ANAMIKA AHIRWAR
@ciitm.org
HOD CS
Compucom Institute of Technology & Management, Jaipur
Ahirwar is working as an Associate Professor in the Department of Science & Technology under Faculty of Education & Methodology in Compucom Institute of Technology & Management, Jaipur (Rajasthan), India. She is having 20 years of experience in Teaching and Research. Her research areas are Medical Imaging, Data Mining and Celestial sound, IOT, Machine learning. She has published more than 50 research papers in repute National/International Journals and Conferences, authored and reviewed many books published by National and International publisher, published five patents in “Publication of the Patent Office”, Journal. She had delivered several Expert/Guest Lectures and Chaired Sessions in Various IEEE, International Conferences.
EDUCATION
Phd. (Computer Applications)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Anamika Ahirwar, Piyush Kumar Shukla, Prashant Kumar Shukla, and Ruby Bhatt
Apple Academic Press
Prashant Kumar Shukla, Jasminder Kaur Sandhu, Anamika Ahirwar, Deepika Ghai, Priti Maheshwary, and Piyush Kumar Shukla
Hindawi Limited
COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019. Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.
Mausumi Dey and Anamika Ahirwar
IEEE
Currently increasing in data exhaustive processing systems of information is now becomes very important for making decisions in the business or companies. Important and responsive knowledgeable designs may consist in process of the business analysis. Now a day privacy-preserving data-mining may obtain a huge enhancement with the help of data-mining and the researchers of the Information security community in the development of the technologies that are merges with the concerns of privacy. In the original data, the random-noise is added in the privacy preserving data Mining (PPDM) method, that is used for publishing the exact information regarding the actual data. The basic perspective of the privacy preserving data mining is to design various algorithms for updating the actual data and for providing the security to the information which is to be get misused, as a result of this the private data and private information still remain same even after the mining process. This paper calculates the results on comparative dependent analytical of the privacy preserving data mining algorithms and implemented those in MATLAB.
Sonal Mishra, Yadunath Pathak, and Anamika Ahirwar
Global Vision Press
The quality of the protein structure can be determined by physical and chemical properties, therefore it has been used to distinguish native or native like structure from other predicted structures. In this study, the machine learning classification models are explored with six physical and chemical properties to classify the root mean square deviation (RMSD) of the protein structure in absence of its true native state and each
Anamika Ahirwar and R.S. Jadon
Global Vision Press
Medical image segmentation is a challenging task. This paper proposed FCSOFM a novel technique for medical image segmentation. FCSOFM technique segments the defected region of brain MRI and digital mammogram images. Then we calculate the efficiency of our proposed technique by the computation of confusion matrix.
Anamika Ahirwar and R.S. Jadon
IEEE
Segmentation of anatomical regions of the medical imaging is a critical problem. In this paper, we propose effectiveness of soft computing techniques for segmenting medical imaging. This paper explores the possibility of applying techniques for segmenting the regions of medical image. We them compute the effectiveness of the applied techniques on medical imaging and compare their results from the database given on the web. We tested the results to calculate the effectiveness of the techniques used for segmenting the tumor region in brain images and digital mammogram images.