Dr.Vanajaroselin Chirchi
@theoxfordengg.org
Professor and Department of Information Science & Engineering
The Oxford College of Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Multidisciplinary, Biomedical Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Vanajaroselin Chirchi, Emmanvelraj Chirchi, E C Khushi, S M Bairavi, and K S Indu
IEEE
The proposed method focuses on the detection of Escherichia coli bacterial contamination in water supplies. The proposed method uses a photonic-based sensor to examine photonic crystals in order to identify bacterial contamination in water. The system accurately predicts and diagnoses water contamination caused by bacteria by integrating an optical sensor with a graphical user interface (GUI). By attentively scrutinizing the early sensor-generated output graphs, we are able to discover variations in drainage patterns that are signaling the presence of bacteria in water samples. These graphs offer crucial insights into the unique characteristics of many samples, such as their refractive index. The sensor’s sensitivity, specificity, and accuracy have all been thoroughly evaluated, and its results have been compared to those of established reference methods. A 95% accuracy rate is attained in the effective detection of the bacterium. These comparisons demonstrate the effectiveness of our AI-driven model and its intuitive graphical user interface (GUI), while also highlighting the potential of our machine learning techniques, such as Decision tree and Naive Bayes algorithm, for effective water bacterial detection.
Manush Prajwal, J Jesy Janet Kumari, Maanas Mitrahass Uppu, Vanajaroselin Chirchi, S Vishalatchi, and D N Darshan
IEEE
The research shows the use of optical ring resonators (ORRs) as a promising non-invasive tool for diagnosing diabetes mellitus, potentially replacing invasive procedures. By accurately measuring glucose levels in biological samples through resonance frequency shifts closely linked to varying glucose concentrations, ORRs point to innovative and precise glucose measurement for early diabetes detection. This advancement could transform disease monitoring by reducing patient discomfort from current invasive techniques. The study develops a highly sensitive, non-invasive method for detecting diabetes by analyzing subtle optical signal changes related to glucose levels in urine is around 165-180 mg/dl (normal) and resonant frequency output is around 36.026459 but in case of blood resonant frequency output is around0.348478amusing advanced optical ring resonator technology. The methodology involves designing optical ring resonators capable of detecting minute refractive index variations connected to glucose concentrations. Major findings demonstrate the precision of diagnosing diabetes, showcasing the potential for a rapid and reliable diagnostic tool. Interpretations emphasize the innovative approach’s ability to significantly improve early detection, enabling timely intervention and enhanced diabetes management for better healthcare outcomes and quality of life for individuals with diabetes.
Vanajaroselin Chirchi, Emmanvelraj Chirchi, and Khushi E Chirchi
Springer Science and Business Media LLC
Vanajaroselin E Chirchi, Chettiyar Vani Vivekanand, N. Vini Antony Grace, R Saranya, S Venkataramana, and K. Praveena
IEEE
In many hospitals, doctors attend to patients either once or twice per day. A situation may arise in which the patient's health worsens during the time if a doctor is unavailable to the patient, and the patient may die as a result. Most problems in today's world are caused by a lack of efficient therapy and appropriate monitoring within the required time period. To solve these issues with the current method, this research study proposes a health monitoring framework using wireless technology, in which the patient's health is followed and communicated to the physician throughout the entire day. The Internet of Things (IoT) is an emergent technology that uses wireless networking phenomenon to transmit data. The advantage of using IoT-based healthcare monitoring systems is that they can assess many physiological characteristics of the human body and is simpler, more accurate, and more precise than traditional methods. Sensors are utilized to measure the patient's bodily functions over a wireless network. The data from the sensors is gathered and communicated to the cloud through a Wi-Fi module linked to the microprocessor. The data is stored in the cloud, and feedback mechanisms are done on the stored data, which may be analyzed distantly by a physician. Virtual monitoring relieves doctors' workload and offers patients accurate health conditions. The proposed system results suggest that the physiological sensor is more effective in terms of availability and portability. The proposed system is easy to use, will save money, and will change how hospitals work in the future.
Vanaja Chirchi, , Laxman Waghmare, and
The Intelligent Networks and Systems Society
In this paper, we propose a novel method for iris recognition system using enhanced isocentric segmentor (EISOS) and wavelet rectangular coder (WRC). At first, we locate the center of the eye within the area of the pupil on low resolution images using EISOS method. Once the iris region is successfully segmented, the next stage is to transform the iris region into the fixed dimensions. Then, we propose a novel feature vector generation method namely, wavelet rectangular coder (WRC). Finally, we recognize the iris image using fuzzy logic classifier to identify whether the iris image is present in the dataset or not. The iris recognition performance is measured using different dataset such as, CASIA, MMU and UBIRIS dataset. Experimental results indicate that the proposed method of EISOS+WRC based iris segmentation and recognition framework have outperformed by having better accuracy of 99.75% which is 94% and 93% for using existing approaches.