UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming Gowtham Rajendiran, Jebakumar Rethnaraj, Shrikant Zade, Ramakrishna Guttula, Krishna Kant Pandey Scientific Reports, 2026 Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop- Lactuca Sativa (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model’s capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.
Lettuce Yield Prediction: ElasticNet Regression Model (ElNetRM) for Indoor Aeroponic Vertical Farming System Gowtham Rajendiran, Jebakumar Rethnaraj International Journal of Electrical and Computer Engineering Systems, 2025 Indoor aeroponic vertical farming systems have revolutionized agriculture by allowing efficient use of space and resources, eliminating the need for soil. These systems improve crop productivity and growth rates. However, accurately predicting lettuce yield in aeroponic environments remains a complex task due to the intricate interactions between environmental, nutrient, and growth parameters. This work aims to address these issues by integrating advanced sensor technologies with ElasticNet Regression Model (ElNetRM) for its hybrid L1 and L2 regularization capabilities, handling multicollinearity and feature selection problems effectively in order to develop a reliable yield prediction framework. The predictive results showcases that the ElNetRM model forecasts lettuce yield with high accuracy of 92% and less error score (RMSE) of 2.28 using a comprehensive dataset from a sensor-equipped indoor aeroponic system. Also, the results demonstrate the superior predictive power of ElNetRM in capturing complex variable relationships, enhancing yield prediction reliability.
Cognitive Large Language Model in Social Media with Local Memory Vigneshwaran N, A. Vijayaraj, V. P. Murugan, R. Jebakumar, N. Mageshkumar, R. Megavannan 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 Enter the era of social media reimagined! This research taps into an interesting combination of advanced Artificial Intelligence, with large language models such as ChatGPT and GEMINI your favorite social platform. Personal information of each user is armed with voice commands and LLM. But this is the first. LLM talk to each other, exchange information. face recognition adds an extra layer in the mass layer the excitement of your Chatbot sharing only the right information Your account security level private, reserved or public. When it comes to security. We got you covered Choose between playing low key or just sharing just the right number of juicy details. Plus, all the classic features It's still fun, like scrolls and scrolls.
Machine Learning-powered Job Offer Verification Vigneshwaran N, A. Vijayaraj, V.P. Murugan, Jebakumar Jebakumar, N. Mageshkumar, R. Megavannan 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 In the modern world, technological developments and inventions have given us the ability to handle practically each aspect of our lives, including keeping track of money, education, job appearances, and security. But this reliance on technology has also made it easier for con artists to defraud people and gain quick money. Fake work notices are a specific approach that has recently become more common. Unsuspecting people submit applications for these fake job openings, handing over their personal information and paying the application fees to the con artists as though they were going to fall for the fraud and waste their hard-earned money. In this paper, the Natural Language Processing (NLP) approach is used to identify bogus job postings. We collected relevant information from job advertisements, after that we applied the random forest classifier to train and evaluate our model. The results we obtained show that our method can recognize fake job advertisements with a 97% accuracy rate.
An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks Vijayaraj Alwarsamy, Jebakumar Rethnaraj, Uma Devi Gurumuni Nathan, Gururama Senthilvel Pandiarajan International Journal of Communication Systems, 2024 SummaryRecently, wireless sensor networks (WSNs) have been used for monitoring, sensing, processing, and communication purposes in real‐time applications. It is employed with a routing protocol that performs an effective data transmission process. However, while transmitting large data, there occurs an over fitting issue, which leads to determining a huge data leakage. Also, the delay is increased with heavy congestion in the network. Hence, a novel method is proposed to diminish the network congestion regarding distributed networks as well as cloud edge computing. Moreover, it diminished the data loss from an overloaded condition. However, the proposed technique controls congestion that resists the traffic in the network through lightweight, ultra‐dense label‐less federation and incorporates adaptive multi‐agent Markov reinforcement learning. Furthermore, a distributed energy‐efficient delay‐aware routing protocol is employed to analyze and regulate congestion control in the network. Also, it varies the network dynamically by adjusting the routing protocol that optimizes the congestion and implements the traffic mechanism. Moreover, the congestion in WSNs overwhelms the nodes and channels distributed in the packets. The evaluation of the proposed method is determined by various metrics such as queuing delay, network lifetime, energy efficiency, throughput, and packet delivery ratio. The experimental results revealed that the proposed method attained an enhanced performance by maximizing energy efficiency and packet delivery ratio by 94% as well as 89% and reducing the delay by 55%, respectively.
IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques Gowtham Rajendiran, Jebakumar Rethnaraj Using Traditional Design Methods to Enhance AI Driven Decision Making, 2024 Precision agriculture driven by the integration of the advanced technologies like internet of things (IoT) and machine learning (ML) is revolutionary precision agriculture, especially the indoor farming techniques. This chapter explores the comprehensive application of IoT and ML in automating indoor cultivation practices, examining their diverse benefits and practical uses in comparison with the traditional farming methodologies. IoT enables the indoor farmers to create controlled environments through interconnected sensors, monitoring crucial variables but not limited to temperature, humidity, and light intensity. Complemented by ML algorithms, data analysis becomes efficient, providing predictive models for crop growth, pest detection, and disease outbreaks. Automated environment climate control systems optimize resource utilization, while precision irrigation minimizes water usage. Real-time monitoring and early detection of plant health issues reduce crop losses, ensuring high-quality produce.
Enhanced CNN Model for Lettuce Disease Identification in Indoor Aeroponic Vertical Farming Systems Gowtham Rajendiran, Jebakumar Rethnaraj, Janani Malaisamy 4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024 India, like many other nations, heavily relies on agricultural exports to fund its national budget. Economic losses may occur when crop and plant diseases reduce production quality and quantity. Hence, it is crucial to detect infections early on in crop farming. Commercial crops grown using aeroponic vertical farming techniques include lettuce in particular. To increase the crop yield, the crop should be free from disease-causing agents like viral, bacterial, or fungal. As a response, scientists have utilized deep learning algorithms for the automatic identification of abnormalities in both indoor and outdoor farming crops by capturing leaf images. In this work, fine-tuned convolutional neural networks (CNN) were utilized to identify diseases in the aeroponic lettuce crops in an effective manner. Further, the Convolutional Neural Networks (CNN) were fine-tuned by training them on lettuce images. While testing, a higher accuracy of 95.6258% was achieved. Hence, this research work signifies that the proposed model would be appropriate for lettuce disease classification in controlled environment agriculture.
A Sentimental Analysis Approach for Personalized Drug Recommendations Using Machine Learning A. Vijayaraj, V.P. Murugan, R. Jebakumar, Gaurav NV, Santhosh V, Shai Kumar R Iccds 2024 International Conference on Computing and Data Science, 2024 Since the virus that causes corona disease was discovered, it has gotten more difficult to find medical professionals with the appropriate licenses, including doctors, nurses, diagnostic equipment, and medications. Due to the intense grieving, a substantial number of people in the medical industry pass away. Because of the scarcity, a lot of individuals began self-medicating without first consulting a doctor, which made the health problem worse. There are numerous well-established applications for machine learning, and recent years have witnessed a growth in the pace and breadth of computerization-related research and development. The goal of this research is to develop a method for recommending medications that might greatly reduce the obligations of specialists. In this work, we built a drug recommendation system that makes estimations about the emotional of patient reviews using a variety of different vectorization techniques, such as using a Bow, The TF-ID Word 2 Vector, or Manually Feature Analysis. The predicted emotions were graded using accuracy, remembrance, flscore, exactness, and the area beneath the slope (AUC). The results show that, when weighed against the other models, the classification technique Linear SVC + The TF -I vector process has the greatest accuracy.
