik A Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, IRAQ
| | - Ahmad Alrubaie
- Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Sami Haddad
- Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Australia
- Department of Oral and Maxillofacial Services, Central Coast Area Health, Australia
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27
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Maharjan N, Alsadoon A, Prasad PWC, Abdullah S, Rashid TA. A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum CorrentropyAlgorithm. Int J Med Robot 2020:e2154. [PMID: 32875672 DOI: 10.1002/rcs.2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND AIM Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. METHODOLOGY markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction. RESULTS This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps. CONCLUSIONS It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nitish Maharjan
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
- Department of Information Technology, Study Group Australia, Sydney Campus, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
- Department of Information Technology, Study Group Australia, Sydney Campus, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
- Department of Information Technology, Study Group Australia, Sydney Campus, Australia
| | - Salma Abdullah
- Department of Computer Engineering, University of Technology, Baghdad, Iraq
| | - Tarik A Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, IRAQ
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28
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Hassan BA, Rashid TA. Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms. Data Brief 2019; 28:105046. [PMID: 31921951 PMCID: PMC6948123 DOI: 10.1016/j.dib.2019.105046] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/10/2019] [Accepted: 12/16/2019] [Indexed: 11/23/2022] Open
Abstract
In this data article, we present the data used to evaluate the statistical success of the backtracking search optimisation algorithm (BSA) in comparison with the other four evolutionary optimisation algorithms. The data presented in this data article is related to the research article entitles ‘Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation’ [1]. Three statistical tests conducted on BSA compared to differential evolution algorithm (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly algorithm (FF). The tests are used to evaluate these mentioned algorithms and to determine which one could solve a specific optimisation problem concerning the statistical success of 16 benchmark problems taking several criteria into account. The criteria are initializing control parameters, dimension of the problems, their search space, and number of iterations needed to minimise a problem, the performance of the computer used to code the algorithms and their programming style, getting a balance on the effect of randomization, and the use of different type of optimisation problem in terms of hardness and their cohort. In addition, all the three tests include necessary statistical measures (Mean: mean-solution, S.D.: standard-deviation of mean-solution, Best: the best solution, Worst: the worst solution, Exec. Time: mean runtime in seconds, No. of succeeds: number of successful minimisation, and No. of Failure: number of failed minimisation).
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Affiliation(s)
- Bryar A Hassan
- Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq.,Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
| | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq
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29
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Arji G, Ahmadi H, Nilashi M, A Rashid T, Hassan Ahmed O, Aljojo N, Zainol A. Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Biocybern Biomed Eng 2019; 39:937-955. [PMID: 32287711 PMCID: PMC7115764 DOI: 10.1016/j.bbe.2019.09.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/15/2019] [Accepted: 09/17/2019] [Indexed: 01/04/2023]
Abstract
This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.
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Affiliation(s)
- Goli Arji
- School of Nursing and Midwifery, Health Information Technology Department, Saveh University of Medical Sciences, Iran
| | - Hossein Ahmadi
- Halal Research Center of IRI, FDA, Tehran, Iran
- Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq
| | - Mehrbakhsh Nilashi
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Kurdistan, Iraq
| | - Omed Hassan Ahmed
- School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, United Kingdom
- University of Human Development, College of Science and Technology, Department of Information Technology, Sulaymaniyah, Iraq
| | - Nahla Aljojo
- College of Computer Science and Engineering, Department of Information Systems and Technology, University of Jeddah, Jeddah, Saudi Arabia
| | - Azida Zainol
- Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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30
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Abstract
Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.
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Affiliation(s)
- Tarik A. Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Kurdistan, Iraq
- Software and Informatics Engineering, Salahaddin University-Erbil, Kurdistan, Iraq
| | - Dosti K. Abbas
- Faculty of Engineering, Soran University, Kurdistan, Iraq
| | - Yalin K. Turel
- Department of Computer Education and Instructional Technology, Firat University, Elazig, Turkey
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Saeed AM, Rashid TA, Mustafa AM, Agha RAAR, Shamsaldin AS, Al-Salihi NK. An evaluation of Reber stemmer with longest match stemmer technique in Kurdish Sorani text classification. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/s42044-018-0007-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Abstract
The rapid increase in the quantity of Kurdish documents over the last several years has created a need for improving information accuracy and precision in text classification and retrieval. Language stemming is an imperative pre-processing step for increasing the possibility of matching terms in a document in text classification tasks. Stemming helps reduce the total number of searchable terms within a document or query. This article proposes an active approach for stemming Kurdish Sorani texts to reduce variations of words to single terms or stems. The outcomes of the process, described in this article, demonstrate that decreasing the dimensionality of feature vectors in documents will increase the effectiveness of retrieval when the stemming process is used. This process applied for Kurdish Sorani can be adapted and applied in Kurdish Kurmanji as well for greater efficiency and effectiveness in digital text classification and applications.
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Affiliation(s)
- Arazo M Mustafa
- School of Computer Science, College of Science, University of Sulaimania, Kurdistan, Iraq
| | - Tarik A Rashid
- Software and Informatics Engineering, College of Engineering, Salahaddin University-Erbil, Kurdistan, Iraq
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Affiliation(s)
- Tarik A. Rashid
- Software and Informatics Engineering, College of EngineeringSalahaddin University – HawlerKirkuk StreetErbil00964Iraq
| | - Asia Latif Jabar
- Computer DepartmentCollege of ScienceUniversity of SulaimaniSulaimaniKurdistanIraq
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