Deep convolutional neural network based Henry gas solubility optimization for disease prediction in data from wireless sensor network
*Hemanta Kumar BhuyanCorresponding authorhmb.bhuyan@gmail.comMuma College of Business8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 34243, U.S.A.0000-0002-9712-7280View full profile → , Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 34243, U.S.A.0000-0003-1118-3837View full profile → , S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and EngineeringKG Reddy College of Engineering and TechnologyHyderabad, Telangana, 500075, India0000-0002-7616-6088View full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and EngineeringSir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, India0000-0001-8062-4144View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 14 Feb 2024
- Published Online:
- 18 Dec 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1802
- Pages:
- 2273–2284
Abstract
Keywords
Subject Classifications
References
[1] B. S. Mahdi, J. H. H. Al-Bayati, and A. J. Al-Mahdawi, “Minimum spanning tree application in Covid-19 network structure analysis in the countries of the Middle East,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 8, pp. 2723-2728 (2022).
[2] D. Sinha and K. Thangavel, “Automatic epileptic signal classification using deep convolutional neural network,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 4, pp. 963-973 (2022).
[3] V. Gupta, P. Dass, V. Bansal, and R. Arora, “A truncated deep neural network for identifying age groups in real time images,” J. Interdiscip. Math., vol. 25, no. 3, pp. 851-861 (2022).
[4] H. Panda, M. Das, and B. Sahu, “Received signal strength prediction model for wireless underground sensor networks using machine learning algorithms,” J. Inf. Optim. Sci., vol. 43, no. 5, pp. 949-962 (2022).
[5] S. L. Yadav and R. L. Ujjwal, “Sensor data fusion and clustering: A congestion detection and avoidance approach in wireless sensor networks,” J. Inf. Optim. Sci., vol. 41, no. 7, pp. 1673-1688 (2020).
[6] K. Sharad, N. Kaur, and I. K. Aulakh, “Evaluation and implementation of cluster head selection in WSN using Contiki/Cooja simulator,” J. Stat. Manag. Syst., vol. 23, no. 2, pp. 407-418 (2020).
[7] N. Singh and D. Virmani, “Competence computation of attacks in wireless sensor network,” J. Stat. Manag. Syst., vol. 23, no. 7, pp. 1227-1239 (2020).
[8] A. Gambhir, A. Payal, and R. Arya, “Water cycle algorithm based optimized clustering protocol for wireless sensor network,” J. Interdiscip. Math., vol. 23, no. 2, pp. 367-377 (2020).
[9] S. Pangaonkar and R. Gunjan, “A consolidative evaluation of extracted EGG speech signal for pathology identification,” Int. J. Simul. Process Modell., vol. 16, no. 4, pp. 300-314 (2021).
[10] N. Kale, S. N. Gunjal, M. Bhalerao, H. E. Khodke, S. Gore, and B. J. Dange, “Crop Yield Estimation Using Deep Learning and Satellite Imagery,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 10s, pp. 464-471 (2023).
[11] M. A. El-Shorbagy, A. Bouaouda, H. A. Nabwey, L. Abualigah, and F. A. Hashim, “Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications,” IEEE Access (2024).
[12] K. Perumal and K. Arockiasamy, “DDoS attack detection in SDN: A special attention towards optimization model,” in AIP Conf. Proc., vol. 2802, no. 1, Jan. (2024).
[13] M. Gokiladevi and S. Santhoshkumar, “Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model,” Int. J. Intell. Eng. Syst., vol. 17, no. 2 (2024).
[14] L. Abualigah, G. Al-Hilo, A. Raza, A. E. Ezugwu, M. R. Al Nasar, A. Mughaid, and M. Al-diabat, “A review of Henry gas solubility optimization algorithm: a robust optimizer and applications,” in Metaheuristic Optimization Algorithms, pp. 177-192 (2024).
[15] M. U. Ali, A. Zafar, J. Tanveer, M. A. Khan, S. H. Kim, M. M. Alsulami, and S. W. Lee, “Deep learning network selection and optimized information fusion for enhanced COVID-19 detection,” Int. J. Imaging Syst. Technol., vol. 34, no. 2, p. e23001 (2024).




