Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning R. Rathiya, M. Kalamani, R. P. Narmadha, L. Sreenivasa Perumal, R. Kalpana Smart Factories for Industry 5 0 Transformation, 2025 To assess a child's early risk of developing schizophrenia. By utilizing complete data, which includes child IDs, age, gender, migraine status, BPRS rating, drug use (including conventional neuroleptics), and EEG data, the study aims to increase prediction accuracy. The study demonstrates that the recommended technique is effective in accurately and reliably identifying individuals who are at-risk by conducting comprehensive investigations on a variety of sample populations. By focusing on particular symptoms and offering prompt support, this technique could improve the early identification and treatment for childhood schizophrenia. Through the use of targeted interventions, symptoms can be addressed quickly, which may slow the disorder's course. Early detection improves impacted children's long-term outcomes by facilitating access to suitable therapies and support resources. This proactive strategy highlights the significance of early detection in children's mental health by lessening the effect of schizophrenia on their development and general well-being.
IoT-based Real-Time Landslide Monitoring System using STM32 Microcontroller 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Smart Shopping Cart using RFID Technology for Automated Billing and Enhanced Retail Experience Rathiya R, Madhumitha S, Elakiya VL, Bharath Kumar P, Chithirika K S, Archana R Proceedings of the 9th International Conference on Electronics Communication and Aerospace Technology Iceca 2025, 2025 Automation technologies are creating a massive change in how people shop in the retail setting. Conventional retailing relationships tend to experience lengthy queues, handheld barcode scanning, billing mistakes and efficiency. To overcome the challenges, it is proposed, and assessed that a Smart Shopping Cart system based on Radio Frequency Identification (RFID) technology be developed. According to this system, RFID readers are installed on the cart and each goods is provided with an RFID tag to allow the detection of the products and generation of the money automatically and on the spot. Experimental findings prove that the Smart Cart will decrease the billing time (by approximately 110 seconds to 33 seconds in case of 20 items), decrease the error rates (8 percent to 3 percent in manual entry and barcode scanning to 1 percent, respectively), and increase the billing accuracy percent to 99 percent. Measures of customers satisfaction such as speed, convenience, accuracy, and ease of use also show that this better than the time-honored method of checkout. The system does not only reduce the human factor and enhances user experience, but also streamlines the operations of the store because it minimizes the dependency of cashiers as well as delays in transactions. These findings confirm the practicability of the application of RFID-based smart carts to supermarkets and retail chains as more efficient, accurate and customer-friendly solution to retail.
IoT-Enabled Smart Healthcare Monitoring: A Low-Cost Real-Time Solution for Vital Signs Tracking Rathiya R, Sreenivasa Perumal L, Anuja T, Srihari S, Sanjay K, Swetha M Proceedings of the 9th International Conference on Electronics Communication and Aerospace Technology Iceca 2025, 2025 The rising need to keep a constant track of patients and identify the presence of health peculiarities early is the factor that has driven the creation of the automated and affordable healthcare systems. The paper will discuss the design and development of an IoT-based Smart Healthcare Monitoring System, where essential vital parameters, such as heart rate, body temperature, and blood oxygen saturation (SpO 2), are measured in real time. The system utilizes pulse sensor, DHT11, and MAX30100 coupled with an STM32 Nucleo microcontroller to capture accurate data and have consistent performance. The sensor results are sent through Wi-Fi and visualized with the help of the Blynk web dashboard which also sends automated notification about abnormal conditions. Experimental evidence showed that it had high measurement accuracy (heart rate 98.5%, temperature 98.2%, SpO 2 97.8%), quick response time (less than 2 seconds) and excellent uptime (99.1%), which is better than conventional low cost monitoring systems. The proposed solution minimized the workload of healthcare staff and also reduced the time spent on diagnosis compared to manual observation. This is due to its small size, low cost, and scalability, which may be adjusted to fit hospital wards, clinic environments, and home-based care, therefore, providing an effective and trustworthy solution to patient safety and healthcare provision.
