Haydar Abdulameer Marhoon
@alayen.edu.iq
Al-ayen University
RESEARCH INTERESTS
Computer Science; Networking; Wireless Sensor Network
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Subhranshu Sekhar Tripathy, Sujit Bebortta, Mazin Abed Mohammed, Jan Nedoma, Radek Martinek, and Haydar Abdulameer Marhoon
Elsevier BV
Zainab Khalid Mohammed, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Dilovan Asaad Zebari, Abdullah Lakhan, Haydar Abdulameer Marhoon, Jan Nedoma, and Radek Martinek
Elsevier BV
Abdullah Lakhan, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Muhammet Deveci, Haydar Abdulameer Marhoon, Jan Nedoma, and Radek Martinek
Elsevier BV
Mazin Abed Mohammed, Abdullah Lakhan, Dilovan Asaad Zebari, Mohd Khanapi Abd Ghani, Haydar Abdulameer Marhoon, Karrar Hameed Abdulkareem, Jan Nedoma, and Radek Martinek
Elsevier BV
Mazin Abed Mohammed, Abdullah Lakhan, Karrar Hameed Abdulkareem, Muhammet Deveci, Ashit Kumar Dutta, Sajida Memon, Haydar Abdulameer Marhoon, and Radek Martinek
Institute of Electrical and Electronics Engineers (IEEE)
Abdullah Lakhan, Mazin Abed Mohammed, Dilovan Asaad Zebari, Karrar Hameed Abdulkareem, Muhammet Deveci, Haydar Abdulameer Marhoon, Jan Nedoma, and Radek Martinek
Institute of Electrical and Electronics Engineers (IEEE)
Nechirvan Asaad Zebari, Chira Nadheef Mohammed, Dilovan Asaad Zebari, Mazin Abed Mohammed, Diyar Qader Zeebaree, Haydar Abdulameer Marhoon, Karrar Hameed Abdulkareem, Seifedine Kadry, Wattana Viriyasitavat, Jan Nedoma,et al.
Institution of Engineering and Technology (IET)
AbstractDetecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.
Abdullah Lakhan, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Mohd khanapi Abd Ghani, Haydar Abdulameer Marhoon, Jan Nedoma, Radek Martinek, and Begonya Garcia-Zapirain
Elsevier BV
Mazin Abed Mohammed, Abdullah Lakhan, Karrar Hameed Abdulkareem, Mohd Khanapi Abd Ghani, Haydar Abdulameer Marhoon, Jan Nedoma, and Radek Martinek
Elsevier BV
Rahi Jobanputra, Prasanna Kulkarni, Haydar Abdulameer Marhoon, and Herlita Palloan
AIP Publishing
Ashish Singh, Sujata Joshi, Haitham Abbas Khalaf, A. H. Radie, and Haydar Abdulameer Marhoon
AIP Publishing
Stuti Bhatt, Giri Gundu Hallur, Ahmed J. Obaid, Atheer Y. Oudah, Karrar Hatif Mohmmed, and Haydar Abdulameer Marhoon
AIP Publishing
Masum Choudhury, Sandeep Prabhu, Ali Kareem Sabri, and Haydar Abdulameer Marhoon
AIP Publishing
Sangitha Biswas, Samaya Pillai, Hayder M. Kadhim, Zaid Abbas Salam, and Haydar Abdulameer Marhoon
AIP Publishing
Samyak Deshbhratar, Sujata Joshi, Rabi N. H. Alwaali, Ali Raheem Saear, and Haydar Abdulameer Marhoon
AIP Publishing
Sulieman I.S. Al-Hawary, José Ricardo Nuñez Alvarez, Amjad Ali, Abhishek Kumar Tripathi, Untung Rahardja, Ibrahim H. Al-Kharsan, Rosario Mireya Romero-Parra, Haydar Abdulameer Marhoon, Vivek John, and Woord Hussian
Elsevier BV
Huseyin Cagan Kilinc, Iman Ahmadianfar, Vahdettin Demir, Salim Heddam, Ahmed M. Al-Areeq, Sani I. Abba, Mou Leong Tan, Bijay Halder, Haydar Abdulameer Marhoon, and Zaher Mundher Yaseen
Springer Science and Business Media LLC
Abeer Abdullah Al Anazi, Oriza Candra, Abdeljelil Chammam, Haydar Abdulameer Marhoon, Inas Ridah Ali, Ibrahim H. Al-Kharsan, Reza Alayi, Yaser Ebazadeh, and Morteza Aladdin
AIP Publishing
