Smart Hydroponic Growth Optimisation System with Real–Time Monitoring and Control with Convolutional Neural Network Algorithm Using Machine Learning

G.  Rajasekaran; N. Mohamed  Kaif; J. Mohammed  Kayuff; B. Mohamed Jaseem  Lathif; M. Mohamed Sameer  Ali; , J. Mohamed Zakkariya  Maricar

G. Rajasekaran Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India
N. Mohamed Kaif Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India
J. Mohammed Kayuff Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India
B. Mohamed Jaseem Lathif Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India
M. Mohamed Sameer Ali Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India
, J. Mohamed Zakkariya Maricar Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India

Keywords: Smart Hydroponic, Real-Time Data, Light Dependent Resistor (LDR), Deep Learning Techniques, Hydroponic Farming Methods, Crop Yields, Crucial Growth, Temperature Sensors, Nutrient Sensors

Abstract

This project aims to develop a smart hydroponic plant monitoring system utilising a range of sensors and actuators to enhance the environment for plant growth. The system automatically changes environmental conditions based on real-time data collected from sensors. This makes hydroponic farming more productive and efficient. The Light Dependent Resistor (LDR) detects light intensity, the DHT11 checks the temperature, and the PH sensor checks the quality of the water. Light, temperature, water pH, and nutrition levels are all controlled by actuators such as bulbs, fans, water pumps, and motors. The main goal of this system is to create an automated and efficient method for monitoring hydroponic plants, thereby enabling them to develop more effectively and reducing the need for human intervention. The study also uses deep learning methods to find diseases in spinach leaves, which adds another layer of plant health monitoring to the system. This combination of technology and software makes it easier to run hydroponic farms and also increases their overall efficiency and output. The smart hydroponic plant monitoring system in this project is a paradigm for sustainable farming. It shows how technology can be used to make farming more effective and environmentally benign. This technology aims to enhance hydroponic farming methods by automating the monitoring and adjustment of key growth factors. This will lead to better crop yields and more efficient use of resources.

References

H. Herman and N. Surantha, "Intelligent monitoring and controlling system for hydroponics precision agriculture," in Proc. 7th Int'l Conf. Inf. Commun. Technol, Kuala Lumpur, Malaysia, 2019.

T. M. Bandara, W. Mudiyanselage, and M. Raza, "Smart farm and monitoring system for measuring the environmental condition using wireless sensor network - IoT technology in farming," Sydney, Australia, 2020.

D. Saraswathi, P. Manibharathy, R. Gokulnath, E. Sureshkumar, and K. Karthikeyan, "Automation of hydroponics greenhouse farming using IoT," in Proc. IEEE Int. Conf. Syst. Comput. Autom. Netw. (ICSCA), Pondicherry, India, 2018.

D. Mishra, T. Pande, K. K. Agrawal, A. Abbas, A. K. Pandey, and R. S. Yadav, "Smart agriculture system using IoT," in Proc. 3rd Int. Conf. Adv. Informat. Comput. Res. (ICAICR), Shimla, India, 2019,

R. A. Tambogon and A. N. Yumang, "Growth of garlic in a hydroponic system with IoT-based monitoring," in Proc. 14th Int. Conf. Comput. Autom. Eng. (ICCAE), Brisbane, Australia, 2022.

K. V. Deshpande and J. Singh, “Weighted transformer neural network for web attack detection using request URL,” Multimedia Tools and Applications, vol. 83, no. 15, pp. 43983–44007, Oct. 2023, doi: 10.1007/s11042-023-17356-9.

J. Singh, S. Rani, and V. Kumar, “Role-based access control (RBAC) enabled secure and efficient data processing framework for IoT networks,” Int. J. Commun. Netw. Inf. Secur. (IJCNIS), Aug. 2024, doi: 10.17762/ijcnis.v16i2.6697.

J. Singh, S. Rani, and P. Kumar, “Blockchain and smart contracts: Evolution, challenges, and future directions,” in Proc. 2024 Int. Conf. Knowledge Eng. Commun. Syst. (ICKECS), Apr. 2024, pp. 1–5, doi: 10.1109/ickecs61492.2024.10616652.

J. Singh, E. al., “Enhancing cloud data privacy with a scalable hybrid approach: HE-DP-SMC,” J. Electr. Syst., vol. 19, no. 4, pp. 350–375, Jan. 2024, doi: 10.52783/jes.643.

