Jasmine J
@kce.ac.in
Associate Professor
karpagam college of engineering
I am Dr J Jasmine completed in my ph.d 2020 june. since have 21 years of teaching experience and Reserach interested experience in various area.
EDUCATION
ASP
Karpagam College of Engineering
Coimbatore
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence, Human-Computer Interaction, Computer Vision and Pattern Recognition
FUTURE PROJECTS
Machine learning on health care
The healthcare sector has long been an early adopter of technological advances. Nowadays, ML (Machine Learning) a subset of AI plays a key role in many health innovations, including the development of new medical procedures. Machine Learning come up with techniques and tools that can help in solving diagnostic and prognostic problems in medical domains. ML is being applied for the analysis of clinical parameters and their combinations for prognosis. Medical diagnostic reasoning is a very important application area of intelligent systems. Machine learning is applied in a broad range of healthcare applications. On large volumes of data, Machine Learning helps healthcare providers to produce medical solutions. In future Machine Learning algorithms are expected to play a critical role in central nervous system clinical trials. The three main areas machine learning is adapted to include medical imaging, natural language processing of medicaldocuments, and genetic information.
Applications Invited
health care
Bloch chain in health care
Quality healthcare services backed up with the latest technology is the need for today. Focusing on quality health care services means ensuring patient health management at a superior level at all times. The misuse or lack of available data is preventing healthcare organizations from delivering appropriate patient care and high-quality services for better health.Upto 40% of healthcare provider data records are filled up with errors or misleading information. Many healthcare facilities today are still dependent on outdated systems for keeping patient records. This can make it difficult for the doctor to diagnose which is time-consuming for the doctor and tedious for the patients too. The healthcare system today not only needs an advance system rather it also needs a system that is smooth, transparent, economically efficient and easily operable.
Applications Invited
health care
Scopus Publications
Scopus Publications
<p>Human activities are directly affected by weather events. In particular, extreme weather events like forest fires, global warming, drought-causing high air temperatures make human life challenging. The use of reliable and accurate weather prediction models is essential to take precautions against these types of climate events. As a result, creating models that accurately forecast the weather is critical. The successful development of deep learning-based weather prediction models has largely aided by technological advancements. With high success rate, this paper proposes a Robust Feature Selection based Hybrid Weather Prediction (RFS-HWP) model for weather prediction. The input dataset is initially pre-processed with the help of Missing Data Imputation and Z-score Standardization. After that, the feature selection process is accomplished using Botox Optimization Algorithm (BxOA) to find the optimal subset of features. The selected features are then fed into the Hybrid Deep Gated Tobler’s Hiking Neural Network (HDGT-HNN) model, which classifies weather conditions into three classes as temperature, pressure and humidity. The hyper-parameters of HPC-DBCN are optimized using Hiking Optimization Algorithm (HiOA). The entire implementation is carried out on Python platform using publicly available Jena climate dataset, and many types of performance measures are calculated. Also, the usefulness of RFS-HWP model is proven by comparing its performance to state-of-the-art approaches. As a result, the RFS-HWP outperforms by accomplishing overall accuracy of 99.3% and proven to be an applicable model for weather forecasting systems.</p>
Anbumani K, Murali Dhar M S, Jasmine J, Subramanian P, Mahaveerakannan R, and John Justin Thangaraj S
Anapub Publications
Inevitably, researchers in the field of medicine must deal with the issue of missing data. Imputation is frequently employed as a solution to this issue. Unfortunately, the perfect would overfit the experiential data distribution due to the uncertainty introduced by imputation, which would have a negative effect on the replica's generalisation presentation. It is unclear how machine learning (ML) approaches are applied in medical research despite claims that they can work around lacking data. We hope to learn if and how machine learning prediction model research discuss how they deal with missing data. Information contained in EHRs is evaluated to ensure it is accurate and comprehensive. The missing information is imputed from the recognised EHR record. The Predictive Modelling approach is used for this, and the Naive Bayesian (NB) model is then used to assess the results in terms of performance metrics related to imputation. An adaptive optimisation technique, called the Adaptive Dolphin Atom Search Optimisation (Adaptive DASO) procedure, is used to teach the NB. The created Adaptive DASO method syndicates the DASO procedure with the adaptive idea. Dolphin Echolocation (DE) and Atom Search Optimisation (ASO) come together to form DASO. This indicator of performance metrics verifies imputation's fullness.
RECENT SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
patents
AI-BASED ANOMALY DETECTION AND MITIGATION IN LARGE-SCALE TELECOMMUNICATION SYSTEMS
PLANT DISEASE PREDICTION
CONSULTANCY
AI automation systems
machine learnig on prediction model