The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Research works printed but not linked: 41
- Alloghani, M., Al-Jumeily, D., Hussain, A., Mustafina, J., Baker, T., Aljaaf, A.J.: Implementation of machine learning and data mining to improve cybersecurity and limit vulnerabilities to cyber attacks. In: Nature-Inspired Computation in Data Mining and Machine Learning, pp. 47–76. Springer (2020) · DOI
- Alloghani, M., Baker, T., Al-Jumeily, D., Hussain, A., Mustafina, J., Aljaaf, A.J.: Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services. In: Nature-Inspired Computation in Data Mining and Machine Learning, pp. 113–136. Springer (2020) · DOI
- Ashqar, B.A., Abu-Naser, S.S.: Image-based tomato leaves diseases detection using deep learning. Int. J. Acad. Eng. Res. 2(12), 10–16 (2018)
- Bhattacharya, S., Kaluri, R., Singh, S., Alazab, M., Tariq, U., et al.: A novel PCA-firefly based xgboost classification model for intrusion detection in networks using GPU. Electronics 9(2), 219 (2020) · DOI · Paper page
- Bosona, T., Gebresenbet, G.: Life cycle analysis of organic tomato production and supply in sweden. J. Clean. Prod. 196, 635–643 (2018) · doi:10.1016/j.jclepro.2018.06.087
- Brahimi, M., Mahmoudi, S., Boukhalfa, K., Moussaoui, A.: Deep interpretable architecture for plant diseases classification. arXiv preprint arXiv:1905.13523 (2019) · DOI
- Fuentes, A.F., Yoon, S., Lee, J., Park, D.S.: High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front. Plant Sci. 9, 1162 (2018) · DOI · Paper page
- Gadekallu, T., Soni, A., Sarkar, D., Kuruva, L.: Application of sentiment analysis in movie reviews. In: Rajput, D.S., Thakur, R.S., Muzamil Basha, S. (eds.) Sentiment Analysis and Knowledge Discovery in Contemporary Business, pp. 77–90. IGI Global, USA (2019) · DOI
- Gadekallu, T.R., Khare, N.: Cuckoo search optimized reduction and fuzzy logic classifier for heart disease and diabetes prediction. Int. J. Fuzzy Syst. Appl. (IJFSA) 6(2), 25–42 (2017)
- Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Reddy Maddikunta, P.K., Ra, I.H., Alazab, M.: Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics 9(2), 274 (2020) · DOI · Paper page
- Garg, S., Kaur, K., Kumar, N., Rodrigues, J.J.: Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in SDN: a social multimedia perspective. IEEE Trans. Multimed. 21(3), 566–578 (2019) · DOI · Paper page
- Haggag, M., Abdelhay, S., Mecheter, A., Gowid, S., Musharavati, F., Ghani, S.: An intelligent hybrid experimental-based deep learning algorithm for tomato-sorting controllers. IEEE Access 7, 106890–106898 (2019) · doi:10.1109/access.2019.2932730
- Hayou, S., Doucet, A., Rousseau, J.: On the selection of initialization and activation function for deep neural networks. arXiv preprint arXiv:1805.08266 (2018) · arXiv
- Iwendi, C., Khan, S., Anajemba, J.H., Bashir, A.K., Noor, F.: Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model. IEEE Access 8, 28462–28474 (2020) · DOI · Paper page
- Iwendi, C., Maddikunta, P.K.R., Gadekallu, T.R., Lakshmanna, K., Bashir, A.K., Piran, M.J.: A metaheuristic optimization approach for energy efficiency in the IoT networks. Softw. Pract. Exp. 1, 1–14 (2020)
- Jindal, A., Aujla, G.S., Kumar, N., Chaudhary, R., Obaidat, M.S., You, I.: Sedative: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems. IEEE Netw. 32(6), 66–73 (2018) · DOI
- Jindal, A., Aujla, G.S., Kumar, N., Prodan, R., Obaidat, M.S.: Drums: Demand response management in a smart city using deep learning and SVR. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2018) · DOI
- Kaljahi, M.A., Shivakumara, P., Hakak, S., Idris, M.Y.I., Anisi, M.H., Rajan, D.: Saliency-based bit plane detection for network applications. Multimedia Tools Appl. 1, 1–19 (2020)
- Lu, J., Ehsani, R., Shi, Y., de Castro, A.I., Wang, S.: Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Sci. Rep. 8(1), 2793 (2018) · doi:10.1038/s41598-018-21191-6
- Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018) · DOI · Paper page
- Mendoza, H., Klein, A., Feurer, M., Springenberg, J.T., Urban, M., Burkart, M., Dippel, M., Lindauer, M., Hutter, F.: Towards automatically-tuned deep neural networks. In: Automated Machine Learning, pp. 135–149. Springer (2019) · DOI
- Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016) · DOI · Paper page
- Moallem, P., Serajoddin, A., Pourghassem, H.: Computer vision-based apple grading for golden delicious apples based on surface features. Inf. Process. Agric. 4(1), 33–40 (2017)
- Mwebaze, E., Owomugisha, G.: Machine learning for plant disease incidence and severity measurements from leaf images. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 158–163. IEEE (2016) · DOI