J. Singh, S. Rani, and G. Srilakshmi, “Towards explainable AI: Interpretable models for complex decision-making,” in Proc. 2024 Int. Conf. Knowledge Eng. Commun. Syst. (ICKECS), Apr. 2024, pp. 1–5, doi: 10.1109/ickecs61492.2024.10616500.

G. Sadineni, J. Singh, S. Rani, G. S. Rao, M. J. Pasha, and A. Lavanya, “Blockchain-enhanced vehicular ad-hoc networks (B-VANETs): Decentralized traffic coordination and anonymized communication,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 1s, pp. 443–456, Sep. 2023.

D. Jadhav and J. Singh, “Web information extraction and fake news detection in Twitter using optimized hybrid bi-gated deep learning network,” Multimedia Tools and Applications, May 2024, doi: 10.1007/s11042-024-19225-5.

S. Jadhav and J. Singh, “Design of EGTBoost classifier for automated external skin defect detection in mango fruit,” Multimedia Tools and Applications, vol. 83, no. 16, pp. 47049–47068, Oct. 2023, doi: 10.1007/s11042-023-17191-y.

P. Das, D. Datta, S. S. Rajest, L. M. M. Visuwasam, A. Thakare, and J. Cypto, "Application of multi-criteria decision-making approach using TOPSIS to identify the vulnerable time zone of earthquake time series signal," Int. J. Crit. Comput.-Based Syst., vol. 11, no. 1/2, pp. 30–47, 2024.

G. Kumaresan and L. M. Visuwasam, "Enhanced in-line data deduplication and secure authorization in hybrid cloud," Int. J. Innov. Res. Sci. Eng. Technol., vol. 4, no. 2, pp. 466–471, 2015.

S. Gomathy, K. Deepa, T. Revathi, and L. M. M. Visuwasam, "Genre specific classification for information search and multimodal semantic indexing for data retrieval," SIJ Trans. Comput. Sci. Eng. Appl. (CSEA), vol. 1, no. 1, pp. 10–15, 2013, doi: 10.9756/sijcsea/v1i1/01010159.

K. Kishore, D. Dhinakaran, N. J. Kumar, S. M. U. Sankar, K. Chandu, and L. M. M. Visuwasam, "Fish farm monitoring system using IoT," in Proc. 2021 Int. Conf. Syst., Comput., Autom. Netw. (ICSCAN), 2021, vol. 10, pp. 1–6.

L. M. V., A. Balakrishna, N. S. R., and K. V., "Level-6 automated IoT integrated with artificial intelligence based big data-driven dynamic vehicular traffic control system," EAI Endorsed Trans. Energy Web, p. 164176, 2018.

N. J. K., M. Shoba, D. Dhinakaran, L. M. M. V., and G. Elangovan, "Bio-inspired optimization to enhance the performance in 6G networks of reconfigurable intelligent surfaces," in Advances in Computational Intelligence and Robotics, pp. 409–444, 2025.

N. J. Kumar, R. Premkumar, L. M. M. Visuwasam, G. Arjunan, G. Yuyaraj, and C. T. Kumar, "Hybrid K-means and firefly algorithm-based load balancer for dynamic task scheduling in fog computing for postoperative healthcare systems," in Proc. 2025 Int. Conf. Adv. Comput. Technol. (ICoACT), Sivalasi, India, 2025, pp. 1–6, doi: 10.1109/ICoACT63339.2025.11004826.

N. J. Kumar, R. Premkumar, L. M. Michael Visuwasam, G. Arjunan, A. Shiny, and K. Dharani, "Adaptive optimization and resource allocation (AORA) model for IoT-edge computing using hybrid Newton-Raphson and dolphin echolocation algorithm (HNR-DEA) technique," in Proc. 2025 Int. Conf. Adv. Comput. Technol. (ICoACT), Sivalasi, India, 2025, pp. 1–6, doi: 10.1109/ICoACT63339.2025.11004948.

