نتائج البحث - "Mansour, Romany F."

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Yafoz et al., "Optimal synergic deep learning for covid-19 classification using chest x-ray images," Computers, Materials & Continua, vol. 75, no.3, pp. 5255–5270, 2023. https://doi.org/10.32604/cmc.2023.033731; https://hdl.handle.net/11323/10601; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/10601
https://doi.org/10.32604/cmc.2023.033731
https://repositorio.cuc.edu.co/

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Academic Journal

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

المؤلفون: Chen, Zhiqing, Xuan, Ping, Asghar Heidari, Ali, Liu, Lei, Wu, Chengwen, Chen, Huiling, Escorcia-Gutierrez, José, Mansour, Romany F.

المصدر: https://www.sciencedirect.com/science/article/pii/S2589004223007563?via%3Dihub.

مصطلحات موضوعية: Genetics, Computational bioinformatics, Algorithms

وصف الملف: 39 páginas; application/pdf

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الاتاحة: https://hdl.handle.net/11323/10499
https://doi.org/10.1016/j.isci.2023.106679
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Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm

المؤلفون: Escorcia-Gutierrez, José, Soto-Diaz, Roosvel, Madera, Natasha, Soto, Carlos, Burgos-Florez, Francisco, Rodríguez, Alexander, Mansour, Romany F.

المصدر: https://www.techscience.com/csse/v46n2/51641.

مصطلحات موضوعية: Computer-aided diagnosis, Water strider optimization, Deep learning, Chest x-rays, Transfer learning

وصف الملف: 17 páginas; application/pdf

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Farahani, “Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble,” Physical and Engineering Sciences in Medicine, vol. 44, no. 1, pp. 291–311, 2021.; [6] A. S. Becker, C. Blüthgen, C. S. Wiltshire, B. Castelnuovo, A. Kambugu et al., “Detection of tuberculosis patterns in digital photographs of chest X-ray images using deep learning: Feasibility study,” The International Journal of Tuberculosis and Lung Disease, vol. 22, no. 3, pp. 328–335, 2018.; [7] S. Rajaraman and S. K. Antani, “Modality-specific deep learning model ensembles toward improving tb detection in chest radiographs,” IEEE Access, vol. 8, pp. 27318–27327, 2020.; [8] R. O. Panicker, K. S. Kalmady, J. Rajan and M. K. Sabu, “Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods,” Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 691–699, 2018.; [9] G. 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H. A. Hijazi and M. Saffree Jeffree, “Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images,” IAES International Journal of Artificial Intelligence, vol. 9, no. 4, pp. 713, 2020.; [24] A. T. Sahlol, M. A. Elaziz, A. T. Jamal, R. Damaševičius and O. Farouk Hassan, “A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features,” Symmetry, vol. 12, no. 7, pp. 1146, 2020.; [25] C. Dasanayaka and M. B. Dissanayake, “Deep learning methods for screening pulmonary tuberculosis using chest x-rays,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 9, no. 1, pp. 39–49, 2021.; [26] A. Haq, J. Li, S. Ahmad, S. Khan, M. Alshara et al., “Diagnostic approach for accurate diagnosis of covid19 employing deep learning and transfer learning techniques through chest x-ray images clinical data in ehealthcare,” Sensors, vol. 21, no. 24, pp. 8219, 2021.; [27] R. F. Mansour, J. Escorcia-Gutierrez, M. Gamarra, D. Gupta, O. Castillo et al., “Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification,” Pattern Recognition Letters, vol. 151, no. 6, pp. 267–274, 2021.; [28] H. Okut, “Deep learning for subtyping and prediction of diseases: Long-short term memory,” in Deep Learning Applications. United Kingdom: IntechOpen, 2021.; [29] R. Karthick, A. Senthilselvi, P. Meenalochini and S. S. Pandi, “Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA,” Circuits, Systems, and Signal Processing, vol. 41, no. 9, pp. 5254–5282, 2022.; 1353; 1337; 46; J. Escorcia-Gutierrez, R. Soto-Diaz, N. Madera, C. Soto, F. Burgos-Florez et al., "Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm," Computer Systems Science and Engineering, vol. 46, no.2, pp. 1337–1353, 2023. https://doi.org/10.32604/csse.2023.035253; https://hdl.handle.net/11323/10111; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

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https://doi.org/10.32604/csse.2023.035253
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Multimodal biomedical image retrieval and indexing system using handcrafted with deep convolution neural network features

المؤلفون: Mansour, Romany F.