Deep Vision Analysis: Unlocking Glaucoma Insights through Analytical Deep Learning in Retinal Fungus Imagery 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Intelligent Nutrient Management System Ensuring Sustainable Farming, Soil Vitality, and Eco-Conscious Crop Growth Rathiya R, Bharathi R, Deepak G, Logeshwari R, Gobi Praveen S, Rohith Kumar S Proceedings of the 6th International Conference on Inventive Research in Computing Applications Icirca 2025, 2025 The intention of this task is to create a mobile app which is able to deliver personalized organic fertilizer recommendation much more accurate and with fewer limitations than the generalization of inorganic fertilizer from the previous systems. The app is a very good application that farmers can use to put in their farm details, e.g. soil type, crops, and exact location for customized fertilizer suggestions according to the requirement of individual. The fertilizer application is equipped with a reminder system i.e. three days prior notifications have been sent in time to manage nutrients. The app has undergone thorough testing and been certified as accurately delivering information. The app is capable of acting as the decision-making assistant to the fertilizer sensor-carrying and ill-informed farmers. The Fertilizer Finder app is a technological approach to eco-friendly farming that allows the farmers to increase their efficiency by using less of the resources and thus, make it possible for sustainable agriculture. This signifies the capacity for further advancement in the fields of precision agriculture and resource recovery.
Advanced Computational Models for Mulberry Leaf Disease Identification 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Convolutional neural network-based strategies for efficient content-based image retrieval Chinnathambi Kamatchi, Rathiya Rajendran, Kopperundevi Nagarajan, Brinda Palanisamy, Deepika Jeyabalan, Rama Subramanian Paperananthamurugesan Indonesian Journal of Electrical Engineering and Computer Science, 2025 Recent years have seen a meteoric rise in the usage of enormous image databases due to advancements in multimedia technologies. One of the most critical technologies for image processing nowadays is image retrieval. This study uses convolutional neural networks (CNNs) for content-based image retrieval (CBIR). With the ever-growing number of digital photos, practical methods for retrieving these images are crucial. CNNs are incredibly efficient in many computer vision applications. Improving the efficacy and precision of image retrieval systems is the primary goal of our research into using deep learning. The paper starts with a thorough analysis of the current state of CBIR methods and the difficulties they face. Afterwards, it explores CNN’s design and operation, focusing on CNN’s capacity to learn hierarchical features from images autonomously. This paper also looks at how the model performs when it alters its hyperparameters, transfer learning techniques, and CNN topologies. The insights obtained from these experiments enhance the comprehension of the elements impacting CNN effectiveness in CBIR. Finally, our study shows that CNNs can change the game for image search by transforming CBIR systems. This research adds to the expanding body of information about using cutting-edge deep learning algorithms to make image retrieval more efficient and accurate.
Ensemble-based Deep Learning Framework for Tuberculosis Detection in Radiographs Shameem Sulthana SH, Rathiya R, J. Partha Sarathy, Gudipudi Kishore, Srimathi S, B. Jegajothi 3rd International Conference on Electronics and Renewable Systems Icears 2025 Proceedings, 2025 Tuberculosis (TB) continues to be a serious worldwide health concern, and in order to effectively treat it, it is necessary to diagnose it accurately and in a timely manner. The purpose of this research is to provide a robust framework for tuberculosis diagnosis that makes use of a deep learning-based model called DenseNet201 in conjunction with a stacking ensemble method in order to improve diagnostic accuracy from chest X-ray pictures. Deep features are extracted in an effective manner by the DenseNet201 architecture, which captures intricate patterns that are suggestive of tuberculosis. Support Vector Machine (SVM), Random Forest (RF), and XGBoost are the three base classifiers that receive these features as input. The outputs of these classifiers are then aggregated with a meta-classifier to get the final prediction. This helps to further improve the performance of the prediction. This stacking strategy helps to minimize the flaws of individual classifiers, hence minimizing the number of false positives and negatives seen in medical diagnostics, which is an extremely important skill. A high accuracy of 95.3% and an area under the curve (AUC) of 0.973 are demonstrated by the model after it has been trained and assessed on the TBX11K dataset. This greatly outperforms the performance of older approaches. In order to increase the quality of the data and better the performance of the model, preprocessing processes were used. These stages included normalization, data augmentation, and contrast enhancement. Due to the fact that the suggested ensemble model generalizes well to data that has not been encountered before, it is a trustworthy and effective instrument for early and accurate tuberculosis screening. This method has the potential to be implemented in clinical settings that are representative of the real world in order to provide assistance to medical professionals in the diagnosis of tuberculosis.