R. Premkumar, N. J. Kumar, L. M. Michael Visuwasam, G. Arjunan, A. Vinothini, and C. T. Kumar, "Hybrid gradient descent and sea lion optimization algorithm (H-GD-SLnO) to optimize task scheduling in fog computing environment," in Proc. 2025 Int. Conf. Adv. Comput. Technol. (ICoACT), Sivalasi, India, 2025, pp. 1–6, doi: 10.1109/ICoACT63339.2025.11005181.

K. Singh, L. M. M. Visuwasam, G. Rajasekaran, R. Regin, S. S. Rajest, and S. T., "Innovations in skeleton-based movement recognition bridging AI and human kinetics," in Advances in Computational Intelligence and Robotics, pp. 125–141, 2024.

S. A. Karthik, S. B. Naga, G. Satish, N. Shobha, H. K. Bhargav, and B. M. Chandrakala, “AI and IoT-infused urban connectivity for smart cities,” in Future of Digital Technology and AI in Social Sectors, D. Ertuğrul and A. Elçi, Eds. IGI Global Scientific Publishing, 2025, pp. 367–394. doi: 10.4018/979-8-3693-5533-6.ch013.

S. Rashmi, B. M. Chandrakala, D. M. Ramani, and M. S. Harsur, “CNN based multi-view classification and ROI segmentation: A survey,” Global Transitions Proceedings, vol. 3, no. 1, pp. 86–90, 2022. doi: 10.1016/j.gltp.2022.04.019.

K. S. N. S. Nischal, N. S. Guvvala, C. Mathew, G. C. S. Gowda, and B. M. Chandrakala, “A survey on recognition of handwritten ZIP codes in a postal sorting system,” International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 3, pp. 1–4, May 2020. [Online]. Available: https://www.academia.edu/download/64527939/IRJET-V7I3842.pdf

B. M. Chandrakala and S. C. Linga Reddy, “Proxy re-encryption using MLBC (Modified Lattice Based Cryptography),” in Proc. Int. Conf. Recent Advances in Energy-efficient Computing and Communication (ICRAECC), Nagercoil, India, 2019, pp. 1–5. doi: 10.1109/ICRAECC43874.2019.8995071.

H. S. Supriya and B. M. Chandrakala, “An efficient multi-layer hybrid neural network and optimized parameter enhancing approach for traffic prediction in Big Data Domain,” The Journal of Special Education, vol. 1, no. 43, pp. 94–96, 2022. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=13925369&AN=159790486

R. Sushmitha, A. K. Gupta, and B. M. Chandrakala, “Automated segmentation technique for detection of myocardial contours in cardiac MRI,” in Proc. Int. Conf. Communication and Electronics Systems (ICCES), Coimbatore, India, 2019, pp. 986–991. doi: 10.1109/ICCES45898.2019.9002554.

V. Hiremath, “Quantum Networking: Strategic Imperatives for Enterprises and Service Providers in the Emerging Quantum Era,” Journal of Computational Analysis and Applications (JoCAAA), vol. 31, no. 3, pp. 617–631, Dec. 2023.

V. Hiremath, “Real-Time BGP Monitoring with BMP and Streaming Telemetry,” International Journal of Environmental Science, vol. 11, no. 1s, pp. 1109–1115, Mar. 2025, doi: 10.64252/1keeap37.

K. Shanthala, B. M. Chandrakala, N. Shobha, and D. D., “Automated diagnosis of brain tumor classification and segmentation of MRI images,” in Proc. Int. Conf. Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), Bangalore, India, 2023, pp. 1–7. doi: 10.1109/IC-RVITM60032.2023.10435084.

B. M. Chandrakala et al., “Harnessing online activism and diversity tech in HR through cloud computing,” in Future of Digital Technology and AI in Social Sectors, D. Ç. Ertuğrul and A. Elçi, Eds. IGI Global Scientific Publishing, 2025, pp. 151–182. doi: 10.4018/979-8-3693-5533-6.ch006.

A. Navya and B. M. Chandrakala, “The effective dashboard to control the intrusion in the private protection of the cloudlet based on the medical mutual data using ECC,” in Proc. Int. Conf. Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2018, pp. 538–543. doi: 10.1109/ICIRCA.2018.8596783.

B. M. Chandrakala and S. C. Lingareddy, “Secure and efficient bi-directional proxy re-encryption technique,” in Proc. Int. Conf. Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, India, 2016, pp. 88–92. doi: 10.1109/ICCICCT.2016.7987923.