المساهمون: New Valley University

المصدر: Journal of Ambient Intelligence and Humanized Computing ; volume 14, issue 4, page 4551-4560 ; ISSN 1868-5137 1868-5145

الاتاحة: http://dx.doi.org/10.1007/s12652-023-04575-z
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Security paradigm for remote health monitoring edge devices in internet of things

المؤلفون: Gupta, Divya, Rani, Shalli, Raza, Saleem, Faseeh Qureshi, Nawab Muhammad, Mansour, Romany F., Ragab, Mahmoud

المصدر: Journal of King Saud University - Computer and Information Sciences ; volume 35, issue 6, page 101478 ; ISSN 1319-1578

الاتاحة: http://dx.doi.org/10.1016/j.jksuci.2022.12.020
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An accelerated sine mapping whale optimizer for feature selection

المؤلفون: Yu, Helong, Zhao, Zisong, Heidari, Ali Asghar, Ma, Li, Hamdi, Monia, Mansour, Romany F., Chen, Huiling

المصدر: iScience ; volume 26, issue 10, page 107896 ; ISSN 2589-0042

الاتاحة: http://dx.doi.org/10.1016/j.isci.2023.107896
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9

Academic Journal

Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment

المؤلفون: Escorcia-Gutierrez, José, Gamarra, Margarita, Leal, Esmeide, Madera, Natasha, Soto, Carlos, Mansour, Romany F., Alharbi, Meshal, Alkhayyat, Ahmed, Gupta, Deepak

المصدر: Computers and Electrical Engineering ; volume 108, page 108704 ; ISSN 0045-7906

الاتاحة: http://dx.doi.org/10.1016/j.compeleceng.2023.108704
https://api.elsevier.com/content/article/PII:S0045790623001283?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S0045790623001283?httpAccept=text/plain

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10

Academic Journal

Retraction notice to “Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference” [Optik 270 (2022) 169956]

المؤلفون: Mansour, Romany F., Alabdulkreem, Eatedal, Eid, Heba F., Sathishkumar, K., Khan, Mohd Abdul Rahim, Kumar, Anil

المصدر: Optik ; volume 288, page 171225 ; ISSN 0030-4026

الاتاحة: https://doi.org/10.1016/j.ijleo.2023.171225
https://api.elsevier.com/content/article/PII:S0030402623007222?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S0030402623007222?httpAccept=text/plain

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11

Academic Journal

[Retracted] Deep Transfer Learning‐Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

المؤلفون: Althobaiti, Maha M., Ashour, Amal Adnan, Alhindi, Nada A., Althobaiti, Asim, Mansour, Romany F., Gupta, Deepak, Khanna, Ashish

المساهمون: Teekaraman, Yuvaraja, Taif University

المصدر: BioMed Research International ; volume 2022, issue 1 ; ISSN 2314-6133 2314-6141

الاتاحة: https://doi.org/10.1155/2022/3714422
http://downloads.hindawi.com/journals/bmri/2022/3714422.pdf
http://downloads.hindawi.com/journals/bmri/2022/3714422.xml
https://onlinelibrary.wiley.com/doi/pdf/10.1155/2022/3714422

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12

Academic Journal

3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User

المؤلفون: Mansour, Romany F., Abdel-Khalek, Sayed, Khayyat, Manal M., Oralhan, Burcu, Oralhan, Zeki

Relation: 35ff5ca2-bdaf-4254-aa5d-465d437dd362; https://avesis.kayseri.edu.tr/publication/details/35ff5ca2-bdaf-4254-aa5d-465d437dd362/oai

الاتاحة: https://doi.org/10.1155/2022/8452002
https://avesis.kayseri.edu.tr/publication/details/35ff5ca2-bdaf-4254-aa5d-465d437dd362/oai