GreenScan Smart Farming Solution for Accurate Diagnosis of Potato Foliage Diseases 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Skin Cancer Diseases Detection using Deep Learning 14th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2023, 2023
Implementation of Secured Online Voting System using Blockchain Technology 14th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2023, 2023
Smart Shopping Cart using RFID Technology for Automated Billing and Enhanced Retail Experience R Rathiya, S Madhumitha, VL Elakiya, P Bharath Kumar, KS Chithirika, ... 2025 9th International Conference on Electronics, Communication and … , 2025 2025.0
IoT-Enabled Smart Healthcare Monitoring: A Low-Cost Real-Time Solution for Vital Signs Tracking R Rathiya, T Anuja, S Srihari, K Sanjay, M Swetha 2025 9th International Conference on Electronics, Communication and … , 2025 2025.0
Intelligent Nutrient Management System Ensuring Sustainable Farming, Soil Vitality, and Eco-Conscious Crop Growth R Rathiya, R Bharathi, G Deepak, R Logeshwari, S Gobi Praveen, ... 2025 6th International Conference on Inventive Research in Computing … , 2025 2025.0
Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning R Rathiya, M Kalamani, RP Narmadha, L Sreenivasa Perumal, R Kalpana Smart Factories for Industry 5.0 Transformation, 269-281 , 2025 2025.0
Ensemble-based Deep Learning Framework for Tuberculosis Detection in Radiographs SS SH, R Rathiya, JP Sarathy, G Kishore, S Srimathi, B Jegajothi 2025 International Conference on Electronics and Renewable Systems (ICEARS … , 2025 2025.0
Machine Learning-Based Detection R Rathiya, LS Perumal, R Ramya, R Krithika, S Devatharshini, ... Innovations in Cybersecurity and Data Science: Proceedings of ICICDS 2024, 133 , 2024 2024.0
Enhanced Sarcasm Detection using Grey Wolf Optimizer with Deep Learning on Social Media M Manimaraboopathy, D Rajeshwari, R Rathiya, CBS Lakshmi, ... 2024 Second International Conference on Intelligent Cyber Physical Systems … , 2024 2024.0 Citations: 2
A Dynamic Strategy Selection Module For Anomaly Detection Wireless Sdns Based On Semisupervised Learning. R Nuthakki, D Mendhe, SN Ghate, SBG Babu Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024 2024.0
Multi-Task Learning for Tomato Crop Disease Detection and Severity Estimation using CNN Framework P Raghul, A Kavitha, S Daniel Madan Raja, R Rathiya, CS Krithik, ... 2024 International Conference on Advances in Data Engineering and … , 2024 2024.0 Citations: 1
Early Detection of Fetal Brain Abnormalities using CNN Framework D Monisha, R Ramya, R Rathiya, T Rajkumar, K Divyabharathi, ... 2024 International Conference on Cognitive Robotics and Intelligent Systems … , 2024 2024.0 Citations: 8
Machine Learning-Based Detection of Children at Risks for Mental Disorders R Rathiya, LS Perumal, R Ramya, R Krithika, S Devatharshini, ... International Conference on Innovations in Cybersecurity and Data Science … , 2024 2024.0
Implementation of a deep learning framework for intelligent intrusion detection in internet of things networks S Jeyapriyanga, CN Ravi, R Rathiya, K Kalaivani, KR Kumar 2023 5th International Conference on Inventive Research in Computing … , 2023 2023.0 Citations: 6
Cosmetic suggestion based on skin condition using artificial intelligence A Kavitha, R Rathiya, T Rajkumar, S Abinaya, M Rajasri 2023 Second International Conference on Electronics and Renewable Systems … , 2023 2023.0 Citations: 6
IoT based energy efficient using wireless sensor network application to smart agriculture R Deepa, M Sankar, C Sankari, R Kalaivani 2023 International Conference on Intelligent Data Communication Technologies … , 2023 2023.0 Citations: 13
Detecting Network Anomalies and Visualization R Rathiya Journal of Advancement in Parallel Computing 1 (1), 1-3 , 2018 2018.0
Efficient QoS oriented vertical handoff scheme in the integration of WiMAX/WLAN networks R Rathiya, A Anitha, J Jayakumari 2013 IEEE Conference on Information & Communication Technologies, 378-381 , 2013 2013.0 Citations: 7
A HYBRID CSBHC-BASED METAHEURISTIC FRAMEWORK FOR ENERGY-BALANCED WIRELESS SENSOR NETWORKS NS Kavitha, R Rathiya, M Sakthivel