Selection of crop varieties and yield prediction based on phenotype applying deep learning Iniyan Shanmugam, Jebakumar Rethnaraj, Gayathri Mani International Journal of Electrical and Computer Engineering, 2023 <div align="center"><span lang="EN-US">In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.</span></div>
Sustainable agriculture practices: Dealing with innovative plant disease management technologies in challenging farming environments Advances in Engineering Research, 2023
Deep Learning Image Classification for Fashion Design A. Vijayaraj, P. T. Vasanth Raj, R. Jebakumar, P. Gururama Senthilvel, N. Kumar, R. Suresh Kumar, R. Dhanagopal Wireless Communications and Mobile Computing, 2022
Learning sparse representation for human action recognition using probabilistic neural network Journal of Advanced Research in Dynamical and Control Systems, 2019
Knowledge base data mining system for newborn screening International Journal of Control Theory and Applications, 2016
Service recommendation system in social networks Arpn Journal of Engineering and Applied Sciences, 2015
E-Learning application using gesture recognition International Journal of Applied Engineering Research, 2015
Cooperative LoadBalancing and frequency reuse for dynamic channelallocationin MANETS International Journal of Applied Engineering Research, 2015
Inline deduplication approach for secure file transactions in hybrid cloud International Journal of Applied Engineering Research, 2015
Distributed data storage through secure routing in wireless sensor networks International Review on Computers and Software, 2014
Multi-level reliable data aggregation in wireless sensor network International Journal of Engineering and Technology, 2013
RECENT SCHOLAR PUBLICATIONS
UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming G Rajendiran, J Rethnaraj, S Zade, R Guttula, KK Pandey Scientific Reports , 2026 2026
Lettuce Yield Prediction: ElasticNet Regression Model (ElNetRM) for Indoor Aeroponic Vertical Farming System G Rajendiran, J Rethnaraj International journal of electrical and computer engineering systems 16 (9 … , 2025 2025 Citations: 3
Machine Learning-powered Job Offer Verification N Vigneshwaran, A Vijayaraj, VP Murugan, J Jebakumar, ... 2025 International Conference on Data Science, Agents & Artificial … , 2025 2025 Citations: 3
Cognitive Large Language Model in Social Media with Local Memory N Vigneshwaran, A Vijayaraj, VP Murugan, R Jebakumar, ... 2025 International Conference on Data Science, Agents & Artificial … , 2025 2025 Citations: 1
Enhanced CNN model for lettuce disease identification in indoor aeroponic vertical farming systems G Rajendiran, J Rethnaraj, J Malaisamy 2024 4th International Conference on Sustainable Expert Systems (ICSES … , 2024 2024 Citations: 3
Prediction Of The Crime Patterns In Big Data By Using The Deep Learning Based Hybrid Descent Gradient-Vgg Classification R Brindha, M Thillaikarasi, R Jebakumar Rare Metal Materials and Engineering 53 (9), 1-13 , 2024 2024
Disease Segmentation in Groundnut Crop J Malaisamy, J Rethnaraj Innovations and Advances in Cognitive Systems: ICIACS 2024, Volume 1 1, 242 , 2024 2024
An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks V Alwarsamy, J Rethnaraj, UD Gurumuni Nathan, GS Pandiarajan International Journal of Communication Systems 37 (10), e5779 , 2024 2024 Citations: 11
Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models. G Rajendiran, J Rethnaraj Revue d'Intelligence Artificielle 38 (3) , 2024 2024 Citations: 12
Disease Segmentation in Groundnut Crop Leaves Using Image Processing Techniques J Malaisamy, J Rethnaraj International Conference on Innovations and Advances in Cognitive Systems … , 2024 2024 Citations: 1
A sentimental analysis approach for personalized drug recommendations using machine learning A Vijayaraj, VP Murugan, R Jebakumar, S Manickam, P Kumar 2024 International conference on computing and data science (ICCDS), 1-6 , 2024 2024 Citations: 5
Sustainable agriculture practices: dealing with innovative plant disease management technologies in challenging farming environments G Rajendiran, J Rethnaraj Electrical resistance-based measurements as fatigue damage Indic. metals 121 , 2024 2024 Citations: 4
IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques G Rajendiran, J Rethnaraj Using traditional design methods to enhance AI-driven decision making, 289-317 , 2024 2024 Citations: 15
Selection of crop varieties and yield prediction based on phenotype applying deep learning I Shanmugam, J Rethnaraj, G Mani International Journal of Electrical and Computer Engineering 13 (6), 6806-6816 , 2023 2023 Citations: 5
Image to Audio Captioning for the Visually Impaired R Sriram, R Haralalka 2023 International Conference on Recent Advances in Science and Engineering … , 2023 2023 Citations: 2