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13

Academic Journal

Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks

المؤلفون: Mansour, Romany F., Soto, Carlos, Soto-Díaz, Roosvel, Escorcia Gutierrez, José, Gupta, Deepak, Khanna, Ashish

مصطلحات موضوعية: artificial intelligence, technological surveillance, internet of things, object detection, wireless sensor networks, IJIMAI

Relation: https://www.ijimai.org/journal/bibcite/reference/3156; https://reunir.unir.net/handle/123456789/13709; https://doi.org/10.9781/ijimai.2022.08.005

الاتاحة: https://reunir.unir.net/handle/123456789/13709
https://doi.org/10.9781/ijimai.2022.08.005

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14

Academic Journal

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

المؤلفون: Escorcia-Gutierrez, Jose, Mansour, Romany F., Beleño, Kelvin, Jiménez-Cabas, Javier, Pérez, Meglys, Madera, Natasha, Velasquez, Kevin

المصدر: https://www.techscience.com/cmc/v71n3/46473.

مصطلحات موضوعية: Breast cancer, Digital mammograms, Deep learning, Wavelet neural network, Resnet 34, Disease diagnosis

وصف الملف: 15 páginas; application/pdf

Relation: Computers, Materials and Continua; [1] S. K. Lakshmanaprabu, S. N. Mohanty, K. Shankar, N. Arunkumar and G. Ramireze, “Optimal deep learning model for classification of lung cancer on CT images,” Future Generation Computer Systems, vol.92, no. 1, pp. 374–382, 2019.; [2] I. V. Pustokhina, D. A. Pustokhin, T. Vaiyapuri, D. Gupta, S. Kumar et al., “An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety,”Safety Science, vol. 142, pp. 105356, 2021.; [3] K. Shankar, E. Perumal, P. Tiwari, M. Shorfuzzaman and D. Gupta, “Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images,” Multimedia Systems, vol. 66, no. 2, pp. 1921, 2021.; [4] I. V. Pustokhina, D. A. Pustokhin, P. K. Pareek, D. Gupta, A. Khanna et al., “Energy-efficient clusterbased unmanned aerial vehicle networks with deep learning-based scene classification model,”International Journal of Communication Systems, vol. 34, no. 8, pp. 1–16, 2021.; [5] T. Vaiyapuri, S. N. Mohanty, M. Sivaram, I. V. Pustokhina, D. A. Pustokhin et al., “Automatic vehicle license plate recognition using optimal deep learning model,” Computers, Materials & Continua, vol. 67, no. 2, pp. 1881–1897, 2021.; [6] R. F. Mansour, A. El Amraoui, I. Nouaouri, V. G. Díaz, D. Gupta et al., “Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems,” IEEE Access, vol. 9, pp. 45137– 45146, 2021.; [7] L. Li, L. Sun, Y. Xue, S. Li, X. Huang et al., “Fuzzy multilevel image thresholding based on improved coyote optimization algorithm,” IEEE Access, vol. 9, pp. 33595–33607, 2021.; [8] R. F. Mansour, J. E. Gutierrez, M. Gamarra, V. García, D. Gupta et al., “Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images,” Neural Computing and Applications, pp. 1–13, 2021. https://doi.org/10.1007/s00521-021-06240-y.; [9] R. F. Mansour, “A robust deep neural network based breast cancer detection and classification,” International Journal of Computational Intelligence and Applications, vol. 19, no. 1, pp. 2050007, 2020.; [10] R. F. Mansour, “Evolutionary computing enriched ridge regression model for craniofacial reconstruction,” Multimedia Tools and Applications, vol. 79, no. 31, pp. 22065–22082, 2020.; [11] C. Zhang, J. Zhao, J. Niu and D. Li, “New convolutional neural network model for screening and diagnosis of mammograms,” PLoS ONE, vol. 15, no. 8, pp. e0237674, 2020.; [12] M. Altaf, “A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulsecoupled neural networks,” Mathematical Biosciences