In this study, energy harvesting using a two-layer piezoelectric sensor in non-linear single-mode mode was investigated, and the optimal performance conditions for power extraction were investigated. Non-linear equations or non-linear electric enthalpy proposal were obtained using Lagrange’s method. In addition, the model was identified with the help of perturbation methods and based on experimental results. The results indicate the presence of second-order damping and third-order stiffness with magnitudes of 2.8 × 106 and −3.9 × 1021. Finally, non-linear energy harvesting was investigated, and the electrical resistance for an optimal electrical power of 185.2 was obtained.
Hai Tao, Ali H. Jawad, A.H. Shather, Zainab Al-Khafaji, Tarik A. Rashid, Mumtaz Ali, Nadhir Al-Ansari, Haydar Abdulameer Marhoon, Shamsuddin Shahid, and Zaher Mundher Yaseen
Elsevier BV
Ghassan Abdul-Majeed, Elameer Amer Saleem, Drai A. Smait, Sadiq H. Abdulhussain, Sadiq M. Sait, Hasan S. Majdi, Haydar Abdulameer Marhoon, and Waleed Khalid Al-Azzawi
Springer Science and Business Media LLC
Mazin Abed Mohammed, Abdullah Lakhan, Karrar Hameed Abdulkareem, Mohd Khanapi Abd Ghani, Haydar Abdulameer Marhoon, Seifedine Kadry, Jan Nedoma, Radek Martinek, and Begonya Garcia Zapirain
Elsevier BV
Mohammed Morad, Atheer Y. Oudah, Mohammed Diykh, Haydar Abdulameer Marhoon, and Hazeem B. Taher
Springer Nature Singapore
Tao Hai, Jincheng Zhou, Mohammad Masdari, and Haydar Abdulameer Marhoon
Springer Science and Business Media LLC
Potharlanka Jhansi Lakshmi, Rubén Apaza Apaza, Ahmed Alkhayyat, Haydar Abdulameer Marhoon, and Ameer A. Alameri
IWA Publishing
Abstract It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as support vector machine (SVM), gene expression programming (GEP), wavelet-SVM (WSVM), and wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River Basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with wavelet improved simulation accuracy, which is now the highest for the WGEP method.
Alim Al Ayub Ahmed, Saurabh Singhal, A. S. Prakaash, Johnry Dayupay, Irwan Rahadi, Haydar Abdulameer Marhoon, A. Heri Iswanto, Saja Fadhil Abbas, and Surendar Aravindhan
Walter de Gruyter GmbH
Abstract The current study examines an essential type of vehicle routing problem (VRP). This type is one where customers are served by limited-capacity vehicles from multiple depots and is known as Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP). The novelty of this study is that in the present case, cars, after Leaving the Depot, can go back to any other depot. Those issues seem to occur in most real-world issues where information, messages, or news are sent electronically from somewhere. The purpose of the problem is to minimize the costs associated with routing. Regarding the literature on such issues, it has been proven in previous studies and research that these problems are among the hard-NP problems, and to solve them using the metaheuristic method, the exact methods justify it. After changing the basic model, this study developed a Tabu Search (TS) algorithm. The study results demonstrate that if the equipment can return to any depot, the cost is significantly reduced in contrast to the case where equipment is forced to return to their depot.