N. J. Maiti, S. Ganguly, K. Choowongkomon, S. Seetaha, S. Saehlee, and T. Aiebchun, "Synthesis, in vitro Anti-HIV-1RT evaluation, molecular modeling, DFT and acute oral toxicity studies of some benzotriazole derivatives," J. Struct. Biol., vol. 216, no. 2, p. 108094, 2024. doi: 10.1016/j.jsb.2024.108094

N. J. Maiti and S. Ganguly, "Synthesis, spectral analysis, antimicrobial evaluation, molecular modelling, DFT, TD‐DFT and SAR studies of novel 4,5,6,7‐tetrabromo‐1H‐benzo[d][1,2,3]triazole derivatives," ChemistrySelect, vol. 9, no. 36, p. e202401746, 2024.

N. J. Maiti and S. Ganguly, "Some new benzotriazole derivatives: Synthesis, antimycobacterial evaluation, antimicrobial efficacy, ADME studies, and molecular docking studies," Indian Journal of Heterocyclic Chemistry, vol. 33, no. 3, pp. 385–392, 2023.

N. J. Maiti, S. Ganguly, B. Sarkar, and R. Saha, "New benzotriazole derivatives: Synthesis, biological assessment, in vivo oral toxicity analysis, docking studies, molecular dynamics, and ADME profiling," Indian Journal of Heterocyclic Chemistry, vol. 33, no. 4, pp. 489–497, 2023.

N. J. Maiti, "A comprehensive review on analytical techniques for the quantification of pharmaceutical compounds in biological matrices," Journal of Cardiovascular Research, vol. 15, no. 9, 2024.

N. J. Maiti and S. Ganguly, "In silico studies of some novel benzotriazole derivatives against the NNIBP of HIV-1 RT," Journal of Pharmaceutical Chemistry, vol. 8, 2022.

N. J. Maiti, Al Rashid, Md Harun, and A. Banerjee, "The queen of herb with potent therapeutic constituent in various disease states: A reappraisal," International Journal of Phytomedicine, vol. 5, no. 2, pp. 125, 2013.

N. J. Maiti, G. N. K. Reddy, V. V. S. R. Prasad, and P. K. Maharana, "Development and validation of a stability indicating UPLC method for determination of moxifloxacin hydrochloride in pharmaceutical formulations," Pharm. Anal. Acta, vol. 2, no. 142, 2011.

N. J. Maiti, B. K. Sahoo, and N. Parwen, "Pharmacological and traditional uses of Paederia foetida Linn: A review," Int. J. Pharm. Eng., vol. 6, no. 4, pp. 839–844, 2018.

S. Kumar, “Challenges in higher education for sustainable development,” South Eastern European Journal of Public Health, vol. 26, no. s1, pp. 4194–4204, Feb. 2025, doi: 10.70135/seejph.vi.4777.

D. Soundararajan, "A novel deep learning framework for rainfall prediction in weather forecasting," Turk. J. Comput. Math. Educ. (TURCOMAT), vol. 12, no. 11, pp. 2685–2692, 2021.

T. B. Sivakumar, L. Maria Michael Visuvasam, V. Sangeetha, S. Bhuvana, K. S. Kumar, and K. Sachet, "Hybrid spotted hyena and simulated annealing optimization algorithm (HSHOSAA-1) for efficient task scheduling in a clustered cloud environment," in Proc. 2024 3rd Int. Conf. Smart Technol. Syst. Next Gener. Comput. (ICSTSN), Villupuram, India, 2024, pp. 1–6, doi: 10.1109/ICSTSN61422.2024.10670915.

L. M. M. Visuwasam and D. P. Raj, "NMA: integrating big data into a novel mobile application using knowledge extraction for big data analytics," Cluster Comput., vol. 22, no. S6, pp. 14287–14298, 2018, doi: 10.1007/s10586-018-2287-8.

L. M. M. Visuwasam and D. P. Raj, "A distributed intelligent mobile application for analyzing travel big data analytics," Peer-to-Peer Netw. Appl., vol. 13, no. 6, pp. 2036–2052, 2019, doi: 10.1007/s12083-019-00799-z.