and Engineering, vol. 18, no. 5, pp. 5029–5046, 2021.; [13] A. Yala, C. Lehman, T. Schuster, T. Portnoi and R. Barzilay, “A deep learning mammography-based model for improved breast cancer risk prediction,” Radiology, vol. 292, no. 1, pp. 60–66, 2019.; [14] L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride et al., “Deep learning to improve breast cancer detection on screening mammography,” Scientific Reports, vol. 9, no. 1, pp. 12495, 2019.; [15] A. Kumar, S. Mukherjee and A. K. Luhach, “Deep learning with perspective modeling for early detection of malignancy in mammograms,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 4, pp. 627–643, 2019.; [16] P. Kaur, G. Singh and P. Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification,” Informatics in Medicine Unlocked, vol. 16, no. 1, pp. 100239, 2019.; [17] S. S. Aboutalib, A. A. Mohamed, W. A. Berg, M. L. Zuley, J. H. Sumkin et al., “Deep learning to distinguish recalled but benign mammography images in breast cancer screening,” Clinical Cancer Research, vol. 24, no. 23, pp. 5902–5909, 2018.; [18] M. A. A. Masni, M. A. A. Antari, J. M. Park, G. Gi, T. Y. Kim et al., “Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system,” Computer Methods and Programs in Biomedicine, vol. 157, no. 1, pp. 85–94, 2018.; [19] M. A. Al-antari, S. M. Han and T. S. Kim, “Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms,” Computer Methods and Programs in Biomedicine, vol. 196, pp. 105584, 2020.; [20] S. Punitha, A. Amuthan and K. S. Joseph, “Benign and malignant breast cancer segmentation using optimized region growing technique,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 348– 358, 2018.; [21] V. Rajinikanth, S. C. Satapathy, S. L. Fernandes and S. Nachiappan, “Entropy based segmentation of tumor from brain MR images–A study with teaching learning based optimization,” Pattern Recognition Letters, vol. 94, no. 1, pp. 87–95, 2017.; [22] A. Helwan, M. K. S. Ma”aitah, R. H. Abiyev, S. Uzelaltinbulat and B. Sonyel, “Deep learning based on residual networks for automatic sorting of bananas,” Journal of Food Quality, vol. 2021, pp. 1–11, 2021.; [23] A. A. Nahid, M. A. Mehrabi and Y. Kong, “Histopathological breast cancer image classification by deep neural network techniques guided by local clustering,”BioMed Research International, vol. 2018, pp. 1–20, 2018.; [24] M. Khishe and M. R. Mosavi, “Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm,” Applied Acoustics, vol. 157, no. 13, pp. 107005, 2020.; [25] L. L. S. Linhares, A. I. R. Fontes, A. M. Martins, F. M. U. Araújo and L. F. Q. Silveira, “Fuzzy wavelet neural network using a correntropy criterion for nonlinear system identification,” Mathematical Problems in Engineering, vol. 2015, no. 5, pp. 1–12, 2015.; [26] Dataset. [Online]. Available: http://peipa.essex.ac.uk/info/mias.html.; 4221; 71; 4235; Escorcia-Gutierrez, J., Mansour, R. F., Beleño, K., Jiménez-Cabas, J., Pérez, M. et al. (2022). Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images. CMC-Computers, Materials & Continua, 71(3), 4221–4235.; https://hdl.handle.net/11323/9076; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/9076
https://doi.org/10.32604/cmc.2022.022322
https://repositorio.cuc.edu.co/

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15

Academic Journal

Improved metaheuristics with machine learning enabled medical decision support system

المؤلفون: Althubiti , Sara, Escorcia-Gutierrez, Jose, Gamarra, Margarita, Soto-Diaz, Roosvel, Mansour, Romany F., Alenezi, Fayadh

المصدر: https://www.techscience.com/cmc/v73n2/48373.