Convolutional neural network-based strategies for efficient content-based image retrieval RSP Chinnathambi Kamatchi , Rathiya Rajendran , Kopperundevi Nagarajan ... Indonesian Journal of Electrical Engineering and Computer Science 37 (ISSN … , 0 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
IoT based energy efficient using wireless sensor network application to smart agriculture R Deepa, M Sankar, C Sankari, R Kalaivani 2023 International Conference on Intelligent Data Communication Technologies … , 2023 2023.0 Citations: 13
Early Detection of Fetal Brain Abnormalities using CNN Framework D Monisha, R Ramya, R Rathiya, T Rajkumar, K Divyabharathi, ... 2024 International Conference on Cognitive Robotics and Intelligent Systems … , 2024 2024.0 Citations: 8
Efficient QoS oriented vertical handoff scheme in the integration of WiMAX/WLAN networks R Rathiya, A Anitha, J Jayakumari 2013 IEEE Conference on Information & Communication Technologies, 378-381 , 2013 2013.0 Citations: 7
Implementation of a deep learning framework for intelligent intrusion detection in internet of things networks S Jeyapriyanga, CN Ravi, R Rathiya, K Kalaivani, KR Kumar 2023 5th International Conference on Inventive Research in Computing … , 2023 2023.0 Citations: 6
Cosmetic suggestion based on skin condition using artificial intelligence A Kavitha, R Rathiya, T Rajkumar, S Abinaya, M Rajasri 2023 Second International Conference on Electronics and Renewable Systems … , 2023 2023.0 Citations: 6
Enhanced Sarcasm Detection using Grey Wolf Optimizer with Deep Learning on Social Media M Manimaraboopathy, D Rajeshwari, R Rathiya, CBS Lakshmi, ... 2024 Second International Conference on Intelligent Cyber Physical Systems … , 2024 2024.0 Citations: 2
Convolutional neural network-based strategies for efficient content-based image retrieval RSP Chinnathambi Kamatchi , Rathiya Rajendran , Kopperundevi Nagarajan ... Indonesian Journal of Electrical Engineering and Computer Science 37 (ISSN … , 0 Citations: 2
Multi-Task Learning for Tomato Crop Disease Detection and Severity Estimation using CNN Framework P Raghul, A Kavitha, S Daniel Madan Raja, R Rathiya, CS Krithik, ... 2024 International Conference on Advances in Data Engineering and … , 2024 2024.0 Citations: 1
Smart Shopping Cart using RFID Technology for Automated Billing and Enhanced Retail Experience R Rathiya, S Madhumitha, VL Elakiya, P Bharath Kumar, KS Chithirika, ... 2025 9th International Conference on Electronics, Communication and … , 2025 2025.0
IoT-Enabled Smart Healthcare Monitoring: A Low-Cost Real-Time Solution for Vital Signs Tracking R Rathiya, T Anuja, S Srihari, K Sanjay, M Swetha 2025 9th International Conference on Electronics, Communication and … , 2025 2025.0
Intelligent Nutrient Management System Ensuring Sustainable Farming, Soil Vitality, and Eco-Conscious Crop Growth R Rathiya, R Bharathi, G Deepak, R Logeshwari, S Gobi Praveen, ... 2025 6th International Conference on Inventive Research in Computing … , 2025 2025.0
Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning R Rathiya, M Kalamani, RP Narmadha, L Sreenivasa Perumal, R Kalpana Smart Factories for Industry 5.0 Transformation, 269-281 , 2025 2025.0
Ensemble-based Deep Learning Framework for Tuberculosis Detection in Radiographs SS SH, R Rathiya, JP Sarathy, G Kishore, S Srimathi, B Jegajothi 2025 International Conference on Electronics and Renewable Systems (ICEARS … , 2025 2025.0
Machine Learning-Based Detection R Rathiya, LS Perumal, R Ramya, R Krithika, S Devatharshini, ... Innovations in Cybersecurity and Data Science: Proceedings of ICICDS 2024, 133 , 2024 2024.0
A Dynamic Strategy Selection Module For Anomaly Detection Wireless Sdns Based On Semisupervised Learning. R Nuthakki, D Mendhe, SN Ghate, SBG Babu Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024 2024.0
Machine Learning-Based Detection of Children at Risks for Mental Disorders R Rathiya, LS Perumal, R Ramya, R Krithika, S Devatharshini, ... International Conference on Innovations in Cybersecurity and Data Science … , 2024 2024.0
Detecting Network Anomalies and Visualization R Rathiya Journal of Advancement in Parallel Computing 1 (1), 1-3 , 2018 2018.0
A HYBRID CSBHC-BASED METAHEURISTIC FRAMEWORK FOR ENERGY-BALANCED WIRELESS SENSOR NETWORKS NS Kavitha, R Rathiya, M Sakthivel