Organic and Recyclable Waste Classification Using Integrated Feature Selection Method A Vijayaraj, S Shreya, G Deepana, PG Senthilvel, R Jebakumar 2023 International Conference on Research Methodologies in Knowledge … , 2023 2023 Citations: 1
Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield. G Rajendiran, J Rethnaraj International Journal of Intelligent Engineering & Systems 16 (5) , 2023 2023 Citations: 21
Lettuce crop yield prediction analysis using random forest regression machine learning model in aeroponics system G Rajendiran, J Rethnaraj 2023 Second International Conference on Augmented Intelligence and … , 2023 2023 Citations: 10
A Machine Learning Approach for Aeroponic Lettuce Crop Growth R Gowtham, R Jebakumar Intelligent Sustainable Systems: Proceedings of ICISS 2023, 99 , 2023 2023
Plant disease identification and detection using support vector machines and artificial neural networks S Iniyan, R Jebakumar, P Mangalraj, M Mohit, A Nanda Artificial intelligence and evolutionary computations in engineering systems … , 2020 2020 Citations: 69
Mutual information feature selection (MIFS) based crop yield prediction on corn and soybean crops using multilayer stacked ensemble regression (MSER) S Iniyan, R Jebakumar Wireless Personal Communications 126 (3), 1935-1964 , 2022 2022 Citations: 60
Object Recommendation based Friendship Selection (ORFS) for navigating smarter social objects in SIoT S Rajendran, R Jebakumar Microprocessors and Microsystems 80, 103358 , 2021 2021 Citations: 54
Detection and classification of groundnut leaf nutrient level extraction in RGB images M Janani, R Jebakumar Advances in Engineering Software 175, 103320 , 2023 2023 Citations: 50
Deep learning image classification for fashion design A Vijayaraj, PT Vasanth Raj, R Jebakumar, P Gururama Senthilvel, ... Wireless Communications and Mobile Computing 2022 (1), 7549397 , 2022 2022 Citations: 48
People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain M Arulprakash, R Jebakumar The Journal of Supercomputing 77 (11), 12582-12608 , 2021 2021 Citations: 37
SSD based waste separation in smart garbage using augmented clustering NMS M Karthikeyan, TS Subashini, R Jebakumar Automated Software Engineering 28 (2), 17 , 2021 2021 Citations: 31
RETRACTED ARTICLE: Sentiment topic sarcasm mixture model to distinguish sarcasm prevalent topics based on the sentiment bearing words in the tweets K Nimala, R Jebakumar, M Saravanan Journal of Ambient Intelligence and Humanized Computing 12 (6), 6801-6810 , 2021 2021 Citations: 27
RETRACTED ARTICLE: Sentiment topic emotion model on students feedback for educational benefits and practices K Nimala, R Jebakumar Behaviour & Information Technology 40 (3), 311-319 , 2021 2021 Citations: 25
A study on smart irrigation using machine learning M Janani, R Jebakumar Cell & Cellular Life Sciences Journal 4 (1), 1-8 , 2019 2019 Citations: 24
Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield. G Rajendiran, J Rethnaraj International Journal of Intelligent Engineering & Systems 16 (5) , 2023 2023 Citations: 21
Retracted: Automatic image captioning using convolution neural networks and lstm R Subash, R Jebakumar, Y Kamdar, N Bhatt Journal of Physics: Conference Series 1362 (1), 012096 , 2019 2019 Citations: 17
Commit-reveal strategy to increase the transaction confidentiality in order to counter the issue of front running in blockchain M Arulprakash, R Jebakumar AIP Conference Proceedings 2460 (1), 020016 , 2022 2022 Citations: 16
A robust user sentiment biterm topic mixture model based on user aggregation strategy to avoid data sparsity for short text K Nimala, R Jebakumar Journal of Medical Systems 43 (4), 1-13 , 2019 2019 Citations: 16
IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques G Rajendiran, J Rethnaraj Using traditional design methods to enhance AI-driven decision making, 289-317 , 2024 2024 Citations: 15
Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models. G Rajendiran, J Rethnaraj Revue d'Intelligence Artificielle 38 (3) , 2024 2024 Citations: 12
A machine learning approach for aeroponic lettuce crop growth monitoring system R Gowtham, R Jebakumar International Conference on Intelligent Sustainable Systems, 99-116 , 2023 2023 Citations: 12
Towards developing a block chain based advanced Data Security-Reward Model (DSecCS) in mobile crowd sensing networks M Arulprakash, R Jebakumar Egyptian Informatics Journal 23 (3), 405-415 , 2022 2022 Citations: 12
An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks V Alwarsamy, J Rethnaraj, UD Gurumuni Nathan, GS Pandiarajan International Journal of Communication Systems 37 (10), e5779 , 2024 2024 Citations: 11
Friendliness based trustworthy relationship management (f-trm) in social internet of things S Rajendran, R Jebakumar Wireless Personal Communications 123 (3), 2625-2647 , 2022 2022 Citations: 11