مصطلحات موضوعية: Ovarian cancer, Decision support system, Smart healthcare, IoMT, Deep learning, Feature selection

وصف الملف: 17 páginas; application/pdf

Relation: Computers, Materials and Continua; [1] M. W. L. Moreira, J. J. P. C. Rodrigues, V. Korotaev, J. Al-Muhtadi and N. Kumar, “A comprehensive review on smart decision support systems for health care,” IEEE Systems Journal, vol. 13, no. 3, pp. 3536–3545, 2019.; [2] E. S. Kumar and P. S. Jayadev, “Deep learning for clinical decision support systems: A review from the panorama of smart healthcare,” Deep Learning Techniques for Biomedical and Health Informatics, Studies in Big Data Book Series, vol. 68, pp. 79–99, 2020.; [3] W. Sun, G. Z. Dai, X. R. Zhang, X. Z. He and X. Chen, “TBE-Net: A three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–13, 2021.; [4] W. Sun, L. Dai, X. R. Zhang, P. S. Chang and X. Z. He, “RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring,” Applied Intelligence, vol. 92, no. 6, pp. 1–16, 2021.; [5] C. A. Kontovas, “The green ship routing and scheduling problem (GSRSP): A conceptual approach,” Transportation Research Part D: Transport and Environment, vol. 31, no. 3, pp. 61–69, 2014.; [6] S. Ganguly, “Multi-objective distributed generation penetration planning with load model using particle SWARM optimization,” Decision Making: Applications in Management and Engineering, vol. 3, no. 1, pp. 30–42, 2020.; [7] H. Engqvist, T. Z. Parris, J. Biermann, E. W. Rönnerman, P. Larsson et al., “Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes,” Scientific Reports, vol. 10, no. 1, pp. 1–13, 2020.; [8] M. Akazawa and K. Hashimoto, “Artificial intelligence in ovarian cancer diagnosis,” Anticancer Research, vol. 40, no. 8, pp. 4795–4800, 2020.; [9] R. F. Mansour, J. Escorcia-Gutierrez, M. Gamarra, V. García, D. Gupta et al., “Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images,” Neural Computing and Applications, vol. 11, no. 1, pp. 1–13, 2021.; [10] K. Muthumayil, S. Manikandan, K. Srinivasan, J. Escorcia-Gutierrez, M. Gamarra et al., “Diagnosis of leukemia disease based on enhanced virtual neural network, Computers, Materials & Continua, vol. 69, no. 2, pp. 2031–2044, 2021.; [11] J. Escorcia-Gutierrez, J. Torrents-Barrena, M. Gamarra, N. Madera, P. Romero-Aroca et al., “A feature selection strategy to optimize retinal vasculature segmentation,” Computers, Materials & Continua, vol. 70, no. 2, pp. 2971–2989, 2021.; [12] J. Escorcia-Gutierrez, R. F. Mansour, K. Beleño, J. Jiménez-Cabas, M. Pérez et al., “Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images,”Computers, Materials & Continua, vol. 71, no. 2, pp. 4221–4235, 2022.; [13] L. Zhang, J. Huang and L. Liu, “Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system,” Journal of MedicalSystems, vol. 43, no. 8, pp. 1–9, 2019.; [14] M. Wu, C. Yan, H. Liu and