- Pham, Q.V., Mirjalili, S., Kumar, N., Alazab, M., Hwang, W.J.: Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Trans. Veh. Technol. 69, 4285–4297 (2020) · DOI · Paper page
- Reddy, G.T., Khare, N.: Hybrid firefly-bat optimized fuzzy artificial neural network based classifier for diabetes diagnosis. Int. J. Intell. Eng. Syst. 10(4), 18–27 (2017)
- Reddy, G.T., Khare, N.: Heart disease classification system using optimised fuzzy rule based algorithm. Int. J. Biomed. Eng. Technol. 27(3), 183–202 (2018) · doi:10.1504/ijbet.2018.094122
- Reddy, G.T., Reddy, M.P.K., Lakshmanna, K., Kaluri, R., Rajput, D.S., Srivastava, G., Baker, T.: Analysis of dimensionality reduction techniques on big data. IEEE Access 8, 54776–54788 (2020) · DOI · Paper page
- Reddy, G.T., Reddy, M.P.K., Lakshmanna, K., Rajput, D.S., Kaluri, R., Srivastava, G.: Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis. Evolut. Intell. 1, 1–12 (2019)
- Reddy, M.P.K., Babu, M.R.: Implementing self adaptiveness in whale optimization for cluster head section in internet of things. Cluster Comput. 22(1), 1361–1372 (2019) · DOI
- Sabrol, H., Satish, K.: Tomato plant disease classification in digital images using classification tree. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1242–1246. IEEE (2016) · DOI
- Saleem, M.H., Potgieter, J., Mahmood Arif, K.: Plant disease detection and classification by deep learning. Plants 8(11), 468 (2019) · DOI · Paper page
- Uddin, Z., Altaf, M., Bilal, M., Nkenyereye, L., Bashir, A.K.: Amateur drones detection: a machine learning approach utilizing the acoustic signals in the presence of strong interference. Comput. Commun. 154, 236–245 (2020) · DOI
- Venkatraman, S., Alazab, M., Vinayakumar, R.: A hybrid deep learning image-based analysis for effective malware detection. J. Inf. Secur. Appl. 47, 377–389 (2019)
- Verma, S., Chug, A., Singh, A.P.: Recent advancements in image-based prediction models for diagnosis of plant diseases. In: Proceedings of 3rd International Conference on Computer Vision and Image Processing, pp. 365–377. Springer (2020) · doi:10.1007/978-981-32-9088-4_31
- Vinayakumar, R., Alazab, M., Srinivasan, S., Pham, Q.V., Padannayil, S.K., Simran, K.: A visualized botnet detection system based deep learning for the internet of things networks of smart cities. IEEE Trans. Ind. Appl. 1, 1–8 (2020)
- Wang, S.H., Phillips, P., Sui, Y., Liu, B., Yang, M., Cheng, H.: Classification of alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42(5), 85 (2018) · DOI · Paper page
- Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017) · DOI · Paper page
- Zerdoumi, S., Sabri, A.Q.M., Kamsin, A., Hashem, I.A.T., Gani, A., Hakak, S., Al-Garadi, M.A., Chang, V.: Image pattern recognition in big data: taxonomy and open challenges: survey. Multimedia Tools Appl. 77(8), 10091–10121 (2018) · DOI
- Zhang, H., Jolfaei, A., Alazab, M.: A face emotion recognition method using convolutional neural network and image edge computing. IEEE Access 7, 159081–159089 (2019) · DOI · Paper page
- Zhou, X., Gong, W., Fu, W., Du, F.: Application of deep learning in object detection. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 631–634. IEEE (2017) · DOI
Cited by
Object detection using YOLO: challenges, architectural successors, datasets and applications
Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13644-y · Cited by 1,549
“A machine learning model is developed for the tomato disease dataset classification wherein PCA based whale optimization is utilized for the efficient features ’ selection [17].”
MethodVGG-ICNN: A Lightweight CNN model for crop disease identification
Thakur, P. S., Sheorey, T., & Ojha, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13144-z · Cited by 259
Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning
Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100081 · Cited by 258
Metadata
- Authors
- Gadekallu, T. R., Rajput, D. S., Reddy, M. P. K., Lakshmanna, K., Bhattacharya, S., Singh, S., Jolfaei, A., & Alazab, M.
- Venue
- Journal of Real-Time Image Processing
- Published
- 2020-06-12
- Volume
- 18
- Issue
- 4
- Pages
- 1383-1396
- ISSN
- 1861-8200 (ISSN-L)
- 1861-8219
- OpenAlex
- W3035503061
- Cited by
- 301
- References
- 1
- FWCI
- 32.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. https://app.cochat.ai/papers/10.1007/s11554-020-00987-8
- The paper
- Gadekallu, T. R., Rajput, D. S., Reddy, M. P. K., Lakshmanna, K., Bhattacharya, S., Singh, S., Jolfaei, A., & Alazab, M. (2021). A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. Journal of Real-Time Image Processing. https://doi.org/10.1007/s11554-020-00987-8
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.