L. M. M. Visuwasam and D. P. Raj, "Spatio temporal tourism tracking system based on adaptive convolutional neural network," Comput. Syst. Sci. Eng., vol. 45, no. 3, pp. 2435–2446, 2022, doi: 10.32604/csse.2023.024742.

L. M. M. Visuwasam, S. V. Deshmukh, N. R. Paul, M. a. M. Raja, S. Kanimozhi, and A. Thakare, "Security and data privacy systems concerns in IoT using consensus algorithm," Int. J. Syst. Syst. Eng., vol. 14, no. 6, pp. 654–675, 2024.

L. M. M. Visuwasam, K. Dhinakaran, G. Kalpana, A. Balakrishna, V. Kowsalyaa, and S. R. N. Keerthana, "SMART—stockpile management with analytical regulation technology," in Cognitive Science and Technology, pp. 835–845, 2022, doi: 10.1007/978-981-19-2350-0_79.

L. M. M. Visuwasam, M. Geetha, G. Gayathri, K. Divya, and D. Elakkiya, "Smart personalised recommendation system for wanderer using prediction analysis," Int. J. Intell. Sustain. Comput., vol. 1, no. 3, p. 223, 2021, doi: 10.1504/ijisc.2021.119078.

L. M. M. Visuwasam, A. K. Gupta, R. Chaudhary, S. C. Gupta, P. Borah, and M. K. Chakravarthi, "Innovative turned and collaborative technology using simulated IoT applications," in Proc. 2022 4th Int. Conf. Inventive Res. Comput. Appl. (ICIRCA), 2022, pp. 369–374.

L. M. M. Visuwasam, G. Kalpana, K. Dhinakaran, N. K. Kumar, and V. Manigandan, "Implementation of unusual human activity detection in warehouse using SSD," in Cognitive Science and Technology, pp. 847–857, 2022.

L. M. M. Visuwasam, D. Paulraj, G. Gayathri, K. Divya, S. Hariprasath, and A. Jayaprakashan, "Intelligent personal digital assistants and smart destination platform (SDP) for globetrotter," J. Comput. Theor. Nanosci., vol. 17, no. 5, pp. 2254–2260, 2020.

L. M. M. Visuwasam, M. Srinath, V. S. A. Raj, A. Sirajudeen, S. S. Maharaaja, and D. Raja, "Tourist behaviour analysis using data analytics," in Advances in Business Information Systems and Analytics, pp. 343–355, 2023, doi: 10.4018/979-8-3693-2193-5.ch023.

L. M. M. Visuwasam, S. Swaminathan, S. Rajalakshmi, and K. P. Kumar, "A hotspot framework for analyzing geolocated travel data using SPARK," Ann. Rom. Soc. Cell Biol., pp. 1956–1966, 2021.

L. M. M. Visuwasam, M. Srinath, V. S. Raj, A. Sirajudeen, S. Sudhir Maharaaja, and D. Raja, "Tourist behaviour analysis using data analytics," in S. Singh, S. Rajest, S. Hadoussa, A. Obaid, and R. Regin, Eds., Data-Driven Decision Making for Long-Term Business Success. IGI Global Scientific Publishing, 2024, pp. 343–355, doi: 10.4018/979-8-3693-2193-5.ch023.

N. J. Kumar, R. Premkumar, L. M. Michael Visuwasam, G. Arjunan, A. Shiny, and K. Dharani, "Adaptive optimization and resource allocation (AORA) model for IoT-edge computing using hybrid Newton-Raphson and dolphin echolocation algorithm (HNR-DEA) technique," in Proc. 2025 Int. Conf. Adv. Comput. Technol. (ICoACT), Sivalasi, India, 2025, pp. 1–6.

P. Das, D. Datta, S. S. Rajest, L. M. M. Visuwasam, A. Thakare, and J. Cypto, "Application of multi-criteria decision-making approach using TOPSIS to identify the vulnerable time zone of earthquake time series signal," Int. J. Crit. Comput.-Based Syst., vol. 11, no. 1/2, pp. 30–47, 2024.

K. V. Deshpande and J. Singh, “A systematic review on website phishing attack detection for online users,” Int. J. Image Graph., Jan. 2025, doi: 10.1142/s0219467827500136.