Q. Liu, “Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks,” Bioscience Reports, vol. 38, no. 3, pp. 1–11, 2018.; [15] A. Consiglio, G. Casalino, G. Castellano, G. Grillo, E. Perlino et al., “Explaining ovarian cancer gene expression profiles with fuzzy rules and genetic algorithms,” Electronics, vol. 10, no. 4, pp. 1–13, 2021.; [16] J. H. Bae, M. Kim, J. S. Lim and Z. W. Geem, “Feature selection for colon cancer detection using k-means clustering and modified harmony search algorithm,” Mathematics, vol. 9, no. 5, pp. 1–14, 2021.; [17] S. Sujamol, E. R. Vimina and U. Krishnakumar, “Improving recurrence prediction accuracy of ovarian cancer using multi-phase feature selection methodology,” Applied Artificial Intelligence, vol. 35, no. 3, pp. 206–226, 2021.; [18] E. S. Paik, J. W. Lee, J. Y. Park, J. H. Kim, M. Kim et al., “Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods,” Journal of Gynecologic Oncology, vol. 30, no.4, pp. 1–13, 2019.; [19] A. Arfiani and Z. Rustam, “Ovarian cancer data classification using bagging and random forest,” AIP Conference Proceedings, vol. 2168, no. 1, pp. 1–6, 2019.; [20] M. Elhoseny, G. B. Bian, S. K. Lakshmanaprabu, K. Shankar, A. K. Singh et al., “Effective features to classify ovarian cancer data in internet of medical things,” Computer Networks, vol. 159, no. 17, pp. 147–156, 2019.; [21] Y. E. Manzalawy, T. Y. Hsieh, M. Shivakumar, D. Kim and V. Honavar, “Min-redundancy and maxrelevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data,”BMC Medical Genomics, vol. 11, no. S3, pp. 20–31, 2018.; [22] U. Ahmed, R. Mumtaz, H. Anwar, A. A. Shah, R. Irfan et al., “Efficient water quality prediction using supervised machine learning,” Water, vol. 11, no. 11, pp. 1–14, 2019.; [23] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, no. 1, pp. 1–28, 2020.; [24] L. T. Brady, C. L. Baldwin, A. Bapat, Y. Kharkov and A. V. Gorshkov, “Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems,” Physical Review Letters, vol. 126, no. 7, pp. 1–11, 2021.; [25] V. B. Semwal, N. Gaud and G. C. Nandi, “Human gait state prediction using cellular automata and classification using ELM,” in Machine Intelligence and Signal Analysis, Advances in Intelligent Systems and Computing Book Series, vol. 748, Singapore: Springer, pp. 135–145, 2019.; [26] A. E. Ezugwu and D. Prayogo, “Symbiotic organisms search algorithm: Theory, recent advances and applications,” Expert Systems with Applications, vol. 119, no. 6, pp. 184–209, 2019.; 2439; 2423; 73; S. A. Althubiti, J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, R. F. Mansour et al., "Improved metaheuristics with machine learning enabled medical decision support system," Computers, Materials & Continua, vol. 73, no.2, pp. 2423–2439, 2022.; https://hdl.handle.net/11323/9382; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/9382
https://doi.org/10.32604/cmc.2022.028878
https://repositorio.cuc.edu.co/

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16

Academic Journal

Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques

المؤلفون: Escorcia-Gutierrez, Jose, Gamarra, Margarita, Soto-Diaz, Roosvel, Pérez, Meglys, Madera, Natasha, Mansour, Romany F.

المصدر: https://www.mdpi.com/2077-0472/12/7/977.

مصطلحات موضوعية: Soil nutrients, pH classification, Agriculture, Soil management, Deep learning, Ensemble model

وصف الملف: 16 páginas; application/pdf

Relation: Agriculture (Switzerland); 1. Patel, H.; Patel, D. A brief survey of data mining techniques applied to agricultural data. Int. J. Comput. Appl. 2014, 95, 80–83. [CrossRef]; 2. Padarian, J.; Minasny, B.; McBratney, A.B. Using deep learning to predict soil properties from regional spectral data. Geoderma Reg. 2019, 16, e00198. [CrossRef]; 3. Ji, C.; Liu, H.; Cha, Z.; Lin, Q.; Feng, G. Spatial-Temporal Variation of N, P, and K Stoichiometry in Cropland of Hainan Island. Agriculture 2021, 12, 39. [CrossRef]; 4. Kayad, A.; Sozzi, M.; Gatto, S.; Whelan, B.; Sartori, L.; Marinello, F. Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy. Comput. Electron. Agric. 2021, 185, 106126. [CrossRef]; 5. Taghizadeh-Mehrjardi, R.; Khademi, H.; Khayamim, F.; Zeraatpisheh, M.; Heung, B.; Scholten, T. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties. Remote Sens. 2022, 14, 472. [CrossRef]; 6. Zeraatpisheh, M.; Garosi, Y.; Owliaie, H.R.; Ayoubi, S.; Taghizadeh-Mehrjardi, R.; Scholten, T.; Xu, M. Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates. Catena 2022, 208, 105723. [CrossRef]; 7. Davenport, J.; Jabro, J. Assessment of hand held ion selective electrode technology for direct measurement of soil chemical properties. Commun. Soil Sci. Plant Anal. 2011, 32, 3077–3085. [CrossRef]; 8. Yang, M.; Xu, D.; Chen, S.; Li, H.; Shi, Z. Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra. Sensors 2019, 19, 263. [CrossRef]; 9. Yu, H.; Liu, D.; Chen, G.; Wan, B.; Wang, S.; Yang, B. A neural network ensemble method for precision fertilization modeling. Math. Comput. Model. 2010, 51, 1375–1382. [CrossRef]; 11. Chambers, O. Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method. Sensors 2021, 21, 4208.; 12. Wu, C.; Chen, Y.; Hong, X.; Liu, Z.; Peng, C. Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques. For. Ecosyst. 2020, 7, 30. [CrossRef]; 13. Rose, S.; Nickolas, S.; Sangeetha, S. Machine Learning and Statistical Approaches used in Estimating Parameters that Affect the Soil Fertility Status: A Survey. In Proceedings of the 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Karnataka, India, 16–18 August 2018; IEEE: New York, NY, USA, 2018; pp. 381–385.; 14. Rajamanickam, J. Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm. INFOCOMP J. Comput. Sci. 2021, 20, 49–55.; 15. Rajamanickam, J.; Mani, S.D. Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction. Concurr. Comput. Pract. Exp. 2021, 33, e6460. [CrossRef]; 16. Sirsat, M.S.; Cernadas, E.; Fernández-Delgado, M.; Barro, S. Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Comput. Electron. Agric. 2018, 154, 120–133. [CrossRef]; 17. Ning, J.; Sheng, M.; Yi, X.; Wang, Y.; Hou, Z.; Zhang, Z.; Gu, X. Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy. Spectrosc. Lett. 2018, 51, 463–471. [CrossRef]; 18. Wang, J.; Wang, Y.; Yang, J. Forecasting of Significant Wave Height Based on Gated Recurrent Unit Network in the Taiwan Straitand Its Adjacent Waters. Water 2021, 13, 86. [CrossRef]; 19. Hinton, G.E. Deep belief network. Scholarpedia 2009, 4, 5947. [CrossRef]; 20. Sokkhey, P.; Okazaki, T. Development and Optimization of Deep Belief Networks Applied for Academic Performance Prediction with Larger Datasets. IEIE Trans. Smart Process. Comput. 2020, 9, 298–311. [CrossRef]; 21. Minh-Tuan, N.; Kim, Y.H. Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems. Appl. Sci. 2019, 9, 3470. [CrossRef]; 22. Hemeida, M.G.; Ibrahim, A.A.; Mohamed, A.A.A.; Alkhalaf, S.; El-Dine, A.M.B. Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO). Ain Shams Eng. J. 2021, 12, 609–619. [CrossRef]; 23. Livieris, I.E.; Kanavos, A.; Tampakas, V.; Pintelas, P. A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays. Algorithms 2019, 12, 64. [CrossRef]; 16; 12; Escorcia-Gutierrez, J.; Gamarra, M.; Soto-Diaz, R.; Pérez, M.; Madera, N.; Mansour, R.F. Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques. Agriculture 2022, 12, 977. https://doi.org/10.3390/agriculture12070977; https://hdl.handle.net/11323/9384; https://doi.org/10.3390/agriculture12070977; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/9384
https://doi.org/10.3390/agriculture12070977
https://repositorio.cuc.edu.co/

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17

Academic Journal

Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection

المؤلفون: Hu, Hanyu, Shan, Weifeng, Tang, Yixiang, Asghar Heidari, Ali, Chen, Huiling, Liu, Haijun, Wang, Maofa, Escorcia-Gutierrez, José, Mansour, Romany F, Chen, Jun

المصدر: https://academic.oup.com/jcde/article/9/6/2524/6795289.

مصطلحات موضوعية: Sine cosine algorithm, Feature selection, Global optimization, Metaheuristic algorithms

وصف الملف: 32 páginas; application/pdf

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