S. Jadhav-Mane and J. Singh, “Mango skin disease detection techniques based on machine learning: A review,” Wireless Pers. Commun., vol. 139, no. 4, pp. 1881–1904, Dec. 2024, doi: 10.1007/s11277-024-11677-0.

R. K. K, P. M, J. Singh, G. Surendra, S. M. Ali, and M. R. B, “BlockStream solutions: Enhancing cloud storage efficiency and transparency through blockchain technology,” Int. J. Electr. Electron. Eng., vol. 11, no. 7, pp. 134–147, Jul. 2024, doi: 10.14445/23488379/ijeee-v11i7p111.

P. Nasra et al., “Optimized ReXNet variants with spatial pyramid pooling, CoordAttention, and convolutional block attention module for money plant disease detection,” Discover Sustainability, vol. 6, no. 1, May 2025, doi: 10.1007/s43621-025-01241-6.

D. Jadhav and J. Singh, “A review on web information extraction and hidden predictive information from large databases,” Multimedia Tools and Applications, May 2025, doi: 10.1007/s11042-025-20863-6.

S. Devi, O. Yadav, S. Rani, J. Singh, C. Dhavale, and S. Khanvilkar, “Blockchain integration in crowdfunding: A smart contract-based approach to fundraising,” in Proc. 2025 7th Int. Conf. Comput. Intell. Commun. Technol. (CCICT), Apr. 2025, pp. 308–312, doi: 10.1109/ccict65753.2025.00055.

J. Singh, S. Rani, S. Devi, and J. Kaur, “A systematic study on recommendation system for e-commerce applications,” in Proc. 2025 7th Int. Conf. Comput. Intell. Commun. Technol. (CCICT), Apr. 2025, pp. 221–226, doi: 10.1109/ccict65753.2025.00043.

D. K. Arora et al., “An in vitro assessment of microleakage of pit and fissure sealants and restorative materials using dye penetration method,” Journal of Pharmacy and Bioallied Sciences, Feb. 2025, doi: 10.4103/jpbs.jpbs_1971_24.

R. Nagar et al., “In vitro analysis of compressive strength of three different aesthetic restorative materials,” Journal of Pharmacy and Bioallied Sciences, Feb. 2025, doi: 10.4103/jpbs.jpbs_1884_24.

N. Maiti et al., “Assessment of the efficacy of photobiomodulation (PBM) therapy in periodontal treatment: a longitudinal study,” Journal of Pharmacy and Bioallied Sciences, vol. 16, no. Suppl 3, pp. S2449–S2451, Jul. 2024, doi: 10.4103/jpbs.jpbs_286_24.

A. Vahora, R. Patel, B. Goradiya, and A. Desai, ‘Heart beat monitoring and wireless data logging using arm cortex A8’, International Journal on Recent and Innovation Trends in Computing and Communication, vol. 2, no. 8, pp. 2321–2325, 2014.

A. Vahora, B. Goradiya, D. Parikh, and A. Shah, ‘Designing a Model for Traffic Rule Violation at Railway Track Using Raspberry Pi in Indian Context’, International Journal of Latest Technology in Engineering,Management & Applied Science, vol. 6, no. 6, pp. 122–125, 2017.

A. Vahora and K. Pandya, ‘Implementation of cylindrical dielectric resonator antenna array for Wi-Fi/wireless LAN/satellite applications’, Progress in Electromagnetics Research M, vol. 90, pp. 157–166, 2020.

A. Vahora and K. Pandya, ‘Triple Band Dielectric Resonator Antenna Array Using Power Divider Network Technique for GPS Navigation/Bluetooth/Satellite Applications’, International Journal of Microwave and Optical Technology, vol. 15, no. 4, pp. 369–378, 2020.

A. Vahora and K. Pandya, ‘A miniaturized cylindrical dielectric resonator antenna array development for GPS/Wi-Fi/wireless LAN applications’, e-Prime-Advances in Electrical Engineering, Electronics and Energy, vol. 2, p. 100044, 2022.

A. Vahora and K. Pandya, ‘A Low-profile 4-element Circularly Polarized Hexagonal DRA Array for Triple-band Wireless Applications’, Advanced Electromagnetics, vol. 11, no. 4, pp. 90–97, 2022.

A. Vahora and M. Munsuri, ‘Smart Embedded System for Physiological Monitoring Using Machine Learning and Sensor Fusion’, Journal of Neonatal Surgery, vol. 14, no. 19s, pp. 694–703, 2025.

M. Fafolawala, Y. Mehta, and A. Vahora, ‘Agricultural Drones: Transforming Farming Practices with Advanced Technology’, International Journal Of Latest Technology In Engineering,Management & Applied Science, vol. 14, no. 4, pp. 877–882, 2025.

A. Vahora, M. Fafolawala, and Y. Mehta, ‘Federated Learning-Enabled Air Quality Monitoring System for Safe Driving in IoT-Integrated Vehicles’, International Journal of Environmental Sciences, vol. 11, no. 4s, pp. 715–723, 2025.

D. Sumathi and P. Poongodi, "Scheduling Based on Hybrid Particle Swarm Optimization with Cuckoo Search Algorithm in Cloud Environment," IIOAB Journal, vol. 7, no. 9, pp. 358-366, 2016.

D. Sumathi and P. Poongodi, "Secure medical information processing in cloud: Trust with swarm based scheduling," Journal of Medical Imaging and Health Informatics, vol. 6, no. 7, pp. 1636-1640, 2016.

D. Sumathi and P. Poongodi, "An improved scheduling strategy in cloud using trust based mechanism," Int. J. Comput. Electr. Autom. Control Inf. Eng, vol. 9, no. 2, pp. 637-641, 2015.

D. Sumathi, B. Melinamath, and R. Goyal, "Iov Traffic Prediction Utilizing Bidirectional Memory and Spatiotemporal Constraints with Local Search and NonLinear Analysis," Journal of Computational Analysis & Applications, vol. 33, no. 2, 2024.

D. Sumathi, A. Singh, A. Sinha, D. Aditya, and M. R. KF, "The Deepfake Dilemma: Enhancing Deepfake Detection with Vision Transformers," in 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, Jan. 2025, pp. 1-7.

V. B. Gowda, M. T. Gopalakrishna, J. Megha, and S. Mohankumar, “Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding,” Neural Netw., vol. 170, pp. 167–175, 2024.

V. B. Gowda, G. M. Thimmaiah, M. Jaishankar, and C. Y. Lokkondra, “Background-foreground segmentation using Multi-scale Attention Net (MA-Net): A deep learning approach,” Rev. Intell. Artif., vol. 37, no. 3, pp. 557–565, 2023, doi: 10.18280/ria.370304.

V. B. Gowda, M. G. Krishna, and J. Megha, “Dynamic Background Modeling and Foreground Detection using Orthogonal Projection onto the Subspace of Moving Objects,” in Proc. IC3, 2023, pp. 171–176.

V. B. Gowda, M. T. Gopalakrishna, J. Megha, and S. Mohankumar, “Background initialization in video data using singular value decomposition and robust principal component analysis,” Int. J. Comput. Appl., vol. 45, no. 9, pp. 600–609, 2023, doi: 10.1080/1206212X.2023.2258329.

A. K. Joshi and S. B. Kulkarni, “Flow analysis of vehicles on a lane using deep learning techniques,” J. Adv. Inf. Technol., vol. 14, no. 6, pp. 1354–1364, 2023.

A. K. Joshi, V. Shirol, S. Jogar, P. Naik, and A. Yaligar, “Credit card fraud detection using machine learning techniques,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 6, no. 3, pp. 436–442, 2020.

A. K. Joshi and S. B. Kulkarni, “Multi-modal information fusion for localization of emergency vehicles,” Int. J. Image Graph., vol. 24, no. 1, Art. no. 2550050, 2024.

A. K. Joshi and S. B. Kulkarni, “Multimodal deep learning information fusion for fine-grained traffic state estimation and intelligent traffic control,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 3, pp. 1020–1029, 2023.

V. S. A. Anala, A. R. Pothu, and S. Chintapalli, “Enhancing Preventive Healthcare with Wearable Health Technology for Early Intervention,” FMDB Transactions on Sustainable Health Science Letters., vol.2, no.4, pp. 211–220, 2024.

V. S. A. Anala and S. Chintapalli, “Scalable Data Partitioning Strategies for Efficient Query Optimization in Cloud Data Warehouses,” FMDB Transactions on Sustainable Computer Letters., vol. 2, no. 4, pp. 195–206, 2024.